Supplementary Results

How Are Daily Events Related to Subjective Well-Being? A Meta-Analytic Review of Intensive Longitudinal Studies

ReadMe

This document includes an overview of all analyses conducted for the manuscript “How Are Daily Events Related to Subjective Well-Being? A Meta-Analytic Review of Intensive Longitudinal Studies”.

Tabs
To make the document easier to read, you can find tabs in many places where you can choose between different domains of daily events (i.e., daily uplifts or daily hassles), within- or between-person associations, and different types of information (e.g., tables or figures).
Note: Often there are several levels of tabs. For example, at the first level, you select whether you are interested in daily uplifts or daily hassles. At the second level, you choose the specific level of association you want to view (within-person or between-person associations). At the third level, you select whether you wish to view tables or figures.

R Code
This document includes all R code used to perform the analysis and create the tables and figures. The code is hidden by default to enhance readability. If you want to see the code, click on the SHOW button on the right hand side just above the figure or table—you will then see the corresponding codechunk. If you want to show all codechunks by default, click CODE at the very top of the document and select Show all Code.

Preregistration

The present meta-analysis has been preregistered at https://osf.io/g2f4d/overview?view_only=478e62129622422ba63702348d203282. Deviations from this preregistration are described in the following table (see Table 1).

deviations <- tibble::tibble(
  `Preregistered Plan` = c(
    
    "We preregistered to estimate the variance-covariance matrix of the effect sizes using the *rcalc*()-function implemented in the *metafor* package.",
    
    "We preregistered the use of the moderator categories *North America*, *Global South*, *Northern Europe*, *Southern Europe*, and *Asia* to examine potential regional differences in daily event-SWB associations.",
    
    "We preregistered to collapse moderator categories that were assessed in less than 5 studies."
  ),
  `Deviation` = c(
    
    "We estimated the variance-covariance matrix of the effect sizes using the *vcalc*()- instead of *rcalc*()-function implemented in the *metafor* package.",
    
    "We did not use the preregistered *Global South* category and introduced *Australia* as an additional moderator category.",
    
    "We set the *Southern Europe* and *Australia* categories of the moderator *Region*, the *Other* category of the moderator *Daily event measurement*, and the  *Global well-being* category of the moderator *SWB dimension* to *NA* for the within- and between-person associations between daily uplifts and SWB. In addition, we set the *Australia* category of the moderator *Region to *NA* for the within-person associations between daily hassles and SWB and set the *Other* category of the moderator *Daily event measurement* to *NA* for the between-person associations between daily hassles and SWB."
  ),
  `Reason` = c(
    
    "The *rcalc*()-function requires the within-study correlation among the variables underlying the dependent effect sizes to compute the exact covariance between the effect sizes. For example, to account for the dependency between two effect sizes from the same study (e.g., a correlation between daily hassles and positive affect and a correlation between daily hassles and negative affect from the same study), *rcalc*() requires the within-study correlation between positive and negative affect. As this information is not always reported in individual studies, we decided to deviate from the preregistration and used *vcalc*() instead. *vcalc*() approximates the covariance across dependent effect sizes using a working-correlation (uniformly applied across all studies) instead. More details on the estimation of the variance-covariance matrix using *vcalc*() can be found in the manuscript.",
    
    "The preregistered *Global South* category was not used because no studies from countries in the Global South were represented in the present meta-analysis. *Australia* was introduced as an additional moderator category because studies from Australia were represented in the meta-analytic dataset but did not fit well within any of the other preregistered regional categories.",
    
    "These categories were not collapsed with other categories because they were conceptually distinct, and doing so could have obscured interpretation of the results of the moderator analyses."
  )
)

kable(deviations
      , caption="**Deviations from the Preregistration**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 1: Deviations from the Preregistration

Preregistered Plan	Deviation	Reason
We preregistered to estimate the variance-covariance matrix of the effect sizes using the rcalc()-function implemented in the metafor package.	We estimated the variance-covariance matrix of the effect sizes using the vcalc()- instead of rcalc()-function implemented in the metafor package.	The rcalc()-function requires the within-study correlation among the variables underlying the dependent effect sizes to compute the exact covariance between the effect sizes. For example, to account for the dependency between two effect sizes from the same study (e.g., a correlation between daily hassles and positive affect and a correlation between daily hassles and negative affect from the same study), rcalc() requires the within-study correlation between positive and negative affect. As this information is not always reported in individual studies, we decided to deviate from the preregistration and used vcalc() instead. vcalc() approximates the covariance across dependent effect sizes using a working-correlation (uniformly applied across all studies) instead. More details on the estimation of the variance-covariance matrix using vcalc() can be found in the manuscript.
We preregistered the use of the moderator categories North America, Global South, Northern Europe, Southern Europe, and Asia to examine potential regional differences in daily event-SWB associations.	We did not use the preregistered Global South category and introduced Australia as an additional moderator category.	The preregistered Global South category was not used because no studies from countries in the Global South were represented in the present meta-analysis. Australia was introduced as an additional moderator category because studies from Australia were represented in the meta-analytic dataset but did not fit well within any of the other preregistered regional categories.
We preregistered to collapse moderator categories that were assessed in less than 5 studies.	We set the Southern Europe and Australia categories of the moderator Region, the Other category of the moderator Daily event measurement, and the Global well-being category of the moderator SWB dimension to NA for the within- and between-person associations between daily uplifts and SWB. In addition, we set the Australia category of the moderator Region to NA* for the within-person associations between daily hassles and SWB and set the Other category of the moderator Daily event measurement to NA for the between-person associations between daily hassles and SWB.	These categories were not collapsed with other categories because they were conceptually distinct, and doing so could have obscured interpretation of the results of the moderator analyses.

Section 1: Study Characteristics of Included Studies

Studies Identified Via Main-Search

Uplifts & SWB

studies.main_uplifts <- data_included %>%
  filter(source == "1", DeDimension == "uplift") %>%
  mutate(ID = paste0(StudyID, ".", SampleID)) %>%
  select(
    ID, Reference, Country, DeDimension, DeDomainAll,
    WbDimension, N, DeOccDay, DeOccTotal, CorrCoeff
  ) %>%
  mutate(
    DeDimension = recode(
      DeDimension,
      "uplift" = "Daily uplifts"
    ),
    DeDomainAll = recode(
      DeDomainAll,
      "work/achievement" = "Work/Achievement",
      "social" = "Social",
      "health" = "Health",
      "other" = "Other",
      "general" = "General"
    ),
    WbDimension = recode(
      WbDimension,
      "life_satisfaction" = "Life satisfaction",
      "positive_affect" = "Positive affect",
      "negative_affect" = "Negative affect",
      "domain_satisfaction" = "Domain satisfaction",
      "global_well-being" = "Global well-being"
    ),
    Country = recode(
      Country,
      "Germany, Austria, Switzerland" = "Germany; Austria; Switzerland",
      "UK" = "United Kingdom",
      "US" = "United States",
      "US & CANADA" = "United States; Canada",
      "US, Canada" = "United States; Canada"
    )
  ) %>%
  group_by(ID) %>%
  summarise(
    Reference = first(Reference),
    Country = first(Country),
    `*N*` = first(N),
    `# Observations (per day)` = first(DeOccDay),
    `# Observations (total)` = first(DeOccTotal),
    `Daily event types` = paste(unique(DeDomainAll), collapse = "; "),
    `Well-being dimensions` = paste(unique(WbDimension), collapse = "; "),
    `Correlation range` = if_else(
      min(CorrCoeff, na.rm = TRUE) == max(CorrCoeff, na.rm = TRUE),
      sprintf("%.2f", min(CorrCoeff, na.rm = TRUE)),
      sprintf(
        "%.2f – %.2f",
        min(CorrCoeff, na.rm = TRUE),
        max(CorrCoeff, na.rm = TRUE)
      )
    ),
    .groups = "drop"
  ) %>%
  arrange(as.numeric(str_remove(ID, "^M"))) %>%
  mutate(across(where(is.character), ~ replace_na(.x, "")))

kable(
  studies.main_uplifts,
  caption = "**Overview of Studies on the Association Between Daily Uplifts and SWB Identified Via Main-Search**",
  escape = FALSE,
  label = NA,
  na = ""
) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    fixed_thead = TRUE,
    full_width = TRUE,
    position = "left"
  )

Table 2: Overview of Studies on the Association Between Daily Uplifts and SWB Identified Via Main-Search

ID	Reference	Country	N	# Observations (per day)	# Observations (total)	Daily event types	Well-being dimensions	Correlation range
M1.1	Chong et al. (2023)	United States	254	1.00	14.00	Other	Positive affect; Negative affect	-0.22 – 0.21
M8.1	Miralles et al. (2015)		117	1.00	10.00	Work/Achievement	Positive affect; Negative affect	0.35 – 0.53
M57.1	Newman et al. (2022)	United States	179	1.00	7.00	General	Positive affect; Negative affect	0.01 – 0.60
M64.1	Nezlek et al. (2019)	Poland	152	1.00	14.00	General	Positive affect; Negative affect	0.05 – 0.62
M88.1	Berhe et al. (2023)	Germany	351	12.51	87.57	General	Positive affect	0.26 – 0.39
M153.1	Diamond et al. (2011)	United States	68	1.00	21.00	Social	Positive affect; Negative affect	-0.05 – 0.31
M153.2	Diamond et al. (2011)	United States	68	1.00	21.00	Social	Positive affect; Negative affect	0.01 – 0.65
M167.1	Longua et al. (2009)	United States	575	1.00	30.00	General	Positive affect; Negative affect	0.02 – 0.20
M343.1	Nezlek et al. (2021)	United States	222	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect	-0.16 – 0.49
M593.1	Nezlek et al. (2008)	United States	90	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect	-0.02 – 0.47
M593.2	Nezlek et al. (2008)	United States	65	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect	-0.25 – 0.40
M593.3	Nezlek et al. (2008)	Canada	89	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect	-0.13 – 0.44
M593.4	Nezlek et al. (2008)	Japan	99	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect	-0.22 – 0.60
M609.1	Junça-Silva (2025)		204	1.00	5.00	Work/Achievement	Positive affect	0.51 – 0.62
M609.2	Junça-Silva (2025)		98	1.00	5.00	Work/Achievement	Positive affect	0.60 – 0.69
M618.1	Newman & Sachs (2023)	United States	151	1.00	14.00	General	Positive affect; Negative affect; Life satisfaction	0.01 – 0.58
M674.1	Mosteo et al. (2023)		195	1.00	5.00	Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.70 – 0.48
M724.1	Newman et al. (2020)	United States	232	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	0.01 – 0.45
M727.1	Demerouti & Cropanzano (2017)	Netherlands	112	1.00	3.00	Work/Achievement	Positive affect	0.32
M740.1	Arbeau et al. (2011)	Canada	81	1.00	14.00	Work/Achievement	Positive affect; Negative affect	0.11 – 0.30
M756.1	Peeters et al. (1995)	Netherlands	38	1.00	5.00	Work/Achievement	Negative affect	-0.20 – 0.41
M876.1	Impett et al. (2005)	United States	121	1.00	14.00	Social	Positive affect; Negative affect; Domain satisfaction	-0.01 – 0.34
M895.1	Doorley & Kashdan (2021)	United States	67	1.00	14.00	General	Positive affect; Negative affect	0.11 – 0.55
M983.1	Wirth et al. (2025)	Germany	268	1.00	14.00	General	Negative affect; Positive affect	0.07 – 0.34
M1009.1	Stephenson et al. (2025)	United States	20	5.00	25.00	Social	Positive affect; Negative affect	0.05 – 0.39
M1018.1	Infurna et al. (2015)	United States	191	1.00	30.00	General	Positive affect; Negative affect	-0.02 – 0.21
M1020.1	Zheng et al. (2023)	Canada	99	1.00	30.00	General	Positive affect; Negative affect	0.11 – 0.64
M1121.1	Nezlek & Allen (2006)	United States	154	1.00	21.00	General	Positive affect; Negative affect	-0.16 – 0.30
M1158.1	Oren-Yagoda & Aderka (2021)		44	1.00	21.00	Social	Positive affect; Negative affect	0.23 – 0.29
M1196.1	David et al. (1997)	United States	96	1.00	8.00	Work/Achievement; Other; Social; General	Positive affect; Negative affect	-0.27 – 0.42
M1229.1	Zhang et al. (2021)	China	158	1.00	10.00	Work/Achievement	Positive affect	0.08 – 0.20
M1289.1	Mahlo & Windsor (2021)	Australia	157	4.00	40.00	General	Positive affect; Negative affect	0.05 – 0.18
M1296.1	Sicorello et al. (2020)	Germany	413	4.00	16.00	General	Positive affect; Negative affect	0.12 – 0.71
M1311.1	Kennedy-Moore et al. (1992)		77	1.00	91.00	General	Positive affect; Negative affect	0.21 – 0.36
M1311.2	Kennedy-Moore et al. (1992)		94	1.00	75.00	General	Positive affect; Negative affect	0.14 – 0.20
M1316.1	Farrell-Reeves (2025)	Canada	257	1.00	10.00	Social	Positive affect; Negative affect	-0.14 – 0.09
M1316.2	Farrell-Reeves (2025)	Canada	129	1.00	5.00	Social	Positive affect; Negative affect	-0.13 – 0.41
M1317.1	Ong (2022)	United States	1212	1.00	7.00	General	Positive affect; Negative affect	0.18 – 0.50
M1366.1	Fingerman et al. (2017)	United States	173	1.00	7.00	Social	Positive affect; Negative affect	-0.03 – 0.17
M1381.1	Yorganson et al. (2020)		191	1.00	14.00	Social	Negative affect	0.16
M1381.2	Yorganson et al. (2020)		191	1.00	14.00	Social	Negative affect	0.13
M1403.1	Pasipanodya et al. (2012)	United States	45	1.00	7.00	General	Negative affect; Positive affect	0.01 – 0.57
M1437.1	Burns et al. (2015)	Australia	45	1.00	14.00	General	Positive affect; Negative affect	0.09 – 0.37
M1469.1	Carmona-Cobo & Lopez-Zafra (2022)	Spain	96	1.00	5.00	Work/Achievement	Positive affect	0.31
M1493.1	Mohr & Sarno (2016)	United States	61	1.00	11.00	Other	Positive affect; Negative affect	-0.04 – 0.23
M1542.1	Galambos et al. (2009)	Canada	191	1.00	14.00	Social	Positive affect; Negative affect	-0.08 – 0.29
M1588.1	Sin et al. (2020)	United States	1982	1.00	8.00	General	Positive affect; Negative affect	-0.03 – 0.12
M1626.1	Bresin (2014)	United States	54	1.00	14.00	General	Positive affect; Negative affect	0.13 – 0.40
M1655.1	Nezlek et al. (2017)	Poland	131	1.00	14.00	General	Positive affect; Negative affect	0.22 – 0.55
M1670.1	Zuffianò et al. (2018)	Spain	56	1.00	5.00	General	Life satisfaction	0.20
M1690.1	Watson (2024)	United States	51	1.00	5.00	Work/Achievement	Domain satisfaction	0.33
M1693.1	Röcke et al. (2009)	Germany	18	1.00	45.00	General	Positive affect; Negative affect	-0.02 – 0.06
M1693.2	Röcke et al. (2009)	Germany	19	1.00	45.00	General	Positive affect; Negative affect	0.03 – 0.39
M1723.1	Galatty & Zimmer-Gembeck (2008)		67	1.00	7.00	Social	Positive affect; Negative affect	-0.09 – 0.33
M1840.1	Veilleux et al. (2020)		85	7.00	49.00	General	Positive affect	0.06
M1925.1	Fritz et al. (2022)	United States	392	1.00	4.00	Work/Achievement	Domain satisfaction; Positive affect	0.04 – 0.66
M1939.1	Shearer (1991)	United States	70	1.00	20.00	General	Global well-being	0.28
M1950.1	Harrison (2009)	United States	132	1.00	8.00	General	Positive affect; Negative affect	0.15 – 0.36
M1992.1	Nezlek et al. (2018)	United States	155	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect	-0.35 – 0.54
M1992.2	Nezlek et al. (2018)	United States	127	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.16 – 0.58
M1992.3	Nezlek et al. (2018)	United States	121	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.10 – 0.67
M2003.1	Congard et al. (2011)	France	49	3.00	120.00	General	Positive affect; Negative affect	-0.09 – 0.52
M2193.1	Early et al. (2024)	United States	440	1.00	13.00	General	Negative affect	0.47
M2253.1	Ion et al. (2025)	Romania	135	1.00	10.00	General	Negative affect; Positive affect	0.06 – 0.24
M2257.1	Baker (2006)	United Kingdom	39	1.00	14.00	General	Positive affect; Negative affect	-0.09 – 0.51
M2322.1	Stanton et al. (2017)		67	1.00	21.00	Social	Negative affect	0.17
M2322.2	Stanton et al. (2017)		67	1.00	21.00	Social	Negative affect	0.16
M2353.1	Nezlek (2022)	United States	124	1.00	14.00	Social; Work/Achievement	Global well-being	0.01 – 0.45
M2380.1	Fingerman et al. (2016)	United States	247	1.00	7.00	Social	Positive affect; Negative affect	-0.06 – 0.16
M2406.1	Newman & Nezlek (2022)	United States	164	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect	-0.39 – 0.55
M2406.2	Newman & Nezlek (2022)	United States	128	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.16 – 0.59
M2410.1	Klaiber et al. (2021)	United States; Canada	776	1.00	7.00	General	Negative affect; Positive affect	0.36 – 0.58
M2415.1	Newman et al. (2023)	United States	130	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.19 – 0.57
M2415.2	Newman et al. (2023)	United States	124	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.12 – 0.59
M2415.3	Newman et al. (2023)	United States	96	1.00	14.00	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.23 – 0.51
M2425.1	Lawhead et al. (2026)	United States	289	5.00	70.00	Social	Positive affect; Negative affect	0.26 – 0.73
M2506.1	Impett et al. (2019)	Canada	116	1.00	21.00	Social	Positive affect; Negative affect; Domain satisfaction	0.01 – 0.27
M2506.2	Impett et al. (2019)	Canada	127	1.00	21.00	Social	Positive affect; Negative affect; Domain satisfaction	0.04 – 0.22
M2566.1	Slavish (2018)		60	1.00	14.00	General	Positive affect; Negative affect	0.21 – 0.57
M2572.1	Totenhagen et al. (2012)	United States	164	1.00	7.00	Social; General	Domain satisfaction	-0.02 – 0.14
M2572.2	Totenhagen et al. (2012)	United States	164	1.00	7.00	Social; General	Domain satisfaction	-0.03 – 0.04
M2573.1	Junça-Silva et al. (2023)		101	1.00	10.00	General	Positive affect	0.43 – 0.48
M2599.1	Davis (2011)	United States	47	1.00	8.00	Work/Achievement	Negative affect; Positive affect	-0.06 – 0.15
M2599.2	Davis (2011)	United States	47	1.00	8.00	General	Negative affect; Positive affect	-0.34 – 0.19
M2607.1	Pollock et al. (2016)	United States	350	1.00	7.00	Work/Achievement; Social	Positive affect; Negative affect	0.02 – 0.37
M2726.1	Nelson-Coffey & Coffey (2024)	United States	191	1.00	7.00	Social	Positive affect; Negative affect; Life satisfaction	-0.06 – -0.00
M2752.1	Sin et al. (2017)	United States	131	1.00	8.00	Work/Achievement; General	Positive affect; Negative affect	-0.08 – 0.10
M2752.2	Sin et al. (2017)	United States	181	1.00	8.00	Work/Achievement; General	Positive affect; Negative affect	-0.05 – 0.13
M2793.1	Klein et al. (2023)		130	1.00	14.00	General	Positive affect; Negative affect	-0.03 – 0.49

Hassles & SWB

studies.main_hassles <- data_included %>%
  filter(source == "1", DeDimension == "hassle") %>%
  mutate(ID = paste0(StudyID, ".", SampleID)) %>%
  select(
    ID, Reference, Country, DeDimension, DeDomainAll,
    WbDimension, N, DeOccDay, DeOccTotal, CorrCoeff
  ) %>%
  mutate(
    DeDimension = recode(
      DeDimension,
      "hassle" = "Daily hassles"
    ),
    DeDomainAll = recode(
      DeDomainAll,
      "work/achievement" = "Work/Achievement",
      "social" = "Social",
      "health" = "Health",
      "other" = "Other",
      "general" = "General"
    ),
    WbDimension = recode(
      WbDimension,
      "life_satisfaction" = "Life satisfaction",
      "positive_affect" = "Positive affect",
      "negative_affect" = "Negative affect",
      "domain_satisfaction" = "Domain satisfaction",
      "global_well-being" = "Global well-being"
    ),
    Country = recode(
      Country,
      "Germany, Austria, Switzerland" = "Germany; Austria; Switzerland",
      "UK" = "United Kingdom",
      "US" = "United States",
      "US & CANADA" = "United States; Canada",
      "US, Canada" = "United States; Canada"
    )
  ) %>%
  group_by(ID) %>%
  summarise(
    Reference = first(Reference),
    Country = first(Country),
    `*N*` = first(N),
    `# Observations (per day)` = first(DeOccDay),
    `# Observations (total)` = first(DeOccTotal),
    `Daily event types` = paste(unique(DeDomainAll), collapse = "; "),
    `Well-being dimensions` = paste(unique(WbDimension), collapse = "; "),
    `Correlation range` = if_else(
      min(CorrCoeff, na.rm = TRUE) == max(CorrCoeff, na.rm = TRUE),
      sprintf("%.2f", min(CorrCoeff, na.rm = TRUE)),
      sprintf(
        "%.2f – %.2f",
        min(CorrCoeff, na.rm = TRUE),
        max(CorrCoeff, na.rm = TRUE)
      )
    ),
    .groups = "drop"
  ) %>%
  arrange(as.numeric(str_remove(ID, "^M"))) %>%
  mutate(across(where(is.character), ~ replace_na(.x, "")))

kable(
  studies.main_hassles,
  caption = "**Overview of Studies on the Association Between Daily Hassles and SWB Identified Via Main-Search**",
  escape = FALSE,
  label = NA,
  na = ""
) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    fixed_thead = TRUE,
    full_width = TRUE,
    position = "left"
  )

Table 3: Overview of Studies on the Association Between Daily Hassles and SWB Identified Via Main-Search

ID	Reference	Country	N	# Observations (per day)	# Observations (total)	Daily event types	Well-being dimensions	Correlation range
M1.1	Chong et al. (2023)	United States	254	1.0	14.0	Other	Positive affect; Negative affect	-0.44 – 0.04
M6.1	Ringwald et al. (2025)	United States	104	6.0	14.0	Social; Work/Achievement; Other; Health; General	Negative affect	-0.22 – 0.05
M57.1	Newman et al. (2022)	United States	180	1.0	7.0	General	Positive affect; Negative affect	-0.51 – -0.04
M64.1	Nezlek et al. (2019)	Poland	152	1.0	14.0	General	Positive affect; Negative affect	-0.67 – -0.05
M125.1	Hunter et al. (2019)	United States	121	1.0	14.0	Work/Achievement	Negative affect; Positive affect	-0.59 – 0.08
M153.1	Diamond et al. (2011)	United States	68	1.0	21.0	Social	Positive affect; Negative affect	-0.59 – -0.23
M153.2	Diamond et al. (2011)	United States	68	1.0	21.0	Social	Positive affect; Negative affect	-0.35 – -0.06
M156.1	Ng et al. (2022)	Singapore	261	1.0	7.0	General	Positive affect; Negative affect	-0.34 – 0.04
M156.2	Ng et al. (2022)	Singapore	253	1.0	7.0	General	Positive affect; Negative affect	-0.51 – -0.01
M158.1	George (2018)	United States	136	4.0	28.0	General	Positive affect; Negative affect	-0.32 – -0.17
M158.2	George (2018)	United States	101	4.0	40.0	General	Positive affect; Negative affect	-0.24 – -0.23
M167.1	Longua et al. (2009)	United States	575	1.0	30.0	General	Positive affect; Negative affect	-0.17 – -0.15
M202.1	Lin & Lachman (2021)	United States	145	1.0	7.0	General	Positive affect; Negative affect	-0.23 – -0.12
M209.1	Stawski et al. (2008)	United States	68	1.0	6.0	General	Negative affect; Positive affect	-0.11 – -0.08
M209.2	Stawski et al. (2008)	United States	116	1.0	6.0	General	Negative affect; Positive affect	-0.33 – 0.12
M215.1	Greaney et al. (2021)	United States	61	1.0	8.0	General	Positive affect; Negative affect	-0.51 – -0.30
M249.1	Gruber et al. (2013)	United States	244	1.0	14.0	General	Positive affect; Life satisfaction	-0.12 – -0.03
M264.1	Finkelstein-Fox et al. (2019)	United States	134	1.0	7.0	General	Positive affect; Negative affect	-0.58 – -0.02
M271.1	Diehl & Hay (2010)	United States	239	1.0	30.0	General	Negative affect	-0.41
M272.1	Brown et al. (2021)	United States	502	1.0	30.0	General	Negative affect	-0.19 – -0.02
M307.1	Lafteron et al. (2020)	Germany; Austria; Switzerland	98	1.0	10.0	Social; Work/Achievement	Positive affect; Negative affect	-0.54 – 0.05
M308.2	Stawski et al. (2019)	United States	172	5.0	70.0	General	Negative affect	-0.33
M308.3	Stawski et al. (2019)	United States	122	6.0	18.0	General	Negative affect	-0.36
M308.4	Stawski et al. (2019)	United States	300	13.6	27.2	General	Negative affect	-0.58
M308.5	Stawski et al. (2019)	United States	311	1.0	8.0	General	Negative affect	-0.40
M343.1	Nezlek et al. (2021)	United States	222	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect	-0.59 – 0.03
M416.1	Toma et al. (2024)	Romania	218	1.0	5.0	General	Negative affect; Positive affect; Global well-being	-0.66 – -0.24
M430.1	Darling et al. (2025)	United States	235	1.0	8.0	General	Positive affect; Negative affect	-0.36 – -0.20
M437.1	Kiang & Buchanan (2014)	United States	76	1.0	14.0	Social; Work/Achievement	Global well-being	-0.13 – -0.09
M437.2	Kiang & Buchanan (2014)	United States	104	1.0	14.0	Social; Work/Achievement	Global well-being	-0.14 – -0.05
M483.1	Girme et al. (2018)	United States	350	1.0	14.0	General	Global well-being; Domain satisfaction	-0.19 – 0.03
M486.1	Harris et al. (2022)	United States	109	1.0	7.0	Social	Negative affect; Positive affect; Global well-being	-0.44 – 0.03
M506.1	Eatough et al. (2016)	United States	90	1.0	10.0	Work/Achievement	Negative affect; Domain satisfaction	-0.22 – -0.06
M511.1	Wilton-Harding et al. (2022)	Australia	152	1.0	10.0	General	Negative affect	-0.41
M532.1	Ben-Ari & Lavee (2011)	Israel	272	1.0	7.0	Social; Health; Work/Achievement; Other	Positive affect	-0.38 – -0.13
M532.2	Ben-Ari & Lavee (2011)	Israel	272	1.0	7.0	Social; Health; Work/Achievement; Other	Positive affect	-0.30 – -0.06
M563.1	Sahl et al. (2009)		127	1.0	7.0	General	Negative affect	-0.50 – -0.22
M593.1	Nezlek et al. (2008)	United States	90	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect	-0.58 – -0.08
M593.2	Nezlek et al. (2008)	United States	65	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect	-0.73 – 0.01
M593.3	Nezlek et al. (2008)	Canada	89	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect	-0.59 – 0.01
M593.4	Nezlek et al. (2008)	Japan	99	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect	-0.49 – 0.30
M594.1	Langer et al. (2022)	Germany	111	1.0	7.0	Work/Achievement	Positive affect; Negative affect	-0.64 – -0.07
M618.1	Newman & Sachs (2023)	United States	151	1.0	14.0	General	Positive affect; Negative affect; Life satisfaction	-0.56 – 0.00
M619.1	Rahal et al. (2024)	United States	330	1.0	15.0	Social	Positive affect; Negative affect	-0.38 – -0.03
M620.1	Monroy et al. (2021)	United States	74	1.0	7.0	General	Positive affect; Negative affect; Life satisfaction	-0.16 – 0.05
M620.2	Monroy et al. (2021)	United States	336	1.0	14.0	General	Positive affect; Negative affect; Life satisfaction	-0.39 – 0.06
M673.1	Wickham et al. (2016)	United States	68	1.0	14.0	Social	Life satisfaction	-0.30 – -0.08
M674.1	Mosteo et al. (2023)		195	1.0	5.0	Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.33 – 0.15
M676.1	Wu (2018)	United States	30	1.0	14.0	Other	Positive affect; Negative affect	-0.40 – 0.03
M688.1	Turner et al. (2022)	United States	258	1.0	14.0	General	Positive affect; Negative affect	-0.14 – -0.12
M688.2	Turner et al. (2022)	United States	303	1.0	14.0	General	Positive affect; Negative affect	-0.31 – -0.27
M697.1	Herres et al. (2016)	United States	132	1.0	8.0	Social	Positive affect; Negative affect	-0.24 – 0.06
M724.1	Newman et al. (2020)	United States	232	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.48 – 0.02
M727.1	Demerouti & Cropanzano (2017)	Netherlands	112	1.0	3.0	Work/Achievement	Positive affect	-0.19
M729.1	Du et al. (2018)	China	108	1.0	5.0	Social	Negative affect	0.02
M738.1	Dunkley et al. (2014)		196	1.0	14.0	General	Positive affect; Negative affect	-0.61 – -0.11
M755.1	Yung et al. (2022)	Australia	98	4.0	56.0	General	Positive affect; Negative affect	-0.48 – -0.09
M756.1	Peeters et al. (1995)	Netherlands	38	1.0	5.0	Work/Achievement	Negative affect	-0.47
M804.1	Li et al. (2025)	China	78	1.0	14.0	Work/Achievement	Positive affect; Negative affect	-0.52 – -0.10
M814.1	Rast et al. (2012)	Canada	178	1.0	7.0	General	Positive affect; Negative affect	-0.55 – -0.01
M818.1	Wang et al. (2023)	China	76	1.0	14.0	Social	Positive affect; Negative affect; Life satisfaction	-0.42 – 0.01
M878.1	Borghiuset al. (2020)	Netherlands	534	1.0	75.0	Social	Negative affect	-0.64 – -0.21
M895.1	Doorley & Kashdan (2021)	United States	67	1.0	14.0	General	Positive affect; Negative affect	-0.57 – -0.18
M901.1	Lu & Shi (2024)	China	69	1.0	30.0	General	Negative affect	-0.49
M919.1	Xu & Zheng (2023)	Canada	87	1.0	30.0	General	Positive affect; Negative affect	-0.80 – -0.03
M928.1	Doane & Thurston (2014)		78	5.0	15.0	General	Positive affect; Negative affect	-0.50 – -0.17
M929.1	Capreol (2000)	Canada	166	1.0	7.0	General	Negative affect	-0.40 – -0.15
M940.1	Blaxton et al. (2023)	United States	894	1.0	56.0	Social; Other; Health	Negative affect	-0.18 – -0.07
M958.1	McDaniel et al. (2018)	United States	174	1.0	14.0	General; Social	Negative affect	-0.39 – -0.19
M958.2	McDaniel et al. (2018)	United States	171	1.0	14.0	General; Social	Negative affect	-0.44 – -0.12
M965.1	Peleg et al. (2024)	Switzerland	108	1.0	14.0	General; Social; Other; Health	Positive affect; Negative affect	-0.53 – -0.04
M981.1	Sliwinski et al. (2009)	United States	116	1.0	6.0	General	Negative affect	-0.32
M983.1	Wirth et al. (2025)	Germany	268	1.0	14.0	General	Negative affect; Positive affect	-0.43 – -0.29
M989.1	Zahavi-Lupo et al. (2023)	Israel	100	1.0	21.0	General; Social; Other	Positive affect	-0.54 – -0.30
M989.2	Zahavi-Lupo et al. (2023)	Israel	100	1.0	21.0	General; Social; Other	Positive affect	-0.34 – -0.24
M997.1	Finkelstein-Fox et al. (2022)	United States	255	1.0	11.0	General; Health	Positive affect; Negative affect	-0.54 – -0.03
M1002.1	Calderwood & Ackerman (2019)	United States	50	1.0	14.0	Work/Achievement	Negative affect; Positive affect	-0.22 – -0.05
M1009.1	Stephenson et al. (2025)	United States	43	5.0	25.0	Social	Positive affect; Negative affect	-0.55 – -0.17
M1018.1	Infurna et al. (2015)	United States	191	1.0	30.0	General	Positive affect; Negative affect	-0.29 – -0.09
M1020.1	Zheng et al. (2023)	Canada	99	1.0	30.0	General	Positive affect; Negative affect	-0.79 – -0.03
M1024.1	Drake et al. (2022)	United States	167	1.0	21.0	General	Negative affect	-0.43 – -0.24
M1032.1	Brose et al. (2011)	Germany	101	1.0	100.0	General	Negative affect	-0.26
M1032.2	Brose et al. (2011)	Germany	103	1.0	100.0	General	Negative affect	-0.19
M1038.1	Koffer et al. (2019)	United States	150	1.0	63.0	General	Positive affect; Negative affect	-0.58 – -0.43
M1059.1	Scheibe (2021)	Germany	123	1.0	10.0	Work/Achievement	Negative affect	-0.25 – -0.24
M1064.1	Keller (2012)	United States	472	1.0	30.0	Work/Achievement; Social	Positive affect; Negative affect	-0.20 – 0.05
M1092.1	Swim et al. (2009)	United States	69	4.0	28.0	Social; Other; General	Positive affect; Negative affect	-0.16 – 0.29
M1107.1	Morse et al. (2024)	United States	62	6.0	42.0	Health	Negative affect	-0.35
M1112.1	Fernández-Castro et al. (2022)	Spain	125	1.0	10.0	General	Positive affect; Negative affect	-0.36 – -0.19
M1121.1	Nezlek & Allen (2006)	United States	154	1.0	21.0	General	Positive affect; Negative affect	-0.59 – -0.04
M1131.1	Alessandri et al. (2020)		172	1.0	18.0	General	Negative affect	-0.29 – -0.23
M1144.1	Mey et al. (2023)	Germany	114	6.0	42.0	General	Negative affect; Positive affect	-0.33 – -0.01
M1147.1	Rowe et al. (2016)	Australia	24	1.0	5.0	General	Domain satisfaction; Life satisfaction	-0.67 – -0.30
M1158.1	Oren-Yagoda & Aderka (2021)		44	1.0	21.0	Social	Positive affect; Negative affect	-0.36 – -0.09
M1163.1	Hu et al. (2025)	China	196	4.0	40.0	General	Life satisfaction	-0.17 – -0.16
M1181.1	Burrow & Ong (2010)	United States	174	1.0	14.0	Other	Negative affect	-0.06
M1196.1	David et al. (1997)	United States	96	1.0	8.0	Work/Achievement; Other; Social; General	Positive affect; Negative affect	-0.34 – -0.01
M1203.1	Mereish et al. (2021)	United States	94	1.0	21.0	Other	Positive affect; Negative affect	-0.70 – 0.13
M1232.1	Vize et al. (2024)	United States	297	6.0	84.0	Social; Other	Negative affect	-0.30 – -0.02
M1240.1	Fang & Galambos (2015)	Canada	170	1.0	14.0	General	Positive affect; Negative affect	-0.56 – 0.13
M1250.1	Rickenbach et al. (2015)	United States	25	1.0	8.0	General	Positive affect; Negative affect	-0.47 – -0.19
M1261.1	H. Zheng & Y. Zheng (2024)	Canada	273	1.0	30.0	General	Positive affect; Negative affect	-0.75 – 0.03
M1270.1	Conway et al. (2015)	United States	100	1.0	14.0	General	Positive affect; Negative affect	-0.51 – 0.05
M1289.1	Mahlo & Windsor (2021)	Australia	157	4.0	40.0	General	Positive affect; Negative affect	-0.42 – -0.40
M1296.1	Sicorello et al. (2020)	Germany	413	4.0	16.0	General	Positive affect; Negative affect	-0.67 – -0.17
M1303.1	Garrison & Schmeichel (2022)	United States	92	5.0	30.0	General	Positive affect; Negative affect	-0.49 – 0.04
M1311.1	Kennedy-Moore et al. (1992)		77	1.0	91.0	General	Positive affect; Negative affect	-0.30 – -0.22
M1311.2	Kennedy-Moore et al. (1992)		94	1.0	75.0	General	Positive affect; Negative affect	-0.34 – -0.05
M1317.1	Ong (2022)	United States	1212	1.0	7.0	Social; Health	Positive affect; Negative affect	-0.49 – -0.17
M1332.1	Dalton & Hammen (2018)	United States	127	1.0	14.0	General	Positive affect; Negative affect	-0.54 – -0.02
M1366.1	Fingerman et al. (2017)	United States	173	1.0	7.0	Social	Positive affect; Negative affect	-0.31 – 0.04
M1381.1	Yorganson et al. (2020)		191	1.0	14.0	Social	Negative affect	-0.21
M1381.2	Yorganson et al. (2020)		191	1.0	14.0	Social	Negative affect	-0.24
M1382.1	Auger et al. (2017)		63	NA	NA	Social	Negative affect; Domain satisfaction	-0.40 – -0.38
M1382.2	Auger et al. (2017)		63	NA	NA	Social	Negative affect; Domain satisfaction	-0.41 – -0.25
M1394.1	Nezlek et al. (2016)	Poland	57	1.0	14.0	General	Positive affect; Negative affect	-0.43 – 0.01
M1403.1	Pasipanodya et al. (2012)	United States	45	1.0	7.0	General	Negative affect; Positive affect	-0.15 – 0.45
M1431.1	Santiago et al. (2017)	United States	58	1.0	7.0	Other; Social; Work/Achievement	Positive affect; Negative affect	-0.68 – -0.01
M1437.1	Burns et al. (2015)	Australia	45	1.0	14.0	General	Positive affect; Negative affect	-0.47 – -0.07
M1469.1	Carmona-Cobo & Lopez-Zafra (2022)	Spain	96	1.0	5.0	Work/Achievement	Positive affect	-0.10
M1486.1	Zielinski et al. (2023)		86	1.0	7.0	Social; General	Negative affect; Positive affect	-0.57 – -0.04
M1493.1	Mohr & Sarno (2016)	United States	61	1.0	11.0	Other	Positive affect; Negative affect	-0.25 – 0.04
M1501.1	Zhaoyang et al. (2020)	United States	176	5.0	35.0	General	Positive affect; Negative affect	-0.32 – -0.14
M1502.1	Park et al. (2004)	United States	137	1.0	28.0	General	Positive affect; Negative affect	-0.29 – -0.01
M1526.1	Chiang et al. (2015)	United States	316	1.0	15.0	General	Negative affect; Positive affect	-0.45 – -0.04
M1531.1	Ekas & Whitman (2011)		46	1.0	30.0	General; Social	Positive affect; Negative affect	-0.64 – -0.02
M1542.1	Galambos et al. (2009)	Canada	191	1.0	14.0	General	Positive affect; Negative affect	-0.55 – 0.12
M1543.1	Tadić et al. (2015)	Croatia	158	1.0	5.0	Work/Achievement	Positive affect	-0.23 – -0.19
M1547.1	Rahal & Fosco (2025)	United States	150	1.0	21.0	Social	Negative affect	-0.65
M1588.1	Sin et al. (2020)	United States	1982	1.0	8.0	General	Positive affect; Negative affect	-0.42 – -0.16
M1600.1	Benson et al. (2023)	United States	101	5.0	35.0	General	Negative affect	-0.39 – -0.25
M1616.1	Lanza et al. (2024)	United States	814	1.0	21.0	Social	Positive affect; Negative affect	-0.35 – 0.01
M1626.1	Bresin (2014)	United States	54	1.0	14.0	General	Positive affect; Negative affect	-0.53 – -0.15
M1648.1	Emmons (1991)	United States	48	2.0	21.0	Health	Positive affect; Negative affect	-0.44 – 0.07
M1655.1	Nezlek et al. (2017)	Poland	131	1.0	14.0	General	Positive affect; Negative affect	-0.73 – 0.04
M1688.1	Blanke et al. (2022)	Germany	156	6.0	72.0	General	Negative affect	-0.27 – -0.26
M1690.1	Watson (2024)	United States	51	1.0	5.0	Work/Achievement	Domain satisfaction	-0.22
M1693.1	Röcke et al. (2009)	Germany	18	1.0	45.0	General	Positive affect; Negative affect	-0.05 – 0.06
M1693.2	Röcke et al. (2009)	Germany	19	1.0	45.0	General	Positive affect; Negative affect	-0.68 – -0.46
M1716.1	Bellingtier et al. (2021)	United States	223	1.0	8.0	General; Health	Positive affect; Negative affect	-0.20 – 0.02
M1723.1	Galatty & Zimmer-Gembeck (2008)		67	1.0	7.0	Social	Positive affect; Negative affect	-0.48 – -0.03
M1731.1	Lavee & Ben-Ari (2007)	Israel	169	1.0	7.0	General	Negative affect	-0.27 – -0.05
M1731.2	Lavee & Ben-Ari (2007)	Israel	169	1.0	7.0	General	Negative affect	-0.19 – -0.11
M1769.1	Yang (2022)	United States	248	1.0	7.0	General	Positive affect; Negative affect	-0.17 – -0.10
M1840.1	Veilleux et al. (2020)		85	7.0	49.0	General	Positive affect	-0.20
M1853.1	Dunkley (2003)	Canada	170	1.0	7.0	General	Positive affect; Negative affect	-0.51 – -0.31
M1919.1	Schilling et al. (2022)	Germany	170	6.0	42.0	General	Negative affect	-0.67 – -0.32
M1939.1	Shearer (1991)	United States	70	1.0	20.0	General; Health	Global well-being	-0.18 – -0.16
M1951.1	Birditt et al. (2020)	United States	293	1.0	5.5	Social	Positive affect; Negative affect	-0.27 – -0.12
M1968.1	Silva et al. (2020)	United States	117	1.0	7.0	Social	Positive affect	-0.27 – -0.25
M1968.2	Silva et al. (2020)	United States	117	1.0	7.0	Social	Positive affect	-0.25 – -0.04
M1992.1	Nezlek et al. (2018)	United States	155	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect	-0.66 – 0.12
M1992.2	Nezlek et al. (2018)	United States	127	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.65 – 0.01
M1992.3	Nezlek et al. (2018)	United States	121	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.45 – 0.00
M2003.1	Congard et al. (2011)	France	49	3.0	120.0	General	Positive affect; Negative affect	-0.61 – 0.12
M2007.1	Scott et al. (2013)	United States	190	5.0	50.0	General	Positive affect; Negative affect	-0.30 – -0.13
M2008.1	MacNeil et al. (2025)	Canada	133	1.0	5.0	Health	Positive affect; Negative affect	-0.20 – -0.00
M2050.1	Goreis et al. (2024)	Austria	90	1.0	30.0	General	Positive affect; Negative affect	-0.85 – -0.01
M2054.1	Schilling & Diehl (2014)	United States	285	1.0	30.0	General	Positive affect; Negative affect	-0.38 – 0.06
M2107.1	Ilen et al. (2024)	Switzerland	55	8.0	48.0	General	Positive affect; Negative affect	-0.69 – -0.18
M2112.1	Sin et al. (2021)	United States; Canada	1028	1.0	7.0	General	Positive affect; Negative affect	-0.44 – -0.28
M2171.1	Richardson & Jiang (2025)		145	1.0	7.0	General	Positive affect; Negative affect	-0.65 – 0.14
M2182.1	Hsu & Raposa (2021)	United States	88	1.0	6.0	General	Negative affect	-0.63
M2196.1	Dyar (2024)	United States	429	2.0	60.0	General; Other	Negative affect	-0.52 – -0.07
M2217.1	Wrzus et al. (2015)	Germany	398	6.0	54.0	General	Negative affect	-0.40 – -0.27
M2221.1	Wang et al. (2025)	China	76	1.0	14.0	Social	Domain satisfaction; Positive affect; Negative affect	-0.54 – 0.01
M2229.1	Starr et al. (2020)	United States	233	3.0	21.0	General	Negative affect	-0.69
M2253.1	Ion et al. (2025)	Romania	135	1.0	10.0	General	Negative affect; Positive affect	-0.24 – 0.03
M2257.1	Baker (2006)	United Kingdom	39	1.0	14.0	General; Health	Positive affect; Negative affect	-0.55 – -0.23
M2310.1	Kotter-Grühn et al. (2015)	United States	43	1.0	8.0	General; Health	Positive affect; Negative affect	-0.56 – 0.23
M2353.1	Nezlek (2022)	United States	124	1.0	14.0	Social; Work/Achievement	Global well-being	-0.31 – -0.03
M2364.1	Smith et al. (2023)	United States	99	1.0	28.0	Social; Health	Negative affect; Positive affect	-0.44 – 0.05
M2367.1	Park et al. (2004)		190	1.0	28.0	General	Positive affect; Negative affect	-0.35 – -0.11
M2380.1	Fingerman et al. (2016)	United States	247	1.0	7.0	Social	Positive affect; Negative affect	-0.37 – 0.05
M2406.1	Newman & Nezlek (2022)	United States	164	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect	-0.66 – 0.07
M2406.2	Newman & Nezlek (2022)	United States	128	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.65 – 0.01
M2407.1	Mey et al. (2020)	Germany	70	5.0	140.0	General	Negative affect	-0.44 – -0.19
M2410.1	Klaiber et al. (2021)	United States; Canada	776	1.0	7.0	General	Negative affect; Positive affect	-0.52 – -0.29
M2415.1	Newman et al. (2023)	United States	130	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.66 – 0.01
M2415.2	Newman et al. (2023)	United States	124	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.46 – 0.00
M2415.3	Newman et al. (2023)	United States	96	1.0	14.0	Social; Work/Achievement	Positive affect; Negative affect; Life satisfaction	-0.67 – 0.17
M2421.1	Neubauer et al. (2018)		180	5.0	105.0	Social; Work/Achievement; Other; Health	Negative affect	-0.32 – -0.01
M2425.1	Lawhead et al. (2026)	United States	289	5.0	70.0	General; Social	Positive affect; Negative affect	-0.62 – -0.21
M2475.1	Pereira et al. (2014)	Switzerland	76	1.0	10.0	Social	Negative affect	-0.51 – -0.11
M2476.1	Richardson (2017)		376	1.0	7.0	General	Positive affect; Negative affect	-0.62 – -0.10
M2485.1	Lawson et al. (2016)	United States	103	1.0	8.0	General	Positive affect; Negative affect	-0.64 – -0.09
M2535.1	Castro (2023)		188	1.0	14.0	Social	Positive affect; Negative affect	-0.35 – 0.19
M2566.1	Slavish (2018)		60	1.0	14.0	General	Positive affect; Negative affect	-0.43 – 0.03
M2572.1	Totenhagen et al. (2012)	United States	164	1.0	7.0	Social; General	Domain satisfaction	-0.11 – -0.03
M2572.2	Totenhagen et al. (2012)	United States	164	1.0	7.0	Social; General	Domain satisfaction	-0.23 – -0.02
M2599.1	Davis (2011)	United States	47	1.0	8.0	Social; Work/Achievement	Negative affect; Positive affect	-0.30 – 0.09
M2599.2	Davis (2011)	United States	47	1.0	8.0	Social; General	Negative affect; Positive affect	-0.15 – 0.13
M2607.1	Pollock et al. (2016)	United States	350	1.0	7.0	Work/Achievement; Social	Positive affect; Negative affect	-0.40 – -0.01
M2611.1	Zhang et al. (2024)	China	107	1.0	7.0	Work/Achievement	Negative affect	-0.14 – -0.03
M2617.1	Buck (2012)		165	1.0	14.0	General	Domain satisfaction	-0.10
M2617.2	Buck (2012)		165	1.0	14.0	General	Domain satisfaction	-0.08
M2650.1	Tung et al. (2022)	Australia	98	4.0	56.0	General	Positive affect; Negative affect	-0.60 – -0.18
M2661.1	Shi & Wang (2022)	China	98	1.0	10.0	Work/Achievement	Negative affect	-0.38
M2690.1	Ilen et al. (2023)	Switzerland	53	8.0	46.0	General	Positive affect; Negative affect	-0.68 – -0.17
M2726.1	Nelson-Coffey & Coffey (2024)	United States	191	1.0	7.0	Social	Positive affect; Negative affect; Life satisfaction	-0.42 – 0.21
M2727.1	Barker (2006)	Canada	61	1.0	14.0	General	Negative affect	-0.46
M2727.2	Barker (2006)	Canada	101	1.0	14.0	General	Negative affect	-0.55
M2731.1	Schmidt et al. (2021)	Germany	562	1.0	21.0	General	Positive affect; Negative affect	-0.49 – -0.35
M2752.1	Sin et al. (2017)	United States	131	1.0	8.0	Work/Achievement; General	Positive affect; Negative affect	-0.30 – 0.06
M2752.2	Sin et al. (2017)	United States	181	1.0	8.0	Work/Achievement; General	Positive affect; Negative affect	-0.31 – 0.01
M2793.1	Klein et al. (2023)		130	1.0	14.0	General	Positive affect; Negative affect	-0.49 – 0.12

Studies Identified Via Forward-Search

Uplifts & SWB

studies.forward_uplifts <- data_included %>%
  filter(source == "2", DeDimension == "uplift") %>%
  mutate(ID = paste0(StudyID, ".", SampleID)) %>%
  select(
    ID, Reference, Country, DeDimension, DeDomainAll,
    WbDimension, N, DeOccDay, DeOccTotal, CorrCoeff
  ) %>%
  mutate(
    DeDimension = recode(
      DeDimension,
      "uplift" = "Daily uplifts"
    ),
    DeDomainAll = recode(
      DeDomainAll,
      "work/achievement" = "Work/Achievement",
      "social" = "Social",
      "health" = "Health",
      "other" = "Other",
      "general" = "General"
    ),
    WbDimension = recode(
      WbDimension,
      "life_satisfaction" = "Life satisfaction",
      "positive_affect" = "Positive affect",
      "negative_affect" = "Negative affect",
      "domain_satisfaction" = "Domain satisfaction",
      "global_well-being" = "Global well-being"
    ),
    Country = recode(
      Country,
      "Germany, Austria, Switzerland" = "Germany; Austria; Switzerland",
      "UK" = "United Kingdom",
      "US" = "United States",
      "US & CANADA" = "United States; Canada",
      "US, Canada" = "United States; Canada"
    )
  ) %>%
  group_by(ID) %>%
  summarise(
    Reference = first(Reference),
    Country = first(Country),
    `*N*` = first(N),
    `# Observations (per day)` = first(DeOccDay),
    `# Observations (total)` = first(DeOccTotal),
    `Daily event types` = paste(unique(DeDomainAll), collapse = "; "),
    `Well-being dimensions` = paste(unique(WbDimension), collapse = "; "),
    `Correlation range` = if_else(
      min(CorrCoeff, na.rm = TRUE) == max(CorrCoeff, na.rm = TRUE),
      sprintf("%.2f", min(CorrCoeff, na.rm = TRUE)),
      sprintf(
        "%.2f – %.2f",
        min(CorrCoeff, na.rm = TRUE),
        max(CorrCoeff, na.rm = TRUE)
      )
    ),
    .groups = "drop"
  ) %>%
  arrange(as.numeric(str_remove(ID, "^M"))) %>%
  mutate(across(where(is.character), ~ replace_na(.x, "")))

kable(
  studies.forward_uplifts,
  caption = "**Overview of Studies on the Association Between Daily Uplifts and SWB Identified Via Forward-Search**",
  escape = FALSE,
  label = NA,
  na = ""
) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    fixed_thead = TRUE,
    full_width = TRUE,
    position = "left"
  )

Table 4: Overview of Studies on the Association Between Daily Uplifts and SWB Identified Via Forward-Search

ID	Reference	Country	N	# Observations (per day)	# Observations (total)	Daily event types	Well-being dimensions	Correlation range
F101.1	Scheibe et al. (2019)	Germany	125	1	10	Work/Achievement	Positive affect; Negative affect	-0.10 – 0.32
F115.1	Tov & Lee (2016)	Singapore	206	1	21	Work/Achievement; Social	Life satisfaction	-0.04 – 0.10
F116.1	Timmons et al. (2016)	United States	70	1	14	Social	Positive affect; Negative affect; Domain satisfaction	0.06 – 0.51
F131.1	Russell et al. (2012)	United States	705	1	56	Social	Positive affect; Negative affect	0.02 – 0.18
F37.1	Disabato et al. (2025)	United States	197	5	50	General	Positive affect; Negative affect	0.17 – 0.54
F51.1	Junça Silva et al. (2023)		181	1	4	Work/Achievement	Life satisfaction	0.30 – 0.34
F51.2	Junça Silva et al. (2023)		51	1	10	Work/Achievement	Life satisfaction	0.02 – 0.33
F62.1	Bayraktaroglu et al. (2022)	Turkey	151	1	21	Social; General	Positive affect	-0.09 – 0.34
F62.2	Bayraktaroglu et al. (2022)	Turkey	151	1	21	Social; General	Positive affect	0.10 – 0.41
F62.3	Bayraktaroglu et al. (2022)	Turkey	168	1	21	Social; General	Positive affect	-0.10 – 0.32
F62.4	Bayraktaroglu et al. (2022)	Turkey	168	1	21	Social; General	Positive affect	0.04 – 0.42

Hassles & SWB

studies.main_hassles <- data_included %>%
  filter(source == "2", DeDimension == "hassle") %>%
  mutate(ID = paste0(StudyID, ".", SampleID)) %>%
  select(
    ID, Reference, Country, DeDimension, DeDomainAll,
    WbDimension, N, DeOccDay, DeOccTotal, CorrCoeff
  ) %>%
  mutate(
    DeDimension = recode(
      DeDimension,
      "hassle" = "Daily hassles"
    ),
    DeDomainAll = recode(
      DeDomainAll,
      "work/achievement" = "Work/Achievement",
      "social" = "Social",
      "health" = "Health",
      "other" = "Other",
      "general" = "General"
    ),
    WbDimension = recode(
      WbDimension,
      "life_satisfaction" = "Life satisfaction",
      "positive_affect" = "Positive affect",
      "negative_affect" = "Negative affect",
      "domain_satisfaction" = "Domain satisfaction",
      "global_well-being" = "Global well-being"
    ),
    Country = recode(
      Country,
      "Germany, Austria, Switzerland" = "Germany; Austria; Switzerland",
      "UK" = "United Kingdom",
      "US" = "United States",
      "US & CANADA" = "United States; Canada",
      "US, Canada" = "United States; Canada"
    )
  ) %>%
  group_by(ID) %>%
  summarise(
    Reference = first(Reference),
    Country = first(Country),
    `*N*` = first(N),
    `# Observations (per day)` = first(DeOccDay),
    `# Observations (total)` = first(DeOccTotal),
    `Daily event types` = paste(unique(DeDomainAll), collapse = "; "),
    `Well-being dimensions` = paste(unique(WbDimension), collapse = "; "),
    `Correlation range` = if_else(
      min(CorrCoeff, na.rm = TRUE) == max(CorrCoeff, na.rm = TRUE),
      sprintf("%.2f", min(CorrCoeff, na.rm = TRUE)),
      sprintf(
        "%.2f – %.2f",
        min(CorrCoeff, na.rm = TRUE),
        max(CorrCoeff, na.rm = TRUE)
      )
    ),
    .groups = "drop"
  ) %>%
  arrange(as.numeric(str_remove(ID, "^M"))) %>%
  mutate(across(where(is.character), ~ replace_na(.x, "")))

kable(
  studies.main_hassles,
  caption = "**Overview of Studies on the Association Between Daily Hassles and SWB Identified Via Forward-Search**",
  escape = FALSE,
  label = NA,
  na = ""
) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    fixed_thead = TRUE,
    full_width = TRUE,
    position = "left"
  )

Table 5: Overview of Studies on the Association Between Daily Hassles and SWB Identified Via Forward-Search

ID	Reference	Country	N	# Observations (per day)	# Observations (total)	Daily event types	Well-being dimensions	Correlation range
F101.1	Scheibe et al. (2019)	Germany	125	1	10	Work/Achievement	Positive affect; Negative affect	-0.44 – -0.25
F11.1	Perelman et al. (2026)	Israel	100	5	80	General	Positive affect; Negative affect	-0.35 – -0.13
F11.2	Perelman et al. (2026)	Israel	100	5	80	General	Positive affect; Negative affect	-0.36 – -0.14
F115.1	Tov & Lee (2016)	Singapore	206	1	21	Work/Achievement; Social	Life satisfaction	-0.11 – -0.01
F116.1	Timmons et al. (2016)	United States	70	1	14	Social	Positive affect; Negative affect; Domain satisfaction	-0.45 – -0.20
F119.1	Volmer (2015)	Germany	98	1	5	Work/Achievement	Negative affect	-0.37
F122.1	Starr (2015)		51	1	14	General	Negative affect	-0.27
F127.1	Wrzus et al. (2013)	Germany	378	6	54	General	Negative affect	-0.28 – -0.17
F13.1	Zhang & Xia (2026)	United States	107	1	30	General	Positive affect; Negative affect	-0.28 – 0.00
F131.1	Russell et al. (2012)	United States	705	1	56	Social	Positive affect; Negative affect	-0.25 – 0.00
F142.1	Todd et al. (2009)	United States	97	1	21	Social	Positive affect; Negative affect	-0.68 – -0.20
F153.1	Neupert et al. (2006)	United States	32	1	8	Health	Positive affect; Negative affect	-0.10
F157.1	Ong et al. (2004)	United States	34	1	98	General	Positive affect; Negative affect	-0.47 – -0.26
F163.1	Reis et al. (2000)		67	1	14	Health	Positive affect; Negative affect	-0.46 – -0.06
F167.1	Peeters et al. (1995)	Netherlands	41	1	5	General	Negative affect	-0.58 – -0.55
F17.1	Forchuk et al. (2026)	United States	262	2	28	General	Positive affect; Negative affect	-0.45 – -0.12
F4.1	Xiong et al. (2026)	China	244	1	12	Social	Negative affect	-0.42 – -0.38
F5.1	Kiebler et al. (2026)	United States	202	1	10	Social	Negative affect	-0.27 – -0.19
F51.1	Junça Silva et al. (2023)		181	1	4	Work/Achievement	Life satisfaction	-0.31 – -0.17
F51.2	Junça Silva et al. (2023)		51	1	10	Work/Achievement	Life satisfaction	-0.37 – -0.34
F58.1	Joshi et al. (2023)	United States	60	3	42	General	Negative affect	-0.47 – -0.31
F61.1	Mereish et al. (2022)	United States	83	5	150	Social	Positive affect	-0.17 – -0.07

Studies Identified Via Review-Screening

Hassles & SWB

studies.review_hassles <- data_included %>%
  filter(source == "3", DeDimension == "hassle") %>%
  mutate(ID = paste0(StudyID, ".", SampleID)) %>%
  select(
    ID, Reference, Country, DeDimension, DeDomainAll,
    WbDimension, N, DeOccDay, DeOccTotal, CorrCoeff
  ) %>%
  mutate(
    DeDimension = recode(
      DeDimension,
      "hassle" = "Daily hassles"
    ),
    DeDomainAll = recode(
      DeDomainAll,
      "work/achievement" = "Work/Achievement",
      "social" = "Social",
      "health" = "Health",
      "other" = "Other",
      "general" = "General"
    ),
    WbDimension = recode(
      WbDimension,
      "life_satisfaction" = "Life satisfaction",
      "positive_affect" = "Positive affect",
      "negative_affect" = "Negative affect",
      "domain_satisfaction" = "Domain satisfaction",
      "global_well-being" = "Global well-being"
    ),
    Country = recode(
      Country,
      "Germany, Austria, Switzerland" = "Germany; Austria; Switzerland",
      "UK" = "United Kingdom",
      "US" = "United States",
      "US & CANADA" = "United States; Canada",
      "US, Canada" = "United States; Canada"
    )
  ) %>%
  group_by(ID) %>%
  summarise(
    Reference = first(Reference),
    Country = first(Country),
    `*N*` = first(N),
    `# Observations (per day)` = first(DeOccDay),
    `# Observations (total)` = first(DeOccTotal),
    `Daily event types` = paste(unique(DeDomainAll), collapse = "; "),
    `Well-being dimensions` = paste(unique(WbDimension), collapse = "; "),
    `Correlation range` = if_else(
      min(CorrCoeff, na.rm = TRUE) == max(CorrCoeff, na.rm = TRUE),
      sprintf("%.2f", min(CorrCoeff, na.rm = TRUE)),
      sprintf(
        "%.2f – %.2f",
        min(CorrCoeff, na.rm = TRUE),
        max(CorrCoeff, na.rm = TRUE)
      )
    ),
    .groups = "drop"
  ) %>%
  arrange(as.numeric(str_remove(ID, "^M"))) %>%
  mutate(across(where(is.character), ~ replace_na(.x, "")))

kable(
  studies.review_hassles,
  caption = "**Overview of Studies on the Association Between Daily Hassles and SWB Identified Via Review-Screening**",
  escape = FALSE,
  label = NA,
  na = ""
) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    fixed_thead = TRUE,
    full_width = TRUE,
    position = "left"
  )

Table 6: Overview of Studies on the Association Between Daily Hassles and SWB Identified Via Review-Screening

ID	Reference	Country	N	# Observations (per day)	# Observations (total)	Daily event types	Well-being dimensions	Correlation range
R1.1	Fleuren et al. (2023)		402	1	30	Health	Positive affect; Negative affect	-0.13 – -0.04
R2.1	Almeida et al. (2020)	United States	115	6	18	General	Negative affect	-0.39 – -0.13

Unpublished Studies Identified by the Research Community

Uplifts & SWB

studies.unpub_uplifts <- data_included %>%
  filter(source == "4", DeDimension == "uplift") %>%
  mutate(ID = paste0(StudyID, ".", SampleID)) %>%
  select(
    ID, Reference, Country, DeDimension, DeDomainAll,
    WbDimension, N, DeOccDay, DeOccTotal, CorrCoeff
  ) %>%
  mutate(
    DeDimension = recode(
      DeDimension,
      "uplift" = "Daily uplifts"
    ),
    DeDomainAll = recode(
      DeDomainAll,
      "work/achievement" = "Work/Achievement",
      "social" = "Social",
      "health" = "Health",
      "other" = "Other",
      "general" = "General"
    ),
    WbDimension = recode(
      WbDimension,
      "life_satisfaction" = "Life satisfaction",
      "positive_affect" = "Positive affect",
      "negative_affect" = "Negative affect",
      "domain_satisfaction" = "Domain satisfaction",
      "global_well-being" = "Global well-being"
    ),
    Country = recode(
      Country,
      "Germany, Austria, Switzerland" = "Germany; Austria; Switzerland",
      "UK" = "United Kingdom",
      "US" = "United States",
      "US & CANADA" = "United States; Canada",
      "US, Canada" = "United States; Canada"
    )
  ) %>%
  group_by(ID) %>%
  summarise(
    Reference = first(Reference),
    Country = first(Country),
    `*N*` = first(N),
    `# Observations (per day)` = first(DeOccDay),
    `# Observations (total)` = first(DeOccTotal),
    `Daily event types` = paste(unique(DeDomainAll), collapse = "; "),
    `Well-being dimensions` = paste(unique(WbDimension), collapse = "; "),
    `Correlation range` = if_else(
      min(CorrCoeff, na.rm = TRUE) == max(CorrCoeff, na.rm = TRUE),
      sprintf("%.2f", min(CorrCoeff, na.rm = TRUE)),
      sprintf(
        "%.2f – %.2f",
        min(CorrCoeff, na.rm = TRUE),
        max(CorrCoeff, na.rm = TRUE)
      )
    ),
    .groups = "drop"
  ) %>%
  arrange(as.numeric(str_remove(ID, "^M"))) %>%
  mutate(across(where(is.character), ~ replace_na(.x, "")))

kable(studies.unpub_uplifts
      , caption="**Overview of Unpublished Studies on the Associations Between Daily Uplifts and SWB Identified by the Research Community**"
      , escape=FALSE
      , label = NA
      , na = "") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 7: Overview of Unpublished Studies on the Associations Between Daily Uplifts and SWB Identified by the Research Community

ID	Reference	Country	N	# Observations (per day)	# Observations (total)	Daily event types	Well-being dimensions	Correlation range
D4.1	Wright et al. (2026)	Germany	951	4	112	General	Positive affect; Negative affect	0.07 – 0.25
D5.1	Wang et al. (2013)	Germany	92	2	16	Work/Achievement	Positive affect; Negative affect; Domain satisfaction; Life satisfaction	0.04 – 0.30
D5.2	Wang et al. (2013)	Germany	97	2	16	Work/Achievement	Positive affect; Negative affect; Domain satisfaction; Life satisfaction	0.01 – 0.53

Hassles & SWB

studies.unpub_hassles <- data_included %>%
  filter(source == "4", DeDimension == "hassle") %>%
  mutate(ID = paste0(StudyID, ".", SampleID)) %>%
  select(
    ID, Reference, Country, DeDimension, DeDomainAll,
    WbDimension, N, DeOccDay, DeOccTotal, CorrCoeff
  ) %>%
  mutate(
    DeDimension = recode(
      DeDimension,
      "hassle" = "Daily hassles"
    ),
    DeDomainAll = recode(
      DeDomainAll,
      "work/achievement" = "Work/Achievement",
      "social" = "Social",
      "health" = "Health",
      "other" = "Other",
      "general" = "General"
    ),
    WbDimension = recode(
      WbDimension,
      "life_satisfaction" = "Life satisfaction",
      "positive_affect" = "Positive affect",
      "negative_affect" = "Negative affect",
      "domain_satisfaction" = "Domain satisfaction",
      "global_well-being" = "Global well-being"
    ),
    Country = recode(
      Country,
      "Germany, Austria, Switzerland" = "Germany; Austria; Switzerland",
      "UK" = "United Kingdom",
      "US" = "United States",
      "US & CANADA" = "United States; Canada",
      "US, Canada" = "United States; Canada"
    )
  ) %>%
  group_by(ID) %>%
  summarise(
    Reference = first(Reference),
    Country = first(Country),
    `*N*` = first(N),
    `# Observations (per day)` = first(DeOccDay),
    `# Observations (total)` = first(DeOccTotal),
    `Daily event types` = paste(unique(DeDomainAll), collapse = "; "),
    `Well-being dimensions` = paste(unique(WbDimension), collapse = "; "),
    `Correlation range` = if_else(
      min(CorrCoeff, na.rm = TRUE) == max(CorrCoeff, na.rm = TRUE),
      sprintf("%.2f", min(CorrCoeff, na.rm = TRUE)),
      sprintf(
        "%.2f – %.2f",
        min(CorrCoeff, na.rm = TRUE),
        max(CorrCoeff, na.rm = TRUE)
      )
    ),
    .groups = "drop"
  ) %>%
  arrange(as.numeric(str_remove(ID, "^M"))) %>%
  mutate(across(where(is.character), ~ replace_na(.x, "")))

kable(
  studies.unpub_hassles,
  caption = "**Overview of Unpublished Studies on the Associations Between Daily Hassles and SWB Identified by the Research Community**",
  escape = FALSE,
  label = NA,
  na = ""
) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    fixed_thead = TRUE,
    full_width = TRUE,
    position = "left"
  )

Table 8: Overview of Unpublished Studies on the Associations Between Daily Hassles and SWB Identified by the Research Community

ID	Reference	Country	N	# Observations (per day)	# Observations (total)	Daily event types	Well-being dimensions	Correlation range
D1.1	Haehner et al. (2025)	Switzerland	429	3	21	General	Positive affect	-0.37 – -0.07
D2.1	Haehner et al. (2026)	Germany	359	6	84	General	Negative affect	-0.39 – -0.08
D3.1	Haehner et al. (2024)	Germany	574	1	14	General	Positive affect; Negative affect	-0.56 – 0.02
D4.1	Wright et al. (2026)	Germany	823	4	112	General	Positive affect; Negative affect	-0.32 – -0.10
D5.1	Wang et al. (2013)	Germany	92	2	16	Work/Achievement	Positive affect; Negative affect; Domain satisfaction; Life satisfaction	-0.34 – 0.04
D5.2	Wang et al. (2013)	Germany	97	2	16	Work/Achievement	Positive affect; Negative affect; Domain satisfaction; Life satisfaction	-0.38 – -0.06
D6.1	Paruzel et al. (2025)		74	1	5	Work/Achievement; Social; Health; Other; General	Positive affect; Negative affect	-0.56 – 0.21

Section 2: Descriptive Statistics

Number of Studies and Effect Sizes

numbers <- data.frame(
  corrtype = c(
    "Within-person",
    "Between-person",
    "Within-person",
    "Between-person",
    ""
  ),

  total_studnum = c(
    length(unique(uplifts_within$StudyID)),
    length(unique(uplifts_between$StudyID)),
    length(unique(hassles_within$StudyID)),
    length(unique(hassles_between$StudyID)),
    length(unique(data_included$StudyID))
  ),

  main_studnum = c(
    length(unique(filter(uplifts_within, source == 1)$StudyID)),
    length(unique(filter(uplifts_between, source == 1)$StudyID)),
    length(unique(filter(hassles_within, source == 1)$StudyID)),
    length(unique(filter(hassles_between, source == 1)$StudyID)),
    length(unique(filter(data_included, source == 1)$StudyID))
  ),

  forward_studnum = c(
    length(unique(filter(uplifts_within, source == 2)$StudyID)),
    length(unique(filter(uplifts_between, source == 2)$StudyID)),
    length(unique(filter(hassles_within, source == 2)$StudyID)),
    length(unique(filter(hassles_between, source == 2)$StudyID)),
    length(unique(filter(data_included, source == 2)$StudyID))
  ),

  review_studnum = c(
    length(unique(filter(uplifts_within, source == 3)$StudyID)),
    length(unique(filter(uplifts_between, source == 3)$StudyID)),
    length(unique(filter(hassles_within, source == 3)$StudyID)),
    length(unique(filter(hassles_between, source == 3)$StudyID)),
    length(unique(filter(data_included, source == 3)$StudyID))
  ),

  unpub_studnum = c(
    length(unique(filter(uplifts_within, source == 4)$StudyID)),
    length(unique(filter(uplifts_between, source == 4)$StudyID)),
    length(unique(filter(hassles_within, source == 4)$StudyID)),
    length(unique(filter(hassles_between, source == 4)$StudyID)),
    length(unique(filter(data_included, source == 4)$StudyID))
  ),

  total_effnum = c(
    nrow(uplifts_within),
    nrow(uplifts_between),
    nrow(hassles_within),
    nrow(hassles_between),
    nrow(data_included)
  ),

  main_effnum = c(
    nrow(filter(uplifts_within, source == 1)),
    nrow(filter(uplifts_between, source == 1)),
    nrow(filter(hassles_within, source == 1)),
    nrow(filter(hassles_between, source == 1)),
    nrow(filter(data_included, source == 1))
  ),

  forward_effnum = c(
    nrow(filter(uplifts_within, source == 2)),
    nrow(filter(uplifts_between, source == 2)),
    nrow(filter(hassles_within, source == 2)),
    nrow(filter(hassles_between, source == 2)),
    nrow(filter(data_included, source == 2))
  ),

  review_effnum = c(
    nrow(filter(uplifts_within, source == 3)),
    nrow(filter(uplifts_between, source == 3)),
    nrow(filter(hassles_within, source == 3)),
    nrow(filter(hassles_between, source == 3)),
    nrow(filter(data_included, source == 3))
  ),

  unpublished_effnum = c(
    nrow(filter(uplifts_within, source == 4)),
    nrow(filter(uplifts_between, source == 4)),
    nrow(filter(hassles_within, source == 4)),
    nrow(filter(hassles_between, source == 4)),
    nrow(filter(data_included, source == 4))
  )
)

names(numbers) <- c("", "Total", "Main search", "Forward search", "Review screening", "Unpublished studies",
                    "Total", "Main search", "Forward search", "Review screening", "Unpublished studies")

kable(numbers
      , caption="**Numbers of Studies and Effect Sizes**"
      , escape=FALSE
      , label = NA) %>%
  pack_rows("Daily uplifts", 1, 2) %>%
  pack_rows("Daily hassles", 3, 4) %>%
  pack_rows("Total", 5, 5) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left") %>% 
  add_header_above(c(" " = 1, "Number of studies" = 5, "Number of effect sizes" = 5))

Table 9: Numbers of Studies and Effect Sizes

	Number of studies					Number of effect sizes
	Total	Main search	Forward search	Review screening	Unpublished studies	Total	Main search	Forward search	Review screening	Unpublished studies
Daily uplifts
Within-person	50	47	1	0	2	305	285	2	0	18
Between-person	74	65	7	0	2	270	231	29	0	10
Daily hassles
Within-person	119	102	9	2	6	531	472	15	3	41
Between-person	191	164	19	2	6	627	550	45	3	29
Total
	217	187	22	2	6	1733	1538	91	6	98

Descriptive Statistics for Moderators

Respective Levels

Overall

Continuous Moderators

# Study-Level

overall_cont_study_level <- data_included %>%
  distinct(StudyID, .keep_all = TRUE) %>%
  select(Year) %>%
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Study")

# Sample-Level

overall_cont_sample_level <- data_included %>% 
  group_by(StudyID) %>%
  distinct(SampleID, .keep_all = TRUE) %>%
  ungroup() %>%
  select(Female, AgeM) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Sample")

# Effect Size-Level

overall_cont_es_level <- data_included %>%
  select(N, Observations, DeOccDay, DeOccTotal, WbReliability) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Effect size")

# Combine

mod_metric_overall <-
  rbind(
    overall_cont_study_level,
    overall_cont_sample_level,
    overall_cont_es_level
  )

# Re-order columns for table

mod_metric_overall <- mod_metric_overall[, c("level", "n", "mean", "sd", "min", "max")]

row.names(mod_metric_overall) <- c("Publication year", "% Female", "Mean age", "Sample size", 
                                   "Number of observations", "Assessments per day", 
                                   "Total assessments", "SWB reliability")

names(mod_metric_overall) <- c("Level", "*n*", "*M*", "*SD*", "Min", "Max")

kable(mod_metric_overall
      , caption="**Descriptive Statistics for Continuous Moderators at Their Respective Levels**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 10: Descriptive Statistics for Continuous Moderators at Their Respective Levels

	Level	n	M	SD	Min	Max
Publication year	Study	217	2017.67	7.10	1991.00	2026.00
% Female	Sample	259	62.32	25.25	0.00	100.00
Mean age	Sample	255	31.70	15.65	10.77	80.30
Sample size	Effect size	1733	175.14	208.46	18.00	1982.00
Number of observations	Effect size	862	3054.35	7884.17	107.00	77474.00
Assessments per day	Effect size	1729	1.51	1.41	1.00	13.60
Total assessments	Effect size	1729	20.61	23.30	3.00	150.00
SWB reliability	Effect size	1339	0.80	0.11	0.54	0.99

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes).

Categorical Moderators

# Recode categorical moderator variables and arrange factor levels

data_included <- data_included %>%
  mutate(
    country_region = case_when(
      Country == "Australia" ~ "Australia",
      
      Country %in% c("Canada", "US", "US & CANADA", "US, Canada") ~ "North America",

      Country %in% c("China", "Israel", "Japan", "Singapore",
                     "Turkey") ~ "Asia",

      Country %in% c("Austria", "France", "Germany",
                     "Germany, Austria, Switzerland",
                     "Netherlands", "Poland",
                     "Switzerland", "UK") ~ "Northern Europe",

      Country %in% c("Croatia", "Romania", "Spain") ~ "Southern Europe",

      TRUE ~ NA_character_
    )
  )

data_included$SampleType <- factor(
  data_included$SampleType,
  levels = c("convenience", "college", "representative")
)

data_included$country_region <- factor(
  data_included$country_region,
  levels = c("North America", "Northern Europe", "Southern Europe", "Asia", "Australia")
)

data_included$StudyDesign <- factor(
  data_included$StudyDesign,
  levels = c("daily_diary", "esm")
)

data_included$DeDomainAll <- factor(
  data_included$DeDomainAll,
  levels = c("general", "work/achievement", "social", "health", "other")
)

data_included$TypeDeMeasure <- factor(
  data_included$TypeDeMeasure,
  levels = c("checklist/questionnaire", "open_response", "interview", "other")
)

data_included$DeDataType <- factor(
  data_included$DeDataType,
  levels = c("dichotomous", "numeric_count", "scale_score", "other")
)

data_included$WbDimension <- factor(
  data_included$WbDimension,
  levels = c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect", "global_well-being")
)

data_included$CorrLag <- factor(
  data_included$CorrLag,
  levels = c("concurrent", "prospective")
)

# Create dataset for computation of descriptive statistics for moderator on sample-level

overall_cat_sample_level_data <- data_included %>% 
  group_by(StudyID) %>%
  distinct(SampleID, .keep_all = TRUE) %>%
  ungroup()

# Compute descriptive statistics for categorical moderator variables

mod_cat_overall <-
  rbind(
    as.data.frame(cbind(Level = "Sample",
                        table(overall_cat_sample_level_data$SampleType),
                        round(prop.table(table(overall_cat_sample_level_data$SampleType)),4) * 100)), 
    as.data.frame(cbind(Level = "Sample",
                        table(overall_cat_sample_level_data$country_region), 
                        round(prop.table(table(overall_cat_sample_level_data$country_region)),4) * 100)),
    as.data.frame(cbind(Level = "Effect size",
                        table(data_included$DeDomainAll), 
                        round(prop.table(table(data_included$DeDomainAll)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
      table(data_included$TypeDeMeasure), 
                        round(prop.table(table(data_included$TypeDeMeasure)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
      table(data_included$DeDataType), 
                        round(prop.table(table(data_included$DeDataType)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
      table(data_included$WbDimension), 
                        round(prop.table(table(data_included$WbDimension)),4) * 100)), 
    as.data.frame(cbind(Level = "Sample",
      table(overall_cat_sample_level_data$StudyDesign),
                        round(prop.table(table(overall_cat_sample_level_data$StudyDesign)),4) * 100)),
    as.data.frame(cbind(Level = "Effect size",
                        table(data_included$CorrLag), 
                        round(prop.table(table(data_included$CorrLag)),4) * 100)))

names(mod_cat_overall) <- c("Level", "*n*", "% *n*")

rownames(mod_cat_overall) <- c("Convenience", "College", "Representative", 
                               "North America", "Northern Europe", "Southern Europe", "Asia", "Australia",
                               "General", "Work/Achievement", "Social", "Health", "Other event",
                               "Checklist/questionnaire", "Open-response", "Interview", "Other measure",
                               "Dichotomous", "Numeric count", "Scale score", "Other type",
                               "Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
                               "Daily diary", "Experience sampling",
                               "Concurrent", "Prospective")

kable(mod_cat_overall
      , caption="**Descriptive Statistics for Categorical Moderators at Their Respective Levels**"
      , escape=FALSE
      , label = NA) %>%  
  pack_rows(index = c("Sample type" = 3, 
                      "Country" = 5, 
                      "Daily event domain" = 5,
                      "Daily event measurement" = 4, 
                      "Daily event data type" = 4, 
                      "Well-being dimension" = 5,
                      "Study design" = 2,
                      "Correlation lag" = 2)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 11: Descriptive Statistics for Categorical Moderators at Their Respective Levels

	Level	n	% n
Sample type
Convenience	Sample	182	70
College	Sample	77	29.62
Representative	Sample	1	0.38
Country
North America	Sample	146	65.47
Northern Europe	Sample	39	17.49
Southern Europe	Sample	6	2.69
Asia	Sample	26	11.66
Australia	Sample	6	2.69
Daily event domain
General	Effect size	607	35.03
Work/Achievement	Effect size	419	24.18
Social	Effect size	565	32.6
Health	Effect size	58	3.35
Other event	Effect size	84	4.85
Daily event measurement
Checklist/questionnaire	Effect size	1572	91.18
Open-response	Effect size	74	4.29
Interview	Effect size	72	4.18
Other measure	Effect size	6	0.35
Daily event data type
Dichotomous	Effect size	392	22.63
Numeric count	Effect size	304	17.55
Scale score	Effect size	1023	59.06
Other type	Effect size	13	0.75
Well-being dimension
Life satisfaction	Effect size	134	7.73
Domain satisfaction	Effect size	61	3.52
Positive affect	Effect size	699	40.33
Negative affect	Effect size	811	46.8
Global well-being	Effect size	28	1.62
Study design
Daily diary	Sample	206	79.23
Experience sampling	Sample	54	20.77
Correlation lag
Concurrent	Effect size	1481	85.46
Prospective	Effect size	252	14.54

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes). % n indicate the proportion of units within the corresponding level assigned to each moderator category.

Uplifts & SWB

Within-Person

Continuous Moderators

# Study-Level

overall_cont_study_level_uplifts_within <- uplifts_within %>%
  distinct(StudyID, .keep_all = TRUE) %>%
  select(Year) %>%
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Study")

# Sample-Level

overall_cont_sample_level_uplifts_within <- uplifts_within %>% 
  group_by(StudyID) %>%
  distinct(SampleID, .keep_all = TRUE) %>%
  ungroup() %>%
  select(Female, AgeM) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Sample")

# Effect Size-Level

overall_cont_es_level_uplifts_within <- uplifts_within %>%
  select(N, Observations, DeOccDay, DeOccTotal, WbReliability) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Effect size")

# Combine

mod_metric_overall_uplifts_within <-
  rbind(
    overall_cont_study_level_uplifts_within,
    overall_cont_sample_level_uplifts_within,
    overall_cont_es_level_uplifts_within
  )

# Re-order columns for table

mod_metric_overall_uplifts_within <- mod_metric_overall_uplifts_within[, c("level", "n", "mean", "sd", "min", "max")]

row.names(mod_metric_overall_uplifts_within) <- c("Publication year", "% Female", "Mean age", "Sample size", 
                                                  "Number of observations", "Assessments per day", 
                                                  "Total assessments", "SWB reliability")

names(mod_metric_overall_uplifts_within) <- c("Level", "*n*", "*M*", "*SD*", "Min", "Max")

kable(mod_metric_overall_uplifts_within
      , caption="**Descriptive Statistics for Continuous Moderators at Their Respective Levels for the Within-Person Association Between Daily Uplifts and SWB**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 12: Descriptive Statistics for Continuous Moderators at Their Respective Levels for the Within-Person Association Between Daily Uplifts and SWB

	Level	n	M	SD	Min	Max
Publication year	Study	50	2018.20	7.21	1992.00	2026.00
% Female	Sample	67	61.54	24.69	0.00	100.00
Mean age	Sample	66	29.19	12.02	10.79	64.47
Sample size	Effect size	305	164.79	201.81	20.00	1982.00
Number of observations	Effect size	304	2588.26	7487.29	150.00	76594.00
Assessments per day	Effect size	305	1.20	0.88	1.00	12.51
Total assessments	Effect size	305	17.01	19.11	4.00	120.00
SWB reliability	Effect size	252	0.75	0.11	0.55	0.98

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes).

Categorical Moderators

uplifts_within <- uplifts_within %>%
  mutate(
    country_region = case_when(
      Country == "Australia" ~ "Australia",
      
      Country %in% c("Canada", "US", "US & CANADA", "US, Canada") ~ "North America",

      Country %in% c("China", "Israel", "Japan", "Singapore",
                     "Turkey") ~ "Asia",

      Country %in% c("Austria", "France", "Germany",
                     "Germany, Austria, Switzerland",
                     "Netherlands", "Poland",
                     "Switzerland", "UK") ~ "Northern Europe",

      Country %in% c("Croatia", "Romania", "Spain") ~ "Southern Europe",

      TRUE ~ NA_character_
    )
  )

uplifts_within$SampleType <- factor(
  uplifts_within$SampleType,
  levels = c("convenience", "college", "representative")
)

uplifts_within$country_region <- factor(
  uplifts_within$country_region,
  levels = c("North America", "Northern Europe", "Southern Europe", "Asia", "Australia")
)

uplifts_within$StudyDesign <- factor(
  uplifts_within$StudyDesign,
  levels = c("daily_diary", "esm")
)

uplifts_within$DeDomainAll <- factor(
  uplifts_within$DeDomainAll,
  levels = c("general", "work/achievement", "social", "health", "other")
)

uplifts_within$TypeDeMeasure <- factor(
  uplifts_within$TypeDeMeasure,
  levels = c("checklist/questionnaire", "open_response", "interview", "other")
)

uplifts_within$DeDataType <- factor(
  uplifts_within$DeDataType,
  levels = c("dichotomous", "numeric_count", "scale_score", "other")
)

uplifts_within$WbDimension <- factor(
  uplifts_within$WbDimension,
  levels = c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect", "global_well-being")
)

uplifts_within$CorrLag <- factor(
  uplifts_within$CorrLag,
  levels = c("concurrent", "prospective")
)

# Create dataset for computation of descriptive statistics for moderator on sample-level

cat_sample_level_data_uplifts_within <- uplifts_within %>% 
  group_by(StudyID) %>%
  distinct(SampleID, .keep_all = TRUE) %>%
  ungroup()

# Compute descriptive statistics for categorical moderator variables

mod_cat_uplifts_within <-
  rbind(
    as.data.frame(cbind(Level = "Sample",
                        table(cat_sample_level_data_uplifts_within$SampleType),
                        round(prop.table(table(cat_sample_level_data_uplifts_within$SampleType)),4) * 100)), 
    as.data.frame(cbind(Level = "Sample",
                        table(cat_sample_level_data_uplifts_within$country_region), 
                        round(prop.table(table(cat_sample_level_data_uplifts_within$country_region)),4) * 100)),
    as.data.frame(cbind(Level = "Effect size",
                        table(uplifts_within$DeDomainAll), 
                        round(prop.table(table(uplifts_within$DeDomainAll)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
      table(uplifts_within$TypeDeMeasure), 
                        round(prop.table(table(uplifts_within$TypeDeMeasure)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
      table(uplifts_within$DeDataType), 
                        round(prop.table(table(uplifts_within$DeDataType)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
      table(uplifts_within$WbDimension), 
                        round(prop.table(table(uplifts_within$WbDimension)),4) * 100)), 
    as.data.frame(cbind(Level = "Sample",
      table(cat_sample_level_data_uplifts_within$StudyDesign),
                        round(prop.table(table(cat_sample_level_data_uplifts_within$StudyDesign)),4) * 100)),
    as.data.frame(cbind(Level = "Effect size",
                        table(uplifts_within$CorrLag), 
                        round(prop.table(table(uplifts_within$CorrLag)),4) * 100)))

names(mod_cat_uplifts_within) <- c("Level", "*n*", "% *n*")

rownames(mod_cat_uplifts_within) <- c("Convenience", "College", "Representative", 
                                     "North America", "Northern Europe", "Southern Europe", "Asia", "Australia",
                                     "General", "Work/Achievement", "Social", "Health", "Other event",
                                     "Checklist/questionnaire", "Open-response", "Interview", "Other measure",
                                     "Dichotomous", "Numeric count", "Scale score", "Other type",
                                     "Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
                                     "Daily diary", "Experience sampling",
                                     "Concurrent", "Prospective")

kable(mod_cat_uplifts_within
      , caption="**Descriptive Statistics for Categorical Moderators at Their Respective Levels for the Within-Person Association Between Daily Uplifts and SWB**"
      , escape=FALSE
      , label = NA) %>%  
  pack_rows(index = c("Sample type" = 3, 
                      "Country" = 5, 
                      "Daily event domain" = 5,
                      "Daily event measurement" = 4, 
                      "Daily event data type" = 4, 
                      "Well-being dimension" = 5,
                      "Study design" = 2,
                      "Correlation lag" = 2)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 13: Descriptive Statistics for Categorical Moderators at Their Respective Levels for the Within-Person Association Between Daily Uplifts and SWB

	Level	n	% n
Sample type
Convenience	Sample	43	64.18
College	Sample	23	34.33
Representative	Sample	1	1.49
Country
North America	Sample	41	74.55
Northern Europe	Sample	9	16.36
Southern Europe	Sample	2	3.64
Asia	Sample	2	3.64
Australia	Sample	1	1.82
Daily event domain
General	Effect size	70	22.95
Work/Achievement	Effect size	111	36.39
Social	Effect size	116	38.03
Health	Effect size	0	0
Other event	Effect size	8	2.62
Daily event measurement
Checklist/questionnaire	Effect size	275	90.16
Open-response	Effect size	12	3.93
Interview	Effect size	18	5.9
Other measure	Effect size	0	0
Daily event data type
Dichotomous	Effect size	68	22.3
Numeric count	Effect size	38	12.46
Scale score	Effect size	196	64.26
Other type	Effect size	3	0.98
Well-being dimension
Life satisfaction	Effect size	35	11.48
Domain satisfaction	Effect size	16	5.25
Positive affect	Effect size	127	41.64
Negative affect	Effect size	123	40.33
Global well-being	Effect size	4	1.31
Study design
Daily diary	Sample	58	86.57
Experience sampling	Sample	9	13.43
Correlation lag
Concurrent	Effect size	193	63.28
Prospective	Effect size	112	36.72

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes). % n indicate the proportion of units within the corresponding level assigned to each moderator category.

Between-Person

Continuous Moderators

# Study-Level

overall_cont_study_level_uplifts_between <- uplifts_between %>%
  distinct(StudyID, .keep_all = TRUE) %>%
  select(Year) %>%
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Study")

# Sample-Level

overall_cont_sample_level_uplifts_between <- uplifts_between %>% 
  group_by(StudyID) %>%
  distinct(SampleID, .keep_all = TRUE) %>%
  ungroup() %>%
  select(Female, AgeM) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Sample")

# Effect Size-Level

overall_cont_es_level_uplifts_between <- uplifts_between %>%
  select(N, DeOccDay, DeOccTotal, WbReliability) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Effect size")

# Combine

mod_metric_overall_uplifts_between <-
  rbind(
    overall_cont_study_level_uplifts_between,
    overall_cont_sample_level_uplifts_between,
    overall_cont_es_level_uplifts_between
  )

# Re-order columns for table

mod_metric_overall_uplifts_between <- mod_metric_overall_uplifts_between[, c("level", "n", "mean", "sd", "min", "max")]

row.names(mod_metric_overall_uplifts_between) <- c("Publication year", "% Female", "Mean age", "Sample size", 
                                                   "Assessments per day", "Total assessments", "SWB reliability")

names(mod_metric_overall_uplifts_between) <- c("Level", "*n*", "*M*", "*SD*", "Min", "Max")

kable(mod_metric_overall_uplifts_between
      , caption="**Descriptive Statistics for Continuous Moderators at Their Respective Levels for the Between-Person Association Between Daily Uplifts and SWB**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 14: Descriptive Statistics for Continuous Moderators at Their Respective Levels for the Between-Person Association Between Daily Uplifts and SWB

	Level	n	M	SD	Min	Max
Publication year	Study	74	2017.34	6.83	1991.00	2026.00
% Female	Sample	96	61.27	26.89	0.00	100.00
Mean age	Sample	95	30.22	14.24	10.79	74.36
Sample size	Effect size	270	181.51	233.32	18.00	1982.00
Assessments per day	Effect size	270	1.26	1.08	1.00	12.51
Total assessments	Effect size	270	17.05	16.61	3.00	120.00
SWB reliability	Effect size	202	0.80	0.11	0.55	0.99

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes).

Categorical Moderators

uplifts_between <- uplifts_between %>%
  mutate(
    country_region = case_when(
      Country == "Australia" ~ "Australia",
      
      Country %in% c("Canada", "US", "US & CANADA", "US, Canada") ~ "North America",

      Country %in% c("China", "Israel", "Japan", "Singapore",
                     "Turkey") ~ "Asia",

      Country %in% c("Austria", "France", "Germany",
                     "Germany, Austria, Switzerland",
                     "Netherlands", "Poland",
                     "Switzerland", "UK") ~ "Northern Europe",

      Country %in% c("Croatia", "Romania", "Spain") ~ "Southern Europe",

      TRUE ~ NA_character_
    )
  )

uplifts_between$SampleType <- factor(
  uplifts_between$SampleType,
  levels = c("convenience", "college", "representative")
)

uplifts_between$country_region <- factor(
  uplifts_between$country_region,
  levels = c("North America", "Northern Europe", "Southern Europe", "Asia", "Australia")
)

uplifts_between$StudyDesign <- factor(
  uplifts_between$StudyDesign,
  levels = c("daily_diary", "esm")
)

uplifts_between$DeDomainAll <- factor(
  uplifts_between$DeDomainAll,
  levels = c("general", "work/achievement", "social", "health", "other")
)

uplifts_between$TypeDeMeasure <- factor(
  uplifts_between$TypeDeMeasure,
  levels = c("checklist/questionnaire", "open_response", "interview", "other")
)

uplifts_between$DeDataType <- factor(
  uplifts_between$DeDataType,
  levels = c("dichotomous", "numeric_count", "scale_score", "other")
)

uplifts_between$WbDimension <- factor(
  uplifts_between$WbDimension,
  levels = c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect", "global_well-being")
)

uplifts_between$CorrLag <- factor(
  uplifts_between$CorrLag,
  levels = c("concurrent", "prospective")
)

# Create dataset for computation of descriptive statistics for moderator on sample-level

cat_sample_level_data_uplifts_between <- uplifts_between %>% 
  group_by(StudyID) %>%
  distinct(SampleID, .keep_all = TRUE) %>%
  ungroup()

# Compute descriptive statistics for categorical moderator variables

mod_cat_uplifts_between <-
  rbind(
    as.data.frame(cbind(Level = "Sample",
                        table(cat_sample_level_data_uplifts_between$SampleType),
                        round(prop.table(table(cat_sample_level_data_uplifts_between$SampleType)),4) * 100)), 
    as.data.frame(cbind(Level = "Sample",
                        table(cat_sample_level_data_uplifts_between$country_region), 
                        round(prop.table(table(cat_sample_level_data_uplifts_between$country_region)),4) * 100)),
    as.data.frame(cbind(Level = "Effect size",
                        table(uplifts_between$DeDomainAll), 
                        round(prop.table(table(uplifts_between$DeDomainAll)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
      table(uplifts_between$TypeDeMeasure), 
                        round(prop.table(table(uplifts_between$TypeDeMeasure)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
      table(uplifts_between$DeDataType), 
                        round(prop.table(table(uplifts_between$DeDataType)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
      table(uplifts_between$WbDimension), 
                        round(prop.table(table(uplifts_between$WbDimension)),4) * 100)), 
    as.data.frame(cbind(Level = "Sample",
      table(cat_sample_level_data_uplifts_between$StudyDesign),
                        round(prop.table(table(cat_sample_level_data_uplifts_between$StudyDesign)),4) * 100)))

names(mod_cat_uplifts_between) <- c("Level", "*n*", "% *n*")

rownames(mod_cat_uplifts_between) <- c("Convenience", "College", "Representative", 
                                     "North America", "Northern Europe", "Southern Europe", "Asia", "Australia",
                                     "General", "Work/Achievement", "Social", "Health", "Other event",
                                     "Checklist/questionnaire", "Open-response", "Interview", "Other measure",
                                     "Dichotomous", "Numeric count", "Scale score", "Other type",
                                     "Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
                                     "Daily diary", "Experience sampling")

kable(mod_cat_uplifts_between
      , caption="**Descriptive Statistics for Categorical Moderators at Their Respective Levels for the Between-Person Association Between Daily Uplifts and SWB**"
      , escape=FALSE
      , label = NA) %>%  
  pack_rows(index = c("Sample type" = 3, 
                      "Country" = 5, 
                      "Daily event domain" = 5,
                      "Daily event measurement" = 4, 
                      "Daily event data type" = 4, 
                      "Well-being dimension" = 5,
                      "Study design" = 2)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 15: Descriptive Statistics for Categorical Moderators at Their Respective Levels for the Between-Person Association Between Daily Uplifts and SWB

	Level	n	% n
Sample type
Convenience	Sample	60	62.5
College	Sample	35	36.46
Representative	Sample	1	1.04
Country
North America	Sample	55	68.75
Northern Europe	Sample	14	17.5
Southern Europe	Sample	2	2.5
Asia	Sample	7	8.75
Australia	Sample	2	2.5
Daily event domain
General	Effect size	77	28.52
Work/Achievement	Effect size	73	27.04
Social	Effect size	108	40
Health	Effect size	0	0
Other event	Effect size	12	4.44
Daily event measurement
Checklist/questionnaire	Effect size	241	89.59
Open-response	Effect size	16	5.95
Interview	Effect size	10	3.72
Other measure	Effect size	2	0.74
Daily event data type
Dichotomous	Effect size	58	21.56
Numeric count	Effect size	44	16.36
Scale score	Effect size	162	60.22
Other type	Effect size	5	1.86
Well-being dimension
Life satisfaction	Effect size	28	10.37
Domain satisfaction	Effect size	11	4.07
Positive affect	Effect size	119	44.07
Negative affect	Effect size	109	40.37
Global well-being	Effect size	3	1.11
Study design
Daily diary	Sample	83	86.46
Experience sampling	Sample	13	13.54

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes). % n indicate the proportion of units within the corresponding level assigned to each moderator category.

Hassles & SWB

Within-Person

Continuous Moderators

# Study-Level

overall_cont_study_level_hassles_within <- hassles_within %>%
  distinct(StudyID, .keep_all = TRUE) %>%
  select(Year) %>%
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Study")

# Sample-Level

overall_cont_sample_level_hassles_within <- hassles_within %>% 
  group_by(StudyID) %>%
  distinct(SampleID, .keep_all = TRUE) %>%
  ungroup() %>%
  select(Female, AgeM) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Sample")

# Effect Size-Level

overall_cont_es_level_hassles_within <- hassles_within %>%
  select(N, Observations, DeOccDay, DeOccTotal, WbReliability) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Effect size")

# Combine

mod_metric_overall_hassles_within <-
  rbind(
    overall_cont_study_level_hassles_within,
    overall_cont_sample_level_hassles_within,
    overall_cont_es_level_hassles_within
  )

# Re-order columns for table

mod_metric_overall_hassles_within <- mod_metric_overall_hassles_within[, c("level", "n", "mean", "sd", "min", "max")]

row.names(mod_metric_overall_hassles_within) <- c("Publication year", "% Female", "Mean age", "Sample size", 
                                                  "Number of observations", "Assessments per day", 
                                                  "Total assessments", "SWB reliability")

names(mod_metric_overall_hassles_within) <- c("Level", "*n*", "*M*", "*SD*", "Min", "Max")

kable(mod_metric_overall_hassles_within
      , caption="**Descriptive Statistics for Continuous Moderators at Their Respective Levels for the Within-Person Association Between Daily Hassles and SWB**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 16: Descriptive Statistics for Continuous Moderators at Their Respective Levels for the Within-Person Association Between Daily Hassles and SWB

	Level	n	M	SD	Min	Max
Publication year	Study	119	2019.15	6.56	1992.00	2026.00
% Female	Sample	138	64.28	21.81	0.00	100.00
Mean age	Sample	135	30.86	13.22	10.77	73.50
Sample size	Effect size	531	172.87	201.12	38.00	1982.00
Number of observations	Effect size	530	3322.65	8287.27	107.00	77474.00
Assessments per day	Effect size	531	1.59	1.46	1.00	8.00
Total assessments	Effect size	531	22.27	26.19	4.00	150.00
SWB reliability	Effect size	419	0.78	0.11	0.54	0.98

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes).

Categorical Moderators

hassles_within <- hassles_within %>%
  mutate(
    country_region = case_when(
      Country == "Australia" ~ "Australia",
      
      Country %in% c("Canada", "US", "US & CANADA", "US, Canada") ~ "North America",

      Country %in% c("China", "Israel", "Japan", "Singapore",
                     "Turkey") ~ "Asia",

      Country %in% c("Austria", "France", "Germany",
                     "Germany, Austria, Switzerland",
                     "Netherlands", "Poland",
                     "Switzerland", "UK") ~ "Northern Europe",

      Country %in% c("Croatia", "Romania", "Spain") ~ "Southern Europe",

      TRUE ~ NA_character_
    )
  )

hassles_within$SampleType <- factor(
  hassles_within$SampleType,
  levels = c("convenience", "college", "representative")
)

hassles_within$country_region <- factor(
  hassles_within$country_region,
  levels = c("North America", "Northern Europe", "Southern Europe", "Asia", "Australia")
)

hassles_within$StudyDesign <- factor(
  hassles_within$StudyDesign,
  levels = c("daily_diary", "esm")
)

hassles_within$DeDomainAll <- factor(
  hassles_within$DeDomainAll,
  levels = c("general", "work/achievement", "social", "health", "other")
)

hassles_within$TypeDeMeasure <- factor(
  hassles_within$TypeDeMeasure,
  levels = c("checklist/questionnaire", "open_response", "interview", "other")
)

hassles_within$DeDataType <- factor(
  hassles_within$DeDataType,
  levels = c("dichotomous", "numeric_count", "scale_score", "other")
)

hassles_within$WbDimension <- factor(
  hassles_within$WbDimension,
  levels = c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect", "global_well-being")
)

hassles_within$CorrLag <- factor(
  hassles_within$CorrLag,
  levels = c("concurrent", "prospective")
)

# Create dataset for computation of descriptive statistics for moderator on sample-level

cat_sample_level_data_hassles_within <- hassles_within %>% 
  group_by(StudyID) %>%
  distinct(SampleID, .keep_all = TRUE) %>%
  ungroup()

# Compute descriptive statistics for categorical moderator variables

mod_cat_hassles_within <-
  rbind(
    as.data.frame(cbind(Level = "Sample",
                        table(cat_sample_level_data_hassles_within$SampleType),
                        round(prop.table(table(cat_sample_level_data_hassles_within$SampleType)),4) * 100)), 
    as.data.frame(cbind(Level = "Sample",
                        table(cat_sample_level_data_hassles_within$country_region), 
                        round(prop.table(table(cat_sample_level_data_hassles_within$country_region)),4) * 100)),
    as.data.frame(cbind(Level = "Effect size",
                        table(hassles_within$DeDomainAll), 
                        round(prop.table(table(hassles_within$DeDomainAll)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
                        table(hassles_within$TypeDeMeasure), 
                        round(prop.table(table(hassles_within$TypeDeMeasure)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
                        table(hassles_within$DeDataType), 
                        round(prop.table(table(hassles_within$DeDataType)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
                        table(hassles_within$WbDimension), 
                        round(prop.table(table(hassles_within$WbDimension)),4) * 100)), 
    as.data.frame(cbind(Level = "Sample",
      table(cat_sample_level_data_hassles_within$StudyDesign),
                        round(prop.table(table(cat_sample_level_data_hassles_within$StudyDesign)),4) * 100)),
    as.data.frame(cbind(Level = "Effect size",
                        table(hassles_within$CorrLag), 
                        round(prop.table(table(hassles_within$CorrLag)),4) * 100)))

names(mod_cat_hassles_within) <- c("Level", "*n*", "% *n*")

rownames(mod_cat_hassles_within) <- c("Convenience", "College", "Representative", 
                                     "North America", "Northern Europe", "Southern Europe", "Asia", "Australia",
                                     "General", "Work/Achievement", "Social", "Health", "Other event",
                                     "Checklist/questionnaire", "Open-response", "Interview", "Other measure",
                                     "Dichotomous", "Numeric count", "Scale score", "Other type",
                                     "Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
                                     "Daily diary", "Experience sampling",
                                     "Concurrent", "Prospective")

kable(mod_cat_hassles_within
      , caption="**Descriptive Statistics for Categorical Moderators at Their Respective Levels for the Within-Person Association Between Daily Hassles and SWB**"
      , escape=FALSE
      , label = NA) %>%  
  pack_rows(index = c("Sample type" = 3, 
                      "Country" = 5, 
                      "Daily event domain" = 5,
                      "Daily event measurement" = 4, 
                      "Daily event data type" = 4, 
                      "Well-being dimension" = 5,
                      "Study design" = 2,
                      "Correlation lag" = 2)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 17: Descriptive Statistics for Categorical Moderators at Their Respective Levels for the Within-Person Association Between Daily Hassles and SWB

	Level	n	% n
Sample type
Convenience	Sample	100	72.46
College	Sample	37	26.81
Representative	Sample	1	0.72
Country
North America	Sample	74	60.66
Northern Europe	Sample	31	25.41
Southern Europe	Sample	5	4.1
Asia	Sample	11	9.02
Australia	Sample	1	0.82
Daily event domain
General	Effect size	184	34.65
Work/Achievement	Effect size	139	26.18
Social	Effect size	159	29.94
Health	Effect size	23	4.33
Other event	Effect size	26	4.9
Daily event measurement
Checklist/questionnaire	Effect size	491	92.99
Open-response	Effect size	13	2.46
Interview	Effect size	24	4.55
Other measure	Effect size	0	0
Daily event data type
Dichotomous	Effect size	115	21.66
Numeric count	Effect size	94	17.7
Scale score	Effect size	322	60.64
Other type	Effect size	0	0
Well-being dimension
Life satisfaction	Effect size	38	7.16
Domain satisfaction	Effect size	14	2.64
Positive affect	Effect size	206	38.79
Negative affect	Effect size	265	49.91
Global well-being	Effect size	8	1.51
Study design
Daily diary	Sample	107	77.54
Experience sampling	Sample	31	22.46
Correlation lag
Concurrent	Effect size	391	73.63
Prospective	Effect size	140	26.37

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes). % n indicate the proportion of units within the corresponding level assigned to each moderator category.

Between-Person

Continuous Moderators

# Study-Level

overall_cont_study_level_hassles_between <- hassles_between %>%
  distinct(StudyID, .keep_all = TRUE) %>%
  select(Year) %>%
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Study")

# Sample-Level

overall_cont_sample_level_hassles_between <- hassles_between %>% 
  group_by(StudyID) %>%
  distinct(SampleID, .keep_all = TRUE) %>%
  ungroup() %>%
  select(Female, AgeM) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Sample")

# Effect Size-Level

overall_cont_es_level_hassles_between <- hassles_between %>%
  select(N, DeOccDay, DeOccTotal, WbReliability) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Effect size")

# Combine

mod_metric_overall_hassles_between <-
  rbind(
    overall_cont_study_level_hassles_between,
    overall_cont_sample_level_hassles_between,
    overall_cont_es_level_hassles_between
  )

# Re-order columns for table

mod_metric_overall_hassles_between <- mod_metric_overall_hassles_between[, c("level", "n", "mean", "sd", "min", "max")]

row.names(mod_metric_overall_hassles_between) <- c("Publication year", "% Female", "Mean age", "Sample size", 
                                                   "Assessments per day", "Total assessments", "SWB reliability")

names(mod_metric_overall_hassles_between) <- c("Level", "*n*", "*M*", "*SD*", "Min", "Max")

kable(mod_metric_overall_hassles_between
      , caption="**Descriptive Statistics for Continuous Moderators at Their Respective Levels for the Between-Person Association Between Daily Hassles and SWB**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 18: Descriptive Statistics for Continuous Moderators at Their Respective Levels for the Between-Person Association Between Daily Hassles and SWB

	Level	n	M	SD	Min	Max
Publication year	Study	191	2017.79	6.88	1991.00	2026.00
% Female	Sample	223	63.07	23.83	0.00	100.00
Mean age	Sample	219	31.54	16.13	10.77	80.30
Sample size	Effect size	627	179.35	206.70	18.00	1982.00
Assessments per day	Effect size	623	1.71	1.64	1.00	13.60
Total assessments	Effect size	623	22.51	24.67	3.00	150.00
SWB reliability	Effect size	466	0.84	0.10	0.55	0.99

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes).

Categorical Moderators

hassles_between <- hassles_between %>%
  mutate(
    country_region = case_when(
      Country == "Australia" ~ "Australia",
      
      Country %in% c("Canada", "US", "US & CANADA", "US, Canada") ~ "North America",

      Country %in% c("China", "Israel", "Japan", "Singapore",
                     "Turkey") ~ "Asia",

      Country %in% c("Austria", "France", "Germany",
                     "Germany, Austria, Switzerland",
                     "Netherlands", "Poland",
                     "Switzerland", "UK") ~ "Northern Europe",

      Country %in% c("Croatia", "Romania", "Spain") ~ "Southern Europe",

      TRUE ~ NA_character_
    )
  )

hassles_between$SampleType <- factor(
  hassles_between$SampleType,
  levels = c("convenience", "college", "representative")
)

hassles_between$country_region <- factor(
  hassles_between$country_region,
  levels = c("North America", "Northern Europe", "Southern Europe", "Asia", "Australia")
)

hassles_between$StudyDesign <- factor(
  hassles_between$StudyDesign,
  levels = c("daily_diary", "esm")
)

hassles_between$DeDomainAll <- factor(
  hassles_between$DeDomainAll,
  levels = c("general", "work/achievement", "social", "health", "other")
)

hassles_between$TypeDeMeasure <- factor(
  hassles_between$TypeDeMeasure,
  levels = c("checklist/questionnaire", "open_response", "interview", "other")
)

hassles_between$DeDataType <- factor(
  hassles_between$DeDataType,
  levels = c("dichotomous", "numeric_count", "scale_score", "other")
)

hassles_between$WbDimension <- factor(
  hassles_between$WbDimension,
  levels = c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect", "global_well-being")
)

hassles_between$CorrLag <- factor(
  hassles_between$CorrLag,
  levels = c("concurrent", "prospective")
)

# Create dataset for computation of descriptive statistics for moderator on sample-level

cat_sample_level_data_hassles_between <- hassles_between %>% 
  group_by(StudyID) %>%
  distinct(SampleID, .keep_all = TRUE) %>%
  ungroup()

# Compute descriptive statistics for categorical moderator variables

mod_cat_hassles_between <-
  rbind(
    as.data.frame(cbind(Level = "Sample",
                        table(cat_sample_level_data_hassles_between$SampleType),
                        round(prop.table(table(cat_sample_level_data_hassles_between$SampleType)),4) * 100)), 
    as.data.frame(cbind(Level = "Sample",
                        table(cat_sample_level_data_hassles_between$country_region), 
                        round(prop.table(table(cat_sample_level_data_hassles_between$country_region)),4) * 100)),
    as.data.frame(cbind(Level = "Effect size",
                        table(hassles_between$DeDomainAll), 
                        round(prop.table(table(hassles_between$DeDomainAll)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
                        table(hassles_between$TypeDeMeasure), 
                        round(prop.table(table(hassles_between$TypeDeMeasure)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
                        table(hassles_between$DeDataType), 
                        round(prop.table(table(hassles_between$DeDataType)),4) * 100)), 
    as.data.frame(cbind(Level = "Effect size",
                        table(hassles_between$WbDimension), 
                        round(prop.table(table(hassles_between$WbDimension)),4) * 100)), 
    as.data.frame(cbind(Level = "Sample",
                        table(cat_sample_level_data_hassles_between$StudyDesign),
                        round(prop.table(table(cat_sample_level_data_hassles_between$StudyDesign)),4) * 100)))

names(mod_cat_hassles_between) <- c("Level", "*n*", "% *n*")

rownames(mod_cat_hassles_between) <- c("Convenience", "College", "Representative", 
                                     "North America", "Northern Europe", "Southern Europe", "Asia", "Australia",
                                     "General", "Work/Achievement", "Social", "Health", "Other event",
                                     "Checklist/questionnaire", "Open-response", "Interview", "Other measure",
                                     "Dichotomous", "Numeric count", "Scale score", "Other type",
                                     "Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
                                     "Daily diary", "Experience sampling")

kable(mod_cat_hassles_between
      , caption="**Descriptive Statistics for Categorical Moderators at Their Respective Levels for the Between-Person Association Between Daily Hassles and SWB**"
      , escape=FALSE
      , label = NA) %>%  
  pack_rows(index = c("Sample type" = 3, 
                      "Country" = 5, 
                      "Daily event domain" = 5,
                      "Daily event measurement" = 4, 
                      "Daily event data type" = 4, 
                      "Well-being dimension" = 5,
                      "Study design" = 2)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 19: Descriptive Statistics for Categorical Moderators at Their Respective Levels for the Between-Person Association Between Daily Hassles and SWB

	Level	n	% n
Sample type
Convenience	Sample	151	67.41
College	Sample	72	32.14
Representative	Sample	1	0.45
Country
North America	Sample	132	67.69
Northern Europe	Sample	34	17.44
Southern Europe	Sample	4	2.05
Asia	Sample	19	9.74
Australia	Sample	6	3.08
Daily event domain
General	Effect size	276	44.02
Work/Achievement	Effect size	96	15.31
Social	Effect size	182	29.03
Health	Effect size	35	5.58
Other event	Effect size	38	6.06
Daily event measurement
Checklist/questionnaire	Effect size	565	90.84
Open-response	Effect size	33	5.31
Interview	Effect size	20	3.22
Other measure	Effect size	4	0.64
Daily event data type
Dichotomous	Effect size	151	24.08
Numeric count	Effect size	128	20.41
Scale score	Effect size	343	54.7
Other type	Effect size	5	0.8
Well-being dimension
Life satisfaction	Effect size	33	5.26
Domain satisfaction	Effect size	20	3.19
Positive affect	Effect size	247	39.39
Negative affect	Effect size	314	50.08
Global well-being	Effect size	13	2.07
Study design
Daily diary	Sample	173	77.23
Experience sampling	Sample	51	22.77

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes). % n indicate the proportion of units within the corresponding level assigned to each moderator category.

Effect Size Level

Overall

Continuous Moderators

mod_metric_overall_es <- data_included %>% 
  select(Year, Female, AgeM, N, Observations, DeOccDay, DeOccTotal, WbReliability) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Effect size")

mod_metric_overall_es <- mod_metric_overall_es[, c("level", "n", "mean", "sd", "min", "max")]

row.names(mod_metric_overall_es) <- c("Publication year", "% Female", "Mean age", "Sample size", "Number of observations", "Assessments per day", 
                                   "Total assessments", "SWB reliability")

names(mod_metric_overall_es) <- c("Level", "*n*", "*M*", "*SD*", "Min", "Max")

kable(mod_metric_overall_es
      , caption="**Descriptive Statistics for Continuous Moderators at the Effect Size Level**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 20: Descriptive Statistics for Continuous Moderators at the Effect Size Level

	Level	n	M	SD	Min	Max
Publication year	Effect size	1733	2017.73	6.61	1991.00	2026.00
% Female	Effect size	1731	63.51	22.60	0.00	100.00
Mean age	Effect size	1709	29.39	13.94	10.77	80.30
Sample size	Effect size	1733	175.14	208.46	18.00	1982.00
Number of observations	Effect size	862	3054.35	7884.17	107.00	77474.00
Assessments per day	Effect size	1729	1.51	1.41	1.00	13.60
Total assessments	Effect size	1729	20.61	23.30	3.00	150.00
SWB reliability	Effect size	1339	0.80	0.11	0.54	0.99

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes).

Categorical Moderators

mod_cat_overall_es <-
  rbind(as.data.frame(cbind(table(data_included$SampleType), 
                                     round(prop.table(table(data_included$SampleType)),4) * 100)), 
                 as.data.frame(cbind(table(data_included$country_region), 
                                     round(prop.table(table(data_included$country_region)),4) * 100)), 
                 as.data.frame(cbind(table(data_included$StudyDesign),
                                     round(prop.table(table(data_included$StudyDesign)),4) * 100)),
                 as.data.frame(cbind(table(data_included$DeDomainAll), 
                                     round(prop.table(table(data_included$DeDomainAll)),4) * 100)), 
                 as.data.frame(cbind(table(data_included$TypeDeMeasure), 
                                     round(prop.table(table(data_included$TypeDeMeasure)),4) * 100)), 
                 as.data.frame(cbind(table(data_included$DeDataType), 
                                     round(prop.table(table(data_included$DeDataType)),4) * 100)), 
                 as.data.frame(cbind(table(data_included$WbDimension), 
                                     round(prop.table(table(data_included$WbDimension)),4) * 100)), 
                 as.data.frame(cbind(table(data_included$CorrLag), 
                                     round(prop.table(table(data_included$CorrLag)),4) * 100)))

mod_cat_overall_es <- mod_cat_overall_es %>%
  mutate(level = "Effect size")

names(mod_cat_overall_es) <- c("*n*", "% *n*", "Level")

mod_cat_overall_es <- mod_cat_overall_es[, c("Level", "*n*", "% *n*")]

rownames(mod_cat_overall_es) <- c("Convenience", "College", "Representative", 
                                     "North America", "Northern Europe", "Southern Europe", "Asia", "Australia",
                                     "General", "Work/Achievement", "Social", "Health", "Other event",
                                     "Checklist/questionnaire", "Open-response", "Interview", "Other measure",
                                     "Dichotomous", "Numeric count", "Scale score", "Other type",
                                     "Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
                                     "Daily diary", "Experience sampling",
                                     "Concurrent", "Prospective")

kable(mod_cat_overall_es
      , caption="**Descriptive Statistics for Categorical Moderators at the Effect Size Level**"
      , escape=FALSE
      , label = NA) %>%  
  pack_rows(index = c("Sample type" = 3, 
                      "Country" = 5, 
                      "Daily event domain" = 5,
                      "Daily event measurement" = 4, 
                      "Daily event data type" = 4, 
                      "Well-being dimension" = 5,
                      "Study design" = 2,
                      "Correlation lag" = 2)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 21: Descriptive Statistics for Categorical Moderators at the Effect Size Level

	Level	n	% n
Sample type
Convenience	Effect size	981	56.61
College	Effect size	744	42.93
Representative	Effect size	8	0.46
Country
North America	Effect size	1115	73.55
Northern Europe	Effect size	238	15.70
Southern Europe	Effect size	24	1.58
Asia	Effect size	114	7.52
Australia	Effect size	25	1.65
Daily event domain
General	Effect size	1402	80.90
Work/Achievement	Effect size	331	19.10
Social	Effect size	607	35.03
Health	Effect size	419	24.18
Other event	Effect size	565	32.60
Daily event measurement
Checklist/questionnaire	Effect size	58	3.35
Open-response	Effect size	84	4.85
Interview	Effect size	1572	91.18
Other measure	Effect size	74	4.29
Daily event data type
Dichotomous	Effect size	72	4.18
Numeric count	Effect size	6	0.35
Scale score	Effect size	392	22.63
Other type	Effect size	304	17.55
Well-being dimension
Life satisfaction	Effect size	1023	59.06
Domain satisfaction	Effect size	13	0.75
Positive affect	Effect size	134	7.73
Negative affect	Effect size	61	3.52
Global well-being	Effect size	699	40.33
Study design
Daily diary	Effect size	811	46.80
Experience sampling	Effect size	28	1.62
Correlation lag
Concurrent	Effect size	1481	85.46
Prospective	Effect size	252	14.54

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes). % n indicate the proportion of units within the corresponding level assigned to each moderator category.

Uplifts & SWB

Within-Person

Continuous Moderators

mod_metric_uplifts_within_es <- uplifts_within %>% 
  select(Year, Female, AgeM, N, Observations, DeOccDay, DeOccTotal, WbReliability) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Effect size")

mod_metric_uplifts_within_es <- mod_metric_uplifts_within_es[, c("level", "n", "mean", "sd", "min", "max")]

row.names(mod_metric_uplifts_within_es) <- c("Publication year", "% Female", "Mean age", "Sample size", "Number of observations", "Assessments per day", 
                                   "Total assessments", "SWB reliability")

names(mod_metric_uplifts_within_es) <- c("Level", "*n*", "*M*", "*SD*", "Min", "Max")

kable(mod_metric_uplifts_within_es
      , caption="**Descriptive Statistics for Continuous Moderators at the Effect Size Level for the Within-Person Association Between Daily Uplifts and SWB**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 22: Descriptive Statistics for Continuous Moderators at the Effect Size Level for the Within-Person Association Between Daily Uplifts and SWB

	Level	n	M	SD	Min	Max
Publication year	Effect size	305	2017.35	6.60	1992.00	2026.00
% Female	Effect size	305	63.67	22.98	0.00	100.00
Mean age	Effect size	301	26.36	10.97	10.79	64.47
Sample size	Effect size	305	164.79	201.81	20.00	1982.00
Number of observations	Effect size	304	2588.26	7487.29	150.00	76594.00
Assessments per day	Effect size	305	1.20	0.88	1.00	12.51
Total assessments	Effect size	305	17.01	19.11	4.00	120.00
SWB reliability	Effect size	252	0.75	0.11	0.55	0.98

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes).

Categorical Moderators

mod_cat_uplifts_within_es <- rbind(as.data.frame(cbind(table(uplifts_within$SampleType), 
                                                    round(prop.table(table(uplifts_within$SampleType)),4) * 100)),
                                as.data.frame(cbind(table(uplifts_within$country_region), 
                                                    round(prop.table(table(uplifts_within$country_region)),4) * 100)), 
                                as.data.frame(cbind(table(uplifts_within$StudyDesign),
                                                    round(prop.table(table(uplifts_within$StudyDesign)),4) * 100)),
                                as.data.frame(cbind(table(uplifts_within$DeDomainAll), 
                                                    round(prop.table(table(uplifts_within$DeDomainAll)),4) * 100)), 
                                as.data.frame(cbind(table(uplifts_within$TypeDeMeasure), 
                                                    round(prop.table(table(uplifts_within$TypeDeMeasure)),4) * 100)), 
                                as.data.frame(cbind(table(uplifts_within$DeDataType), 
                                                    round(prop.table(table(uplifts_within$DeDataType)),4) * 100)), 
                                as.data.frame(cbind(table(uplifts_within$WbDimension), 
                                                    round(prop.table(table(uplifts_within$WbDimension)),4) * 100)), 
                                as.data.frame(cbind(table(uplifts_within$CorrLag), 
                                                    round(prop.table(table(uplifts_within$CorrLag)),4) * 100)))

mod_cat_uplifts_within_es <- mod_cat_uplifts_within_es %>%
  mutate(level = "Effect size")

names(mod_cat_uplifts_within_es) <- c("*n*", "% *n*", "Level")

mod_cat_uplifts_within_es <- mod_cat_uplifts_within_es[, c("Level", "*n*", "% *n*")]

rownames(mod_cat_uplifts_within_es) <- c("Convenience", "College", "Representative", 
                                     "North America", "Northern Europe", "Southern Europe", "Asia", "Australia",
                                     "General", "Work/Achievement", "Social", "Health", "Other event",
                                     "Checklist/questionnaire", "Open-response", "Interview", "Other measure",
                                     "Dichotomous", "Numeric count", "Scale score", "Other type",
                                     "Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
                                     "Daily diary", "Experience sampling",
                                     "Concurrent", "Prospective")

kable(mod_cat_uplifts_within_es
      , caption="**Descriptive Statistics for Categorical Moderators at the Effect Size Level for the Within-Person Association Between Daily Uplifts and SWB**"
      , escape=FALSE
      , label = NA) %>%  
  pack_rows(index = c("Sample type" = 3, 
                      "Country" = 5, 
                      "Daily event domain" = 5,
                      "Daily event measurement" = 4, 
                      "Daily event data type" = 4, 
                      "Well-being dimension" = 5,
                      "Study design" = 2,
                      "Correlation lag" = 2)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 23: Descriptive Statistics for Categorical Moderators at the Effect Size Level for the Within-Person Association Between Daily Uplifts and SWB

	Level	n	% n
Sample type
Convenience	Effect size	132	43.28
College	Effect size	171	56.07
Representative	Effect size	2	0.66
Country
North America	Effect size	235	83.04
Northern Europe	Effect size	34	12.01
Southern Europe	Effect size	3	1.06
Asia	Effect size	9	3.18
Australia	Effect size	2	0.71
Daily event domain
General	Effect size	267	87.54
Work/Achievement	Effect size	38	12.46
Social	Effect size	70	22.95
Health	Effect size	111	36.39
Other event	Effect size	116	38.03
Daily event measurement
Checklist/questionnaire	Effect size	0	0.00
Open-response	Effect size	8	2.62
Interview	Effect size	275	90.16
Other measure	Effect size	12	3.93
Daily event data type
Dichotomous	Effect size	18	5.90
Numeric count	Effect size	0	0.00
Scale score	Effect size	68	22.30
Other type	Effect size	38	12.46
Well-being dimension
Life satisfaction	Effect size	196	64.26
Domain satisfaction	Effect size	3	0.98
Positive affect	Effect size	35	11.48
Negative affect	Effect size	16	5.25
Global well-being	Effect size	127	41.64
Study design
Daily diary	Effect size	123	40.33
Experience sampling	Effect size	4	1.31
Correlation lag
Concurrent	Effect size	193	63.28
Prospective	Effect size	112	36.72

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes). % n indicate the proportion of units within the corresponding level assigned to each moderator category.

Between-Person

Continuous Moderators

mod_metric_uplifts_between_es <- uplifts_between %>% 
  select(Year, Female, AgeM, N, DeOccDay, DeOccTotal, WbReliability) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Effect size")

mod_metric_uplifts_between_es <- mod_metric_uplifts_between_es[, c("level", "n", "mean", "sd", "min", "max")]

row.names(mod_metric_uplifts_between_es) <- c("Publication year", "% Female", "Mean age", "Sample size", "Assessments per day", 
                                   "Total assessments", "SWB reliability")

names(mod_metric_uplifts_between_es) <- c("Level", "*n*", "*M*", "*SD*", "Min", "Max")

kable(mod_metric_uplifts_between_es
      , caption="**Descriptive Statistics for Continuous Moderators at the Effect Size Level for the Between-Person Association Between Daily Uplifts and SWB**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 24: Descriptive Statistics for Continuous Moderators at the Effect Size Level for the Between-Person Association Between Daily Uplifts and SWB

	Level	n	M	SD	Min	Max
Publication year	Effect size	270	2016.37	7.00	1991.00	2026.00
% Female	Effect size	270	61.24	26.20	0.00	100.00
Mean age	Effect size	268	28.55	13.22	10.79	74.36
Sample size	Effect size	270	181.51	233.32	18.00	1982.00
Assessments per day	Effect size	270	1.26	1.08	1.00	12.51
Total assessments	Effect size	270	17.05	16.61	3.00	120.00
SWB reliability	Effect size	202	0.80	0.11	0.55	0.99

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes).

Categorical Moderators

mod_cat_uplifts_between_es <- rbind(as.data.frame(cbind(table(uplifts_between$SampleType), 
                                                    round(prop.table(table(uplifts_between$SampleType)),4) * 100)), 
                 as.data.frame(cbind(table(uplifts_between$country_region), 
                                     round(prop.table(table(uplifts_between$country_region)),4) * 100)), 
                 as.data.frame(cbind(table(uplifts_between$StudyDesign), round(prop.table(table(uplifts_between$StudyDesign)),4) * 100)),
                 as.data.frame(cbind(table(uplifts_between$DeDomainAll), 
                                     round(prop.table(table(uplifts_between$DeDomainAll)),4) * 100)), 
                 as.data.frame(cbind(table(uplifts_between$TypeDeMeasure), 
                                     round(prop.table(table(uplifts_between$TypeDeMeasure)),4) * 100)), 
                 as.data.frame(cbind(table(uplifts_between$DeDataType), 
                                     round(prop.table(table(uplifts_between$DeDataType)),4) * 100)), 
                 as.data.frame(cbind(table(uplifts_between$WbDimension), 
                                     round(prop.table(table(uplifts_between$WbDimension)),4) * 100)))

mod_cat_uplifts_between_es <- mod_cat_uplifts_between_es %>%
  mutate(level = "Effect size")

names(mod_cat_uplifts_between_es) <- c("*n*", "% *n*", "Level")

mod_cat_uplifts_between_es <- mod_cat_uplifts_between_es[, c("Level", "*n*", "% *n*")]

rownames(mod_cat_uplifts_between_es) <- c("Convenience", "College", "Representative", 
                                     "North America", "Northern Europe", "Southern Europe", "Asia", "Australia",
                                     "General", "Work/Achievement", "Social", "Health", "Other event",
                                     "Checklist/questionnaire", "Open-response", "Interview", "Other measure",
                                     "Dichotomous", "Numeric count", "Scale score", "Other type",
                                     "Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
                                     "Daily diary", "Experience sampling")

kable(mod_cat_uplifts_between_es
      , caption="**Descriptive Statistics for Categorical Moderators at the Effect Size Level for the Between-Person Association Between Daily Uplifts and SWB**"
      , escape=FALSE
      , label = NA) %>%  
  pack_rows(index = c("Sample type" = 3, 
                      "Country" = 5, 
                      "Daily event domain" = 5,
                      "Daily event measurement" = 4, 
                      "Daily event data type" = 4, 
                      "Well-being dimension" = 5,
                      "Study design" = 2)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 25: Descriptive Statistics for Categorical Moderators at the Effect Size Level for the Between-Person Association Between Daily Uplifts and SWB

	Level	n	% n
Sample type
Convenience	Effect size	139	51.48
College	Effect size	129	47.78
Representative	Effect size	2	0.74
Country
North America	Effect size	177	75.32
Northern Europe	Effect size	32	13.62
Southern Europe	Effect size	3	1.28
Asia	Effect size	19	8.09
Australia	Effect size	4	1.70
Daily event domain
General	Effect size	241	89.26
Work/Achievement	Effect size	29	10.74
Social	Effect size	77	28.52
Health	Effect size	73	27.04
Other event	Effect size	108	40.00
Daily event measurement
Checklist/questionnaire	Effect size	0	0.00
Open-response	Effect size	12	4.44
Interview	Effect size	241	89.59
Other measure	Effect size	16	5.95
Daily event data type
Dichotomous	Effect size	10	3.72
Numeric count	Effect size	2	0.74
Scale score	Effect size	58	21.56
Other type	Effect size	44	16.36
Well-being dimension
Life satisfaction	Effect size	162	60.22
Domain satisfaction	Effect size	5	1.86
Positive affect	Effect size	28	10.37
Negative affect	Effect size	11	4.07
Global well-being	Effect size	119	44.07
Study design
Daily diary	Effect size	109	40.37
Experience sampling	Effect size	3	1.11

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes). % n indicate the proportion of units within the corresponding level assigned to each moderator category.

Hassles & SWB

Within-Person

Continuous Moderators

mod_metric_hassles_within_es <- hassles_within %>% 
  select(Year, Female, AgeM, N, Observations, DeOccDay, DeOccTotal, WbReliability) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Effect size")

mod_metric_hassles_within_es <- mod_metric_hassles_within_es[, c("level", "n", "mean", "sd", "min", "max")]

row.names(mod_metric_hassles_within_es) <- c("Publication year", "% Female", "Mean age", "Sample size", "Number of observations", "Assessments per day", 
                                   "Total assessments", "SWB reliability")

names(mod_metric_hassles_within_es) <- c("Level", "*n*", "*M*", "*SD*", "Min", "Max")

kable(mod_metric_hassles_within_es
      , caption="**Descriptive Statistics for Continuous Moderators at the Effect Size Level for the Within-Person Association Between Daily Hassles and SWB**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 26: Descriptive Statistics for Continuous Moderators at the Effect Size Level for the Within-Person Association Between Daily Hassles and SWB

	Level	n	M	SD	Min	Max
Publication year	Effect size	531	2018.86	6.07	1992.00	2026.00
% Female	Effect size	531	65.26	19.56	0.00	100.00
Mean age	Effect size	524	29.46	13.04	10.77	73.50
Sample size	Effect size	531	172.87	201.12	38.00	1982.00
Number of observations	Effect size	530	3322.65	8287.27	107.00	77474.00
Assessments per day	Effect size	531	1.59	1.46	1.00	8.00
Total assessments	Effect size	531	22.27	26.19	4.00	150.00
SWB reliability	Effect size	419	0.78	0.11	0.54	0.98

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes).

Categorical Moderators

mod_cat_hassles_within_es <- rbind(as.data.frame(cbind(table(hassles_within$SampleType), 
                                                    round(prop.table(table(hassles_within$SampleType)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_within$country_region), 
                                     round(prop.table(table(hassles_within$country_region)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_within$StudyDesign), round(prop.table(table(hassles_within$StudyDesign)),4) * 100)),
                 as.data.frame(cbind(table(hassles_within$DeDomainAll), 
                                     round(prop.table(table(hassles_within$DeDomainAll)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_within$TypeDeMeasure), 
                                     round(prop.table(table(hassles_within$TypeDeMeasure)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_within$DeDataType), 
                                     round(prop.table(table(hassles_within$DeDataType)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_within$WbDimension), 
                                     round(prop.table(table(hassles_within$WbDimension)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_within$CorrLag), 
                                     round(prop.table(table(hassles_within$CorrLag)),4) * 100)))

mod_cat_hassles_within_es <- mod_cat_hassles_within_es %>%
  mutate(level = "Effect size")

names(mod_cat_hassles_within_es) <- c("*n*", "% *n*", "Level")

mod_cat_hassles_within_es <- mod_cat_hassles_within_es[, c("Level", "*n*", "% *n*")]

rownames(mod_cat_hassles_within_es) <- c("Convenience", "College", "Representative", 
                                     "North America", "Northern Europe", "Southern Europe", "Asia", "Australia",
                                     "General", "Work/Achievement", "Social", "Health", "Other event",
                                     "Checklist/questionnaire", "Open-response", "Interview", "Other measure",
                                     "Dichotomous", "Numeric count", "Scale score", "Other type",
                                     "Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
                                     "Daily diary", "Experience sampling",
                                     "Concurrent", "Prospective")

kable(mod_cat_hassles_within_es
      , caption="**Descriptive Statistics for Categorical Moderators at the Effect Size Level for the Within-Person Association Between Daily Hassles and SWB**"
      , escape=FALSE
      , label = NA) %>%  
  pack_rows(index = c("Sample type" = 3, 
                      "Country" = 5, 
                      "Daily event domain" = 5,
                      "Daily event measurement" = 4, 
                      "Daily event data type" = 4, 
                      "Well-being dimension" = 5,
                      "Study design" = 2,
                      "Correlation lag" = 2)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 27: Descriptive Statistics for Categorical Moderators at the Effect Size Level for the Within-Person Association Between Daily Hassles and SWB

	Level	n	% n
Sample type
Convenience	Effect size	308	58.00
College	Effect size	221	41.62
Representative	Effect size	2	0.38
Country
North America	Effect size	328	70.24
Northern Europe	Effect size	89	19.06
Southern Europe	Effect size	10	2.14
Asia	Effect size	38	8.14
Australia	Effect size	2	0.43
Daily event domain
General	Effect size	410	77.21
Work/Achievement	Effect size	121	22.79
Social	Effect size	184	34.65
Health	Effect size	139	26.18
Other event	Effect size	159	29.94
Daily event measurement
Checklist/questionnaire	Effect size	23	4.33
Open-response	Effect size	26	4.90
Interview	Effect size	491	92.99
Other measure	Effect size	13	2.46
Daily event data type
Dichotomous	Effect size	24	4.55
Numeric count	Effect size	0	0.00
Scale score	Effect size	115	21.66
Other type	Effect size	94	17.70
Well-being dimension
Life satisfaction	Effect size	322	60.64
Domain satisfaction	Effect size	0	0.00
Positive affect	Effect size	38	7.16
Negative affect	Effect size	14	2.64
Global well-being	Effect size	206	38.79
Study design
Daily diary	Effect size	265	49.91
Experience sampling	Effect size	8	1.51
Correlation lag
Concurrent	Effect size	391	73.63
Prospective	Effect size	140	26.37

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes). % n indicate the proportion of units within the corresponding level assigned to each moderator category.

Between-Person

Continuous Moderators

mod_metric_hassles_between_es <- hassles_between %>% 
  select(Year, Female, AgeM, N, DeOccDay, DeOccTotal, WbReliability) %>% 
  describe() %>% 
  select(c(2:4, 8, 9)) %>% 
  round(2) %>%
  mutate(level = "Effect size")

mod_metric_hassles_between_es <- mod_metric_hassles_between_es[, c("level", "n", "mean", "sd", "min", "max")]

row.names(mod_metric_hassles_between_es) <- c("Publication year", "% Female", "Mean age", "Sample size", "Assessments per day", 
                                   "Total assessments", "SWB reliability")

names(mod_metric_hassles_between_es) <- c("Level", "*n*", "*M*", "*SD*", "Min", "Max")

kable(mod_metric_hassles_between_es
      , caption="**Descriptive Statistics for Continuous Moderators at the Effect Size Level for the Between-Person Association Between Daily Hassles and SWB**"
      , escape=FALSE
      , label = NA) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 28: Descriptive Statistics for Continuous Moderators at the Effect Size Level for the Between-Person Association Between Daily Hassles and SWB

	Level	n	M	SD	Min	Max
Publication year	Effect size	627	2017.55	6.76	1991.00	2026.00
% Female	Effect size	625	62.92	23.07	0.00	100.00
Mean age	Effect size	616	31.17	15.90	10.77	80.30
Sample size	Effect size	627	179.35	206.70	18.00	1982.00
Assessments per day	Effect size	623	1.71	1.64	1.00	13.60
Total assessments	Effect size	623	22.51	24.67	3.00	150.00
SWB reliability	Effect size	466	0.84	0.10	0.55	0.99

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes).

Categorical Moderators

mod_cat_hassles_between_es <- rbind(as.data.frame(cbind(table(hassles_between$SampleType), 
                                                    round(prop.table(table(hassles_between$SampleType)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_between$country_region), 
                                     round(prop.table(table(hassles_between$country_region)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_between$StudyDesign), round(prop.table(table(hassles_between$StudyDesign)),4) * 100)),
                 as.data.frame(cbind(table(hassles_between$DeDomainAll), 
                                     round(prop.table(table(hassles_between$DeDomainAll)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_between$TypeDeMeasure), 
                                     round(prop.table(table(hassles_between$TypeDeMeasure)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_between$DeDataType), 
                                     round(prop.table(table(hassles_between$DeDataType)),4) * 100)), 
                 as.data.frame(cbind(table(hassles_between$WbDimension), 
                                     round(prop.table(table(hassles_between$WbDimension)),4) * 100)))

mod_cat_hassles_between_es <- mod_cat_hassles_between_es %>%
  mutate(level = "Effect size")

names(mod_cat_hassles_between_es) <- c("*n*", "% *n*", "Level")

mod_cat_hassles_between_es <- mod_cat_hassles_between_es[, c("Level", "*n*", "% *n*")]

rownames(mod_cat_hassles_between_es) <- c("Convenience", "College", "Representative", 
                                          "North America", "Northern Europe", "Southern Europe", "Asia", "Australia",
                                          "General", "Work/Achievement", "Social", "Health", "Other event",
                                          "Checklist/questionnaire", "Open-response", "Interview", "Other measure",
                                          "Dichotomous", "Numeric count", "Scale score", "Other type",
                                          "Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
                                          "Daily diary", "Experience sampling")

kable(mod_cat_hassles_between_es
      , caption="**Descriptive Statistics for Categorical Moderators at the Effect Size Level for the Between-Person Association Between Daily Hassles and SWB**"
      , escape=FALSE
      , label = NA) %>%  
  pack_rows(index = c("Sample type" = 3, 
                      "Country" = 5, 
                      "Daily event domain" = 5,
                      "Daily event measurement" = 4, 
                      "Daily event data type" = 4, 
                      "Well-being dimension" = 5,
                      "Study design" = 2)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 29: Descriptive Statistics for Categorical Moderators at the Effect Size Level for the Between-Person Association Between Daily Hassles and SWB

	Level	n	% n
Sample type
Convenience	Effect size	402	64.11
College	Effect size	223	35.57
Representative	Effect size	2	0.32
Country
North America	Effect size	375	70.62
Northern Europe	Effect size	83	15.63
Southern Europe	Effect size	8	1.51
Asia	Effect size	48	9.04
Australia	Effect size	17	3.20
Daily event domain
General	Effect size	484	77.19
Work/Achievement	Effect size	143	22.81
Social	Effect size	276	44.02
Health	Effect size	96	15.31
Other event	Effect size	182	29.03
Daily event measurement
Checklist/questionnaire	Effect size	35	5.58
Open-response	Effect size	38	6.06
Interview	Effect size	565	90.84
Other measure	Effect size	33	5.31
Daily event data type
Dichotomous	Effect size	20	3.22
Numeric count	Effect size	4	0.64
Scale score	Effect size	151	24.08
Other type	Effect size	128	20.41
Well-being dimension
Life satisfaction	Effect size	343	54.70
Domain satisfaction	Effect size	5	0.80
Positive affect	Effect size	33	5.26
Negative affect	Effect size	20	3.19
Global well-being	Effect size	247	39.39
Study design
Daily diary	Effect size	314	50.08
Experience sampling	Effect size	13	2.07

Note. n denotes the number of units at the respective level (studies, samples, or effect sizes). % n indicate the proportion of units within the corresponding level assigned to each moderator category.

Section 3: Meta-Analysis

Meta-Analytic Correlation Table

# Define Function for Meta-Analytic Model

run_ma <- function(data, SamV) {
  
  # Multi-level meta-analytic model
  
  model <- rma.mv(
    yi = ES,
    V = SamV,
    random = list(
      ~ 1 | EffSizeID,
      ~ 1 | StudyID
    ),
    data = data
  )
  
  # Cluster-robust standard errors
  
  robust_model <- robust(
    model,
    cluster = StudyID,
    clubSandwich = TRUE
  )
  
  list(
    model = model,
    robust = robust_model
  )
}

# Define Function for Back-Transformation
back_transform <- function(data) {

  rob <- data$robust
  
  data.frame(
    r = fisherz2r(rob$b[1]),
    z = rob$zval[1],
    p = rob$pval[1],
    ci.lb = fisherz2r(rob$ci.lb[1]),
    ci.ub = fisherz2r(rob$ci.ub[1]),
    ES = rob$k.eff
  )
}

# Define Function for Computation of I2

calc_i2 <- function(model, SamV) {

  X <- model$X

  W <- solve(SamV[model$not.na, model$not.na])

  P <- W -
    W %*% X %*%
    solve(t(X) %*% W %*% X) %*%
    t(X) %*% W
  
  k <- model$k
  
  p <- ncol(X)

  vbar <- (k - p) / sum(diag(P))

  sigma1 <- model$sigma2[1]
  sigma2   <- model$sigma2[2]

  I2_L1 <- 100 * sigma1 /
    (sigma1 + sigma2 + vbar)

  I2_L2 <- 100 * sigma2 /
    (sigma1 + sigma2 + vbar)

  data.frame(
    I2_Level1 = I2_L1,
    I2_Level2 = I2_L2,
    I2_Total = I2_L1 + I2_L2
  )
}

# Uplifts

## Within-Person Associations

### Fisher z-Transformation 

uplifts_within$ES <- escalc(measure = "ZCOR", ri = uplifts_within$CorrCoeff,ni = uplifts_within$Observations)$yi
uplifts_within$SamVar <- escalc(measure = "ZCOR", ri = uplifts_within$CorrCoeff,ni = uplifts_within$Observations)$vi

### Create Effect Size ID

uplifts_within$EffSizeID <- 1:nrow(uplifts_within)

### Compute Sampling-Variance/Covariance Matrix

uplifts_within$CorrLagID <- ifelse(uplifts_within$CorrLag == "prospective", 1, 0)

V_uplifts_within <- vcalc(
  SamVar,
  cluster = StudyID,
  subgroup = SampleID,
  obs = DeID,
  type = WbID,
  time1 = CorrLagID,
  rho = c(0.3, 0.4), # Based on correlations from previous research
  phi = 0.55, # Based on auto-correlations in existing intensive longitudinal studies
  data = uplifts_within
)

### Meta-Analysis

ma_uplifts_within <- run_ma(uplifts_within, V_uplifts_within)

sigma1_uplifts_within <- ma_uplifts_within[["model"]]$sigma2[1]
sigma2_uplifts_within <- ma_uplifts_within[["model"]]$sigma2[2]

results_ma_uplifts_within <- cbind(
  back_transform(ma_uplifts_within),
  calc_i2(ma_uplifts_within$model, V_uplifts_within)
)

## Between-Person Associations

### Fisher z-Transformation

uplifts_between$ES <- escalc(measure = "ZCOR", ri = uplifts_between$CorrCoeff,ni = uplifts_between$N)$yi
uplifts_between$SamVar <- escalc(measure = "ZCOR", ri = uplifts_between$CorrCoeff,ni = uplifts_between$N)$vi

### Create Effect Size ID

uplifts_between$EffSizeID <- 1:nrow(uplifts_between)

### Compute Sampling Variance/Covariance Matrix (Uplifts & SWB: Between-Person Associations)

V_uplifts_between <- vcalc(
  SamVar,
  cluster = StudyID,
  subgroup = SampleID,
  obs = DeID,
  type = WbID,
  rho = c(0.5, 0.4),
  data = uplifts_between
)

### Meta-Analysis

ma_uplifts_between <- run_ma(uplifts_between, V_uplifts_between)

sigma1_uplifts_between <- ma_uplifts_between[["model"]]$sigma2[1]
sigma2_uplifts_between <- ma_uplifts_between[["model"]]$sigma2[2]

results_ma_uplifts_between <- cbind(
  back_transform(ma_uplifts_between),
  calc_i2(ma_uplifts_between$model, V_uplifts_between)
)

# Hassles

## Within-Person Associations

### Fisher z-Transformation

hassles_within$ES <- escalc(measure = "ZCOR", ri = hassles_within$CorrCoeff,ni = hassles_within$Observations)$yi
hassles_within$SamVar <- escalc(measure = "ZCOR", ri = hassles_within$CorrCoeff,ni = hassles_within$Observations)$vi

### Create Effect Size ID

hassles_within$EffSizeID <- 1:nrow(hassles_within)

### Compute Sampling-Variance/Covariance Matrix

hassles_within$CorrLagID <- ifelse(hassles_within$CorrLag == "prospective", 1, 0)

V_hassles_within <- vcalc(
  SamVar,
  cluster = StudyID,
  subgroup = SampleID,
  obs = DeID,
  type = WbID,
   time1 = CorrLagID,
  rho = c(0.35, 0.4), # Based on correlations from previous research
    phi = 0.55, # Based on auto-correlations in existing intensive longitudinal studies
  data = hassles_within
)

### Meta-Analysis

ma_hassles_within <- run_ma(hassles_within, V_hassles_within)

sigma1_hassles_between <- ma_hassles_within[["model"]]$sigma2[1]
sigma2_hassles_between <- ma_hassles_within[["model"]]$sigma2[2]

results_ma_hassles_within <- cbind(
  back_transform(ma_hassles_within),
  calc_i2(ma_hassles_within$model, V_hassles_within)
)
## Between-Person Associations

### Fisher z-Transformation

hassles_between$ES <- escalc(measure = "ZCOR", ri = hassles_between$CorrCoeff,ni = hassles_between$N)$yi
hassles_between$SamVar <- escalc(measure = "ZCOR", ri = hassles_between$CorrCoeff,ni = hassles_between$N)$vi

### Create Effect Size ID

hassles_between$EffSizeID <- 1:nrow(hassles_between)

### Compute Sampling-Variance/Covariance Matrix

V_hassles_between <- vcalc(
  SamVar,
  cluster = StudyID,
  subgroup = SampleID,
  obs = DeID,
  type = WbID,
  rho = c(0.5, 0.4),
  data = hassles_between
  )

### Meta-Analysis

ma_hassles_between <- run_ma(hassles_between, V_hassles_between)

sigma1_hassles_between <- ma_hassles_between[["model"]]$sigma2[1]
sigma2_hassles_between <- ma_hassles_between[["model"]]$sigma2[2]

results_ma_hassles_between <- cbind(
  back_transform(ma_hassles_between),
  calc_i2(ma_hassles_between$model, V_hassles_between)
)

# Create table

table_results_uplifts <- rbind(results_ma_uplifts_within, results_ma_uplifts_between) %>%
  as.data.frame(row.names = c("Within-person", "Between-person")) %>%
  round(3) %>%
  mutate('CI (95%)' = paste0("[", ci.lb, ", ", ci.ub, "]")) %>%
  select(ES, r, z, p, 'CI (95%)', I2_Level1, I2_Level2, I2_Total, -ci.lb, -ci.ub) %>%
  setNames(c("ES", "*r*", "*t*", "*p*", "CI (95%)", "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~"))

table_results_hassles <- rbind(results_ma_hassles_within, results_ma_hassles_between) %>%
  as.data.frame(row.names = c("Within-Person", "Between-Person")) %>%
  round(3) %>%
  mutate('CI (95%)' = paste0("[", ci.lb, ", ", ci.ub, "]")) %>%
  select(ES, r, z, p, 'CI (95%)', I2_Level1, I2_Level2, I2_Total, -ci.lb, -ci.ub) %>%
  setNames(c("ES", "*r*", "*t*", "*p*", "CI (95%)", "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~"))

table_results <- cbind(table_results_uplifts, table_results_hassles)

kable(
  table_results,
  caption = "**Meta-Analytic Correlations for the Associations of Daily Uplifts and Daily Hassles with SWB**",
  digits = 3,
  escape = FALSE,
  label = NA
  ) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    fixed_thead = TRUE,
    full_width = TRUE,
    position = "left"
  ) %>%
  add_header_above(c(" " = 1, "Daily uplifts" = 8, "Daily hassles" = 8))

Table 30: Meta-Analytic Correlations for the Associations of Daily Uplifts and Daily Hassles with SWB

	Daily uplifts								Daily hassles
	ES	r	t	p	CI (95%)	I2Level1	I2Level2	I2Total	ES	r	t	p	CI (95%)	I2Level1	I2Level2	I2Total
Within-person	304	0.160	9.517	0	[0.127, 0.193]	79.414	19.894	99.308	530	-0.183	-19.962	0	[-0.201, -0.165]	87.177	12.001	99.178
Between-person	270	0.209	13.726	0	[0.179, 0.239]	93.501	0.000	93.501	627	-0.279	-25.719	0	[-0.299, -0.259]	86.299	5.616	91.915

Meta-Analytic Correlation Plot

plot_data <- rbind(
  results_ma_uplifts_within,
  results_ma_uplifts_between,
  results_ma_hassles_within,
  results_ma_hassles_between) %>%
  select(-z, -p) %>%
  mutate(
    dedimension = c("Daily uplifts", "Daily uplifts", "Daily hassles", "Daily hassles"),
    corrlevel = c("Within-person", "Between-person", "Within-person", "Between-person")
  ) %>%
  mutate(
    across(c(r, ci.lb, ci.ub, ES), as.numeric)
  )

main_plot <- ggplot(
  plot_data,
  aes(
    x = r,
    y = corrlevel,
    color = dedimension,
    shape = dedimension
  )
) +
  geom_vline(
    xintercept = 0,
    color = "grey60"
  ) +
  geom_errorbarh(
    aes(xmin = ci.lb, xmax = ci.ub),
    height = 0.2,
    linewidth = 0.8,
    position = position_dodge(width = 0.4)
  ) +
  geom_point_interactive(
    aes(
      x = r,
      y = corrlevel,
      tooltip = paste0(
        "<b>Daily ", dedimension, " & SWB</b>", " (", corrlevel, ")",
        "<br>r = ", round(r, 3),
        "<br>95% CI = [", round(ci.lb, 3), ", ", round(ci.ub, 3), "]",
        "<br>ES = ", ES
       )
    ),
    position = position_dodge(width = 0.4)
  ) +
  scale_color_manual(
    values = c(
      "Daily hassles" = "#4E8A8A",
      "Daily uplifts" = "#5C1f95"
    )
  ) +
scale_shape_manual(
  name = NULL,
  values = c(
    "Daily uplifts" = 17,
    "Daily hassles" = 16
    )
  ) +
  theme_apa(base_size = 16) +
  labs(
    title = "Meta-Analytic Correlations Between Daily Events and SWB",
    x = "Correlation (r)",
    y = NULL,
    color = NULL
  ) +
  theme(
    plot.title = element_text(
      size = 16,
      face = "bold",
      hjust = 0.5
    ),
    legend.position = "top",
    legend.text = element_text(size = 16),
    axis.text.y = element_text(color = "black"),
    axis.text.x = element_text(color = "black"),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank()
  ) +
  scale_x_continuous(
    limits = c(-0.3, 0.3),
    breaks = c(-0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3)
  )


girafe(
  ggobj = main_plot,
  width_svg = 8,
  height_svg = 4,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Hover over the points in the plot to display more information on the corresponding meta-analytic correlation.

Within- vs. Between-Person Correlations

# Create combind dataset for uplifts

uplifts_between$CorrLagID <- 0

uplifts <- bind_rows(
  uplifts_within,
  uplifts_between
) %>%
  mutate(
    EffSizeID = row_number(),
    DeID_CorrType = interaction(DeID, CorrType, drop = TRUE)
  )

# Variance-covariance matrix for uplifts
V_uplifts <- vcalc(
  SamVar,
  cluster  = StudyID,
  subgroup = SampleID,
  obs      = DeID_CorrType,
  type     = WbID,
  time1    = CorrLagID,
  rho      = c(0.4, 0.4),
  phi      = 0.55,
  data     = uplifts
)

# Meta-analytic moderation model
mod_uplifts <- rma.mv(
  yi = ES,
  V = V_uplifts,
  mods = ~ CorrType,
  random = list(
    ~ 1 | EffSizeID,
    ~ 1 | StudyID
  ),
  data = uplifts,
  sparse = TRUE
)

# Cluster-robust results
robust_uplifts <- robust(
  mod_uplifts,
  cluster = uplifts$StudyID,
  clubSandwich = TRUE
)

# Null model for R2
rows_uplifts <- mod_uplifts$not.na

null_uplifts <- rma.mv(
  yi = ES,
  V = V_uplifts[rows_uplifts, rows_uplifts],
  random = list(
    ~ 1 | EffSizeID,
    ~ 1 | StudyID
  ),
  data = uplifts[rows_uplifts, ],
  sparse = TRUE
)

# Variance components
tau1_null <- null_uplifts$sigma2[1]
tau2_null <- null_uplifts$sigma2[2]

tau1_mod <- mod_uplifts$sigma2[1]
tau2_mod <- mod_uplifts$sigma2[2]

# R2
R2_uplifts <- c(
  R2_Level1 = 100 * (tau1_null - tau1_mod) / tau1_null,
  R2_Level2 = 100 * (tau2_null - tau2_mod) / tau2_null,
  R2_Total  = 100 * (
    (tau1_null + tau2_null) -
    (tau1_mod + tau2_mod)
  ) / (tau1_null + tau2_null)
)

# Extract CorrType coefficient
uplifts_result <- data.frame(
  ES = nrow(uplifts),
  b = robust_uplifts$b[2],
  SE = robust_uplifts$se[2],
  t = robust_uplifts$zval[2],
  p = robust_uplifts$pval[2],
  tau2_Level1 = tau1_mod,
  tau2_Level2 = tau2_mod,
  R2_Level1 = R2_uplifts["R2_Level1"],
  R2_Level2 = R2_uplifts["R2_Level2"],
  R2_Total = R2_uplifts["R2_Total"]
)

# Create combined dataset for hassles

hassles_between$CorrLagID <- 0

hassles <- bind_rows(
  hassles_within,
  hassles_between
) %>%
  mutate(
    EffSizeID = row_number(),
    DeID_CorrType = interaction(DeID, CorrType, drop = TRUE)
  )

# Variance-covariance matrix
V_hassles <- vcalc(
  SamVar,
  cluster  = StudyID,
  subgroup = SampleID,
  obs      = DeID_CorrType,
  type     = WbID,
  time1    = CorrLagID,
  rho      = c(0.4, 0.4),
  phi      = 0.55,
  data     = hassles
)

# Meta-analytic moderation model
mod_hassles <- rma.mv(
  yi = ES,
  V = V_hassles,
  mods = ~ CorrType,
  random = list(
    ~ 1 | EffSizeID,
    ~ 1 | StudyID
  ),
  data = hassles,
  sparse = TRUE
)

# Cluster-robust results
robust_hassles <- robust(
  mod_hassles,
  cluster = hassles$StudyID,
  clubSandwich = TRUE
)

# Null model for R2
rows_hassles <- mod_hassles$not.na

null_hassles <- rma.mv(
  yi = ES,
  V = V_hassles[rows_hassles, rows_hassles],
  random = list(
    ~ 1 | EffSizeID,
    ~ 1 | StudyID
  ),
  data = hassles[rows_hassles, ],
  sparse = TRUE
)

# Variance components
tau1_null <- null_hassles$sigma2[1]
tau2_null <- null_hassles$sigma2[2]

tau1_mod <- mod_hassles$sigma2[1]
tau2_mod <- mod_hassles$sigma2[2]

# R2
R2_hassles <- c(
  R2_Level1 = 100 * (tau1_null - tau1_mod) / tau1_null,
  R2_Level2 = 100 * (tau2_null - tau2_mod) / tau2_null,
  R2_Total  = 100 * (
    (tau1_null + tau2_null) -
    (tau1_mod + tau2_mod)
  ) / (tau1_null + tau2_null)
)

# Extract CorrType coefficient
hassles_result <- data.frame(
  ES = nrow(hassles),
  b = robust_hassles$b[2],
  SE = robust_hassles$se[2],
  t = robust_hassles$zval[2],
  p = robust_hassles$pval[2],
  tau2_Level1 = tau1_mod,
  tau2_Level2 = tau2_mod,
  R2_Level1 = R2_hassles["R2_Level1"],
  R2_Level2 = R2_hassles["R2_Level2"],
  R2_Total = R2_hassles["R2_Total"]
)

# Assuming calc_i2() is your existing function
i2_uplifts <- calc_i2(mod_uplifts, V_uplifts)
i2_hassles <- calc_i2(mod_hassles, V_hassles)

uplifts_result <- cbind(uplifts_result, i2_uplifts)
hassles_result <- cbind(hassles_result, i2_hassles)

# Combine results

within_between_diff <- bind_rows(
  uplifts_result,
  hassles_result
) %>%
  select(
    ES, b, SE, t, p,
    tau2_Level1,
    tau2_Level2,
    everything()
  )

# Replace negative R2 values with zero
within_between_diff <- within_between_diff %>%
  mutate(
    R2_Level1 = pmax(R2_Level1, 0),
    R2_Level2 = pmax(R2_Level2, 0),
    R2_Total  = pmax(R2_Total, 0)
)

# Rename columns
names(within_between_diff) <- c(
  "ES",
  "*b*",
  "*SE*",
  "*t*",
  "*p*",
  "τ^2^~Level1~",
  "τ^2^~Level2~",
  "*I*^2^~Level1~",
  "*I*^2^~Level2~",
  "*I*^2^~Total~",
  "R^2^~Level1~",
  "R^2^~Level2~",
  "R^2^~Total~"
)

rownames(within_between_diff) <- c(
  "Within-person",
  "Within-person "
)

# Create table

kable(
  round(within_between_diff, 3),
  caption = "**Within- vs. Between-Person Associations Between Daily Events and SWB**",
  escape = FALSE,
  label = NA,
  digits = 3
) %>%
  pack_rows("Daily uplifts", 1, 1) %>%
  pack_rows("Daily hassles", 2, 2) %>%
  column_spec(
    3:6,
    bold = ifelse(within_between_diff[, 5] < 0.05, TRUE, FALSE)
  ) %>%
  kable_styling(
    bootstrap_options = c("striped", "hover", "condensed"),
    fixed_thead = TRUE,
    full_width = TRUE,
    position = "left"
  )

Table 31: Within- vs. Between-Person Associations Between Daily Events and SWB

	ES	b	SE	t	p	τ2Level1	τ2Level2	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Daily uplifts
Within-person	575	-0.068	0.014	-4.983	0	0.038	0.008	1.798	3.964	2.173	82.297	16.845	99.143
Daily hassles
Within-person	1158	0.109	0.011	9.540	0	0.031	0.004	6.538	14.399	7.553	87.093	11.827	98.919

Note. Between-person correlations serve as the reference category. Level-specific \(R^2\) values were calculated as the proportional reduction in the estimated random-effect variance components relative to the corresponding null model fitted to the same observations (see the corresponding analysis code for more details).

Section 4: Moderator Analyses

# Preparation Steps

## Recode/Drop Moderator-Categories With Less Than 5 Observations for Within-Person Uplifts & SWB Associations

levels(uplifts_within$SampleType)[
  levels(uplifts_within$SampleType) == "representative"
] <- "convenience"
uplifts_within$SampleType <- droplevels(uplifts_within$SampleType)
uplifts_within$SampleType <- relevel(uplifts_within$SampleType, ref = "convenience")

uplifts_within <- uplifts_within %>%
  mutate(
    country_region = replace(
      country_region,
      country_region %in% c("Southern Europe", "Australia"),
      NA
    )
  ) %>%
  mutate(
    DeDataType = replace(
      DeDataType,
      DeDataType == "other",
      NA
    )
  ) %>%
  mutate(
    WbDimension = replace(
      WbDimension,
      WbDimension == "global_well-being",
      NA
    )
  ) %>%
  mutate(
    country_region = droplevels(country_region),
    DeDomainAll = droplevels(DeDomainAll),
    TypeDeMeasure = droplevels(TypeDeMeasure),
    DeDataType = droplevels(DeDataType),
    WbDimension = droplevels(WbDimension)
  )

## Recode Moderator-Categories With Less Than 5 Observations for Between-Person Uplifts & SWB Associations

levels(uplifts_between$SampleType)[
  levels(uplifts_between$SampleType) == "representative"
] <- "convenience"
uplifts_between$SampleType <- droplevels(uplifts_between$SampleType)
uplifts_between$SampleType <- relevel(uplifts_between$SampleType, ref = "convenience")

uplifts_between <- uplifts_between %>%
  mutate(
    country_region = replace(
      country_region,
      country_region %in% c("Southern Europe", "Australia"),
      NA
    )
  ) %>%
  mutate(
    TypeDeMeasure = replace(
      TypeDeMeasure,
      TypeDeMeasure == "other",
      NA
    )
  ) %>%
  mutate(
    WbDimension = replace(
      WbDimension,
      WbDimension == "global_well-being",
      NA
    )
  ) %>%
  mutate(
    country_region = droplevels(country_region),
    DeDomainAll = droplevels(DeDomainAll),
    TypeDeMeasure = droplevels(TypeDeMeasure),
    WbDimension = droplevels(WbDimension)
  )

## Recode Moderator-Categories With Less Than 5 Observations for Within-Person Hassles & SWB Associations

levels(hassles_within$SampleType)[
  levels(hassles_within$SampleType) == "representative"
] <- "convenience"
hassles_within$SampleType <- droplevels(hassles_within$SampleType)
hassles_within$SampleType <- relevel(hassles_within$SampleType, ref = "convenience")

hassles_within <- hassles_within %>%
  mutate(
    country_region = replace(
      country_region,
      country_region == "Australia",
      NA
    )
  ) %>%
  mutate(
    country_region = droplevels(country_region),
    TypeDeMeasure = droplevels(TypeDeMeasure),
    DeDataType = droplevels(DeDataType)
  )

## Recode Moderator-Categories With Less Than 5 Observations for Between-Person Hassles & SWB Associations

levels(hassles_between$SampleType)[
  levels(hassles_between$SampleType) == "representative"
] <- "convenience"
hassles_between$SampleType <- droplevels(hassles_between$SampleType)
hassles_between$SampleType <- relevel(hassles_between$SampleType, ref = "convenience")

hassles_between <- hassles_between %>%
  mutate(
    TypeDeMeasure = replace(
      TypeDeMeasure,
      TypeDeMeasure == "other",
      NA
    )
  ) %>%
  mutate(
    TypeDeMeasure = droplevels(TypeDeMeasure),
  )

# Center Metric Moderators

## Percentage of females in a sample

uplifts_within <- uplifts_within %>%
  mutate(Female_c = scale(Female, center = TRUE, scale = FALSE))

uplifts_between <- uplifts_between %>%
  mutate(Female_c = scale(Female, center = TRUE, scale = FALSE))

hassles_within <- hassles_within %>%
  mutate(Female_c = scale(Female, center = TRUE, scale = FALSE))

hassles_between <- hassles_between %>%
  mutate(Female_c = scale(Female, center = TRUE, scale = FALSE))

## Mean age

uplifts_within <- uplifts_within %>%
  mutate(AgeM_c = scale(AgeM, center = TRUE, scale = FALSE))

uplifts_between <- uplifts_between %>%
  mutate(AgeM_c = scale(AgeM, center = TRUE, scale = FALSE))

hassles_within <- hassles_within %>%
  mutate(AgeM_c = scale(AgeM, center = TRUE, scale = FALSE))

hassles_between <- hassles_between %>%
  mutate(AgeM_c = scale(AgeM, center = TRUE, scale = FALSE))

## Publication year

uplifts_within <- uplifts_within %>%
  mutate(Year_c = scale(Year, center = TRUE, scale = FALSE))

uplifts_between <- uplifts_between %>%
  mutate(Year_c = scale(Year, center = TRUE, scale = FALSE))

hassles_within <- hassles_within %>%
  mutate(Year_c = scale(Year, center = TRUE, scale = FALSE))

hassles_between <- hassles_between %>%
  mutate(Year_c = scale(Year, center = TRUE, scale = FALSE))

## SWB Reliability (Center)

uplifts_within <- uplifts_within %>%
  mutate(WbReliability_c = scale(WbReliability, center = TRUE, scale = FALSE))

uplifts_between <- uplifts_between %>%
  mutate(WbReliability_c = scale(WbReliability, center = TRUE, scale = FALSE))

hassles_within <- hassles_within %>%
  mutate(WbReliability_c = scale(WbReliability, center = TRUE, scale = FALSE))

hassles_between <- hassles_between %>%
  mutate(WbReliability_c = scale(WbReliability, center = TRUE, scale = FALSE))

# Define Function for Meta-Analytic Model Including Moderators

run_ma_mod <- function(mod, data, SamV) {
  
  formula <- as.formula(paste("~", mod))
  
  ma_mod <- rma.mv(
    yi = ES,
    V = SamV,
    mods = formula,
    random = list(~ 1 | EffSizeID,
                  ~ 1 | StudyID),
    data = data,
    sparse = TRUE
  )
  
  robust_ma_mod <- robust(ma_mod, cluster = data$StudyID, clubSandwich = TRUE)
  
  list(
    model = ma_mod,
    robust = robust_ma_mod
  )
}

# Define Function for the Computation of R2

calc_r2 <- function(model, data, SamV) {
  
  rows_used <- model$not.na
  
  null_model <- rma.mv(
    yi = ES,
    V = SamV[rows_used, rows_used],
    random = list(
      ~ 1 | EffSizeID,
      ~ 1 | StudyID
    ),
    data = data[rows_used, ],
    sparse = TRUE
  )

  tau1_null <- null_model$sigma2[1]
  tau2_null <- null_model$sigma2[2]

  tau1_mod <- model$sigma2[1]
  tau2_mod <- model$sigma2[2]

  R2_L1 <- 100 * (tau1_null - tau1_mod) / tau1_null
  
  R2_L2 <- 100 * (tau2_null - tau2_mod) / tau2_null

  R2_Total <- 100 *
    ((tau1_null + tau2_null) -
       (tau1_mod + tau2_mod)) /
    (tau1_null + tau2_null)

  data.frame(
    R2_Level1 = R2_L1,
    R2_Level2 = R2_L2,
    R2_Total = R2_Total
  )
}

# Define Function to Extract Results from Moderation Analysis

extract_mod_results <- function(x, data, SamV) {
  
  i2 <- calc_i2(x$model, SamV)
  
  r2 <- calc_r2(
    model = x$model,
    data = data,
    SamV = SamV
  )
  
  coef_df <- data.frame(
    term = rownames(x$robust$b),
    b    = x$robust$b,
    se   = x$robust$se,
    z    = x$robust$zval,
    p    = x$robust$pval,
    ci.lb = x$robust$ci.lb,
    ci.ub = x$robust$ci.ub
  )
  
  coef_df <- subset(coef_df, term != "intrcpt")

  cbind(
    k = x$model$k,
    coef_df,
    sigma1 = x$model$sigma2[1],
    sigma2 = x$model$sigma2[2],
    i2,
    r2
  )
}

Single-Moderator Models

Uplifts & SWB

Within-Person Associations

Regression Coefficient Table

# Sample Characteristics

sample_mods <- c("Female_c", "SampleType", "AgeM_c", "country_region")

sample_mod_uplifts_within <- setNames(
  lapply(
    sample_mods,
    run_ma_mod,
    data = uplifts_within,
    SamV = V_uplifts_within
  ),
  sample_mods
)

sample_mod_results_uplifts_within <- lapply(
  sample_mod_uplifts_within,
  extract_mod_results,
  data = uplifts_within,
  SamV = V_uplifts_within
) %>%
  bind_rows(.id = "moderator")

# Publication Characteristics

pub_mods <- c("Year_c")

pub_mod_uplifts_within <- setNames(
  lapply(
    pub_mods,
    run_ma_mod,
    data = uplifts_within,
    SamV = V_uplifts_within
  ),
  pub_mods
)

pub_mod_results_uplifts_within <- lapply(
  pub_mod_uplifts_within,
  extract_mod_results,
  data = uplifts_within,
  SamV = V_uplifts_within
) %>%
  bind_rows(.id = "moderator")

# Study-Design Characteristics

design_mods <- c("StudyDesign")

design_mod_uplifts_within <- setNames(
  lapply(
    design_mods,
    run_ma_mod,
    data = uplifts_within,
    SamV = V_uplifts_within
  ),
  design_mods
)

design_mod_results_uplifts_within <- lapply(
  design_mod_uplifts_within,
  extract_mod_results,
  data = uplifts_within,
  SamV = V_uplifts_within
) %>%
  bind_rows(.id = "moderator")

# Assessment Characteristics

assess_mods <- c("DeDomainAll", "TypeDeMeasure", "DeDataType", 
                 "WbDimension", "WbReliability_c")

assess_mod_uplifts_within <- setNames(
  lapply(
    assess_mods,
    run_ma_mod,
    data = uplifts_within,
    SamV = V_uplifts_within
  ),
  assess_mods
)

assess_mod_results_uplifts_within <- lapply(
  assess_mod_uplifts_within,
  extract_mod_results,
  data = uplifts_within,
  SamV = V_uplifts_within
) %>%
  bind_rows(.id = "moderator")

# Correlation Characteristics

corr_mods <- c("CorrLag")

corr_mod_uplifts_within <- setNames(
  lapply(
    corr_mods,
    run_ma_mod,
    data = uplifts_within,
    SamV = V_uplifts_within
  ),
  corr_mods
)

corr_mod_results_uplifts_within <- lapply(
  corr_mod_uplifts_within,
  extract_mod_results,
  data = uplifts_within,
  SamV = V_uplifts_within
) %>%
  bind_rows(.id = "moderator")

# Create table

mod_uplifts_within_metric <- rbind(
  sample_mod_results_uplifts_within, 
  pub_mod_results_uplifts_within,
  design_mod_results_uplifts_within,
  assess_mod_results_uplifts_within,
  corr_mod_results_uplifts_within) %>%
  filter(
    moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c")
  )

mod_uplifts_within_categorical <- rbind(
  sample_mod_results_uplifts_within, 
  pub_mod_results_uplifts_within,
  design_mod_results_uplifts_within,
  assess_mod_results_uplifts_within,
  corr_mod_results_uplifts_within) %>%
  filter(
    !moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c")
  )

mod_uplifts_within <- rbind(
  mod_uplifts_within_metric,
  mod_uplifts_within_categorical
) %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

mod_uplifts_within$R2_Level1 <- ifelse(mod_uplifts_within$R2_Level1 < 0, 0, mod_uplifts_within$R2_Level1)
mod_uplifts_within$R2_Level2 <- ifelse(mod_uplifts_within$R2_Level2 < 0, 0, mod_uplifts_within$R2_Level2)
mod_uplifts_within$R2_Total <- ifelse(mod_uplifts_within$R2_Total < 0, 0, mod_uplifts_within$R2_Total)

names(mod_uplifts_within) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                               "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                               "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(mod_uplifts_within) <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                   "College", "Northern Europe", "Asia",
                                   "ESM", "Work/Achievement", "Social",
                                   "Other event", "Open-response", "Interview", "Numeric count",
                                   "Scale score", "Domain satisfaction", "Positive affect", "Negative affect",
                                   "Prospective")

order_mod_uplifts_within <- c(
  "Publication year",
  "College",
  "% Female",
  "Mean age",
  "Northern Europe","Asia",
  "Work/Achievement", "Social", "Other event",
  "Open-response", "Interview",
  "Numeric count", "Scale score",
  "Domain satisfaction", "Positive affect", "Negative affect",
  "SWB reliability",
  "ESM",
  "Prospective"
)

mod_uplifts_within <- mod_uplifts_within[order_mod_uplifts_within, ]

kable(round(mod_uplifts_within, 3),
      caption = "**Results of Single-Moderator Analyses for the Within-Person Association Between Daily Uplifts and SWB**",
      escape = FALSE,
      label = NA,
      digits = 3) %>%
  pack_rows("Region", 5, 6) %>%
  pack_rows("Daily event type", 7, 9) %>%
  pack_rows("Daily event measurement", 10, 11) %>%
  pack_rows("Daily event data type", 12, 13) %>%
  pack_rows("SWB dimension", 14, 16) %>%
  column_spec(c(3:6), bold = ifelse(mod_uplifts_within[,5] < 0.05, TRUE, FALSE)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 32: Results of Single-Moderator Analyses for the Within-Person Association Between Daily Uplifts and SWB

	ES	b	SE	t	p	τ2Level1	τ2Level2	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Publication year	304	0.002	0.002	1.006	0.347	0.026	0.007	77.811	21.504	99.314	0.718	0.000	0.000
College	304	-0.005	0.029	-0.168	0.868	0.027	0.007	77.539	21.782	99.321	0.543	0.000	0.000
% Female	304	0.000	0.000	0.666	0.534	0.027	0.007	79.423	19.887	99.310	0.000	0.000	0.000
Mean age	300	0.000	0.001	0.097	0.924	0.027	0.008	76.792	22.536	99.329	0.297	0.000	0.000
Region
Northern Europe	278	0.065	0.043	1.506	0.185	0.023	0.000	97.038	2.007	99.045	2.496	0.000	0.479
Asia	278	-0.002	0.018	-0.092	0.941	0.023	0.000	97.038	2.007	99.045	2.496	0.000	0.479
Daily event type
Work/Achievement	304	-0.091	0.038	-2.397	0.024	0.024	0.013	63.714	35.659	99.373	11.746	0.000	0.000
Social	304	0.014	0.037	0.387	0.702	0.024	0.013	63.714	35.659	99.373	11.746	0.000	0.000
Other event	304	-0.112	0.024	-4.721	0.103	0.024	0.013	63.714	35.659	99.373	11.746	0.000	0.000
Daily event measurement
Open-response	304	0.049	0.106	0.461	0.673	0.027	0.006	81.318	17.977	99.295	0.000	11.373	1.931
Interview	304	-0.097	0.026	-3.687	0.031	0.027	0.006	81.318	17.977	99.295	0.000	11.373	1.931
Daily event data type
Numeric count	301	0.078	0.033	2.347	0.040	0.027	0.000	99.153	0.000	99.153	2.904	59.143	2.904
Scale score	301	0.078	0.019	4.022	0.001	0.027	0.000	99.153	0.000	99.153	2.904	59.143	2.904
SWB dimension
Domain satisfaction	300	0.023	0.057	0.415	0.693	0.023	0.008	73.618	25.634	99.252	13.326	0.000	8.107
Positive affect	300	0.068	0.041	1.654	0.162	0.023	0.008	73.618	25.634	99.252	13.326	0.000	8.107
Negative affect	300	-0.058	0.040	-1.430	0.215	0.023	0.008	73.618	25.634	99.252	13.326	0.000	8.107
SWB reliability	251	0.366	0.120	3.061	0.011	0.028	0.008	76.138	22.886	99.024	13.160	0.000	0.000
ESM	304	0.026	0.047	0.554	0.594	0.027	0.007	78.195	21.115	99.310	0.353	0.000	0.000
Prospective	304	-0.175	0.011	-15.454	0.000	0.019	0.010	64.106	35.088	99.194	28.408	0.000	11.414

Note. Level-specific \(R^2\) values were calculated as the proportional reduction in the estimated random-effect variance components relative to the corresponding null model fitted to the same observations (see the corresponding analysis code for more details). Because the addition of moderators changes how remaining residual heterogeneity is partitioned between the within- and between-study levels, individual variance components may increase or decrease after including a moderator. Consequently, \(R^2\) values can get negative. Negative \(R^2\) values were truncated to 0.000% for presentation.

Regression Coefficient Plot

mod_uplifts_within_plot <- rbind(
  mod_uplifts_within_metric,
  mod_uplifts_within_categorical
)

table_extract_mod_uplifts_within <- mod_uplifts_within_plot %>%
  select(b, ci.lb, ci.ub) %>%
  mutate(across(everything(), ~ round(.x, 2)))

mod_uplifts_within_plot$modlabel <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                      "College", "Northern Europe", "Asia", "ESM", "Work/Achievement", "Social",
                                      "Other event", "Open-response", "Interview", "Numeric count",
                                      "Scale score", "Domain satisfaction", "Positive affect", "Negative affect",
                                      "Prospective")

mod_uplifts_within_plot <- mod_uplifts_within_plot %>%
  mutate(
    modcat = case_when(
      modlabel %in% c("Work/Achievement", "Social", "Other event") ~ "Daily event type",
      modlabel %in% c("Open-response", "Interview", "Other measure") ~ "Daily event measurement",
      modlabel %in% c("Numeric count", "Scale score") ~ "Daily event data type",
      modlabel %in% c("Domain satisfaction", "Positive affect", "Negative affect") ~ "SWB dimension",
      modlabel %in% c("College") ~ "Sample type",
      modlabel %in% c("Northern Europe", "Asia") ~ "Region",
      modlabel %in% c("ESM") ~ "Study design",
      modlabel == "Prospective" ~ "Correlation lag",
      TRUE ~ modlabel
    )
  ) %>%
  mutate(
    modtype = ifelse(
      moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c"),
      "continuous",
      "categorical"
    ),
    significance = ifelse(
      p < .05,
      "significant",
      "non-significant"
    )
  )

order_modcat_within <- c("Publication year",
                        "Sample type", 
                        "% Female", 
                        "Mean age", 
                        "Region", 
                        "Daily event type", 
                        "Daily event measurement", 
                        "Daily event data type", 
                        "SWB dimension", 
                        "SWB reliability",
                        "Study design", 
                        "Correlation lag")

mod_uplifts_within_plot <- mod_uplifts_within_plot %>%
  mutate(modcat = factor(modcat, levels = order_modcat_within)) %>%
  arrange(modcat)

mod_uplifts_within_plot$modcat <- factor(
  mod_uplifts_within_plot$modcat,
  levels = rev(unique(mod_uplifts_within_plot$modcat))
)

mod_uplifts_within_plot <- mod_uplifts_within_plot %>%
  mutate(modlabel = factor(modlabel, levels = rev(order_mod_uplifts_within)))

# Compute x-axis limits
x_min <- min(mod_uplifts_within_plot$ci.lb)
x_max <- max(mod_uplifts_within_plot$ci.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

plot <- ggplot(data = mod_uplifts_within_plot,
       aes(
         x = b,
         y = modcat,
         group = modlabel
         )
       ) +
  
   geom_point_interactive(
    aes(
      colour = significance,
      tooltip = paste0(
      "<b>", modlabel, "</b>",
      " (",ifelse(modtype == "categorical", as.character(modcat), "Continuous"), ")",
      "<br>b = ", round(b, 3),
      "<br>95% CI = [", round(ci.ub, 3), ", ", round(ci.lb, 3), "]",
      "<br>p = ", round(p, 3),
      "<br>ES = ", k
      ),
      data_id = modlabel
    ),
    position = position_dodge(width = 0.75),
    size = 2.5
  ) +

  geom_errorbarh(
    aes(
      colour = significance,
      xmin = ci.lb,
      xmax = ci.ub
      ),
    position = position_dodge(width = 0.75),
    height = 0.15,
    linewidth = .5
    ) +
geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
    ) +
  geom_text(
  data = subset(mod_uplifts_within_plot, modtype == "categorical"),
  aes(
    x = ci.ub + right_pad,
    label = modlabel
  ),
  position = position_dodge(width = 0.75),
  hjust = 0,
  size = 3
) +
  theme_pub() +

  labs(
    x = "Meta-regression coefficient (b)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +
  
  theme(
    axis.text.y = element_text(hjust = 1)
    )

girafe(
  ggobj = plot,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Hover over the points in the plot to display more information for the corresponding moderator. Significant moderation effects are depicted in black, non-significant moderation effects are depicted in grey.

Predicted Correlations Plot

## Publication year

sd_year_uplifts_within <- sd(uplifts_within$Year_c, na.rm = TRUE)

pred_year_uplifts_within <- predict(
  pub_mod_uplifts_within$Year_c$robust,
  newmods = c(-sd_year_uplifts_within, 0, sd_year_uplifts_within)
)

df_pred_year_uplifts_within <- transform(
  as.data.frame(pred_year_uplifts_within),
  moderator = "Publication year",
  label = c(
    round(mean(uplifts_within$Year, na.rm = TRUE) - sd_year_uplifts_within, 2),
    round(mean(uplifts_within$Year, na.rm = TRUE), 2),
    round(mean(uplifts_within$Year, na.rm = TRUE) + sd_year_uplifts_within, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = pub_mod_uplifts_within$Year_c$robust$k
)

## Sample type

sampletype_cats_uplifts_within <- data.frame(
  SampleType = factor(
    c("convenience", "college"),
    levels = c("convenience", "college")
  )
)

sampletype_mods_uplifts_within <-
  model.matrix(~ SampleType, sampletype_cats_uplifts_within)[, -1]

pred_sampletype_uplifts_within <- predict(
  sample_mod_uplifts_within$SampleType$robust,
  newmods = sampletype_mods_uplifts_within
)

df_pred_sampletype_uplifts_within <- transform(
  as.data.frame(pred_sampletype_uplifts_within),
  moderator = "Sample type",
  label = c("Convenience", "College"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_uplifts_within$SampleType$robust$k
)

## Percentage female

sd_female_uplifts_within <- sd(uplifts_within$Female_c, na.rm = TRUE)

pred_female_uplifts_within <- predict(
  sample_mod_uplifts_within$Female_c$robust,
  newmods = c(-sd_female_uplifts_within, 0, sd_female_uplifts_within)
)

df_pred_female_uplifts_within <- transform(
  as.data.frame(pred_female_uplifts_within),
  moderator = "% Female",
  label = c(
    paste0(round(mean(uplifts_within$Female, na.rm = TRUE) - sd_female_uplifts_within, 2), "%"),
    paste0(round(mean(uplifts_within$Female, na.rm = TRUE), 2), "%"),
    paste0(round(mean(uplifts_within$Female, na.rm = TRUE) +  sd_female_uplifts_within, 2), "%")
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_uplifts_within$Female_c$robust$k
)

## Mean age

sd_mean_age_uplifts_within <- sd(uplifts_within$AgeM_c, na.rm = TRUE)

pred_agem_uplifts_within <- predict(
  sample_mod_uplifts_within$AgeM_c$robust,
  newmods = c(- sd_mean_age_uplifts_within, 0, + sd_mean_age_uplifts_within)
)

df_pred_agem_uplifts_within <- transform(
  as.data.frame(pred_agem_uplifts_within),
  moderator = "Mean age",
  label = c(
    round(mean(uplifts_within$AgeM, na.rm = TRUE) - sd_mean_age_uplifts_within, 2),
    round(mean(uplifts_within$AgeM, na.rm = TRUE), 2),
    round(mean(uplifts_within$AgeM, na.rm = TRUE) + sd_mean_age_uplifts_within, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_uplifts_within$AgeM_c$robust$k
)

## Region

region_cats_uplifts_within <- data.frame(
  country_region = factor(
    c("North America", "Northern Europe", "Asia"),
    levels = c("North America", "Northern Europe", "Asia")
  )
)

region_mods_uplifts_within <-
  model.matrix(~ country_region, region_cats_uplifts_within)[, -1]

pred_region_uplifts_within <- predict(
  sample_mod_uplifts_within$country_region$robust,
  newmods = region_mods_uplifts_within
)

df_pred_region_uplifts_within <- transform(
  as.data.frame(pred_region_uplifts_within),
  moderator = "Region",
  label = c("North America", "Northern Europe", "Asia"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_uplifts_within$country_region$robust$k
)

## Daily event type

eventtype_cats_uplifts_within <- data.frame(
  DeDomainAll = factor(
    c("general", "work/achievement", "social", "other"),
    levels = c("general", "work/achievement", "social", "other"),
  )
)

eventtype_mods_uplifts_within <-
  model.matrix(~ DeDomainAll, eventtype_cats_uplifts_within)[, -1]

pred_eventtype_uplifts_within <- predict(
  assess_mod_uplifts_within$DeDomainAll$robust,
  newmods = eventtype_mods_uplifts_within
)

df_pred_eventtype_uplifts_within <- transform(
  as.data.frame(pred_eventtype_uplifts_within),
  moderator = "Event type",
  label = c("General", "Work/Achievement", "Social", "Other"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_uplifts_within$DeDomainAll$robust$k
)

## Daily event measurement

eventmeasure_cats_uplifts_within <- data.frame(
  TypeDeMeasure = factor(
    c("checklist/questionnaire", "open_response", "interview"),
    levels = c("checklist/questionnaire", "open_response", "interview"),
  )
)

eventmeasure_mods_uplifts_within <-
  model.matrix(~ TypeDeMeasure, eventmeasure_cats_uplifts_within)[, -1]

pred_eventmeasure_uplifts_within <- predict(
  assess_mod_uplifts_within$TypeDeMeasure$robust,
  newmods = eventmeasure_mods_uplifts_within
)

df_pred_eventmeasure_uplifts_within <- transform(
  as.data.frame(pred_eventmeasure_uplifts_within),
  moderator = "Event measurement",
  label = c("Checklist/Questionnaire", "Open-response", "Interview"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_uplifts_within$TypeDeMeasure$robust$k
)

## Daily event operationalization

eventdata_cats_uplifts_within <- data.frame(
  DeDataType = factor(
    c("dichotomous", "numeric_count", "scale_score"),
    levels = c("dichotomous", "numeric_count", "scale_score"),
  )
)

eventdata_mods_uplifts_within <-
  model.matrix(~ DeDataType, eventdata_cats_uplifts_within)[, -1]

pred_eventdata_uplifts_within <- predict(
  assess_mod_uplifts_within$DeDataType$robust,
  newmods = eventdata_mods_uplifts_within
)

df_pred_eventdata_uplifts_within <- transform(
  as.data.frame(pred_eventdata_uplifts_within),
  moderator = "Event operationalization",
  label = c("Dichotomous", "Numeric count", "Scale score"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_uplifts_within$DeDataType$robust$k
)

## SWB dimension

swb_cats_uplifts_within <- data.frame(
  WbDimension = factor(
    c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect"),
    levels =c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect"),
  )
)

swb_mods_uplifts_within <- 
  model.matrix(~ WbDimension, data = swb_cats_uplifts_within)[, -1]

pred_swb_mods_uplifts_within <- predict(
  assess_mod_uplifts_within$WbDimension$robust,
  newmods = swb_mods_uplifts_within
  )

df_pred_swb_mods_uplifts_within <- transform(
  as.data.frame(pred_swb_mods_uplifts_within),
  moderator = "SWB dimension",
  label = c("Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_uplifts_within$WbDimension$robust$k
)

## SWB reliability

sd_reliability_uplifts_within <- sd(uplifts_within$WbReliability_c, na.rm = TRUE)

pred_reliability_uplifts_within <- predict(
  assess_mod_uplifts_within$WbReliability_c$robust,
  newmods = c(- sd_reliability_uplifts_within, 0, + sd_reliability_uplifts_within)
)

df_pred_reliability_uplifts_within <- transform(
  as.data.frame(pred_reliability_uplifts_within),
  moderator = "SWB reliability",
  label = c(
    round(mean(uplifts_within$WbReliability, na.rm = TRUE) - sd_reliability_uplifts_within, 2),
    round(mean(uplifts_within$WbReliability, na.rm = TRUE), 2),
    round(mean(uplifts_within$WbReliability, na.rm = TRUE) + sd_reliability_uplifts_within, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_uplifts_within$WbReliability$robust$k
)

## Study design

design_cats_uplifts_within <- data.frame(
  StudyDesign = factor(
    c("daily_diary", "esm"),
    levels =c("daily_diary", "esm"),
  )
)

design_mods_uplifts_within <- 
  model.matrix(~ StudyDesign, data = design_cats_uplifts_within)[, -1]

pred_design_mods_uplifts_within <- predict(
  design_mod_uplifts_within$StudyDesign$robust,
  newmods = design_mods_uplifts_within
  )

df_pred_design_mods_uplifts_within <- transform(
  as.data.frame(pred_design_mods_uplifts_within),
  moderator = "Study design",
  label = c("Daily diary", "Experience sampling"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = design_mod_uplifts_within$StudyDesign$robust$k
  
)

## Correlation lag

corr_cats_uplifts_within <- data.frame(
  CorrLag = factor(
    c("concurrent", "prospective"),
    levels =c("concurrent", "prospective"),
  )
)

corr_mods_uplifts_within <- 
  model.matrix(~ CorrLag, data = corr_cats_uplifts_within)[, -1]

pred_corr_mods_uplifts_within <- predict(
  corr_mod_uplifts_within$CorrLag$robust,
  newmods = corr_mods_uplifts_within
  )

df_pred_corr_mods_uplifts_within <- transform(
  as.data.frame(pred_corr_mods_uplifts_within),
  moderator = "Correlation lag",
  label = c("Concurrent", "Prospective"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = corr_mod_uplifts_within$CorrLag$robust$k
)

## Create dataset for plot

df_pred_year_uplifts_within$label <-
  as.character(df_pred_year_uplifts_within$label)

df_pred_female_uplifts_within$label <-
  as.character(df_pred_female_uplifts_within$label)

df_pred_agem_uplifts_within$label <-
  as.character(df_pred_agem_uplifts_within$label)

df_pred_reliability_uplifts_within$label <-
  as.character(df_pred_reliability_uplifts_within$label)

df_pred_year_uplifts_within$order <- rev(seq_len(nrow(df_pred_year_uplifts_within)))
df_pred_sampletype_uplifts_within$order <- rev(seq_len(nrow(df_pred_sampletype_uplifts_within)))
df_pred_female_uplifts_within$order <- rev(seq_len(nrow(df_pred_female_uplifts_within)))
df_pred_agem_uplifts_within$order <- rev(seq_len(nrow(df_pred_agem_uplifts_within)))
df_pred_region_uplifts_within$order <- rev(seq_len(nrow(df_pred_region_uplifts_within)))
df_pred_eventtype_uplifts_within$order <- rev(seq_len(nrow(df_pred_eventtype_uplifts_within)))
df_pred_eventmeasure_uplifts_within$order <- rev(seq_len(nrow(df_pred_eventmeasure_uplifts_within)))
df_pred_eventdata_uplifts_within$order <- rev(seq_len(nrow(df_pred_eventdata_uplifts_within)))
df_pred_swb_mods_uplifts_within$order <- rev(seq_len(nrow(df_pred_swb_mods_uplifts_within)))
df_pred_reliability_uplifts_within$order <- rev(seq_len(nrow(df_pred_reliability_uplifts_within)))
df_pred_design_mods_uplifts_within$order <- rev(seq_len(nrow(df_pred_design_mods_uplifts_within)))
df_pred_corr_mods_uplifts_within$order <- rev(seq_len(nrow(df_pred_corr_mods_uplifts_within)))

plot_pred_mod_uplifts_within <- bind_rows(
  df_pred_year_uplifts_within,
  df_pred_sampletype_uplifts_within,
  df_pred_female_uplifts_within,
  df_pred_agem_uplifts_within,
  df_pred_region_uplifts_within,
  df_pred_eventtype_uplifts_within,
  df_pred_eventmeasure_uplifts_within,
  df_pred_eventdata_uplifts_within,
  df_pred_swb_mods_uplifts_within,
  df_pred_reliability_uplifts_within,
  df_pred_design_mods_uplifts_within,
  df_pred_corr_mods_uplifts_within
) %>%
  mutate(
    significance = if_else(
      (r.lb>0)&(r.ub>0) | (r.lb<0)&(r.ub<0),
      "significant",
      "non-significant"
    )
  )

# Order moderators
plot_pred_mod_uplifts_within$moderator <- factor(
  plot_pred_mod_uplifts_within$moderator,
  levels = rev(c("Publication year", "Sample type", "% Female", "Mean age", "Region", 
                 "Event type", "Event measurement", "Event operationalization", 
                 "SWB dimension", "SWB reliability", "Study design", "Correlation lag"))
)

# Order moderator categories and assign y-positions
plot_pred_mod_uplifts_within <- plot_pred_mod_uplifts_within %>%
  arrange(moderator, order) %>%
  mutate(
    tooltip = paste0(
      "<b>", label, "</b>",
      "<br>Predicted r = ", round(r, 3),
      "<br>95% CI = [", round(r.lb, 3), ", ", round(r.ub, 3), "]",
      "<br>ES = ", k
    )
  )

# Compute x-axis limits
x_min <- min(plot_pred_mod_uplifts_within$r.lb)
x_max <- max(plot_pred_mod_uplifts_within$r.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

# Create plot
simple_slopes_uplifts_within <-
  ggplot(
    plot_pred_mod_uplifts_within,
    aes(
      x = r,
      y = moderator,
      group = interaction(moderator, order),
      color = significance
      )
  ) +

  geom_errorbarh(
    aes(
      xmin = r.lb,
      xmax = r.ub
    ),
    height = 0.15,
    linewidth = 0.5,
    position = position_dodge(width = 0.75)
  ) +

  geom_point_interactive(
    aes(
      tooltip = tooltip,
      data_id = paste(moderator, label),
    ),
    size = 2.5,
    position = position_dodge(width = 0.75)
  ) +

  geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
  ) +

  coord_cartesian(
    xlim = c(
      x_min,
      x_max + right_pad + x_range * 0.15
    ),
    clip = "off"
  ) +

  labs(
    x = "Predicted correlation (r)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +

  geom_text(
    aes(
      x = r.ub + right_pad,
      label = label
    ),
    hjust = 0,
    size = 3,
    position = position_dodge(width = 0.75),
    colour = "black"
  ) +

  theme_pub() +

  theme(
    axis.text.y = element_text(hjust = 1),
    panel.grid.major.y = element_blank()
  )

# Interactive plot
girafe(
  ggobj = simple_slopes_uplifts_within,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Points represent model-predicted correlations. Predictions for continuous moderators are estimated at the mean of the moderator, whereas predictions for categorical moderators are reported for each category. Hover over the points in the plot to display more information for the corresponding moderator. Significant predicted correlations are depicted in black, non-significant correlations are depicted in grey.

Between-Person Associations

Regression Coefficient Table

# Sample Characteristics

sample_mods <- c("Female_c", "SampleType", "AgeM_c", "country_region")

sample_mod_uplifts_between <- setNames(
  lapply(
    sample_mods,
    run_ma_mod,
    data = uplifts_between,
    SamV = V_uplifts_between
  ),
  sample_mods
)

sample_mod_results_uplifts_between <- lapply(
  sample_mod_uplifts_between,
  extract_mod_results,
  data = uplifts_between,
  SamV = V_uplifts_between
) %>%
  bind_rows(.id = "moderator")

# Publication Characteristics

pub_mods <- c("Year_c")

pub_mod_uplifts_between <- setNames(
  lapply(
    pub_mods,
    run_ma_mod,
    data = uplifts_between,
    SamV = V_uplifts_between
  ),
  pub_mods
)

pub_mod_results_uplifts_between <- lapply(
  pub_mod_uplifts_between,
  extract_mod_results,
  data = uplifts_between,
  SamV = V_uplifts_between
) %>%
  bind_rows(.id = "moderator")

# Study-Design Characteristics

design_mods <- c("StudyDesign")

design_mod_uplifts_between <- setNames(
  lapply(
    design_mods,
    run_ma_mod,
    data = uplifts_between,
    SamV = V_uplifts_between
  ),
  design_mods
)

design_mod_results_uplifts_between <- lapply(
  design_mod_uplifts_between,
  extract_mod_results,
  data = uplifts_between,
  SamV = V_uplifts_between
) %>%
  bind_rows(.id = "moderator")

# Assessment Characteristics

assess_mods <- c("DeDomainAll", "TypeDeMeasure", "DeDataType", 
                 "WbDimension", "WbReliability_c")

assess_mod_uplifts_between <- setNames(
  lapply(
    assess_mods,
    run_ma_mod,
    data = uplifts_between,
    SamV = V_uplifts_between
  ),
  assess_mods
)

assess_mod_results_uplifts_between <- lapply(
  assess_mod_uplifts_between,
  extract_mod_results,
  data = uplifts_between,
  SamV = V_uplifts_between
) %>%
  bind_rows(.id = "moderator")

# Create table

mod_uplifts_between_metric <- rbind(
  sample_mod_results_uplifts_between, 
  pub_mod_results_uplifts_between,
  design_mod_results_uplifts_between,
  assess_mod_results_uplifts_between) %>%
  filter(
    moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c")
  )

mod_uplifts_between_categorical <- rbind(
  sample_mod_results_uplifts_between, 
  pub_mod_results_uplifts_between,
  design_mod_results_uplifts_between,
  assess_mod_results_uplifts_between) %>%
  filter(
    !moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c")
  )

mod_uplifts_between <- rbind(
  mod_uplifts_between_metric,
  mod_uplifts_between_categorical
) %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )


mod_uplifts_between$R2_Level1 <- ifelse(mod_uplifts_between$R2_Level1 < 0, 0, mod_uplifts_between$R2_Level1)
mod_uplifts_between$R2_Level2 <- ifelse(mod_uplifts_between$R2_Level2 < 0, 0, mod_uplifts_between$R2_Level2)
mod_uplifts_between$R2_Total <- ifelse(mod_uplifts_between$R2_Total < 0, 0, mod_uplifts_between$R2_Total)

names(mod_uplifts_between) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                               "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                               "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(mod_uplifts_between) <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                    "College", "Northern Europe", "Asia", "ESM", "Work/Achievement", "Social", 
                                    "Other event", "Open-response", "Interview","Numeric count",
                                    "Scale score", "Other type", "Domain satisfaction", "Positive affect", 
                                    "Negative affect")

order_mod_uplifts_between <- c(
  "Publication year",
  "College",
  "% Female",
  "Mean age",
  "Northern Europe","Asia",
  "Work/Achievement", "Social", "Other event",
  "Open-response", "Interview",
  "Numeric count", "Scale score", "Other type",
  "Domain satisfaction", "Positive affect", "Negative affect",
  "SWB reliability",
  "ESM"
)

mod_uplifts_between <- mod_uplifts_between[order_mod_uplifts_between, ]

kable(round(mod_uplifts_between, 3),
      caption = "**Results of Single-Moderator Analyses for the Between-Person Association Between Daily Uplifts and SWB**",
      escape = FALSE,
      label = NA,
      digits = 3) %>%
  pack_rows("Region", 5, 6) %>%
  pack_rows("Daily event type", 7, 9) %>%
  pack_rows("Daily event measurement", 10, 11) %>%
  pack_rows("Daily event data type", 12, 14) %>%
  pack_rows("SWB dimension", 15, 17) %>%
  column_spec(c(3:6), bold = ifelse(mod_uplifts_between[,5] < 0.05, TRUE, FALSE)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 33: Results of Single-Moderator Analyses for the Between-Person Association Between Daily Uplifts and SWB

	ES	b	SE	t	p	τ2Level1	τ2Level2	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Publication year	270	0.005	0.002	2.242	0.046	0.060	0.000	93.424	0.000	93.424	1.275	0.000	1.275
College	270	-0.017	0.030	-0.567	0.574	0.061	0.000	93.519	0.000	93.519	0.000	0.000	0.000
% Female	270	0.001	0.000	1.578	0.141	0.060	0.000	93.494	0.000	93.494	0.214	63.146	0.214
Mean age	268	0.000	0.001	-0.192	0.849	0.061	0.000	93.509	0.000	93.509	0.000	0.000	0.000
Region
Northern Europe	228	0.067	0.042	1.592	0.141	0.062	0.000	94.150	0.000	94.150	0.405	19.497	0.405
Asia	228	-0.060	0.057	-1.053	0.381	0.062	0.000	94.150	0.000	94.150	0.405	19.497	0.405
Daily event type
Work/Achievement	270	-0.044	0.045	-0.984	0.332	0.060	0.000	93.473	0.000	93.473	0.327	0.000	0.327
Social	270	-0.035	0.043	-0.821	0.416	0.060	0.000	93.473	0.000	93.473	0.327	0.000	0.327
Other event	270	-0.167	0.064	-2.630	0.095	0.060	0.000	93.473	0.000	93.473	0.327	0.000	0.327
Daily event measurement
Open-response	267	-0.073	0.072	-1.007	0.370	0.060	0.000	93.434	0.000	93.434	1.508	0.000	1.508
Interview	267	-0.214	0.030	-7.005	0.020	0.060	0.000	93.434	0.000	93.434	1.508	0.000	1.508
Daily event data type
Numeric count	269	0.125	0.052	2.417	0.024	0.059	0.000	93.308	0.000	93.308	2.797	17.169	2.797
Scale score	269	0.097	0.030	3.238	0.003	0.059	0.000	93.308	0.000	93.308	2.797	17.169	2.797
Other type	269	0.308	0.176	1.755	0.175	0.059	0.000	93.308	0.000	93.308	2.797	17.169	2.797
SWB dimension
Domain satisfaction	267	0.043	0.126	0.341	0.743	0.030	0.012	64.843	25.947	90.790	51.161	0.000	31.618
Positive affect	267	0.080	0.079	1.012	0.341	0.030	0.012	64.843	25.947	90.790	51.161	0.000	31.618
Negative affect	267	-0.256	0.096	-2.663	0.029	0.030	0.012	64.843	25.947	90.790	51.161	0.000	31.618
SWB reliability	202	1.101	0.321	3.427	0.004	0.051	0.017	70.173	23.854	94.027	21.694	0.000	0.000
ESM	270	0.079	0.041	1.908	0.086	0.060	0.000	93.454	0.000	93.454	0.489	5.055	0.489

Note. Level-specific \(R^2\) values were calculated as the proportional reduction in the estimated random-effect variance components relative to the corresponding null model fitted to the same observations (see the corresponding analysis code for more details). Because the addition of moderators changes how remaining residual heterogeneity is partitioned between the within- and between-study levels, individual variance components may increase or decrease after including a moderator. Consequently, \(R^2\) values can get negative. Negative \(R^2\) values were truncated to 0.000% for presentation.

Regression Coefficient Plot

mod_uplifts_between_plot <- rbind(
  mod_uplifts_between_metric,
  mod_uplifts_between_categorical
)

table_extract_mod_uplifts_between <- mod_uplifts_between_plot %>%
  select(b, ci.lb, ci.ub) %>%
  mutate(across(everything(), ~ round(.x, 2)))

mod_uplifts_between_plot$modlabel <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                      "College", "Northern Europe", "Asia", "ESM", 
                                      "Work/Achievement", "Social", "Other event" ,"Open-response", "Interview", 
                                      "Numeric count", "Scale score", "Other type", "Domain satisfaction", 
                                      "Positive affect", "Negative affect")

mod_uplifts_between_plot <- mod_uplifts_between_plot %>%
  mutate(
    modcat = case_when(
      modlabel %in% c("Work/Achievement", "Social", "Other event") ~ "Daily event type",
      modlabel %in% c("Open-response", "Interview", "Other measure") ~ "Daily event measurement",
      modlabel %in% c("Numeric count", "Scale score", "Other type") ~ "Daily event data type",
      modlabel %in% c("Domain satisfaction", "Positive affect", "Negative affect") ~ "SWB dimension",
      modlabel %in% c("College") ~ "Sample type",
      modlabel %in% c("ESM") ~ "Study design",
      modlabel %in% c("Northern Europe", "Asia") ~ "Region",
      TRUE ~ modlabel
    )
  ) %>%
  mutate(
    modtype = ifelse(
      moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c"),
      "continuous",
      "categorical"
    ),
    significance = ifelse(
      p < .05,
      "significant",
      "non-significant"
    )
  )

order_modcat_between <- c("Publication year",
                          "Sample type", 
                          "% Female", 
                          "Mean age", 
                          "Region", 
                          "Daily event type", 
                          "Daily event measurement", 
                          "Daily event data type", 
                          "SWB dimension", 
                          "SWB reliability",
                          "Study design", 
                          "Correlation lag")

mod_uplifts_between_plot <- mod_uplifts_between_plot %>%
  mutate(modcat = factor(modcat, levels = order_modcat_between)) %>%
  arrange(modcat)

mod_uplifts_between_plot$modcat <- factor(
  mod_uplifts_between_plot$modcat,
  levels = rev(unique(mod_uplifts_between_plot$modcat))
)

mod_uplifts_between_plot <- mod_uplifts_between_plot %>%
  mutate(modlabel = factor(modlabel, levels = rev(order_mod_uplifts_between)))

x_min <- min(mod_uplifts_between_plot$ci.lb)
x_max <- max(mod_uplifts_between_plot$ci.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

plot <- ggplot(data = mod_uplifts_between_plot,
       aes(
         x = b,
         y = modcat,
         group = modlabel
         )
       ) +
  
   geom_point_interactive(
    aes(
      colour = significance,
      tooltip = paste0(
      "<b>", modlabel, "</b>",
      " (",ifelse(modtype == "categorical", as.character(modcat), "Continuous"), ")",
      "<br>b = ", round(b, 3),
      "<br>95% CI = [", round(ci.lb, 3), ", ", round(ci.ub, 3), "]",
      "<br>p = ", round(p, 3),
      "<br>ES = ", k
      ),
      data_id = modlabel
    ),
    position = position_dodge(width = 0.75),
    size = 2.5
  ) +

  geom_errorbarh(
    aes(
      colour = significance,
      xmin = ci.lb,
      xmax = ci.ub
      ),
    position = position_dodge(width = 0.75),
    height = 0.15,
    linewidth = .5
    ) +
geom_text(
  data = subset(mod_uplifts_between_plot, modtype == "categorical"),
  aes(
    x = ci.ub + right_pad,
    label = modlabel
  ),
  position = position_dodge(width = 0.75),
  hjust = 0,
  size = 3
) +
geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
    ) +
  
  theme_pub() +

  labs(
    x = "Meta-regression coefficient (b)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +
  
  theme(
    axis.text.y = element_text(hjust = 1)
    )

girafe(
  ggobj = plot,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Hover over the points in the plot to display more information for the corresponding moderator. Significant moderation effects are depicted in black, non-significant moderation effects are depicted in grey.

Predicted Correlations Plot

## Publication year

sd_year_uplifts_between <- sd(uplifts_between$Year_c, na.rm = TRUE)

pred_year_uplifts_between <- predict(
  pub_mod_uplifts_between$Year_c$robust,
  newmods = c(-sd_year_uplifts_between, 0, sd_year_uplifts_between)
)

df_pred_year_uplifts_between <- transform(
  as.data.frame(pred_year_uplifts_between),
  moderator = "Publication year",
  label = c(
    round(mean(uplifts_between$Year, na.rm = TRUE) - sd_year_uplifts_between, 2),
    round(mean(uplifts_between$Year, na.rm = TRUE), 2),
    round(mean(uplifts_between$Year, na.rm = TRUE) + sd_year_uplifts_between, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = pub_mod_uplifts_between$Year_c$robust$k
)

## Sample type

sampletype_cats_uplifts_between <- data.frame(
  SampleType = factor(
    c("convenience", "college"),
    levels = c("convenience", "college")
  )
)

sampletype_mods_uplifts_between <-
  model.matrix(~ SampleType, sampletype_cats_uplifts_between)[, -1]

pred_sampletype_uplifts_between <- predict(
  sample_mod_uplifts_between$SampleType$robust,
  newmods = sampletype_mods_uplifts_between
)

df_pred_sampletype_uplifts_between <- transform(
  as.data.frame(pred_sampletype_uplifts_between),
  moderator = "Sample type",
  label = c("Convenience", "College"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_uplifts_between$SampleType$robust$k
)

df_pred_sampletype_uplifts_between$label <- factor(
  df_pred_sampletype_uplifts_between$label,
  levels = c("Convenience", "College")
)

## Percentage female

sd_female_uplifts_between <- sd(uplifts_between$Female_c, na.rm = TRUE)

pred_female_uplifts_between <- predict(
  sample_mod_uplifts_between$Female_c$robust,
  newmods = c(-sd_female_uplifts_between, 0, sd_female_uplifts_between)
)

df_pred_female_uplifts_between <- transform(
  as.data.frame(pred_female_uplifts_between),
  moderator = "% Female",
  label = c(
    paste0(round(mean(uplifts_between$Female, na.rm = TRUE) - sd_female_uplifts_between, 2), "%"),
    paste0(round(mean(uplifts_between$Female, na.rm = TRUE), 2), "%"),
    paste0(round(mean(uplifts_between$Female, na.rm = TRUE) +  sd_female_uplifts_between, 2), "%")
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_uplifts_between$Female_c$robust$k
)

## Mean age

sd_mean_age_uplifts_between <- sd(uplifts_between$AgeM_c, na.rm = TRUE)

pred_agem_uplifts_between <- predict(
  sample_mod_uplifts_between$AgeM_c$robust,
  newmods = c(- sd_mean_age_uplifts_between, 0, + sd_mean_age_uplifts_between)
)

df_pred_agem_uplifts_between <- transform(
  as.data.frame(pred_agem_uplifts_between),
  moderator = "Mean age",
  label = c(
    round(mean(uplifts_between$AgeM, na.rm = TRUE) - sd_mean_age_uplifts_between, 2),
    round(mean(uplifts_between$AgeM, na.rm = TRUE), 2),
    round(mean(uplifts_between$AgeM, na.rm = TRUE) + sd_mean_age_uplifts_between, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_uplifts_between$AgeM_c$robust$k
)

## Region

region_cats_uplifts_between <- data.frame(
  country_region = factor(
    c("North America", "Northern Europe", "Asia"),
    levels = c("North America", "Northern Europe", "Asia")
  )
)

region_mods_uplifts_between <-
  model.matrix(~ country_region, region_cats_uplifts_between)[, -1]

pred_region_uplifts_between <- predict(
  sample_mod_uplifts_between$country_region$robust,
  newmods = region_mods_uplifts_between
)

df_pred_region_uplifts_between <- transform(
  as.data.frame(pred_region_uplifts_between),
  moderator = "Region",
  label = c("North America", "Northern Europe", "Asia"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_uplifts_between$country_region$robust$k
)

## Daily event type

eventtype_cats_uplifts_between <- data.frame(
  DeDomainAll = factor(
    c("general", "work/achievement", "social", "other"),
    levels = c("general", "work/achievement", "social", "other"),
  )
)

eventtype_mods_uplifts_between <-
  model.matrix(~ DeDomainAll, eventtype_cats_uplifts_between)[, -1]

pred_eventtype_uplifts_between <- predict(
  assess_mod_uplifts_between$DeDomainAll$robust,
  newmods = eventtype_mods_uplifts_between
)

df_pred_eventtype_uplifts_between <- transform(
  as.data.frame(pred_eventtype_uplifts_between),
  moderator = "Event type",
  label = c("General", "Work/Achievement", "Social", "Other"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_uplifts_between$DeDomainAll$robust$k
)

## Daily event measurement

eventmeasure_cats_uplifts_between <- data.frame(
  TypeDeMeasure = factor(
    c("checklist/questionnaire", "open_response", "interview"),
    levels = c("checklist/questionnaire", "open_response", "interview"),
  )
)

eventmeasure_mods_uplifts_between <-
  model.matrix(~ TypeDeMeasure, eventmeasure_cats_uplifts_between)[, -1]

pred_eventmeasure_uplifts_between <- predict(
  assess_mod_uplifts_between$TypeDeMeasure$robust,
  newmods = eventmeasure_mods_uplifts_between
)

df_pred_eventmeasure_uplifts_between <- transform(
  as.data.frame(pred_eventmeasure_uplifts_between),
  moderator = "Event measurement",
  label = c("Checklist/Questionnaire", "Open-response", "Interview"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_uplifts_between$TypeDeMeasure$robust$k
)

## Daily event operationalization

eventdata_cats_uplifts_between <- data.frame(
  DeDataType = factor(
    c("dichotomous", "numeric_count", "scale_score", "other"),
    levels = c("dichotomous", "numeric_count", "scale_score", "other"),
  )
)

eventdata_mods_uplifts_between <-
  model.matrix(~ DeDataType, eventdata_cats_uplifts_between)[, -1]

pred_eventdata_uplifts_between <- predict(
  assess_mod_uplifts_between$DeDataType$robust,
  newmods = eventdata_mods_uplifts_between
)

df_pred_eventdata_uplifts_between <- transform(
  as.data.frame(pred_eventdata_uplifts_between),
  moderator = "Event operationalization",
  label = c("Dichotomous", "Numeric count", "Scale score", "Other"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_uplifts_between$DeDataType$robust$k
)

## SWB dimension

swb_cats_uplifts_between <- data.frame(
  WbDimension = factor(
    c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect"),
    levels =c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect"),
  )
)

swb_mods_uplifts_between <- 
  model.matrix(~ WbDimension, data = swb_cats_uplifts_between)[, -1]

pred_swb_mods_uplifts_between <- predict(
  assess_mod_uplifts_between$WbDimension$robust,
  newmods = swb_mods_uplifts_between
  )

df_pred_swb_mods_uplifts_between <- transform(
  as.data.frame(pred_swb_mods_uplifts_between),
  moderator = "SWB dimension",
  label = c("Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_uplifts_between$WbDimension$robust$k
)

## SWB reliability

sd_reliability_uplifts_between <- sd(uplifts_between$WbReliability_c, na.rm = TRUE)

pred_reliability_uplifts_between <- predict(
  assess_mod_uplifts_between$WbReliability_c$robust,
  newmods = c(- sd_reliability_uplifts_between, 0, + sd_reliability_uplifts_between)
)

df_pred_reliability_uplifts_between <- transform(
  as.data.frame(pred_reliability_uplifts_between),
  moderator = "SWB reliability",
  label = c(
    round(mean(uplifts_between$WbReliability, na.rm = TRUE) - sd_reliability_uplifts_between, 2),
    round(mean(uplifts_between$WbReliability, na.rm = TRUE), 2),
    round(mean(uplifts_between$WbReliability, na.rm = TRUE) + sd_reliability_uplifts_between, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_uplifts_between$WbReliability$robust$k
)

## Study design

design_cats_uplifts_between <- data.frame(
  StudyDesign = factor(
    c("daily_diary", "esm"),
    levels =c("daily_diary", "esm"),
  )
)

design_mods_uplifts_between <- 
  model.matrix(~ StudyDesign, data = design_cats_uplifts_between)[, -1]

pred_design_mods_uplifts_between <- predict(
  design_mod_uplifts_between$StudyDesign$robust,
  newmods = design_mods_uplifts_between
  )

df_pred_design_mods_uplifts_between <- transform(
  as.data.frame(pred_design_mods_uplifts_between),
  moderator = "Study design",
  label = c("Daily diary", "Experience sampling"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = design_mod_uplifts_between$StudyDesign$robust$k
  
)

## Create dataset for plot

## Convert to character for merging

df_pred_year_uplifts_between$label <-
  as.character(df_pred_year_uplifts_between$label)

df_pred_female_uplifts_between$label <-
  as.character(df_pred_female_uplifts_between$label)

df_pred_agem_uplifts_between$label <-
  as.character(df_pred_agem_uplifts_between$label)

df_pred_reliability_uplifts_between$label <-
  as.character(df_pred_reliability_uplifts_between$label)

## Create order column for order of moderator labels inside the plot

df_pred_year_uplifts_between$order <- rev(seq_len(nrow(df_pred_year_uplifts_between)))
df_pred_sampletype_uplifts_between$order <- rev(seq_len(nrow(df_pred_sampletype_uplifts_between)))
df_pred_female_uplifts_between$order <- rev(seq_len(nrow(df_pred_female_uplifts_between)))
df_pred_agem_uplifts_between$order <- rev(seq_len(nrow(df_pred_agem_uplifts_between)))
df_pred_region_uplifts_between$order <- rev(seq_len(nrow(df_pred_region_uplifts_between)))
df_pred_eventtype_uplifts_between$order <- rev(seq_len(nrow(df_pred_eventtype_uplifts_between)))
df_pred_eventmeasure_uplifts_between$order <- rev(seq_len(nrow(df_pred_eventmeasure_uplifts_between)))
df_pred_eventdata_uplifts_between$order <- rev(seq_len(nrow(df_pred_eventdata_uplifts_between)))
df_pred_swb_mods_uplifts_between$order <- rev(seq_len(nrow(df_pred_swb_mods_uplifts_between)))
df_pred_reliability_uplifts_between$order <- rev(seq_len(nrow(df_pred_reliability_uplifts_between)))
df_pred_design_mods_uplifts_between$order <- rev(seq_len(nrow(df_pred_design_mods_uplifts_between)))

plot_pred_mod_uplifts_between <- bind_rows(
  df_pred_year_uplifts_between,
  df_pred_sampletype_uplifts_between,
  df_pred_female_uplifts_between,
  df_pred_agem_uplifts_between,
  df_pred_region_uplifts_between,
  df_pred_eventtype_uplifts_between,
  df_pred_eventmeasure_uplifts_between,
  df_pred_eventdata_uplifts_between,
  df_pred_swb_mods_uplifts_between,
  df_pred_reliability_uplifts_between,
  df_pred_design_mods_uplifts_between
) %>%
  mutate(
    significance = if_else(
      (r.lb>0)&(r.ub>0) | (r.lb<0)&(r.ub<0),
      "significant",
      "non-significant"
    )
  )

# Order moderators
plot_pred_mod_uplifts_between$moderator <- factor(
  plot_pred_mod_uplifts_between$moderator,
  levels = rev(c("Publication year", "Sample type", "% Female", "Mean age", "Region", 
                 "Event type", "Event measurement", "Event operationalization", 
                 "SWB dimension", "SWB reliability", "Study design", "Correlation lag"))
)

# Order moderator categories and assign y-positions
plot_pred_mod_uplifts_between <- plot_pred_mod_uplifts_between %>%
  arrange(moderator, order) %>%
  mutate(
    tooltip = paste0(
      "<b>", label, "</b>",
      "<br>Predicted r = ", round(r, 3),
      "<br>95% CI = [", round(r.lb, 3), ", ", round(r.ub, 3), "]",
      "<br>ES = ", k
    )
  )

# Compute x-axis limits
x_min <- min(plot_pred_mod_uplifts_between$r.lb)
x_max <- max(plot_pred_mod_uplifts_between$r.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

# Create plot
simple_slopes_uplifts_between <-
  ggplot(
    plot_pred_mod_uplifts_between,
    aes(
      x = r,
      y = moderator,
      group = interaction(moderator, order),
      color = significance
      )
  ) +

  geom_errorbarh(
    aes(
      xmin = r.lb,
      xmax = r.ub
    ),
    height = 0.15,
    linewidth = 0.5,
    position = position_dodge(width = 0.75)
  ) +

  geom_point_interactive(
    aes(
      tooltip = tooltip,
      data_id = paste(moderator, label),
    ),
    size = 2.5,
    position = position_dodge(width = 0.75)
  ) +

  geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
  ) +

  coord_cartesian(
    xlim = c(
      x_min,
      x_max + right_pad + x_range * 0.15
    ),
    clip = "off"
  ) +

  labs(
    x = "Predicted correlation (r)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +

  geom_text(
    aes(
      x = r.ub + right_pad,
      label = label
    ),
    hjust = 0,
    size = 3,
    position = position_dodge(width = 0.75),
    colour = "black"
  ) +

  theme_pub() +

  theme(
    axis.text.y = element_text(hjust = 1),
    panel.grid.major.y = element_blank()
  )

# Interactive plot
girafe(
  ggobj = simple_slopes_uplifts_between,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Points represent model-predicted correlations. Predictions for continuous moderators are estimated at the mean of the moderator, whereas predictions for categorical moderators are reported for each category. Hover over the points in the plot to display more information for the corresponding moderator. Significant predicted correlations are depicted in black, non-significant correlations are depicted in grey.

Hassles & SWB

Within-Person Associations

Regression Coefficient Table

# Sample Characteristics

sample_mods <- c("Female_c", "SampleType", "AgeM_c", "country_region")

sample_mod_hassles_within <- setNames(
  lapply(
    sample_mods,
    run_ma_mod,
    data = hassles_within,
    SamV = V_hassles_within
  ),
  sample_mods
)

sample_mod_results_hassles_within <- lapply(
  sample_mod_hassles_within,
  extract_mod_results,
  data = hassles_within,
  SamV = V_hassles_within
) %>%
  bind_rows(.id = "moderator")

# Publication Characteristics

pub_mods <- c("Year_c")

pub_mod_hassles_within <- setNames(
  lapply(
    pub_mods,
    run_ma_mod,
    data = hassles_within,
    SamV = V_hassles_within
  ),
  pub_mods
)

pub_mod_results_hassles_within <- lapply(
  pub_mod_hassles_within,
  extract_mod_results,
  data = hassles_within,
  SamV = V_hassles_within
) %>%
  bind_rows(.id = "moderator")

# Study-Design Characteristics

design_mods <- c("StudyDesign")

design_mod_hassles_within <- setNames(
  lapply(
    design_mods,
    run_ma_mod,
    data = hassles_within,
    SamV = V_hassles_within
  ),
  design_mods
)

design_mod_results_hassles_within <- lapply(
  design_mod_hassles_within,
  extract_mod_results,
  data = hassles_within,
  SamV = V_hassles_within
) %>%
  bind_rows(.id = "moderator")

# Assessment Characteristics

assess_mods <- c("DeDomainAll", "TypeDeMeasure", "DeDataType", 
                 "WbDimension", "WbReliability_c")

assess_mod_hassles_within <- setNames(
  lapply(
    assess_mods,
    run_ma_mod,
    data = hassles_within,
    SamV = V_hassles_within
  ),
  assess_mods
)

assess_mod_results_hassles_within <- lapply(
  assess_mod_hassles_within,
  extract_mod_results,
  data = hassles_within,
  SamV = V_hassles_within
) %>%
  bind_rows(.id = "moderator")

# Correlation Characteristics

corr_mods <- c("CorrLag")

corr_mod_hassles_within <- setNames(
  lapply(
    corr_mods,
    run_ma_mod,
    data = hassles_within,
    SamV = V_hassles_within
  ),
  corr_mods
)

corr_mod_results_hassles_within <- lapply(
  corr_mod_hassles_within,
  extract_mod_results,
  data = hassles_within,
  SamV = V_hassles_within
) %>%
  bind_rows(.id = "moderator")

# Create table

mod_hassles_within_metric <- rbind(
  sample_mod_results_hassles_within, 
  pub_mod_results_hassles_within,
  design_mod_results_hassles_within,
  assess_mod_results_hassles_within,
  corr_mod_results_hassles_within) %>%
  filter(
    moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c")
  )

mod_hassles_within_categorical <- rbind(
  sample_mod_results_hassles_within, 
  pub_mod_results_hassles_within,
  design_mod_results_hassles_within,
  assess_mod_results_hassles_within,
  corr_mod_results_hassles_within) %>%
  filter(
    !moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c")
  )

mod_hassles_within <- rbind(
  mod_hassles_within_metric,
  mod_hassles_within_categorical
) %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

mod_hassles_within$R2_Level1 <- ifelse(mod_hassles_within$R2_Level1 < 0, 0, mod_hassles_within$R2_Level1)
mod_hassles_within$R2_Level2 <- ifelse(mod_hassles_within$R2_Level2 < 0, 0, mod_hassles_within$R2_Level2)
mod_hassles_within$R2_Total <- ifelse(mod_hassles_within$R2_Total < 0, 0, mod_hassles_within$R2_Total)

names(mod_hassles_within) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                               "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                               "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(mod_hassles_within) <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                   "College", "Northern Europe", "Southern Europe","Asia",
                                   "ESM", "Work/Achievement",
                                   "Social", "Health", "Other event", "Open-response",
                                   "Interview", "Numeric count", "Scale score", "Domain satisfaction",
                                   "Positive affect", "Negative affect", "Global well-being", "Prospective")

order_mod_hassles_within <- c(
  "Publication year",
  "College",
  "% Female",
  "Mean age",
  "Northern Europe", "Southern Europe","Asia",
  "Work/Achievement", "Social", "Health", "Other event",
  "Open-response", "Interview",
  "Numeric count", "Scale score",
  "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
  "SWB reliability",
  "ESM",
  "Prospective"
)

mod_hassles_within <- mod_hassles_within[order_mod_hassles_within, ]

kable(round(mod_hassles_within, 3),
      caption = "**Results of Single-Moderator Analyses for the Within-Person Association Between Daily Hassles and SWB**",
      escape = FALSE,
      label = NA,
      digits = 3) %>%
  pack_rows("Region", 5, 7) %>%
  pack_rows("Daily event type", 8, 11) %>%
  pack_rows("Daily event measurement", 12, 13) %>%
  pack_rows("Daily event data type", 14, 15) %>%
  pack_rows("SWB dimension", 16, 19) %>%
  column_spec(c(3:6), bold = ifelse(mod_hassles_within[,5] < 0.05, TRUE, FALSE)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 34: Results of Single-Moderator Analyses for the Within-Person Association Between Daily Hassles and SWB

	ES	b	SE	t	p	τ2Level1	τ2Level2	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Publication year	530	0.001	0.002	0.354	0.728	0.022	0.003	86.490	12.692	99.182	0.239	0.000	0.000
College	530	0.022	0.018	1.223	0.228	0.022	0.003	86.962	12.217	99.179	0.181	0.000	0.000
% Female	530	0.000	0.000	-0.968	0.353	0.022	0.003	87.118	12.058	99.176	0.000	0.000	0.000
Mean age	523	-0.001	0.001	-0.939	0.358	0.022	0.003	86.352	12.839	99.191	0.173	0.000	0.000
Region
Northern Europe	464	-0.083	0.023	-3.536	0.001	0.021	0.002	90.318	8.840	99.158	0.847	31.591	4.667
Southern Europe	464	-0.113	0.103	-1.101	0.358	0.021	0.002	90.318	8.840	99.158	0.847	31.591	4.667
Asia	464	0.008	0.031	0.274	0.793	0.021	0.002	90.318	8.840	99.158	0.847	31.591	4.667
Daily event type
Work/Achievement	530	0.058	0.015	3.805	0.001	0.022	0.001	95.147	3.930	99.078	0.506	70.145	8.933
Social	530	0.102	0.017	5.951	0.000	0.022	0.001	95.147	3.930	99.078	0.506	70.145	8.933
Health	530	0.085	0.025	3.465	0.004	0.022	0.001	95.147	3.930	99.078	0.506	70.145	8.933
Other event	530	0.115	0.030	3.798	0.004	0.022	0.001	95.147	3.930	99.078	0.506	70.145	8.933
Daily event measurement
Open-response	527	-0.024	0.081	-0.292	0.786	0.022	0.003	87.468	11.706	99.174	0.000	0.000	0.000
Interview	527	0.050	0.049	1.020	0.367	0.022	0.003	87.468	11.706	99.174	0.000	0.000	0.000
Daily event data type
Numeric count	530	-0.029	0.028	-1.044	0.302	0.022	0.003	88.794	10.368	99.161	0.000	14.865	1.466
Scale score	530	-0.050	0.022	-2.329	0.025	0.022	0.003	88.794	10.368	99.161	0.000	14.865	1.466
SWB dimension
Domain satisfaction	530	-0.002	0.049	-0.031	0.976	0.017	0.004	78.936	20.117	99.053	20.687	0.000	12.517
Positive affect	530	0.046	0.017	2.697	0.038	0.017	0.004	78.936	20.117	99.053	20.687	0.000	12.517
Negative affect	530	-0.086	0.017	-4.967	0.003	0.017	0.004	78.936	20.117	99.053	20.687	0.000	12.517
Global well-being	530	-0.028	0.051	-0.543	0.619	0.017	0.004	78.936	20.117	99.053	20.687	0.000	12.517
SWB reliability	418	0.181	0.076	2.386	0.026	0.022	0.004	83.894	15.162	99.056	1.889	0.000	0.000
ESM	530	-0.033	0.021	-1.570	0.125	0.022	0.003	87.229	11.943	99.172	0.256	0.797	0.321
Prospective	530	0.202	0.016	12.452	0.000	0.014	0.006	70.160	28.797	98.956	35.869	0.000	20.492

Note. Level-specific \(R^2\) values were calculated as the proportional reduction in the estimated random-effect variance components relative to the corresponding null model fitted to the same observations (see the corresponding analysis code for more details). Because the addition of moderators changes how remaining residual heterogeneity is partitioned between the within- and between-study levels, individual variance components may increase or decrease after including a moderator. Consequently, \(R^2\) values can get negative. Negative \(R^2\) values were truncated to 0.000% for presentation.

Regression Coefficient Plot

mod_hassles_within_plot <- rbind(
  mod_hassles_within_metric,
  mod_hassles_within_categorical
)

table_extract_mod_hassles_within <- mod_hassles_within_plot %>%
  select(b, ci.lb, ci.ub) %>%
  mutate(across(everything(), ~ round(.x, 2)))

mod_hassles_within_plot$modlabel <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                      "College", "Northern Europe", "Southern Europe", "Asia",
                                      "ESM", "Work/Achievement", "Social", "Health", 
                                      "Other event", "Open-response", "Interview", "Numeric count",
                                      "Scale score", "Domain satisfaction", "Positive affect", "Negative affect",
                                      "Global well-being", "Prospective")

mod_hassles_within_plot <- mod_hassles_within_plot %>%
  mutate(
    modcat = case_when(
      modlabel %in% c("Work/Achievement", "Social", "Health", "Other event") ~ "Daily event type",
      modlabel %in% c("Open-response", "Interview", "Other measure") ~ "Daily event measurement",
      modlabel %in% c("Numeric count", "Scale score") ~ "Daily event data type",
      modlabel %in% c("Domain satisfaction", "Positive affect", "Negative affect", "Global well-being") ~ "SWB dimension",
      modlabel %in% c("College") ~ "Sample type",
      modlabel %in% c("Northern Europe", "Southern Europe", "Asia") ~ "Region",
      modlabel %in% c("ESM") ~ "Study design",
      modlabel == "Prospective" ~ "Correlation lag",
      TRUE ~ modlabel
    )
  ) %>%
  mutate(
    modtype = ifelse(
      moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c"),
      "continuous",
      "categorical"
    ),
    significance = ifelse(
      p < .05,
      "significant",
      "non-significant"
    )
  )

mod_hassles_within_plot <- mod_hassles_within_plot %>%
  mutate(modcat = factor(modcat, levels = order_modcat_within)) %>%
  arrange(modcat)

mod_hassles_within_plot$modcat <- factor(
  mod_hassles_within_plot$modcat,
  levels = rev(unique(mod_hassles_within_plot$modcat))
)

mod_hassles_within_plot <- mod_hassles_within_plot %>%
  mutate(modlabel = factor(modlabel, levels = rev(order_mod_hassles_within)))

# Compute x-axis limits
x_min <- min(mod_hassles_within_plot$ci.lb)
x_max <- max(mod_hassles_within_plot$ci.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

plot <- ggplot(data = mod_hassles_within_plot,
       aes(
         x = b,
         y = modcat,
         group = modlabel
         )
       ) +
  
   geom_point_interactive(
    aes(
      colour = significance,
      tooltip = paste0(
      "<b>", modlabel, "</b>",
      " (",ifelse(modtype == "categorical", as.character(modcat), "Continuous"), ")",
      "<br>b = ", round(b, 3),
      "<br>95% CI = [", round(ci.lb, 3), ", ", round(ci.ub, 3), "]",
      "<br>p = ", round(p, 3),
      "<br>ES = ", k
      ),
      data_id = modlabel
    ),
    position = position_dodge(width = 0.75),
    size = 2.5
  ) +

  geom_errorbarh(
    aes(
      colour = significance,
      xmin = ci.lb,
      xmax = ci.ub
      ),
    position = position_dodge(width = 0.75),
    height = 0.15,
    linewidth = .5
    ) +
geom_text(
  data = subset(mod_hassles_within_plot, modtype == "categorical"),
  aes(
    x = ci.ub + right_pad,
    label = modlabel
  ),
  position = position_dodge(width = 0.75),
  hjust = 0,
  size = 3
) +
geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
    ) +
  
  theme_pub() +

  labs(
    x = "Meta-regression coefficient (b)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +
  
  theme(
    axis.text.y = element_text(hjust = 1)
    )

girafe(
  ggobj = plot,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Hover over the points in the plot to display more information for the corresponding moderator. Significant moderation effects are depicted in black, non-significant moderation effects are depicted in grey.

Predicted Correlations Plot

## Publication year

sd_year_hassles_within <- sd(hassles_within$Year_c, na.rm = TRUE)

pred_year_hassles_within <- predict(
  pub_mod_hassles_within$Year_c$robust,
  newmods = c(-sd_year_hassles_within, 0, sd_year_hassles_within)
)

df_pred_year_hassles_within <- transform(
  as.data.frame(pred_year_hassles_within),
  moderator = "Publication year",
  label = c(
    round(mean(hassles_within$Year, na.rm = TRUE) - sd_year_hassles_within, 2),
    round(mean(hassles_within$Year, na.rm = TRUE), 2),
    round(mean(hassles_within$Year, na.rm = TRUE) + sd_year_hassles_within, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = pub_mod_hassles_within$Year_c$robust$k
)

## Sample type

sampletype_cats_hassles_within <- data.frame(
  SampleType = factor(
    c("convenience", "college"),
    levels = c("convenience", "college")
  )
)

sampletype_mods_hassles_within <-
  model.matrix(~ SampleType, sampletype_cats_hassles_within)[, -1]

pred_sampletype_hassles_within <- predict(
  sample_mod_hassles_within$SampleType$robust,
  newmods = sampletype_mods_hassles_within
)

df_pred_sampletype_hassles_within <- transform(
  as.data.frame(pred_sampletype_hassles_within),
  moderator = "Sample type",
  label = c("Convenience", "College"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_hassles_within$SampleType$robust$k
)

## Percentage female

sd_female_hassles_within <- sd(hassles_within$Female_c, na.rm = TRUE)

pred_female_hassles_within <- predict(
  sample_mod_hassles_within$Female_c$robust,
  newmods = c(-sd_female_hassles_within, 0, sd_female_hassles_within)
)

df_pred_female_hassles_within <- transform(
  as.data.frame(pred_female_hassles_within),
  moderator = "% Female",
  label = c(
    paste0(round(mean(hassles_within$Female, na.rm = TRUE) - sd_female_hassles_within, 2), "%"),
    paste0(round(mean(hassles_within$Female, na.rm = TRUE), 2), "%"),
    paste0(round(mean(hassles_within$Female, na.rm = TRUE) +  sd_female_hassles_within, 2), "%")
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_hassles_within$Female_c$robust$k
)

## Mean age

sd_mean_age_hassles_within <- sd(hassles_within$AgeM_c, na.rm = TRUE)

pred_agem_hassles_within <- predict(
  sample_mod_hassles_within$AgeM_c$robust,
  newmods = c(- sd_mean_age_hassles_within, 0, + sd_mean_age_hassles_within)
)

df_pred_agem_hassles_within <- transform(
  as.data.frame(pred_agem_hassles_within),
  moderator = "Mean age",
  label = c(
    round(mean(hassles_within$AgeM, na.rm = TRUE) - sd_mean_age_hassles_within, 2),
    round(mean(hassles_within$AgeM, na.rm = TRUE), 2),
    round(mean(hassles_within$AgeM, na.rm = TRUE) + sd_mean_age_hassles_within, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_hassles_within$AgeM_c$robust$k
)

## Region

region_cats_hassles_within <- data.frame(
  country_region = factor(
    c("North America", "Northern Europe", "Southern Europe", "Asia"),
    levels = c("North America", "Northern Europe", "Southern Europe","Asia")
  )
)

region_mods_hassles_within <-
  model.matrix(~ country_region, region_cats_hassles_within)[, -1]

pred_region_hassles_within <- predict(
  sample_mod_hassles_within$country_region$robust,
  newmods = region_mods_hassles_within
)

df_pred_region_hassles_within <- transform(
  as.data.frame(pred_region_hassles_within),
  moderator = "Region",
  label = c("North America", "Northern Europe", "Southern Europe","Asia"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_hassles_within$country_region$robust$k
)

## Daily event type

eventtype_cats_hassles_within <- data.frame(
  DeDomainAll = factor(
    c("general", "work/achievement", "social", "health", "other"),
    levels = c("general", "work/achievement", "social", "health", "other"),
  )
)

eventtype_mods_hassles_within <-
  model.matrix(~ DeDomainAll, eventtype_cats_hassles_within)[, -1]

pred_eventtype_hassles_within <- predict(
  assess_mod_hassles_within$DeDomainAll$robust,
  newmods = eventtype_mods_hassles_within
)

df_pred_eventtype_hassles_within <- transform(
  as.data.frame(pred_eventtype_hassles_within),
  moderator = "Event type",
  label = c("General", "Work/Achievement", "Social", "Health", "Other"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_hassles_within$DeDomainAll$robust$k
)

df_pred_eventtype_hassles_within$label <- factor(
  df_pred_eventtype_hassles_within$label,
  levels = c("General", "Work/Achievement", "Social", "Health", "Other")
)

## Daily event measurement

eventmeasure_cats_hassles_within <- data.frame(
  TypeDeMeasure = factor(
    c("checklist/questionnaire", "open_response", "interview"),
    levels = c("checklist/questionnaire", "open_response", "interview"),
  )
)

eventmeasure_mods_hassles_within <-
  model.matrix(~ TypeDeMeasure, eventmeasure_cats_hassles_within)[, -1]

pred_eventmeasure_hassles_within <- predict(
  assess_mod_hassles_within$TypeDeMeasure$robust,
  newmods = eventmeasure_mods_hassles_within
)

df_pred_eventmeasure_hassles_within <- transform(
  as.data.frame(pred_eventmeasure_hassles_within),
  moderator = "Event measurement",
  label = c("Checklist/Questionnaire", "Open-response", "Interview"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_hassles_within$TypeDeMeasure$robust$k
)

## Daily event operationalization

eventdata_cats_hassles_within <- data.frame(
  DeDataType = factor(
    c("dichotomous", "numeric_count", "scale_score"),
    levels = c("dichotomous", "numeric_count", "scale_score"),
  )
)

eventdata_mods_hassles_within <-
  model.matrix(~ DeDataType, eventdata_cats_hassles_within)[, -1]

pred_eventdata_hassles_within <- predict(
  assess_mod_hassles_within$DeDataType$robust,
  newmods = eventdata_mods_hassles_within
)

df_pred_eventdata_hassles_within <- transform(
  as.data.frame(pred_eventdata_hassles_within),
  moderator = "Event operationalization",
  label = c("Dichotomous", "Numeric count", "Scale score"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_hassles_within$DeDataType$robust$k
)

## SWB dimension

swb_cats_hassles_within <- data.frame(
  WbDimension = factor(
    c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect", "global_well-being"),
    levels =c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect", "global_well-being"),
  )
)

swb_mods_hassles_within <- 
  model.matrix(~ WbDimension, data = swb_cats_hassles_within)[, -1]

pred_swb_mods_hassles_within <- predict(
  assess_mod_hassles_within$WbDimension$robust,
  newmods = swb_mods_hassles_within
  )

df_pred_swb_mods_hassles_within <- transform(
  as.data.frame(pred_swb_mods_hassles_within),
  moderator = "SWB dimension",
  label = c("Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_hassles_within$WbDimension$robust$k
)

## SWB reliability

sd_reliability_hassles_within <- sd(hassles_within$WbReliability_c, na.rm = TRUE)

pred_reliability_hassles_within <- predict(
  assess_mod_hassles_within$WbReliability_c$robust,
  newmods = c(- sd_reliability_hassles_within, 0, + sd_reliability_hassles_within)
)

df_pred_reliability_hassles_within <- transform(
  as.data.frame(pred_reliability_hassles_within),
  moderator = "SWB reliability",
  label = c(
    round(mean(hassles_within$WbReliability, na.rm = TRUE) - sd_reliability_hassles_within, 2),
    round(mean(hassles_within$WbReliability, na.rm = TRUE), 2),
    round(mean(hassles_within$WbReliability, na.rm = TRUE) + sd_reliability_hassles_within, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_hassles_within$WbReliability$robust$k
)

## Study design

design_cats_hassles_within <- data.frame(
  StudyDesign = factor(
    c("daily_diary", "esm"),
    levels =c("daily_diary", "esm"),
  )
)

design_mods_hassles_within <- 
  model.matrix(~ StudyDesign, data = design_cats_hassles_within)[, -1]

pred_design_mods_hassles_within <- predict(
  design_mod_hassles_within$StudyDesign$robust,
  newmods = design_mods_hassles_within
  )

df_pred_design_mods_hassles_within <- transform(
  as.data.frame(pred_design_mods_hassles_within),
  moderator = "Study design",
  label = c("Daily diary", "Experience sampling"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = design_mod_hassles_within$StudyDesign$robust$k
  
)

## Correlation lag

corr_cats_hassles_within <- data.frame(
  CorrLag = factor(
    c("concurrent", "prospective"),
    levels =c("concurrent", "prospective"),
  )
)

corr_mods_hassles_within <- 
  model.matrix(~ CorrLag, data = corr_cats_hassles_within)[, -1]

pred_corr_mods_hassles_within <- predict(
  corr_mod_hassles_within$CorrLag$robust,
  newmods = corr_mods_hassles_within
  )

df_pred_corr_mods_hassles_within <- transform(
  as.data.frame(pred_corr_mods_hassles_within),
  moderator = "Correlation lag",
  label = c("Concurrent", "Prospective"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = corr_mod_hassles_within$CorrLag$robust$k
)

## Create dataset for plot

df_pred_year_hassles_within$label <-
  as.character(df_pred_year_hassles_within$label)

df_pred_female_hassles_within$label <-
  as.character(df_pred_female_hassles_within$label)

df_pred_agem_hassles_within$label <-
  as.character(df_pred_agem_hassles_within$label)

df_pred_reliability_hassles_within$label <-
  as.character(df_pred_reliability_hassles_within$label)

df_pred_year_hassles_within$order <- rev(seq_len(nrow(df_pred_year_hassles_within)))
df_pred_sampletype_hassles_within$order <- rev(seq_len(nrow(df_pred_sampletype_hassles_within)))
df_pred_female_hassles_within$order <- rev(seq_len(nrow(df_pred_female_hassles_within)))
df_pred_agem_hassles_within$order <- rev(seq_len(nrow(df_pred_agem_hassles_within)))
df_pred_region_hassles_within$order <- rev(seq_len(nrow(df_pred_region_hassles_within)))
df_pred_eventtype_hassles_within$order <- rev(seq_len(nrow(df_pred_eventtype_hassles_within)))
df_pred_eventmeasure_hassles_within$order <- rev(seq_len(nrow(df_pred_eventmeasure_hassles_within)))
df_pred_eventdata_hassles_within$order <- rev(seq_len(nrow(df_pred_eventdata_hassles_within)))
df_pred_swb_mods_hassles_within$order <- rev(seq_len(nrow(df_pred_swb_mods_hassles_within)))
df_pred_reliability_hassles_within$order <- rev(seq_len(nrow(df_pred_reliability_hassles_within)))
df_pred_design_mods_hassles_within$order <- rev(seq_len(nrow(df_pred_design_mods_hassles_within)))
df_pred_corr_mods_hassles_within$order <- rev(seq_len(nrow(df_pred_corr_mods_hassles_within)))

plot_pred_mod_hassles_within <- bind_rows(
  df_pred_year_hassles_within,
  df_pred_sampletype_hassles_within,
  df_pred_female_hassles_within,
  df_pred_agem_hassles_within,
  df_pred_region_hassles_within,
  df_pred_eventtype_hassles_within,
  df_pred_eventmeasure_hassles_within,
  df_pred_eventdata_hassles_within,
  df_pred_swb_mods_hassles_within,
  df_pred_reliability_hassles_within,
  df_pred_design_mods_hassles_within,
  df_pred_corr_mods_hassles_within
) %>%
  mutate(
    significance = if_else(
      (r.lb>0)&(r.ub>0) | (r.lb<0)&(r.ub<0),
      "significant",
      "non-significant"
    )
  )


# Order moderators
plot_pred_mod_hassles_within$moderator <- factor(
  plot_pred_mod_hassles_within$moderator,
  levels = rev(c("Publication year", "Sample type", "% Female", "Mean age", "Region", 
                 "Event type", "Event measurement", "Event operationalization", 
                 "SWB dimension", "SWB reliability", "Study design", "Correlation lag"))
)

# Order moderator categories and assign y-positions
plot_pred_mod_hassles_within <- plot_pred_mod_hassles_within %>%
  arrange(moderator, order) %>%
  mutate(
    tooltip = paste0(
      "<b>", label, "</b>",
      "<br>Predicted r = ", round(r, 3),
      "<br>95% CI = [", round(r.lb, 3), ", ", round(r.ub, 3), "]",
      "<br>ES = ", k
    )
  )

# Compute x-axis limits
x_min <- min(plot_pred_mod_hassles_within$r.lb)
x_max <- max(plot_pred_mod_hassles_within$r.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

# Create plot
simple_slopes_hassles_within <-
  ggplot(
    plot_pred_mod_hassles_within,
    aes(
      x = r,
      y = moderator,
      group = interaction(moderator, order),
      color = significance
      )
  ) +

  geom_errorbarh(
    aes(
      xmin = r.lb,
      xmax = r.ub
    ),
    height = 0.15,
    linewidth = 0.5,
    position = position_dodge(width = 0.75)
  ) +

  geom_point_interactive(
    aes(
      tooltip = tooltip,
      data_id = paste(moderator, label),
    ),
    size = 2.5,
    position = position_dodge(width = 0.75)
  ) +

  geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
  ) +

  coord_cartesian(
    xlim = c(
      x_min,
      x_max + right_pad + x_range * 0.15
    ),
    clip = "off"
  ) +

  labs(
    x = "Predicted correlation (r)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +

  geom_text(
    aes(
      x = r.ub + right_pad,
      label = label
    ),
    hjust = 0,
    size = 3,
    position = position_dodge(width = 0.75),
    colour = "black"
  ) +

  theme_pub() +

  theme(
    axis.text.y = element_text(hjust = 1),
    panel.grid.major.y = element_blank()
  )

# Interactive plot
girafe(
  ggobj = simple_slopes_hassles_within,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Points represent model-predicted correlations. Predictions for continuous moderators are estimated at the mean of the moderator, whereas predictions for categorical moderators are reported for each category. Hover over the points in the plot to display more information for the corresponding moderator. Significant predicted correlations are depicted in black, non-significant correlations are depicted in grey.

Between-Person Associations

Regression Coefficient Table

# Sample Characteristics

sample_mods <- c("Female_c", "SampleType", "AgeM_c", "country_region")

sample_mod_hassles_between <- setNames(
  lapply(
    sample_mods,
    run_ma_mod,
    data = hassles_between,
    SamV = V_hassles_between
  ),
  sample_mods
)

sample_mod_results_hassles_between <- lapply(
  sample_mod_hassles_between,
  extract_mod_results,
  data = hassles_between,
  SamV = V_hassles_between
) %>%
  bind_rows(.id = "moderator")

# Publication Characteristics

pub_mods <- c("Year_c")

pub_mod_hassles_between <- setNames(
  lapply(
    pub_mods,
    run_ma_mod,
    data = hassles_between,
    SamV = V_hassles_between
  ),
  pub_mods
)

pub_mod_results_hassles_between <- lapply(
  pub_mod_hassles_between,
  extract_mod_results,
  data = hassles_between,
  SamV = V_hassles_between
) %>%
  bind_rows(.id = "moderator")

# Study-Design Characteristics

design_mods <- c("StudyDesign")

design_mod_hassles_between <- setNames(
  lapply(
    design_mods,
    run_ma_mod,
    data = hassles_between,
    SamV = V_hassles_between
  ),
  design_mods
)

design_mod_results_hassles_between <- lapply(
  design_mod_hassles_between,
  extract_mod_results,
  data = hassles_between,
  SamV = V_hassles_between
) %>%
  bind_rows(.id = "moderator")

# Assessment Characteristics

assess_mods <- c("DeDomainAll", "TypeDeMeasure", "DeDataType", 
                 "WbDimension", "WbReliability_c")

assess_mod_hassles_between <- setNames(
  lapply(
    assess_mods,
    run_ma_mod,
    data = hassles_between,
    SamV = V_hassles_between
  ),
  assess_mods
)

assess_mod_results_hassles_between <- lapply(
  assess_mod_hassles_between,
  extract_mod_results,
  data = hassles_between,
  SamV = V_hassles_between
) %>%
  bind_rows(.id = "moderator")

# Create table

mod_hassles_between_metric <- rbind(
  sample_mod_results_hassles_between, 
  pub_mod_results_hassles_between,
  design_mod_results_hassles_between,
  assess_mod_results_hassles_between) %>%
  filter(
    moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c")
  )

mod_hassles_between_categorical <- rbind(
  sample_mod_results_hassles_between, 
  pub_mod_results_hassles_between,
  design_mod_results_hassles_between,
  assess_mod_results_hassles_between) %>%
  filter(
    !moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c")
  )

mod_hassles_between <- rbind(
  mod_hassles_between_metric,
  mod_hassles_between_categorical
) %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

mod_hassles_between$R2_Level1 <- ifelse(mod_hassles_between$R2_Level1 < 0, 0, mod_hassles_between$R2_Level1)
mod_hassles_between$R2_Level2 <- ifelse(mod_hassles_between$R2_Level2 < 0, 0, mod_hassles_between$R2_Level2)
mod_hassles_between$R2_Total <- ifelse(mod_hassles_between$R2_Total < 0, 0, mod_hassles_between$R2_Total)

names(mod_hassles_between) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                               "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                               "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(mod_hassles_between) <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                    "College", "Northern Europe", "Southern Europe", "Asia",
                                    "Australia", "ESM", "Work/Achievement", "Social", 
                                    "Health", "Other event",
                                    "Open-response", "Interview", "Numeric count", "Scale score",
                                    "Other type ", "Domain satisfaction", "Positive affect", "Negative affect",
                                    "Global well-being")

order_mod_hassles_between <- c(
  "Publication year",
  "College",
  "% Female",
  "Mean age",
  "Northern Europe", "Southern Europe", "Asia", "Australia",
  "Work/Achievement", "Social", "Health", "Other event",
  "Open-response", "Interview",
  "Numeric count", "Scale score", "Other type",
  "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being",
  "SWB reliability",
  "ESM"
)

mod_hassles_between <- mod_hassles_between[order_mod_hassles_between, ]

kable(round(mod_hassles_between, 3),
      caption = "**Results of Single-Moderator Analyses for the Between-Person Association Between Daily Hassles and SWB**",
      escape = FALSE,
      label = NA,
      digits = 3) %>%
  pack_rows("Region", 5, 8) %>%
  pack_rows("Daily event type", 9, 12) %>%
  pack_rows("Daily event measurement", 13, 14) %>%
  pack_rows("Daily event data type", 15, 17) %>%
  pack_rows("SWB dimension", 18, 21) %>%
  column_spec(c(3:6), bold = ifelse(mod_hassles_between[,5] < 0.05, TRUE, FALSE)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 35: Results of Single-Moderator Analyses for the Between-Person Association Between Daily Hassles and SWB

	ES	b	SE	t	p	τ2Level1	τ2Level2	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Publication year	627	-0.004	0.002	-2.464	0.018	0.044	0.002	87.057	4.766	91.823	0.302	16.134	1.270
College	627	0.011	0.022	0.515	0.608	0.044	0.003	85.770	6.170	91.940	0.326	0.000	0.000
% Female	625	0.000	0.000	-0.816	0.421	0.044	0.003	85.843	5.991	91.834	0.000	4.044	0.000
Mean age	616	0.001	0.001	0.778	0.440	0.045	0.003	86.270	5.737	92.008	0.000	3.573	0.000
Region
Northern Europe	531	-0.103	0.033	-3.095	0.004	0.046	0.003	87.577	5.034	92.611	1.270	17.162	2.288
Southern Europe	531	-0.124	0.153	-0.812	0.480	0.046	0.003	87.577	5.034	92.611	1.270	17.162	2.288
Asia	531	0.019	0.041	0.477	0.642	0.046	0.003	87.577	5.034	92.611	1.270	17.162	2.288
Australia	531	-0.139	0.039	-3.581	0.015	0.046	0.003	87.577	5.034	92.611	1.270	17.162	2.288
Daily event type
Work/Achievement	627	0.053	0.024	2.217	0.033	0.044	0.002	87.422	4.246	91.668	0.770	25.948	2.309
Social	627	0.065	0.021	3.069	0.003	0.044	0.002	87.422	4.246	91.668	0.770	25.948	2.309
Health	627	0.125	0.027	4.728	0.000	0.044	0.002	87.422	4.246	91.668	0.770	25.948	2.309
Other event	627	0.110	0.031	3.502	0.003	0.044	0.002	87.422	4.246	91.668	0.770	25.948	2.309
Daily event measurement
Open-response	618	0.007	0.041	0.161	0.876	0.045	0.003	86.281	5.665	91.946	0.000	0.000	0.000
Interview	618	0.044	0.076	0.583	0.594	0.045	0.003	86.281	5.665	91.946	0.000	0.000	0.000
Daily event data type
Numeric count	627	-0.001	0.031	-0.031	0.976	0.045	0.003	86.864	4.948	91.812	0.000	12.218	0.471
Scale score	627	-0.045	0.023	-1.909	0.060	0.045	0.003	86.864	4.948	91.812	0.000	12.218	0.471
Other type	627	-0.051	0.037	-1.361	0.313	0.045	0.003	86.864	4.948	91.812	0.000	12.218	0.471
SWB dimension
Domain satisfaction	627	-0.105	0.069	-1.527	0.158	0.018	0.011	54.196	33.575	87.771	58.372	0.000	36.702
Positive affect	627	0.029	0.039	0.744	0.473	0.018	0.011	54.196	33.575	87.771	58.372	0.000	36.702
Negative affect	627	-0.275	0.035	-7.916	0.000	0.018	0.011	54.196	33.575	87.771	58.372	0.000	36.702
Global well-being	627	-0.037	0.049	-0.753	0.475	0.018	0.011	54.196	33.575	87.771	58.372	0.000	36.702
SWB reliability	466	0.511	0.145	3.516	0.002	0.044	0.004	84.869	6.836	91.705	4.582	0.000	2.776
ESM	627	-0.021	0.026	-0.809	0.422	0.044	0.003	85.995	5.928	91.923	0.237	0.000	0.000

Note. Level-specific \(R^2\) values were calculated as the proportional reduction in the estimated random-effect variance components relative to the corresponding null model fitted to the same observations (see the corresponding analysis code for more details). Because the addition of moderators changes how remaining residual heterogeneity is partitioned between the within- and between-study levels, individual variance components may increase or decrease after including a moderator. Consequently, \(R^2\) values can get negative. Negative \(R^2\) values were truncated to 0.000% for presentation.

Regression Coefficient Plot

mod_hassles_between_plot <- rbind(
  mod_hassles_between_metric,
  mod_hassles_between_categorical
)

table_extract_mod_hassles_between <- mod_hassles_between_plot %>%
  select(b, ci.lb, ci.ub) %>%
  mutate(across(everything(), ~ round(.x, 2)))

mod_hassles_between_plot$modlabel <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                      "College", "Northern Europe", "Southern Europe", "Asia", 
                                      "Australia", "ESM", "Work/Achievement", "Social", "Health",
                                      "Other event", "Open-response", "Interview", "Numeric count",
                                      "Scale score", "Other type", "Domain satisfaction", "Positive affect", 
                                      "Negative affect", "Global well-being")

mod_hassles_between_plot <- mod_hassles_between_plot %>%
  mutate(
    modcat = case_when(
      modlabel %in% c("Work/Achievement", "Social", "Health", "Other event") ~ "Daily event type",
      modlabel %in% c("Open-response", "Interview", "Other measure") ~ "Daily event measurement",
      modlabel %in% c("Numeric count", "Scale score", "Other type") ~ "Daily event data type",
      modlabel %in% c("Domain satisfaction", "Positive affect", "Negative affect", "Global well-being") ~ "SWB dimension",
      modlabel %in% c("College") ~ "Sample type",
      modlabel %in% c("Northern Europe", "Southern Europe", "Asia", "Australia") ~ "Region",
      modlabel %in% c("ESM") ~ "Study design",
      modlabel %in% c("Thesis", "Unpublished") ~ "Publication type",
      TRUE ~ modlabel
    )
  ) %>%
  mutate(
    modtype = ifelse(
      moderator %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c"),
      "continuous",
      "categorical"
    ),
    significance = ifelse(
      p < .05,
      "significant",
      "non-significant"
    )
  )

mod_hassles_between_plot <- mod_hassles_between_plot %>%
  mutate(modcat = factor(modcat, levels = order_modcat_between)) %>%
  arrange(modcat)

mod_hassles_between_plot$modcat <- factor(
  mod_hassles_between_plot$modcat,
  levels = rev(unique(mod_hassles_between_plot$modcat))
)

mod_hassles_between_plot <- mod_hassles_between_plot %>%
  mutate(modlabel = factor(modlabel, levels = rev(order_mod_hassles_between)))

# Compute x-axis limits
x_min <- min(mod_hassles_between_plot$ci.lb)
x_max <- max(mod_hassles_between_plot$ci.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

plot <- ggplot(data = mod_hassles_between_plot,
       aes(
         x = b,
         y = modcat,
         group = modlabel
         )
       ) +
  
   geom_point_interactive(
    aes(
      colour = significance,
      tooltip = paste0(
      "<b>", modlabel, "</b>",
      " (",ifelse(modtype == "categorical", as.character(modcat), "Continuous"), ")",
      "<br>b = ", round(b, 3),
      "<br>95% CI = [", round(ci.lb, 3), ", ", round(ci.ub, 3), "]",
      "<br>p = ", round(p, 3),
      "<br>ES = ", k
      ),
      data_id = modlabel
    ),
    position = position_dodge(width = 0.75),
    size = 2.5
  ) +
  geom_errorbarh(
    aes(
      colour = significance,
      xmin = ci.lb,
      xmax = ci.ub
      ),
    position = position_dodge(width = 0.75),
    height = 0.15,
    linewidth = .5
    ) +
geom_text(
  data = subset(mod_hassles_between_plot, modtype == "categorical"),
  aes(
    x = ci.ub + right_pad,
    label = modlabel
  ),
  position = position_dodge(width = 0.75),
  hjust = 0,
  size = 3
) +
geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
    ) +
  
  theme_pub() +

  labs(
    x = "Meta-regression coefficient (b)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +
  scale_x_continuous(
  expand = expansion(mult = c(0.02, 0.20))
  ) +
  theme(
    axis.text.y = element_text(hjust = 1)
    )

girafe(
  ggobj = plot,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Hover over the points in the plot to display more information for the corresponding moderator. Significant moderation effects are depicted in black, non-significant moderation effects are depicted in grey.

Predicted Correlations Plot

## Publication year

sd_year_hassles_between <- sd(hassles_between$Year_c, na.rm = TRUE)

pred_year_hassles_between <- predict(
  pub_mod_hassles_between$Year_c$robust,
  newmods = c(-sd_year_hassles_between, 0, sd_year_hassles_between)
)

df_pred_year_hassles_between <- transform(
  as.data.frame(pred_year_hassles_between),
  moderator = "Publication year",
  label = c(
    round(mean(hassles_between$Year, na.rm = TRUE) - sd_year_hassles_between, 2),
    round(mean(hassles_between$Year, na.rm = TRUE), 2),
    round(mean(hassles_between$Year, na.rm = TRUE) + sd_year_hassles_between, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = pub_mod_hassles_between$Year_c$robust$k
)

## Sample type

sampletype_cats_hassles_between <- data.frame(
  SampleType = factor(
    c("convenience", "college"),
    levels = c("convenience", "college")
  )
)

sampletype_mods_hassles_between <-
  model.matrix(~ SampleType, sampletype_cats_hassles_between)[, -1]

pred_sampletype_hassles_between <- predict(
  sample_mod_hassles_between$SampleType$robust,
  newmods = sampletype_mods_hassles_between
)

df_pred_sampletype_hassles_between <- transform(
  as.data.frame(pred_sampletype_hassles_between),
  moderator = "Sample type",
  label = c("Convenience", "College"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_hassles_between$SampleType$robust$k
)

df_pred_sampletype_hassles_between$label <- factor(
  df_pred_sampletype_hassles_between$label,
  levels = c("Convenience", "College")
)

## Percentage female

sd_female_hassles_between <- sd(hassles_between$Female_c, na.rm = TRUE)

pred_female_hassles_between <- predict(
  sample_mod_hassles_between$Female_c$robust,
  newmods = c(-sd_female_hassles_between, 0, sd_female_hassles_between)
)

df_pred_female_hassles_between <- transform(
  as.data.frame(pred_female_hassles_between),
  moderator = "% Female",
  label = c(
    paste0(round(mean(hassles_between$Female, na.rm = TRUE) - sd_female_hassles_between, 2), "%"),
    paste0(round(mean(hassles_between$Female, na.rm = TRUE), 2), "%"),
    paste0(round(mean(hassles_between$Female, na.rm = TRUE) +  sd_female_hassles_between, 2), "%")
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_hassles_between$Female_c$robust$k
)

## Mean age

sd_mean_age_hassles_between <- sd(hassles_between$AgeM_c, na.rm = TRUE)

pred_agem_hassles_between <- predict(
  sample_mod_hassles_between$AgeM_c$robust,
  newmods = c(- sd_mean_age_hassles_between, 0, + sd_mean_age_hassles_between)
)

df_pred_agem_hassles_between <- transform(
  as.data.frame(pred_agem_hassles_between),
  moderator = "Mean age",
  label = c(
    round(mean(hassles_between$AgeM, na.rm = TRUE) - sd_mean_age_hassles_between, 2),
    round(mean(hassles_between$AgeM, na.rm = TRUE), 2),
    round(mean(hassles_between$AgeM, na.rm = TRUE) + sd_mean_age_hassles_between, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_hassles_between$AgeM_c$robust$k
)

## Region

region_cats_hassles_between <- data.frame(
  country_region = factor(
    c("North America", "Northern Europe", "Southern Europe","Asia", "Australia"),
    levels = c("North America", "Northern Europe", "Southern Europe", "Asia", "Australia")
  )
)

region_mods_hassles_between <-
  model.matrix(~ country_region, region_cats_hassles_between)[, -1]

pred_region_hassles_between <- predict(
  sample_mod_hassles_between$country_region$robust,
  newmods = region_mods_hassles_between
)

df_pred_region_hassles_between <- transform(
  as.data.frame(pred_region_hassles_between),
  moderator = "Region",
  label = c("North America", "Northern Europe", "Southern Europe", "Asia", "Australia"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = sample_mod_hassles_between$country_region$robust$k
)

## Daily event type

eventtype_cats_hassles_between <- data.frame(
  DeDomainAll = factor(
    c("general", "work/achievement", "social", "health", "other"),
    levels = c("general", "work/achievement", "social", "health", "other"),
  )
)

eventtype_mods_hassles_between <-
  model.matrix(~ DeDomainAll, eventtype_cats_hassles_between)[, -1]

pred_eventtype_hassles_between <- predict(
  assess_mod_hassles_between$DeDomainAll$robust,
  newmods = eventtype_mods_hassles_between
)

df_pred_eventtype_hassles_between <- transform(
  as.data.frame(pred_eventtype_hassles_between),
  moderator = "Event type",
  label = c("General", "Work/Achievement", "Social", "Health", "Other"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_hassles_between$DeDomainAll$robust$k
)

## Daily event measurement

eventmeasure_cats_hassles_between <- data.frame(
  TypeDeMeasure = factor(
    c("checklist/questionnaire", "open_response", "interview"),
    levels = c("checklist/questionnaire", "open_response", "interview"),
  )
)

eventmeasure_mods_hassles_between <-
  model.matrix(~ TypeDeMeasure, eventmeasure_cats_hassles_between)[, -1]

pred_eventmeasure_hassles_between <- predict(
  assess_mod_hassles_between$TypeDeMeasure$robust,
  newmods = eventmeasure_mods_hassles_between
)

df_pred_eventmeasure_hassles_between <- transform(
  as.data.frame(pred_eventmeasure_hassles_between),
  moderator = "Event measurement",
  label = c("Checklist/Questionnaire", "Open-response", "Interview"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_hassles_between$TypeDeMeasure$robust$k
)

## Daily event operationalization

eventdata_cats_hassles_between <- data.frame(
  DeDataType = factor(
    c("dichotomous", "numeric_count", "scale_score", "other"),
    levels = c("dichotomous", "numeric_count", "scale_score", "other"),
  )
)

eventdata_mods_hassles_between <-
  model.matrix(~ DeDataType, eventdata_cats_hassles_between)[, -1]

pred_eventdata_hassles_between <- predict(
  assess_mod_hassles_between$DeDataType$robust,
  newmods = eventdata_mods_hassles_between
)

df_pred_eventdata_hassles_between <- transform(
  as.data.frame(pred_eventdata_hassles_between),
  moderator = "Event operationalization",
  label = c("Dichotomous", "Numeric count", "Scale score", "Other"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_hassles_between$DeDataType$robust$k
)

## SWB dimension

swb_cats_hassles_between <- data.frame(
  WbDimension = factor(
    c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect", "global_well-being"),
    levels =c("life_satisfaction", "domain_satisfaction", "positive_affect", "negative_affect", "global_well-being"),
  )
)

swb_mods_hassles_between <- 
  model.matrix(~ WbDimension, data = swb_cats_hassles_within)[, -1]

pred_swb_mods_hassles_between <- predict(
  assess_mod_hassles_between$WbDimension$robust,
  newmods = swb_mods_hassles_between
  )

df_pred_swb_mods_hassles_between <- transform(
  as.data.frame(pred_swb_mods_hassles_between),
  moderator = "SWB dimension",
  label = c("Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_hassles_between$WbDimension$robust$k
)

## SWB reliability

sd_reliability_hassles_between <- sd(hassles_between$WbReliability_c, na.rm = TRUE)

pred_reliability_hassles_between <- predict(
  assess_mod_hassles_between$WbReliability_c$robust,
  newmods = c(- sd_reliability_hassles_between, 0, + sd_reliability_hassles_between)
)

df_pred_reliability_hassles_between <- transform(
  as.data.frame(pred_reliability_hassles_between),
  moderator = "SWB reliability",
  label = c(
    round(mean(hassles_between$WbReliability, na.rm = TRUE) - sd_reliability_hassles_between, 2),
    round(mean(hassles_between$WbReliability, na.rm = TRUE), 2),
    round(mean(hassles_between$WbReliability, na.rm = TRUE) + sd_reliability_hassles_between, 2)
  ),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = assess_mod_hassles_between$WbReliability$robust$k
)

## Study design

design_cats_hassles_between <- data.frame(
  StudyDesign = factor(
    c("daily_diary", "esm"),
    levels =c("daily_diary", "esm"),
  )
)

design_mods_hassles_between <- 
  model.matrix(~ StudyDesign, data = design_cats_hassles_between)[, -1]

pred_design_mods_hassles_between <- predict(
  design_mod_hassles_between$StudyDesign$robust,
  newmods = design_mods_hassles_between
  )

df_pred_design_mods_hassles_between <- transform(
  as.data.frame(pred_design_mods_hassles_between),
  moderator = "Study design",
  label = c("Daily diary", "Experience sampling"),
  r = tanh(pred),
  r.lb = tanh(ci.lb),
  r.ub = tanh(ci.ub),
  k = design_mod_hassles_between$StudyDesign$robust$k
  
)

## Create dataset for plot

df_pred_year_hassles_between$label <-
  as.character(df_pred_year_hassles_between$label)

df_pred_female_hassles_between$label <-
  as.character(df_pred_female_hassles_between$label)

df_pred_agem_hassles_between$label <-
  as.character(df_pred_agem_hassles_between$label)

df_pred_reliability_hassles_between$label <-
  as.character(df_pred_reliability_hassles_between$label)

df_pred_year_hassles_between$order <- rev(seq_len(nrow(df_pred_year_hassles_between)))
df_pred_sampletype_hassles_between$order <- rev(seq_len(nrow(df_pred_sampletype_hassles_between)))
df_pred_female_hassles_between$order <- rev(seq_len(nrow(df_pred_female_hassles_between)))
df_pred_agem_hassles_between$order <- rev(seq_len(nrow(df_pred_agem_hassles_between)))
df_pred_region_hassles_between$order <- rev(seq_len(nrow(df_pred_region_hassles_between)))
df_pred_eventtype_hassles_between$order <- rev(seq_len(nrow(df_pred_eventtype_hassles_between)))
df_pred_eventmeasure_hassles_between$order <- rev(seq_len(nrow(df_pred_eventmeasure_hassles_between)))
df_pred_eventdata_hassles_between$order <- rev(seq_len(nrow(df_pred_eventdata_hassles_between)))
df_pred_swb_mods_hassles_between$order <- rev(seq_len(nrow(df_pred_swb_mods_hassles_between)))
df_pred_reliability_hassles_between$order <- rev(seq_len(nrow(df_pred_reliability_hassles_between)))
df_pred_design_mods_hassles_between$order <- rev(seq_len(nrow(df_pred_design_mods_hassles_between)))

plot_pred_mod_hassles_between <- bind_rows(
  df_pred_year_hassles_between,
  df_pred_sampletype_hassles_between,
  df_pred_female_hassles_between,
  df_pred_agem_hassles_between,
  df_pred_region_hassles_between,
  df_pred_eventtype_hassles_between,
  df_pred_eventmeasure_hassles_between,
  df_pred_eventdata_hassles_between,
  df_pred_swb_mods_hassles_between,
  df_pred_reliability_hassles_between,
  df_pred_design_mods_hassles_between
) %>%
  mutate(
    significance = if_else(
      (r.lb>0)&(r.ub>0) | (r.lb<0)&(r.ub<0),
      "significant",
      "non-significant"
    )
  )

# Order moderators
plot_pred_mod_hassles_between$moderator <- factor(
  plot_pred_mod_hassles_between$moderator,
  levels = rev(c("Publication year", "Sample type", "% Female", "Mean age", "Region", 
                 "Event type", "Event measurement", "Event operationalization", 
                 "SWB dimension", "SWB reliability", "Study design"))
)

# Order moderator categories and assign y-positions
plot_pred_mod_hassles_between <- plot_pred_mod_hassles_between %>%
  arrange(moderator, order) %>%
  mutate(
    tooltip = paste0(
      "<b>", label, "</b>",
      "<br>Predicted r = ", round(r, 3),
      "<br>95% CI = [", round(r.lb, 3), ", ", round(r.ub, 3), "]",
      "<br>ES = ", k
    )
  )

# Compute x-axis limits
x_min <- min(plot_pred_mod_hassles_between$r.lb)
x_max <- max(plot_pred_mod_hassles_between$r.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

# Create plot
simple_slopes_hassles_between <-
  ggplot(
    plot_pred_mod_hassles_between,
    aes(
      x = r,
      y = moderator,
      group = interaction(moderator, order),
      color = significance
      )
  ) +

  geom_errorbarh(
    aes(
      xmin = r.lb,
      xmax = r.ub
    ),
    height = 0.15,
    linewidth = 0.5,
    position = position_dodge(width = 0.75)
  ) +

  geom_point_interactive(
    aes(
      tooltip = tooltip,
      data_id = paste(moderator, label),
    ),
    size = 2.5,
    position = position_dodge(width = 0.75)
  ) +

  geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
  ) +

  coord_cartesian(
    xlim = c(
      x_min,
      x_max + right_pad + x_range * 0.15
    ),
    clip = "off"
  ) +

  labs(
    x = "Predicted correlation (r)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +

  geom_text(
    aes(
      x = r.ub + right_pad,
      label = label
    ),
    hjust = 0,
    size = 3,
    position = position_dodge(width = 0.75),
    colour = "black"
  ) +

  theme_pub() +

  theme(
    axis.text.y = element_text(hjust = 1),
    panel.grid.major.y = element_blank()
  )

# Interactive plot
girafe(
  ggobj = simple_slopes_hassles_between,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Points represent model-predicted correlations. Predictions for continuous moderators are estimated at the mean of the moderator, whereas predictions for categorical moderators are reported for each category. Hover over the points in the plot to display more information for the corresponding moderator. Significant predicted correlations are depicted in black, non-significant correlations are depicted in grey.

Single-Moderator Comparisons

# Preparation steps

# Extend function for moderator analysis created earliert to easily swap the reference category

run_ma_mod_ref <- function(mod, data, SamV, ref = NULL) {

  data2 <- data

  if (!is.null(ref)) {
    data2[[mod]] <- relevel(droplevels(data2[[mod]]), ref = ref)
  }

  run_ma_mod(mod = mod,
             data = data2,
             SamV = SamV)
}

# Create function to prepare tables

prepare_mod_table <- function(tbl, moderator) {

  rownames(tbl) <- sub(
    paste0("^", moderator),
    "",
    rownames(tbl)
  )

  tbl <- tbl %>%
    mutate(
      across(where(is.numeric), ~ round(.x, 3)),
      across(c(R2_Level1, R2_Level2, R2_Total), ~ pmax(.x, 0))
    ) %>%
    select(
      k, b, se, z, p,
      I2_Level1, I2_Level2, I2_Total,
      R2_Level1, R2_Level2, R2_Total
    )

  names(tbl) <- c(
    "ES", "*b*", "*SE*", "*t*", "*p*",
    "*I*^2^~Level1~", "*I*^2^~Level2~", "*I*^2^~Total~",
    "R^2^~Level1~", "R^2^~Level2~", "R^2^~Total~"
  )

  tbl
}

Uplifts & SWB

Within-Person Associations

Region

# Run moderator analysis with all possible reference levels
refs_region_uplifts_within <- c("North America",
                                "Northern Europe",
                                "Asia")

res_region_uplifts_within <- lapply(refs_region_uplifts_within, function(ref) {
  run_ma_mod_ref(
    mod = "country_region",
    data = uplifts_within,
    SamV = V_uplifts_within,
    ref = ref
  )
})

names(res_region_uplifts_within) <- refs_region_uplifts_within

# Extract results for all reference levels
tables_region_uplifts_within <- lapply(
  names(res_region_uplifts_within),
  function(ref) {
    extract_mod_results(
      res_region_uplifts_within[[ref]],
      uplifts_within,
      V_uplifts_within
    )
  }
)

names(tables_region_uplifts_within) <- names(res_region_uplifts_within)

Reference: North America

tbl_region_na_uplifts_within <- prepare_mod_table(
  tables_region_uplifts_within[["North America"]],
  moderator = "country_region"
)

rownames(tbl_region_na_uplifts_within) <- c("Northern Europe", "Asia")

    kable(
     tbl_region_na_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: North America)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_na_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 36: Moderator Analysis for Region (Reference: North America)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Northern Europe	278	0.065	0.043	1.506	0.185	97.038	2.007	99.045	2.496	0	0.479
Asia	278	-0.002	0.018	-0.092	0.941	97.038	2.007	99.045	2.496	0	0.479

Reference: Northern Europe

tbl_region_ne_uplifts_within <- prepare_mod_table(
  tables_region_uplifts_within[["Northern Europe"]],
  moderator = "country_region"
)

rownames(tbl_region_ne_uplifts_within) <- c("North America", "Asia")

    kable(
     tbl_region_ne_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Northern Europe)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_ne_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 37: Moderator Analysis for Region (Reference: Northern Europe)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	278	-0.065	0.043	-1.506	0.185	97.038	2.007	99.045	2.496	0	0.479
Asia	278	-0.067	0.046	-1.443	0.314	97.038	2.007	99.045	2.496	0	0.479

Reference: Asia

tbl_region_asia_uplifts_within <- prepare_mod_table(
  tables_region_uplifts_within[["Asia"]],
  moderator = "country_region"
)

rownames(tbl_region_asia_uplifts_within) <- c("North America", "Northern Europe")

    kable(
     tbl_region_asia_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Asia)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_asia_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 38: Moderator Analysis for Region (Reference: Asia)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	278	0.002	0.018	0.092	0.941	97.038	2.007	99.045	2.496	0	0.479
Northern Europe	278	0.067	0.046	1.443	0.314	97.038	2.007	99.045	2.496	0	0.479

Event Type

# Run moderator analysis with all possible reference levels
refs_detype_uplifts_within <- c("general",
                 "work/achievement",
                 "social",
                 "other")

res_detype_uplifts_within <- lapply(refs_detype_uplifts_within, function(ref) {
  run_ma_mod_ref(
    mod = "DeDomainAll",
    data = uplifts_within,
    SamV = V_uplifts_within,
    ref = ref
  )
})

names(res_detype_uplifts_within) <- refs_detype_uplifts_within

# Extract results for all reference levels
tables_detype_uplifts_within <- lapply(
  names(res_detype_uplifts_within),
  function(ref) {
    extract_mod_results(
      res_detype_uplifts_within[[ref]],
      uplifts_within,
      V_uplifts_within
    )
  }
)

names(tables_detype_uplifts_within) <- names(res_detype_uplifts_within)

Reference: General

tbl_detype_gen_uplifts_within <- prepare_mod_table(
  tables_detype_uplifts_within[["general"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_gen_uplifts_within) <- c("Work/Achievement", "Social", "Other")

    kable(
     tbl_detype_gen_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: General)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_gen_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 39: Moderator Analysis for Event Type (Reference: General)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Work/Achievement	304	-0.091	0.038	-2.397	0.024	63.714	35.659	99.373	11.746	0	0
Social	304	0.014	0.037	0.387	0.702	63.714	35.659	99.373	11.746	0	0
Other	304	-0.112	0.024	-4.721	0.103	63.714	35.659	99.373	11.746	0	0

Reference: Work/Achievement

tbl_detype_wa_uplifts_within <- prepare_mod_table(
  tables_detype_uplifts_within[["work/achievement"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_wa_uplifts_within) <- c("General", "Social", "Other")

    kable(
     tbl_detype_wa_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Work/Achievement)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_wa_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 40: Moderator Analysis for Event Type (Reference: Work/Achievement)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	304	0.091	0.038	2.397	0.024	63.714	35.659	99.373	11.746	0	0
Social	304	0.105	0.036	2.927	0.026	63.714	35.659	99.373	11.746	0	0
Other	304	-0.021	0.038	-0.545	0.671	63.714	35.659	99.373	11.746	0	0

Reference: Social

tbl_detype_soc_uplifts_within <- prepare_mod_table(
  tables_detype_uplifts_within[["social"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_soc_uplifts_within) <- c("General", "Work/Achievement", "Other")

    kable(
     tbl_detype_soc_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Social)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_soc_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 41: Moderator Analysis for Event Type (Reference: Social)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	304	-0.014	0.037	-0.387	0.702	63.714	35.659	99.373	11.746	0	0
Work/Achievement	304	-0.105	0.036	-2.927	0.026	63.714	35.659	99.373	11.746	0	0
Other	304	-0.126	0.036	-3.528	0.144	63.714	35.659	99.373	11.746	0	0

Reference: Other

tbl_detype_oth_uplifts_within <- prepare_mod_table(
  tables_detype_uplifts_within[["other"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_oth_uplifts_within) <- c("General", "Work/Achievement", "Social")

    kable(
     tbl_detype_oth_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Other)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_oth_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 42: Moderator Analysis for Event Type (Reference: Other)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	304	0.112	0.024	4.721	0.103	63.714	35.659	99.373	11.746	0	0
Work/Achievement	304	0.021	0.038	0.545	0.671	63.714	35.659	99.373	11.746	0	0
Social	304	0.126	0.036	3.528	0.144	63.714	35.659	99.373	11.746	0	0

Event Measurement

# Run moderator analysis with all possible reference levels
refs_demeas_uplifts_within <- c("checklist/questionnaire",
                                 "open_response",
                                  "interview")

res_demeas_uplifts_within <- lapply(refs_demeas_uplifts_within, function(ref) {
  run_ma_mod_ref(
    mod = "TypeDeMeasure",
    data = uplifts_within,
    SamV = V_uplifts_within,
    ref = ref
  )
})

names(res_demeas_uplifts_within) <- refs_demeas_uplifts_within

# Extract results for all reference levels
tables_demeas_uplifts_within <- lapply(
  names(res_demeas_uplifts_within),
  function(ref) {
    extract_mod_results(
      res_demeas_uplifts_within[[ref]],
      uplifts_within,
      V_uplifts_within
    )
  }
)

names(tables_demeas_uplifts_within) <- names(res_demeas_uplifts_within)

Reference: Checklist/Questionnaire

tbl_demeas_check_uplifts_within <- prepare_mod_table(
  tables_demeas_uplifts_within[["checklist/questionnaire"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_check_uplifts_within) <- c("Open-response", "Interview")

    kable(
     tbl_demeas_check_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Checklist/Questionnaire)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_check_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 43: Moderator Analysis for Event Measurement (Reference: Checklist/Questionnaire)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Open-response	304	0.049	0.106	0.461	0.673	81.318	17.977	99.295	0	11.373	1.931
Interview	304	-0.097	0.026	-3.687	0.031	81.318	17.977	99.295	0	11.373	1.931

Reference: Open-Response

tbl_demeas_opem_uplifts_within <- prepare_mod_table(
  tables_demeas_uplifts_within[["open_response"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_opem_uplifts_within) <- c("Checklist/Questionnaire", "Interview")

    kable(
     tbl_demeas_opem_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Open-Response)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_opem_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 44: Moderator Analysis for Event Measurement (Reference: Open-Response)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Checklist/Questionnaire	304	-0.049	0.106	-0.461	0.673	81.318	17.977	99.295	0	11.373	1.931
Interview	304	-0.146	0.107	-1.369	0.224	81.318	17.977	99.295	0	11.373	1.931

Reference: Interview

tbl_demeas_int_uplifts_within <- prepare_mod_table(
  tables_demeas_uplifts_within[["interview"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_int_uplifts_within) <- c("Checklist/Questionnaire", "Open-response")

    kable(
     tbl_demeas_int_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Interview)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_int_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 45: Moderator Analysis for Event Measurement (Reference: Interview)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Checklist/Questionnaire	304	0.097	0.026	3.687	0.031	81.318	17.977	99.295	0	11.373	1.931
Open-response	304	0.146	0.107	1.369	0.224	81.318	17.977	99.295	0	11.373	1.931

Event Operationalization

# Run moderator analysis with all possible reference levels
refs_deop_uplifts_within <- c("dichotomous",
               "numeric_count",
               "scale_score")

res_deop_uplifts_within <- lapply(refs_deop_uplifts_within, function(ref) {
  run_ma_mod_ref(
    mod = "DeDataType",
    data = uplifts_within,
    SamV = V_uplifts_within,
    ref = ref
  )
})

names(res_deop_uplifts_within) <- refs_deop_uplifts_within

# Extract results for all reference levels
tables_deop_uplifts_within <- lapply(
  names(res_deop_uplifts_within),
  function(ref) {
    extract_mod_results(
      res_deop_uplifts_within[[ref]],
      uplifts_within,
      V_uplifts_within
    )
  }
)

names(tables_deop_uplifts_within) <- names(res_deop_uplifts_within)

Reference: Dichotomous

tbl_deop_dic_uplifts_within <- prepare_mod_table(
  tables_deop_uplifts_within[["dichotomous"]],
  moderator = "DeDataType"
)

rownames(tbl_deop_dic_uplifts_within) <- c("Numeric count", "Scale score")

    kable(
     tbl_deop_dic_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Dichotomous)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_deop_dic_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 46: Moderator Analysis for Event Operationalization (Reference: Dichotomous)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Numeric count	301	0.078	0.033	2.347	0.040	99.153	0	99.153	2.904	59.143	2.904
Scale score	301	0.078	0.019	4.022	0.001	99.153	0	99.153	2.904	59.143	2.904

Reference: Numeric Count

tbl_deop_num_uplifts_within <- prepare_mod_table(
  tables_deop_uplifts_within[["numeric_count"]],
  moderator = "DeDataType"
)

rownames(tbl_deop_num_uplifts_within) <- c("Dichotomous", "Scale score")

    kable(
     tbl_deop_num_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Numeric Count)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_deop_num_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 47: Moderator Analysis for Event Operationalization (Reference: Numeric Count)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Dichotomous	301	-0.078	0.033	-2.347	0.040	99.153	0	99.153	2.904	59.116	2.904
Scale score	301	0.000	0.031	-0.010	0.993	99.153	0	99.153	2.904	59.116	2.904

Reference: Scale Score

tbl_detype_scal_uplifts_within <- prepare_mod_table(
  tables_deop_uplifts_within[["scale_score"]],
  moderator = "DeDataType"
)

rownames(tbl_detype_scal_uplifts_within) <- c("Dichotomous", "Numeric count")

    kable(
     tbl_detype_scal_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Scale Score)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_scal_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 48: Moderator Analysis for Event Operationalization (Reference: Scale Score)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Dichotomous	301	-0.078	0.019	-4.022	0.001	99.153	0	99.153	2.904	59.147	2.904
Numeric count	301	0.000	0.031	0.010	0.993	99.153	0	99.153	2.904	59.147	2.904

SWB Dimension

# Run moderator analysis with all possible reference levels
refs_swb_uplifts_within <- c("life_satisfaction",
              "domain_satisfaction",
              "positive_affect",
              "negative_affect")

res_swb_uplifts_within <- lapply(refs_swb_uplifts_within, function(ref) {
  run_ma_mod_ref(
    mod = "WbDimension",
    data = uplifts_within,
    SamV = V_uplifts_within,
    ref = ref
  )
})

names(res_swb_uplifts_within) <- refs_swb_uplifts_within

# Extract results for all reference levels
tables_swb_uplifts_within <- lapply(
  names(res_swb_uplifts_within),
  function(ref) {
    extract_mod_results(
      res_swb_uplifts_within[[ref]],
      uplifts_within,
      V_uplifts_within
    )
  }
)

names(tables_swb_uplifts_within) <- names(res_swb_uplifts_within)

Reference: Life Satisfaction

tbl_swb_ls_uplifts_within <- prepare_mod_table(
  tables_swb_uplifts_within[["life_satisfaction"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_ls_uplifts_within) <- c("Domain satisfaction", "Positive affect", "Negative affect")

    kable(
     tbl_swb_ls_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Life Satisfaction)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_ls_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 49: Moderator Analysis for SWB Dimension (Reference: Life Satisfaction)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Domain satisfaction	300	0.023	0.057	0.415	0.693	73.618	25.634	99.252	13.326	0	8.107
Positive affect	300	0.068	0.041	1.654	0.162	73.618	25.634	99.252	13.326	0	8.107
Negative affect	300	-0.058	0.040	-1.430	0.215	73.618	25.634	99.252	13.326	0	8.107

Reference: Domain Satisfaction

tbl_swb_ds_uplifts_within <- prepare_mod_table(
  tables_swb_uplifts_within[["domain_satisfaction"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_ds_uplifts_within) <- c("Life satisfaction", "Positive affect", "Negative affect")

    kable(
     tbl_swb_ds_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Domain Satisfaction)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_ds_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 50: Moderator Analysis for SWB Dimension (Reference: Domain Satisfaction)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	300	-0.023	0.057	-0.415	0.693	73.618	25.634	99.252	13.326	0	8.107
Positive affect	300	0.044	0.044	1.016	0.365	73.618	25.634	99.252	13.326	0	8.107
Negative affect	300	-0.081	0.042	-1.920	0.124	73.618	25.634	99.252	13.326	0	8.107

Reference: Positive Affect

tbl_swb_pa_uplifts_within <- prepare_mod_table(
  tables_swb_uplifts_within[["positive_affect"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_pa_uplifts_within) <- c("Life satisfaction", "Domain satisfaction", "Negative affect")

    kable(
     tbl_swb_pa_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Positive Affect)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_pa_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 51: Moderator Analysis for SWB Dimension (Reference: Positive Affect)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	300	-0.068	0.041	-1.654	0.162	73.618	25.634	99.252	13.326	0	8.107
Domain satisfaction	300	-0.044	0.044	-1.016	0.365	73.618	25.634	99.252	13.326	0	8.107
Negative affect	300	-0.125	0.013	-9.565	0.000	73.618	25.634	99.252	13.326	0	8.107

Reference: Negative Affect

tbl_swb_na_uplifts_within <- prepare_mod_table(
  tables_swb_uplifts_within[["negative_affect"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_na_uplifts_within) <- c("Life satisfaction", "Domain satisfaction", "Positive affect")

    kable(
     tbl_swb_na_uplifts_within,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Negative Affect)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_na_uplifts_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 52: Moderator Analysis for SWB Dimension (Reference: Negative Affect)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	300	0.058	0.040	1.430	0.215	73.618	25.634	99.252	13.326	0	8.107
Domain satisfaction	300	0.081	0.042	1.920	0.124	73.618	25.634	99.252	13.326	0	8.107
Positive affect	300	0.125	0.013	9.565	0.000	73.618	25.634	99.252	13.326	0	8.107

Between-Person Associations

Region

# Run moderator analysis with all possible reference levels
refs_region_uplifts_between <- c("North America",
                 "Northern Europe",
                 "Asia")

res_region_uplifts_between <- lapply(refs_region_uplifts_between, function(ref) {
  run_ma_mod_ref(
    mod = "country_region",
    data = uplifts_between,
    SamV = V_uplifts_between,
    ref = ref
  )
})

names(res_region_uplifts_between) <- refs_region_uplifts_between

# Extract results for all reference levels
tables_region_uplifts_between <- lapply(
  names(res_region_uplifts_between),
  function(ref) {
    extract_mod_results(
      res_region_uplifts_between[[ref]],
      uplifts_between,
      V_uplifts_between
    )
  }
)

names(tables_region_uplifts_between) <- names(res_region_uplifts_between)

Reference: North America

tbl_region_na_uplifts_between <- prepare_mod_table(
  tables_region_uplifts_between[["North America"]],
  moderator = "country_region"
)

rownames(tbl_region_na_uplifts_between) <- c("Northern Europe", "Asia")

    kable(
     tbl_region_na_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: North America)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_na_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 53: Moderator Analysis for Region (Reference: North America)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Northern Europe	228	0.067	0.042	1.592	0.141	94.15	0	94.15	0.405	19.497	0.405
Asia	228	-0.060	0.057	-1.053	0.381	94.15	0	94.15	0.405	19.497	0.405

Reference: Northern Europe

tbl_region_ne_uplifts_between <- prepare_mod_table(
  tables_region_uplifts_between[["Northern Europe"]],
  moderator = "country_region"
)

rownames(tbl_region_ne_uplifts_between) <- c("North America", "Asia")

    kable(
     tbl_region_ne_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Northern Europe)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_ne_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 54: Moderator Analysis for Region (Reference: Northern Europe)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	228	-0.067	0.042	-1.592	0.141	94.15	0	94.15	0.405	19.478	0.405
Asia	228	-0.127	0.065	-1.951	0.108	94.15	0	94.15	0.405	19.478	0.405

Reference: Asia

tbl_region_asia_uplifts_between <- prepare_mod_table(
  tables_region_uplifts_between[["Asia"]],
  moderator = "country_region"
)

rownames(tbl_region_asia_uplifts_between) <- c("North America", "Northern Europe")

    kable(
     tbl_region_asia_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Asia)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_asia_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 55: Moderator Analysis for Region (Reference: Asia)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	228	0.060	0.057	1.053	0.381	94.15	0	94.15	0.405	19.395	0.405
Northern Europe	228	0.127	0.065	1.951	0.108	94.15	0	94.15	0.405	19.395	0.405

Event Type

# Run moderator analysis with all possible reference levels
refs_detype_uplifts_between <- c("general",
                 "work/achievement",
                 "social",
                 "other")

res_detype_uplifts_between <- lapply(refs_detype_uplifts_between, function(ref) {
  run_ma_mod_ref(
    mod = "DeDomainAll",
    data = uplifts_between,
    SamV = V_uplifts_between,
    ref = ref
  )
})

names(res_detype_uplifts_between) <- refs_detype_uplifts_between

# Extract results for all reference levels
tables_detype_uplifts_between <- lapply(
  names(res_detype_uplifts_between),
  function(ref) {
    extract_mod_results(
      res_detype_uplifts_between[[ref]],
      uplifts_between,
      V_uplifts_between
    )
  }
)

names(tables_detype_uplifts_between) <- names(res_detype_uplifts_between)

Reference: General

tbl_detype_gen_uplifts_between <- prepare_mod_table(
  tables_detype_uplifts_between[["general"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_gen_uplifts_between) <- c("Work/Achievement", "Social", "Other")

    kable(
     tbl_detype_gen_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: General)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_gen_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 56: Moderator Analysis for Event Type (Reference: General)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Work/Achievement	270	-0.044	0.045	-0.984	0.332	93.473	0	93.473	0.327	0	0.327
Social	270	-0.035	0.043	-0.821	0.416	93.473	0	93.473	0.327	0	0.327
Other	270	-0.167	0.064	-2.630	0.095	93.473	0	93.473	0.327	0	0.327

Reference: Work/Achievement

tbl_detype_wa_uplifts_between <- prepare_mod_table(
  tables_detype_uplifts_between[["work/achievement"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_wa_uplifts_between) <- c("General", "Social", "Other")

    kable(
     tbl_detype_wa_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Work/Achievement)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_wa_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 57: Moderator Analysis for Event Type (Reference: Work/Achievement)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	270	0.044	0.045	0.984	0.332	93.473	0	93.473	0.327	0	0.327
Social	270	0.009	0.043	0.198	0.845	93.473	0	93.473	0.327	0	0.327
Other	270	-0.123	0.059	-2.105	0.143	93.473	0	93.473	0.327	0	0.327

Reference: Social

tbl_detype_soc_uplifts_between <- prepare_mod_table(
  tables_detype_uplifts_between[["social"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_soc_uplifts_between) <- c("General", "Work/Achievement", "Other")

    kable(
     tbl_detype_soc_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Social)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_soc_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 58: Moderator Analysis for Event Type (Reference: Social)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	270	0.035	0.043	0.821	0.416	93.473	0	93.473	0.327	0	0.327
Work/Achievement	270	-0.009	0.043	-0.198	0.845	93.473	0	93.473	0.327	0	0.327
Other	270	-0.132	0.057	-2.301	0.129	93.473	0	93.473	0.327	0	0.327

Reference: Other

tbl_detype_oth_uplifts_between <- prepare_mod_table(
  tables_detype_uplifts_between[["other"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_oth_uplifts_between) <- c("General", "Work/Achievement", "Social")

    kable(
     tbl_detype_oth_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Other)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_oth_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 59: Moderator Analysis for Event Type (Reference: Other)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	270	0.167	0.064	2.630	0.095	93.473	0	93.473	0.327	0	0.327
Work/Achievement	270	0.123	0.059	2.105	0.143	93.473	0	93.473	0.327	0	0.327
Social	270	0.132	0.057	2.301	0.129	93.473	0	93.473	0.327	0	0.327

Event Measurement

# Run moderator analysis with all possible reference levels
refs_demeas_uplifts_between <- c("checklist/questionnaire",
                                 "open_response",
                                  "interview")

res_demeas_uplifts_between <- lapply(refs_demeas_uplifts_between, function(ref) {
  run_ma_mod_ref(
    mod = "TypeDeMeasure",
    data = uplifts_between,
    SamV = V_uplifts_between,
    ref = ref
  )
})

names(res_demeas_uplifts_between) <- refs_demeas_uplifts_between

# Extract results for all reference levels
tables_demeas_uplifts_between <- lapply(
  names(res_demeas_uplifts_between),
  function(ref) {
    extract_mod_results(
      res_demeas_uplifts_between[[ref]],
      uplifts_between,
      V_uplifts_between
    )
  }
)

names(tables_demeas_uplifts_between) <- names(res_demeas_uplifts_between)

Reference: Checklist/Questionnaire

tbl_demeas_check_uplifts_between <- prepare_mod_table(
  tables_demeas_uplifts_between[["checklist/questionnaire"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_check_uplifts_between) <- c("Open-response", "Interview")

    kable(
     tbl_demeas_check_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Checklist/Questionnaire)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_check_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 60: Moderator Analysis for Event Measurement (Reference: Checklist/Questionnaire)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Open-response	267	-0.073	0.072	-1.007	0.37	93.434	0	93.434	1.508	0	1.508
Interview	267	-0.214	0.030	-7.005	0.02	93.434	0	93.434	1.508	0	1.508

Reference: Open-Response

tbl_demeas_opem_uplifts_between <- prepare_mod_table(
  tables_demeas_uplifts_between[["open_response"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_opem_uplifts_between) <- c("Checklist/Questionnaire", "Interview")

    kable(
     tbl_demeas_opem_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Open-Response)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_opem_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 61: Moderator Analysis for Event Measurement (Reference: Open-Response)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Checklist/Questionnaire	267	0.073	0.072	1.007	0.370	93.434	0	93.434	1.508	0	1.508
Interview	267	-0.141	0.075	-1.870	0.135	93.434	0	93.434	1.508	0	1.508

Reference: Interview

tbl_demeas_int_uplifts_between <- prepare_mod_table(
  tables_demeas_uplifts_between[["interview"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_int_uplifts_between) <- c("Checklist/Questionnaire", "Open-response")

    kable(
     tbl_demeas_int_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Interview)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_int_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 62: Moderator Analysis for Event Measurement (Reference: Interview)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Checklist/Questionnaire	267	0.214	0.030	7.005	0.020	93.434	0	93.434	1.508	0	1.508
Open-response	267	0.141	0.075	1.870	0.135	93.434	0	93.434	1.508	0	1.508

Event Operationalization

# Run moderator analysis with all possible reference levels
refs_deop_uplifts_between <- c("dichotomous",
               "numeric_count",
               "scale_score",
               "other")

res_deop_uplifts_between <- lapply(refs_deop_uplifts_between, function(ref) {
  run_ma_mod_ref(
    mod = "DeDataType",
    data = uplifts_between,
    SamV = V_uplifts_between,
    ref = ref
  )
})

names(res_deop_uplifts_between) <- refs_deop_uplifts_between

# Extract results for all reference levels
tables_deop_uplifts_between <- lapply(
  names(res_deop_uplifts_between),
  function(ref) {
    extract_mod_results(
      res_deop_uplifts_between[[ref]],
      uplifts_between,
      V_uplifts_between
    )
  }
)

names(tables_deop_uplifts_between) <- names(res_deop_uplifts_between)

Reference: Dichotomous

tbl_deop_dic_uplifts_between <- prepare_mod_table(
  tables_deop_uplifts_between[["dichotomous"]],
  moderator = "DeDataType"
)

rownames(tbl_deop_dic_uplifts_between) <- c("Numeric count", "Scale score", "Other")

    kable(
     tbl_deop_dic_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Dichotomous)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_deop_dic_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 63: Moderator Analysis for Event Operationalization (Reference: Dichotomous)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Numeric count	269	0.125	0.052	2.417	0.024	93.308	0	93.308	2.797	17.169	2.797
Scale score	269	0.097	0.030	3.238	0.003	93.308	0	93.308	2.797	17.169	2.797
Other	269	0.308	0.176	1.755	0.175	93.308	0	93.308	2.797	17.169	2.797

Reference: Numeric Count

tbl_deop_num_uplifts_between <- prepare_mod_table(
  tables_deop_uplifts_between[["numeric_count"]],
  moderator = "DeDataType"
)

rownames(tbl_deop_num_uplifts_between) <- c("Dichotomous", "Scale score", "Other")

    kable(
     tbl_deop_num_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Numeric Count)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_deop_num_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 64: Moderator Analysis for Event Operationalization (Reference: Numeric Count)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Dichotomous	269	-0.125	0.052	-2.417	0.024	93.308	0	93.308	2.797	17.167	2.797
Scale score	269	-0.028	0.048	-0.585	0.567	93.308	0	93.308	2.797	17.167	2.797
Other	269	0.184	0.180	1.019	0.378	93.308	0	93.308	2.797	17.167	2.797

Reference: Scale Score

tbl_deop_scal_uplifts_between <- prepare_mod_table(
  tables_deop_uplifts_between[["scale_score"]],
  moderator = "DeDataType"
)

rownames(tbl_deop_scal_uplifts_between) <- c("Dichotomous", "Numeric count", "Other")

    kable(
     tbl_deop_scal_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Scale Score)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_deop_scal_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 65: Moderator Analysis for Event Operationalization (Reference: Scale Score)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Dichotomous	269	-0.097	0.030	-3.238	0.003	93.308	0	93.308	2.797	17.167	2.797
Numeric count	269	0.028	0.048	0.585	0.567	93.308	0	93.308	2.797	17.167	2.797
Other	269	0.212	0.175	1.210	0.319	93.308	0	93.308	2.797	17.167	2.797

Reference: Other

tbl_detype_oth_uplifts_between <- prepare_mod_table(
  tables_deop_uplifts_between[["other"]],
  moderator = "DeDataType"
)

rownames(tbl_detype_oth_uplifts_between) <- c("Dichotomous", "Numeric Count" ,"Scale score")

    kable(
     tbl_detype_oth_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Other)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_oth_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 66: Moderator Analysis for Event Operationalization (Reference: Other)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Dichotomous	269	-0.308	0.176	-1.755	0.175	93.308	0	93.308	2.797	17.166	2.797
Numeric Count	269	-0.184	0.180	-1.019	0.378	93.308	0	93.308	2.797	17.166	2.797
Scale score	269	-0.212	0.175	-1.210	0.319	93.308	0	93.308	2.797	17.166	2.797

SWB Dimension

# Run moderator analysis with all possible reference levels
refs_swb_uplifts_between <- c("life_satisfaction",
              "domain_satisfaction",
              "positive_affect",
              "negative_affect")

res_swb_uplifts_between <- lapply(refs_swb_uplifts_between, function(ref) {
  run_ma_mod_ref(
    mod = "WbDimension",
    data = uplifts_between,
    SamV = V_uplifts_between,
    ref = ref
  )
})

names(res_swb_uplifts_between) <- refs_swb_uplifts_between

# Extract results for all reference levels
tables_swb_uplifts_between <- lapply(
  names(res_swb_uplifts_between),
  function(ref) {
    extract_mod_results(
      res_swb_uplifts_between[[ref]],
      uplifts_between,
      V_uplifts_between
    )
  }
)

names(tables_swb_uplifts_between) <- names(res_swb_uplifts_between)

Reference: Life Satisfaction

tbl_swb_ls_uplifts_between <- prepare_mod_table(
  tables_swb_uplifts_between[["life_satisfaction"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_ls_uplifts_between) <- c("Domain satisfaction", "Positive affect", "Negative affect")

    kable(
     tbl_swb_ls_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Life Satisfaction)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_ls_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 67: Moderator Analysis for SWB Dimension (Reference: Life Satisfaction)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Domain satisfaction	267	0.043	0.126	0.341	0.743	64.843	25.947	90.79	51.161	0	31.618
Positive affect	267	0.080	0.079	1.012	0.341	64.843	25.947	90.79	51.161	0	31.618
Negative affect	267	-0.256	0.096	-2.663	0.029	64.843	25.947	90.79	51.161	0	31.618

Reference: Domain Satisfaction

tbl_swb_ds_uplifts_between <- prepare_mod_table(
  tables_swb_uplifts_between[["domain_satisfaction"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_ds_uplifts_between) <- c("Life satisfaction", "Positive affect", "Negative affect")

    kable(
     tbl_swb_ds_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Domain Satisfaction)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_ds_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 68: Moderator Analysis for SWB Dimension (Reference: Domain Satisfaction)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	267	-0.043	0.126	-0.341	0.743	64.843	25.947	90.79	51.161	0	31.618
Positive affect	267	0.037	0.099	0.370	0.727	64.843	25.947	90.79	51.161	0	31.618
Negative affect	267	-0.299	0.101	-2.956	0.032	64.843	25.947	90.79	51.161	0	31.618

Reference: Positive Affect

tbl_swb_pa_uplifts_between <- prepare_mod_table(
  tables_swb_uplifts_between[["positive_affect"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_pa_uplifts_between) <- c("Life satisfaction", "Domain satisfaction", "Negative affect")

    kable(
     tbl_swb_pa_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Positive Affect)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_pa_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 69: Moderator Analysis for SWB Dimension (Reference: Positive Affect)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	267	-0.080	0.079	-1.012	0.341	64.843	25.947	90.79	51.161	0	31.618
Domain satisfaction	267	-0.037	0.099	-0.370	0.727	64.843	25.947	90.79	51.161	0	31.618
Negative affect	267	-0.335	0.039	-8.678	0.000	64.843	25.947	90.79	51.161	0	31.618

Reference: Negative Affect

tbl_swb_na_uplifts_between <- prepare_mod_table(
  tables_swb_uplifts_between[["negative_affect"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_na_uplifts_between) <- c("Life satisfaction", "Domain satisfaction", "Positive affect")

    kable(
     tbl_swb_na_uplifts_between,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Negative Affect)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_na_uplifts_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 70: Moderator Analysis for SWB Dimension (Reference: Negative Affect)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	267	0.256	0.096	2.663	0.029	64.843	25.947	90.79	51.161	0	31.618
Domain satisfaction	267	0.299	0.101	2.956	0.032	64.843	25.947	90.79	51.161	0	31.618
Positive affect	267	0.335	0.039	8.678	0.000	64.843	25.947	90.79	51.161	0	31.618

Hassles & SWB

Within-Person Associations

Region

# Run moderator analysis with all possible reference levels
refs_region_hassles_within <- c("North America",
                 "Northern Europe",
                 "Southern Europe",
                 "Asia")

res_region_hassles_within <- lapply(refs_region_hassles_within, function(ref) {
  run_ma_mod_ref(
    mod = "country_region",
    data = hassles_within,
    SamV = V_hassles_within,
    ref = ref
  )
})

names(res_region_hassles_within) <- refs_region_hassles_within

# Extract results for all reference levels
tables_region_hassles_within <- lapply(
  names(res_region_hassles_within),
  function(ref) {
    extract_mod_results(
      res_region_hassles_within[[ref]],
      hassles_within,
      V_hassles_within
    )
  }
)

names(tables_region_hassles_within) <- names(res_region_hassles_within)

Reference: North America

tbl_region_na_hassles_within <- prepare_mod_table(
  tables_region_hassles_within[["North America"]],
  moderator = "country_region"
)

rownames(tbl_region_na_hassles_within) <- c("Northern Europe", "Southern Europe", "Asia")

    kable(
     tbl_region_na_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: North America)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_na_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 71: Moderator Analysis for Region (Reference: North America)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Northern Europe	464	-0.083	0.023	-3.536	0.001	90.318	8.84	99.158	0.847	31.591	4.667
Southern Europe	464	-0.113	0.103	-1.101	0.358	90.318	8.84	99.158	0.847	31.591	4.667
Asia	464	0.008	0.031	0.274	0.793	90.318	8.84	99.158	0.847	31.591	4.667

Reference: Northern Europe

tbl_region_ne_hassles_within <- prepare_mod_table(
  tables_region_hassles_within[["Northern Europe"]],
  moderator = "country_region"
)

rownames(tbl_region_ne_hassles_within) <- c("North America", "Southern Europe", "Asia")

    kable(
     tbl_region_ne_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Northern Europe)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_ne_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 72: Moderator Analysis for Region (Reference: Northern Europe)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	464	0.083	0.023	3.536	0.001	90.318	8.84	99.158	0.847	31.591	4.667
Southern Europe	464	-0.031	0.105	-0.294	0.787	90.318	8.84	99.158	0.847	31.591	4.667
Asia	464	0.091	0.036	2.560	0.028	90.318	8.84	99.158	0.847	31.591	4.667

Reference: Southern Europe

tbl_region_se_hassles_within <- prepare_mod_table(
  tables_region_hassles_within[["Southern Europe"]],
  moderator = "country_region"
)

rownames(tbl_region_se_hassles_within) <- c("North America", "Northern Europe", "Asia")

    kable(
     tbl_region_se_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Southern Europe)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_se_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 73: Moderator Analysis for Region (Reference: Southern Europe)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	464	0.113	0.103	1.101	0.358	90.318	8.84	99.158	0.847	31.591	4.667
Northern Europe	464	0.031	0.105	0.294	0.787	90.318	8.84	99.158	0.847	31.591	4.667
Asia	464	0.122	0.107	1.145	0.316	90.318	8.84	99.158	0.847	31.591	4.667

Reference: Asia

tbl_region_asia_hassles_within <- prepare_mod_table(
  tables_region_hassles_within[["Asia"]],
  moderator = "country_region"
)

rownames(tbl_region_asia_hassles_within) <- c("North America", "Northern Europe", "Southern Europe")

    kable(
     tbl_region_asia_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Asia)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_asia_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 74: Moderator Analysis for Region (Reference: Asia)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	464	-0.008	0.031	-0.274	0.793	90.318	8.84	99.158	0.847	31.591	4.667
Northern Europe	464	-0.091	0.036	-2.560	0.028	90.318	8.84	99.158	0.847	31.591	4.667
Southern Europe	464	-0.122	0.107	-1.145	0.316	90.318	8.84	99.158	0.847	31.591	4.667

Event Type

# Run moderator analysis with all possible reference levels
refs_detype_hassles_within <- c("general",
                 "work/achievement",
                 "social",
                 "health",
                 "other")

res_detype_hassles_within <- lapply(refs_detype_hassles_within, function(ref) {
  run_ma_mod_ref(
    mod = "DeDomainAll",
    data = hassles_within,
    SamV = V_hassles_within,
    ref = ref
  )
})

names(res_detype_hassles_within) <- refs_detype_hassles_within

# Extract results for all reference levels
tables_detype_hassles_within <- lapply(
  names(res_detype_hassles_within),
  function(ref) {
    extract_mod_results(
      res_detype_hassles_within[[ref]],
      hassles_within,
      V_hassles_within
    )
  }
)

names(tables_detype_hassles_within) <- names(res_detype_hassles_within)

Reference: General

tbl_detype_gen_hassles_within <- prepare_mod_table(
  tables_detype_hassles_within[["general"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_gen_hassles_within) <- c("Work/Achievement", "Social", "Health", "Other")

    kable(
     tbl_detype_gen_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: General)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_gen_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 75: Moderator Analysis for Event Type (Reference: General)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Work/Achievement	530	0.058	0.015	3.805	0.001	95.147	3.93	99.078	0.506	70.145	8.933
Social	530	0.102	0.017	5.951	0.000	95.147	3.93	99.078	0.506	70.145	8.933
Health	530	0.085	0.025	3.465	0.004	95.147	3.93	99.078	0.506	70.145	8.933
Other	530	0.115	0.030	3.798	0.004	95.147	3.93	99.078	0.506	70.145	8.933

Reference: Work/Achievement

tbl_detype_wa_hassles_within <- prepare_mod_table(
  tables_detype_hassles_within[["work/achievement"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_wa_hassles_within) <- c("General", "Social", "Health", "Other")

    kable(
     tbl_detype_wa_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Work/Achievement)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_wa_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 76: Moderator Analysis for Event Type (Reference: Work/Achievement)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	530	-0.058	0.015	-3.805	0.001	95.147	3.93	99.078	0.506	70.145	8.933
Social	530	0.044	0.013	3.302	0.005	95.147	3.93	99.078	0.506	70.145	8.933
Health	530	0.027	0.024	1.122	0.281	95.147	3.93	99.078	0.506	70.145	8.933
Other	530	0.057	0.028	2.050	0.066	95.147	3.93	99.078	0.506	70.145	8.933

Reference: Social

tbl_detype_soc_hassles_within <- prepare_mod_table(
  tables_detype_hassles_within[["social"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_soc_hassles_within) <- c("General", "Work/Achievement", "Health", "Other")

    kable(
     tbl_detype_soc_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Social)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_soc_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 77: Moderator Analysis for Event Type (Reference: Social)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	530	-0.102	0.017	-5.951	0.000	95.147	3.93	99.078	0.506	70.145	8.933
Work/Achievement	530	-0.044	0.013	-3.302	0.005	95.147	3.93	99.078	0.506	70.145	8.933
Health	530	-0.017	0.027	-0.611	0.552	95.147	3.93	99.078	0.506	70.145	8.933
Other	530	0.013	0.030	0.436	0.672	95.147	3.93	99.078	0.506	70.145	8.933

Reference: Health

tbl_detype_hlth_hassles_within <- prepare_mod_table(
  tables_detype_hassles_within[["health"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_hlth_hassles_within) <- c("General", "Work/Achievement", "Social","Other")

    kable(
     tbl_detype_hlth_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Health)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_hlth_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 78: Moderator Analysis for Event Type (Reference: Health)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	530	-0.085	0.025	-3.465	0.004	95.147	3.93	99.078	0.506	70.145	8.933
Work/Achievement	530	-0.027	0.024	-1.122	0.281	95.147	3.93	99.078	0.506	70.145	8.933
Social	530	0.017	0.027	0.611	0.552	95.147	3.93	99.078	0.506	70.145	8.933
Other	530	0.030	0.039	0.770	0.455	95.147	3.93	99.078	0.506	70.145	8.933

Reference: Other

tbl_detype_oth_hassles_within <- prepare_mod_table(
  tables_detype_hassles_within[["other"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_oth_hassles_within) <- c("General", "Work/Achievement", "Social", "Health")

    kable(
     tbl_detype_oth_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Other)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_oth_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 79: Moderator Analysis for Event Type (Reference: Other)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	530	-0.115	0.030	-3.798	0.004	95.147	3.93	99.078	0.506	70.145	8.933
Work/Achievement	530	-0.057	0.028	-2.050	0.066	95.147	3.93	99.078	0.506	70.145	8.933
Social	530	-0.013	0.030	-0.436	0.672	95.147	3.93	99.078	0.506	70.145	8.933
Health	530	-0.030	0.039	-0.770	0.455	95.147	3.93	99.078	0.506	70.145	8.933

Event Measurement

# Run moderator analysis with all possible reference levels
refs_demeas_hassles_within <- c("checklist/questionnaire",
                                 "open_response",
                                  "interview")

res_demeas_hassles_within <- lapply(refs_demeas_hassles_within, function(ref) {
  run_ma_mod_ref(
    mod = "TypeDeMeasure",
    data = hassles_within,
    SamV = V_hassles_within,
    ref = ref
  )
})

names(res_demeas_hassles_within) <- refs_demeas_hassles_within

# Extract results for all reference levels
tables_demeas_hassles_within <- lapply(
  names(res_demeas_hassles_within),
  function(ref) {
    extract_mod_results(
      res_demeas_hassles_within[[ref]],
      hassles_within,
      V_hassles_within
    )
  }
)

names(tables_demeas_hassles_within) <- names(res_demeas_hassles_within)

Reference: Checklist/Questionnaire

tbl_demeas_check_hassles_within <- prepare_mod_table(
  tables_demeas_hassles_within[["checklist/questionnaire"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_check_hassles_within) <- c("Open-response", "Interview")

    kable(
     tbl_demeas_check_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Checklist/Questionnaire)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_check_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 80: Moderator Analysis for Event Measurement (Reference: Checklist/Questionnaire)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Open-response	527	-0.024	0.081	-0.292	0.786	87.468	11.706	99.174	0	0	0
Interview	527	0.050	0.049	1.020	0.367	87.468	11.706	99.174	0	0	0

Reference: Open-Response

tbl_demeas_opem_hassles_within <- prepare_mod_table(
  tables_demeas_hassles_within[["open_response"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_opem_hassles_within) <- c("Checklist/Questionnaire", "Interview")

    kable(
     tbl_demeas_opem_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Open-Response)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_opem_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 81: Moderator Analysis for Event Measurement (Reference: Open-Response)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Checklist/Questionnaire	527	0.024	0.081	0.292	0.786	87.468	11.706	99.174	0	0	0
Interview	527	0.073	0.094	0.784	0.460	87.468	11.706	99.174	0	0	0

Reference: Interview

tbl_demeas_int_hassles_within <- prepare_mod_table(
  tables_demeas_hassles_within[["interview"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_int_hassles_within) <- c("Checklist/Questionnaire", "Open-response")

    kable(
     tbl_demeas_int_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Interview)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_int_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 82: Moderator Analysis for Event Measurement (Reference: Interview)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Checklist/Questionnaire	527	-0.050	0.049	-1.020	0.367	87.468	11.706	99.174	0	0	0
Open-response	527	-0.073	0.094	-0.784	0.460	87.468	11.706	99.174	0	0	0

Event Operationalization

# Run moderator analysis with all possible reference levels
refs_deop_hassles_within <- c("dichotomous",
               "numeric_count",
               "scale_score")

res_deop_hassles_within <- lapply(refs_deop_hassles_within, function(ref) {
  run_ma_mod_ref(
    mod = "DeDataType",
    data = hassles_within,
    SamV = V_hassles_within,
    ref = ref
  )
})

names(res_deop_hassles_within) <- refs_deop_hassles_within

# Extract results for all reference levels
tables_deop_hassles_within <- lapply(
  names(res_deop_hassles_within),
  function(ref) {
    extract_mod_results(
      res_deop_hassles_within[[ref]],
      hassles_within,
      V_hassles_within
    )
  }
)

names(tables_deop_hassles_within) <- names(res_deop_hassles_within)

Reference: Dichotomous

tbl_deop_dic_hassles_within <- prepare_mod_table(
  tables_deop_hassles_within[["dichotomous"]],
  moderator = "DeDataType"
)

rownames(tbl_deop_dic_hassles_within) <- c("Numeric count", "Scale score")

    kable(
     tbl_deop_dic_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Dichotomous)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_deop_dic_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 83: Moderator Analysis for Event Operationalization (Reference: Dichotomous)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Numeric count	530	-0.029	0.028	-1.044	0.302	88.794	10.368	99.161	0	14.865	1.466
Scale score	530	-0.050	0.022	-2.329	0.025	88.794	10.368	99.161	0	14.865	1.466

Reference: Numeric Count

tbl_deop_num_hassles_within <- prepare_mod_table(
  tables_deop_hassles_within[["numeric_count"]],
  moderator = "DeDataType"
)

rownames(tbl_deop_num_hassles_within) <- c("Dichotomous", "Scale score")

    kable(
     tbl_deop_num_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Numeric Count)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_deop_num_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 84: Moderator Analysis for Event Operationalization (Reference: Numeric Count)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Dichotomous	530	0.029	0.028	1.044	0.302	88.794	10.368	99.161	0	14.865	1.466
Scale score	530	-0.021	0.022	-0.984	0.331	88.794	10.368	99.161	0	14.865	1.466

Reference: Scale Score

tbl_detype_scal_hassles_within <- prepare_mod_table(
  tables_deop_hassles_within[["scale_score"]],
  moderator = "DeDataType"
)

rownames(tbl_detype_scal_hassles_within) <- c("Dichotomous", "Numeric count")

    kable(
     tbl_detype_scal_hassles_within,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Scale Score)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_scal_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 85: Moderator Analysis for Event Operationalization (Reference: Scale Score)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Dichotomous	530	0.050	0.022	2.329	0.025	88.794	10.368	99.161	0	14.865	1.466
Numeric count	530	0.021	0.022	0.984	0.331	88.794	10.368	99.161	0	14.865	1.466

SWB Dimension

# Run moderator analysis with all possible reference levels
refs_swb_hassles_within <- c("life_satisfaction",
              "domain_satisfaction",
              "positive_affect",
              "negative_affect",
              "global_well-being")

res_swb_hassles_within <- lapply(refs_swb_hassles_within, function(ref) {
  run_ma_mod_ref(
    mod = "WbDimension",
    data = hassles_within,
    SamV = V_hassles_within,
    ref = ref
  )
})

names(res_swb_hassles_within) <- refs_swb_hassles_within

# Extract results for all reference levels
tables_swb_hassles_within <- lapply(
  names(res_swb_hassles_within),
  function(ref) {
    extract_mod_results(
      res_swb_hassles_within[[ref]],
      hassles_within,
      V_hassles_within
    )
  }
)

names(tables_swb_hassles_within) <- names(res_swb_hassles_within)

Reference: Life Satisfaction

tbl_swb_ls_hassles_within <- prepare_mod_table(
  tables_swb_hassles_within[["life_satisfaction"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_ls_hassles_within) <- c("Domain satisfaction", "Positive affect", "Negative affect", "Global well-being")

    kable(
     tbl_swb_ls_hassles_within,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Life Satisfaction)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_ls_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 86: Moderator Analysis for SWB Dimension (Reference: Life Satisfaction)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Domain satisfaction	530	-0.002	0.049	-0.031	0.976	78.936	20.117	99.053	20.687	0	12.517
Positive affect	530	0.046	0.017	2.697	0.038	78.936	20.117	99.053	20.687	0	12.517
Negative affect	530	-0.086	0.017	-4.967	0.003	78.936	20.117	99.053	20.687	0	12.517
Global well-being	530	-0.028	0.051	-0.543	0.619	78.936	20.117	99.053	20.687	0	12.517

Reference: Domain Satisfaction

tbl_swb_ds_hassles_within <- prepare_mod_table(
  tables_swb_hassles_within[["domain_satisfaction"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_ds_hassles_within) <- c("Life satisfaction", "Positive affect", "Negative affect", "Global well-being")

    kable(
     tbl_swb_ds_hassles_within,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Domain Satisfaction)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_ds_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 87: Moderator Analysis for SWB Dimension (Reference: Domain Satisfaction)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	530	0.002	0.049	0.031	0.976	78.936	20.117	99.053	20.687	0	12.517
Positive affect	530	0.047	0.051	0.934	0.400	78.936	20.117	99.053	20.687	0	12.517
Negative affect	530	-0.085	0.050	-1.685	0.164	78.936	20.117	99.053	20.687	0	12.517
Global well-being	530	-0.026	0.068	-0.382	0.719	78.936	20.117	99.053	20.687	0	12.517

Reference: Positive Affect

tbl_swb_pa_hassles_within <- prepare_mod_table(
  tables_swb_hassles_within[["positive_affect"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_pa_hassles_within) <- c("Life satisfaction", "Domain satisfaction", "Negative affect", "Global well-being")

    kable(
     tbl_swb_pa_hassles_within,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Positive Affect)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_pa_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 88: Moderator Analysis for SWB Dimension (Reference: Positive Affect)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	530	-0.046	0.017	-2.697	0.038	78.936	20.117	99.053	20.687	0	12.517
Domain satisfaction	530	-0.047	0.051	-0.934	0.400	78.936	20.117	99.053	20.687	0	12.517
Negative affect	530	-0.132	0.011	-12.510	0.000	78.936	20.117	99.053	20.687	0	12.517
Global well-being	530	-0.073	0.048	-1.514	0.235	78.936	20.117	99.053	20.687	0	12.517

Reference: Negative Affect

tbl_swb_na_hassles_within <- prepare_mod_table(
  tables_swb_hassles_within[["negative_affect"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_na_hassles_within) <- c("Life satisfaction", "Domain satisfaction", "Positive affect", "Global well-being")

    kable(
     tbl_swb_na_hassles_within,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Negative Affect)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_na_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 89: Moderator Analysis for SWB Dimension (Reference: Negative Affect)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	530	0.086	0.017	4.967	0.003	78.936	20.117	99.053	20.687	0	12.517
Domain satisfaction	530	0.085	0.050	1.685	0.164	78.936	20.117	99.053	20.687	0	12.517
Positive affect	530	0.132	0.011	12.510	0.000	78.936	20.117	99.053	20.687	0	12.517
Global well-being	530	0.059	0.048	1.220	0.317	78.936	20.117	99.053	20.687	0	12.517

Reference: Global Well-Being

tbl_swb_gwb_hassles_within <- prepare_mod_table(
  tables_swb_hassles_within[["negative_affect"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_gwb_hassles_within) <- c("Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect")

    kable(
     tbl_swb_gwb_hassles_within,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Global Well-Being)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_gwb_hassles_within$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 90: Moderator Analysis for SWB Dimension (Reference: Global Well-Being)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	530	0.086	0.017	4.967	0.003	78.936	20.117	99.053	20.687	0	12.517
Domain satisfaction	530	0.085	0.050	1.685	0.164	78.936	20.117	99.053	20.687	0	12.517
Positive affect	530	0.132	0.011	12.510	0.000	78.936	20.117	99.053	20.687	0	12.517
Negative affect	530	0.059	0.048	1.220	0.317	78.936	20.117	99.053	20.687	0	12.517

Between-Person Associations

Region

# Run moderator analysis with all possible reference levels
refs_region_hassles_between <- c("North America",
                 "Northern Europe",
                 "Southern Europe",
                 "Asia",
                 "Australia")

res_region_hassles_between <- lapply(refs_region_hassles_between, function(ref) {
  run_ma_mod_ref(
    mod = "country_region",
    data = hassles_between,
    SamV = V_hassles_between,
    ref = ref
  )
})

names(res_region_hassles_between) <- refs_region_hassles_between

# Extract results for all reference levels
tables_region_hassles_between <- lapply(
  names(res_region_hassles_between),
  function(ref) {
    extract_mod_results(
      res_region_hassles_between[[ref]],
      hassles_between,
      V_hassles_between
    )
  }
)

names(tables_region_hassles_between) <- names(res_region_hassles_between)

Reference: North America

tbl_region_na_hassles_between <- prepare_mod_table(
  tables_region_hassles_between[["North America"]],
  moderator = "country_region"
)

rownames(tbl_region_na_hassles_between) <- c("Northern Europe", "Southern Europe", "Asia", "Australia")

    kable(
     tbl_region_na_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: North America)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_na_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 91: Moderator Analysis for Region (Reference: North America)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Northern Europe	531	-0.103	0.033	-3.095	0.004	87.577	5.034	92.611	1.27	17.162	2.288
Southern Europe	531	-0.124	0.153	-0.812	0.480	87.577	5.034	92.611	1.27	17.162	2.288
Asia	531	0.019	0.041	0.477	0.642	87.577	5.034	92.611	1.27	17.162	2.288
Australia	531	-0.139	0.039	-3.581	0.015	87.577	5.034	92.611	1.27	17.162	2.288

Reference: Northern Europe

tbl_region_ne_hassles_between <- prepare_mod_table(
  tables_region_hassles_between[["Northern Europe"]],
  moderator = "country_region"
)

rownames(tbl_region_ne_hassles_between) <- c("North America", "Southern Europe", "Asia", "Australia")

    kable(
     tbl_region_ne_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Northern Europe)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_ne_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 92: Moderator Analysis for Region (Reference: Northern Europe)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	531	0.103	0.033	3.095	0.004	87.577	5.034	92.611	1.27	17.162	2.288
Southern Europe	531	-0.021	0.155	-0.134	0.901	87.577	5.034	92.611	1.27	17.162	2.288
Asia	531	0.123	0.046	2.641	0.015	87.577	5.034	92.611	1.27	17.162	2.288
Australia	531	-0.035	0.047	-0.762	0.474	87.577	5.034	92.611	1.27	17.162	2.288

Reference: Southern Europe

tbl_region_se_hassles_between <- prepare_mod_table(
  tables_region_hassles_between[["Southern Europe"]],
  moderator = "country_region"
)

rownames(tbl_region_se_hassles_between) <- c("North America", "Northern Europe", "Asia", "Australia")

    kable(
     tbl_region_se_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Southern Europe)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_se_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 93: Moderator Analysis for Region (Reference: Southern Europe)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	531	0.124	0.153	0.812	0.480	87.577	5.034	92.611	1.27	17.162	2.288
Northern Europe	531	0.021	0.155	0.134	0.901	87.577	5.034	92.611	1.27	17.162	2.288
Asia	531	0.143	0.156	0.918	0.413	87.577	5.034	92.611	1.27	17.162	2.288
Australia	531	-0.015	0.156	-0.095	0.928	87.577	5.034	92.611	1.27	17.162	2.288

Reference: Asia

tbl_region_asia_hassles_between <- prepare_mod_table(
  tables_region_hassles_between[["Asia"]],
  moderator = "country_region"
)

rownames(tbl_region_asia_hassles_between) <- c("North America", "Northern Europe", "Southern Europe", "Australia")

    kable(
     tbl_region_asia_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Asia)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_asia_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 94: Moderator Analysis for Region (Reference: Asia)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	531	-0.019	0.041	-0.477	0.642	87.577	5.034	92.611	1.27	17.162	2.288
Northern Europe	531	-0.123	0.046	-2.641	0.015	87.577	5.034	92.611	1.27	17.162	2.288
Southern Europe	531	-0.143	0.156	-0.918	0.413	87.577	5.034	92.611	1.27	17.162	2.288
Australia	531	-0.158	0.050	-3.135	0.015	87.577	5.034	92.611	1.27	17.162	2.288

Reference: Australia

tbl_region_aus_hassles_between <- prepare_mod_table(
  tables_region_hassles_between[["Australia"]],
  moderator = "country_region"
)

rownames(tbl_region_aus_hassles_between) <- c("North America", "Northern Europe", "Southern Europe", "Asia")

    kable(
     tbl_region_aus_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Region (Reference: Australia)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_region_aus_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 95: Moderator Analysis for Region (Reference: Australia)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
North America	531	0.139	0.039	3.581	0.015	87.577	5.034	92.611	1.27	17.162	2.288
Northern Europe	531	0.035	0.047	0.762	0.474	87.577	5.034	92.611	1.27	17.162	2.288
Southern Europe	531	0.015	0.156	0.095	0.928	87.577	5.034	92.611	1.27	17.162	2.288
Asia	531	0.158	0.050	3.135	0.015	87.577	5.034	92.611	1.27	17.162	2.288

Event Type

# Run moderator analysis with all possible reference levels
refs_detype_hassles_between <- c("general",
                 "work/achievement",
                 "social",
                 "health",
                 "other")

res_detype_hassles_between <- lapply(refs_detype_hassles_between, function(ref) {
  run_ma_mod_ref(
    mod = "DeDomainAll",
    data = hassles_between,
    SamV = V_hassles_between,
    ref = ref
  )
})

names(res_detype_hassles_between) <- refs_detype_hassles_between

# Extract results for all reference levels
tables_detype_hassles_between <- lapply(
  names(res_detype_hassles_between),
  function(ref) {
    extract_mod_results(
      res_detype_hassles_between[[ref]],
      hassles_between,
      V_hassles_between
    )
  }
)

names(tables_detype_hassles_between) <- names(res_detype_hassles_between)

Reference: General

tbl_detype_gen_hassles_between <- prepare_mod_table(
  tables_detype_hassles_between[["general"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_gen_hassles_between) <- c("Work/Achievement", "Social", "Health", "Other")

    kable(
     tbl_detype_gen_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: General)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_gen_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 96: Moderator Analysis for Event Type (Reference: General)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Work/Achievement	627	0.053	0.024	2.217	0.033	87.422	4.246	91.668	0.77	25.948	2.309
Social	627	0.065	0.021	3.069	0.003	87.422	4.246	91.668	0.77	25.948	2.309
Health	627	0.125	0.027	4.728	0.000	87.422	4.246	91.668	0.77	25.948	2.309
Other	627	0.110	0.031	3.502	0.003	87.422	4.246	91.668	0.77	25.948	2.309

Reference: Work/Achievement

tbl_detype_wa_hassles_between <- prepare_mod_table(
  tables_detype_hassles_between[["work/achievement"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_wa_hassles_between) <- c("General", "Social", "Health", "Other")

    kable(
     tbl_detype_wa_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Work/Achievement)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_wa_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 97: Moderator Analysis for Event Type (Reference: Work/Achievement)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	627	-0.053	0.024	-2.217	0.033	87.422	4.246	91.668	0.77	25.948	2.309
Social	627	0.011	0.019	0.592	0.559	87.422	4.246	91.668	0.77	25.948	2.309
Health	627	0.072	0.029	2.514	0.019	87.422	4.246	91.668	0.77	25.948	2.309
Other	627	0.057	0.034	1.677	0.109	87.422	4.246	91.668	0.77	25.948	2.309

Reference: Social

tbl_detype_soc_hassles_between <- prepare_mod_table(
  tables_detype_hassles_between[["social"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_soc_hassles_between) <- c("General", "Work/Achievement", "Health", "Other")

    kable(
     tbl_detype_soc_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Social)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_soc_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 98: Moderator Analysis for Event Type (Reference: Social)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	627	-0.065	0.021	-3.069	0.003	87.422	4.246	91.668	0.77	25.948	2.309
Work/Achievement	627	-0.011	0.019	-0.592	0.559	87.422	4.246	91.668	0.77	25.948	2.309
Health	627	0.061	0.026	2.338	0.029	87.422	4.246	91.668	0.77	25.948	2.309
Other	627	0.045	0.032	1.420	0.173	87.422	4.246	91.668	0.77	25.948	2.309

Reference: Health

tbl_detype_hlth_hassles_between <- prepare_mod_table(
  tables_detype_hassles_between[["health"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_hlth_hassles_between) <- c("General", "Work/Achievement", "Social", "Other")

    kable(
     tbl_detype_hlth_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Health)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_hlth_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 99: Moderator Analysis for Event Type (Reference: Health)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	627	-0.125	0.027	-4.728	0.000	87.422	4.246	91.668	0.77	25.948	2.309
Work/Achievement	627	-0.072	0.029	-2.514	0.019	87.422	4.246	91.668	0.77	25.948	2.309
Social	627	-0.061	0.026	-2.338	0.029	87.422	4.246	91.668	0.77	25.948	2.309
Other	627	-0.015	0.034	-0.447	0.660	87.422	4.246	91.668	0.77	25.948	2.309

Reference: Other

tbl_detype_oth_hassles_between <- prepare_mod_table(
  tables_detype_hassles_between[["other"]],
  moderator = "DeDomainAll"
)

rownames(tbl_detype_oth_hassles_between) <- c("General", "Work/Achievement", "Social", "Health")

    kable(
     tbl_detype_oth_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Type (Reference: Other)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_oth_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 100: Moderator Analysis for Event Type (Reference: Other)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
General	627	-0.110	0.031	-3.502	0.003	87.422	4.246	91.668	0.77	25.948	2.309
Work/Achievement	627	-0.057	0.034	-1.677	0.109	87.422	4.246	91.668	0.77	25.948	2.309
Social	627	-0.045	0.032	-1.420	0.173	87.422	4.246	91.668	0.77	25.948	2.309
Health	627	0.015	0.034	0.447	0.660	87.422	4.246	91.668	0.77	25.948	2.309

Event Measurement

# Run moderator analysis with all possible reference levels
refs_demeas_hassles_between <- c("checklist/questionnaire",
                                 "open_response",
                                  "interview")

res_demeas_hassles_between <- lapply(refs_demeas_hassles_between, function(ref) {
  run_ma_mod_ref(
    mod = "TypeDeMeasure",
    data = hassles_between,
    SamV = V_hassles_between,
    ref = ref
  )
})

names(res_demeas_hassles_between) <- refs_demeas_hassles_between

# Extract results for all reference levels
tables_demeas_hassles_between <- lapply(
  names(res_demeas_hassles_between),
  function(ref) {
    extract_mod_results(
      res_demeas_hassles_between[[ref]],
      hassles_between,
      V_hassles_between
    )
  }
)

names(tables_demeas_hassles_between) <- names(res_demeas_hassles_between)

Reference: Checklist/Questionnaire

tbl_demeas_check_hassles_between <- prepare_mod_table(
  tables_demeas_hassles_between[["checklist/questionnaire"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_check_hassles_between) <- c("Open-response", "Interview")

    kable(
     tbl_demeas_check_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Checklist/Questionnaire)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_check_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 101: Moderator Analysis for Event Measurement (Reference: Checklist/Questionnaire)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Open-response	618	0.007	0.041	0.161	0.876	86.281	5.665	91.946	0	0	0
Interview	618	0.044	0.076	0.583	0.594	86.281	5.665	91.946	0	0	0

Reference: Open-Response

tbl_demeas_opem_hassles_between <- prepare_mod_table(
  tables_demeas_hassles_between[["open_response"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_opem_hassles_between) <- c("Checklist/Questionnaire", "Interview")

    kable(
     tbl_demeas_opem_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Open-Response)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_opem_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 102: Moderator Analysis for Event Measurement (Reference: Open-Response)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Checklist/Questionnaire	618	-0.007	0.041	-0.161	0.876	86.281	5.665	91.946	0	0	0
Interview	618	0.038	0.085	0.443	0.670	86.281	5.665	91.946	0	0	0

Reference: Interview

tbl_demeas_int_hassles_between <- prepare_mod_table(
  tables_demeas_hassles_between[["interview"]],
  moderator = "TypeDeMeasure"
)

rownames(tbl_demeas_int_hassles_between) <- c("Checklist/Questionnaire", "Open-response")

    kable(
     tbl_demeas_int_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Measurement (Reference: Interview)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_demeas_int_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 103: Moderator Analysis for Event Measurement (Reference: Interview)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Checklist/Questionnaire	618	-0.044	0.076	-0.583	0.594	86.281	5.665	91.946	0	0	0
Open-response	618	-0.038	0.085	-0.443	0.670	86.281	5.665	91.946	0	0	0

Event Operationalization

# Run moderator analysis with all possible reference levels
refs_deop_hassles_between <- c("dichotomous",
               "numeric_count",
               "scale_score",
               "other")

res_deop_hassles_between <- lapply(refs_deop_hassles_between, function(ref) {
  run_ma_mod_ref(
    mod = "DeDataType",
    data = hassles_between,
    SamV = V_hassles_between,
    ref = ref
  )
})

names(res_deop_hassles_between) <- refs_deop_hassles_between

# Extract results for all reference levels
tables_deop_hassles_between <- lapply(
  names(res_deop_hassles_between),
  function(ref) {
    extract_mod_results(
      res_deop_hassles_between[[ref]],
      hassles_between,
      V_hassles_between
    )
  }
)

names(tables_deop_hassles_between) <- names(res_deop_hassles_between)

Reference: Dichotomous

tbl_deop_dic_hassles_between <- prepare_mod_table(
  tables_deop_hassles_between[["dichotomous"]],
  moderator = "DeDataType"
)

rownames(tbl_deop_dic_hassles_between) <- c("Numeric count", "Scale score", "Other")

    kable(
     tbl_deop_dic_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Dichotomous)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_deop_dic_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 104: Moderator Analysis for Event Operationalization (Reference: Dichotomous)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Numeric count	627	-0.001	0.031	-0.031	0.976	86.864	4.948	91.812	0	12.218	0.471
Scale score	627	-0.045	0.023	-1.909	0.060	86.864	4.948	91.812	0	12.218	0.471
Other	627	-0.051	0.037	-1.361	0.313	86.864	4.948	91.812	0	12.218	0.471

Reference: Numeric Count

tbl_deop_num_hassles_between <- prepare_mod_table(
  tables_deop_hassles_between[["numeric_count"]],
  moderator = "DeDataType"
)

rownames(tbl_deop_num_hassles_between) <- c("Dichotomous", "Scale score", "Other")

    kable(
     tbl_deop_num_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Numeric Count)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_deop_num_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 105: Moderator Analysis for Event Operationalization (Reference: Numeric Count)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Dichotomous	627	0.001	0.031	0.031	0.976	86.864	4.948	91.812	0	12.218	0.471
Scale score	627	-0.044	0.027	-1.605	0.114	86.864	4.948	91.812	0	12.218	0.471
Other	627	-0.050	0.040	-1.247	0.344	86.864	4.948	91.812	0	12.218	0.471

Reference: Scale Score

tbl_deop_scal_hassles_between <- prepare_mod_table(
  tables_deop_hassles_between[["scale_score"]],
  moderator = "DeDataType"
)

rownames(tbl_deop_scal_hassles_between) <- c("Dichotomous", "Numeric count", "Other")

    kable(
     tbl_deop_scal_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Scale Score)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_deop_scal_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 106: Moderator Analysis for Event Operationalization (Reference: Scale Score)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Dichotomous	627	0.045	0.023	1.909	0.060	86.864	4.948	91.812	0	12.218	0.471
Numeric count	627	0.044	0.027	1.605	0.114	86.864	4.948	91.812	0	12.218	0.471
Other	627	-0.006	0.033	-0.187	0.871	86.864	4.948	91.812	0	12.218	0.471

Reference: Other

tbl_detype_oth_hassles_between <- prepare_mod_table(
  tables_deop_hassles_between[["other"]],
  moderator = "DeDataType"
)

    kable(
     tbl_detype_oth_hassles_between,
      caption = paste0(
        "**Moderator Analysis for Event Operationalization (Reference: Other)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_detype_oth_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 107: Moderator Analysis for Event Operationalization (Reference: Other)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
dichotomous	627	0.051	0.037	1.361	0.313	86.864	4.948	91.812	0	12.218	0.471
numeric_count	627	0.050	0.040	1.247	0.344	86.864	4.948	91.812	0	12.218	0.471
scale_score	627	0.006	0.033	0.187	0.871	86.864	4.948	91.812	0	12.218	0.471

SWB Dimension

# Run moderator analysis with all possible reference levels
refs_swb_hassles_between <- c("life_satisfaction",
              "domain_satisfaction",
              "positive_affect",
              "negative_affect",
              "global_well-being")

res_swb_hassles_between <- lapply(refs_swb_hassles_between, function(ref) {
  run_ma_mod_ref(
    mod = "WbDimension",
    data = hassles_between,
    SamV = V_hassles_between,
    ref = ref
  )
})

names(res_swb_hassles_between) <- refs_swb_hassles_between

# Extract results for all reference levels
tables_swb_hassles_between <- lapply(
  names(res_swb_hassles_between),
  function(ref) {
    extract_mod_results(
      res_swb_hassles_between[[ref]],
      hassles_between,
      V_hassles_between
    )
  }
)

names(tables_swb_hassles_between) <- names(res_swb_hassles_between)

Reference: Life Satisfaction

tbl_swb_ls_hassles_between <- prepare_mod_table(
  tables_swb_hassles_between[["life_satisfaction"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_ls_hassles_between) <- c("Domain satisfaction", "Positive affect", "Negative affect", "Global well-being")

    kable(
     tbl_swb_ls_hassles_between,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Life Satisfaction)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_ls_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 108: Moderator Analysis for SWB Dimension (Reference: Life Satisfaction)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Domain satisfaction	627	-0.105	0.069	-1.527	0.158	54.196	33.575	87.771	58.372	0	36.702
Positive affect	627	0.029	0.039	0.744	0.473	54.196	33.575	87.771	58.372	0	36.702
Negative affect	627	-0.275	0.035	-7.916	0.000	54.196	33.575	87.771	58.372	0	36.702
Global well-being	627	-0.037	0.049	-0.753	0.475	54.196	33.575	87.771	58.372	0	36.702

Reference: Domain Satisfaction

tbl_swb_ds_hassles_between <- prepare_mod_table(
  tables_swb_hassles_between[["domain_satisfaction"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_ds_hassles_between) <- c("Life satisfaction", "Positive affect", "Negative affect", "Global well-being")

    kable(
     tbl_swb_ds_hassles_between,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Domain Satisfaction)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_ds_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 109: Moderator Analysis for SWB Dimension (Reference: Domain Satisfaction)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	627	0.105	0.069	1.527	0.158	54.196	33.575	87.771	58.372	0	36.702
Positive affect	627	0.135	0.065	2.059	0.077	54.196	33.575	87.771	58.372	0	36.702
Negative affect	627	-0.170	0.065	-2.623	0.034	54.196	33.575	87.771	58.372	0	36.702
Global well-being	627	0.069	0.076	0.902	0.391	54.196	33.575	87.771	58.372	0	36.702

Reference: Positive Affect

tbl_swb_pa_hassles_between <- prepare_mod_table(
  tables_swb_hassles_between[["positive_affect"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_pa_hassles_between) <- c("Life satisfaction", "Domain satisfaction", "Negative affect", "Global well-being")

    kable(
     tbl_swb_pa_hassles_between,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Positive Affect)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_pa_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 110: Moderator Analysis for SWB Dimension (Reference: Positive Affect)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	627	-0.029	0.039	-0.744	0.473	54.196	33.575	87.771	58.372	0	36.702
Domain satisfaction	627	-0.135	0.065	-2.059	0.077	54.196	33.575	87.771	58.372	0	36.702
Negative affect	627	-0.304	0.022	-13.602	0.000	54.196	33.575	87.771	58.372	0	36.702
Global well-being	627	-0.066	0.036	-1.831	0.125	54.196	33.575	87.771	58.372	0	36.702

Reference: Negative Affect

tbl_swb_na_hassles_between <- prepare_mod_table(
  tables_swb_hassles_between[["negative_affect"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_na_hassles_between) <- c("Life satisfaction", "Domain satisfaction", "Positive affect", "Global well-being")

    kable(
     tbl_swb_na_hassles_between,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Negative Affect)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_na_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 111: Moderator Analysis for SWB Dimension (Reference: Negative Affect)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	627	0.275	0.035	7.916	0.000	54.196	33.575	87.771	58.372	0	36.702
Domain satisfaction	627	0.170	0.065	2.623	0.034	54.196	33.575	87.771	58.372	0	36.702
Positive affect	627	0.304	0.022	13.602	0.000	54.196	33.575	87.771	58.372	0	36.702
Global well-being	627	0.238	0.037	6.455	0.001	54.196	33.575	87.771	58.372	0	36.702

Reference: Global Well-Being

tbl_swb_gwb_hassles_between <- prepare_mod_table(
  tables_swb_hassles_between[["global_well-being"]],
  moderator = "WbDimension"
)

rownames(tbl_swb_gwb_hassles_between) <- c("Life satisfaction", "Domain satisfaction", "Positive affect", "Negative affect")

    kable(
     tbl_swb_gwb_hassles_between,
      caption = paste0(
        "**Moderator Analysis for SWB Dimension (Reference: Global Well-Being)**"
      ),
     escape = FALSE,
     digits = 3,
     label = NA
    ) %>%
      column_spec(
        c(3:6),
        bold = tbl_swb_gwb_hassles_between$`*p*` < .05
      ) %>%
      kable_styling(
        bootstrap_options = c("striped", "hover", "condensed"),
        fixed_thead = TRUE,
        full_width = TRUE,
        position = "left"
      )

Table 112: Moderator Analysis for SWB Dimension (Reference: Global Well-Being)

	ES	b	SE	t	p	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Life satisfaction	627	0.037	0.049	0.753	0.475	54.196	33.575	87.771	58.372	0	36.702
Domain satisfaction	627	-0.069	0.076	-0.902	0.391	54.196	33.575	87.771	58.372	0	36.702
Positive affect	627	0.066	0.036	1.831	0.125	54.196	33.575	87.771	58.372	0	36.702
Negative affect	627	-0.238	0.037	-6.455	0.001	54.196	33.575	87.771	58.372	0	36.702

Multivariate Moderator Models

Uplifts & SWB

Within-Person Associations

Regression Coefficient Table

# All Moderators Combined

comb_mods_within <- c("Female_c + AgeM_c + Year_c + WbReliability_c + SampleType + country_region +
                      StudyDesign + DeDomainAll + TypeDeMeasure + DeDataType + WbDimension + 
                      CorrLag")

comb_mods_uplifts_within <- setNames(
  lapply(
    comb_mods_within,
    run_ma_mod,
    data = uplifts_within,
    SamV = V_uplifts_within
  ),
  comb_mods_within
)

comb_mods_results_uplifts_within <- lapply(
  comb_mods_uplifts_within,
  extract_mod_results,
  data = uplifts_within,
  SamV = V_uplifts_within
) %>%
  bind_rows(.id = "moderator")

comb_mods_table_uplifts_within <- comb_mods_results_uplifts_within %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

comb_mods_table_uplifts_within$R2_Level1 <- ifelse(comb_mods_table_uplifts_within$R2_Level1 < 0, 0, comb_mods_table_uplifts_within$R2_Level1)
comb_mods_table_uplifts_within$R2_Level2 <- ifelse(comb_mods_table_uplifts_within$R2_Level2 < 0, 0, comb_mods_table_uplifts_within$R2_Level2)
comb_mods_table_uplifts_within$R2_Total <- ifelse(comb_mods_table_uplifts_within$R2_Total < 0, 0, comb_mods_table_uplifts_within$R2_Total)

names(comb_mods_table_uplifts_within) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                                           "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                                           "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(comb_mods_table_uplifts_within) <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                        "College", "Northern Europe", "Asia", "ESM",
                                        "Work/Achievement", "Social", "Other event", "Open-response", 
                                        "Interview", "Numeric count", "Scale score", "Domain satisfaction", 
                                        "Positive affect", "Negative affect", "Prospective")

order_comb_mods_uplifts_within <- c(
  "Publication year",
  "College",
  "% Female",
  "Mean age",
  "Northern Europe","Asia",
  "Work/Achievement", "Social", "Other event",
  "Open-response", "Interview",
  "Numeric count", "Scale score",
  "Domain satisfaction", "Positive affect", "Negative affect",
  "SWB reliability",
  "ESM",
  "Prospective"
)

comb_mods_table_uplifts_within <- comb_mods_table_uplifts_within[order_comb_mods_uplifts_within, ]

kable(round(comb_mods_table_uplifts_within, 3),
      caption = "**Results of Multivariate Moderator Analyses for the Within-Person Association Between Daily Uplifts and SWB**",
      escape = FALSE,
      label = NA,
      digits = 3) %>%
  pack_rows("Region", 5, 6) %>%
  pack_rows("Daily event type", 7, 9) %>%
  pack_rows("Daily event measurement", 10, 11) %>%
  pack_rows("Daily event data type", 12, 13) %>%
  pack_rows("SWB dimension", 14, 16) %>%
  column_spec(c(3:6), bold = ifelse(comb_mods_table_uplifts_within[,5] < 0.05, TRUE, FALSE)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 113: Results of Multivariate Moderator Analyses for the Within-Person Association Between Daily Uplifts and SWB

	ES	b	SE	t	p	τ2Level1	τ2Level2	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Publication year	232	0.003	0.004	0.909	0.399	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
College	232	0.089	0.067	1.330	0.207	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
% Female	232	0.000	0.000	0.964	0.398	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Mean age	232	0.000	0.002	0.107	0.917	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Region
Northern Europe	232	0.165	0.081	2.028	0.089	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Asia	232	-0.010	0.029	-0.357	0.776	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Daily event type
Work/Achievement	232	-0.093	0.040	-2.321	0.047	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Social	232	0.048	0.047	1.032	0.326	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Other event	232	-0.028	0.041	-0.694	0.548	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Daily event measurement
Open-response	232	-0.037	0.034	-1.069	0.312	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Interview	232	-0.004	0.050	-0.075	0.943	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Daily event data type
Numeric count	232	0.006	0.098	0.056	0.956	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Scale score	232	0.086	0.069	1.242	0.244	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
SWB dimension
Domain satisfaction	232	0.149	0.126	1.179	0.377	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Positive affect	232	0.033	0.018	1.861	0.161	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Negative affect	232	-0.079	0.025	-3.141	0.027	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
SWB reliability	232	0.122	0.146	0.835	0.421	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
ESM	232	0.026	0.060	0.429	0.686	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194
Prospective	232	-0.180	0.014	-12.997	0.000	0.01	0.003	72.465	24.996	97.46	62.224	0	49.194

Note. Level-specific \(R^2\) values were calculated as the proportional reduction in the estimated random-effect variance components relative to the corresponding null model fitted to the same observations (see the corresponding analysis code for more details). Because the addition of moderators changes how remaining residual heterogeneity is partitioned between the within- and between-study levels, individual variance components may increase or decrease after including a moderator. Consequently, \(R^2\) values can get negative. Negative \(R^2\) values were truncated to 0.000% for presentation.

Regression Coefficient Plot

comb_mods_plot_uplifts_within <- comb_mods_results_uplifts_within

comb_mods_plot_uplifts_within$modlabel <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                           "College", "Northern Europe", "Asia", "ESM", 
                                           "Work/Achievement", "Social", "Other event", "Open-response", 
                                           "Interview", "Numeric count", "Scale score", "Domain satisfaction", 
                                           "Positive affect", "Negative affect", "Prospective")

comb_mods_plot_uplifts_within <- comb_mods_plot_uplifts_within %>%
  mutate(
    modcat = case_when(
      modlabel %in% c("Work/Achievement", "Social", "Health", "Other event") ~ "Daily event type",
      modlabel %in% c("Open-response", "Interview", "Other measure") ~ "Daily event measurement",
      modlabel %in% c("Numeric count", "Scale score", "Other type") ~ "Daily event data type",
      modlabel %in% c("Domain satisfaction", "Positive affect", "Negative affect", "Global well-being") ~ "SWB dimension",
      modlabel %in% c("College") ~ "Sample type",
      modlabel %in% c("Northern Europe", "Southern Europe", "Asia", "Australia") ~ "Region",
      modlabel %in% c("ESM") ~ "Study design",
      modlabel == "Prospective" ~ "Correlation lag",
      TRUE ~ modlabel
    )
  ) %>%
  mutate(
    modtype = ifelse(
      term %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c"),
      "continuous",
      "categorical"
    ),
    significance = ifelse(
      p < .05,
      "significant",
      "non-significant"
    )
  )

comb_mods_plot_uplifts_within <- comb_mods_plot_uplifts_within %>%
  mutate(modcat = factor(modcat, levels = order_modcat_within)) %>%
  arrange(modcat)

comb_mods_plot_uplifts_within$modcat <- factor(
  comb_mods_plot_uplifts_within$modcat,
  levels = rev(unique(comb_mods_plot_uplifts_within$modcat))
)

comb_mods_plot_uplifts_within <- comb_mods_plot_uplifts_within %>%
  mutate(modlabel = factor(modlabel, levels = rev(order_comb_mods_uplifts_within)))

# Compute x-axis limits
x_min <- min(comb_mods_plot_uplifts_within$ci.lb)
x_max <- max(comb_mods_plot_uplifts_within$ci.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

plot <- ggplot(data = comb_mods_plot_uplifts_within,
       aes(
         x = b,
         y = modcat,
         group = modlabel
         )
       ) +
  
   geom_point_interactive(
    aes(
      colour = significance,
      tooltip = paste0(
      "<b>", modlabel, "</b>",
      " (",ifelse(modtype == "categorical", as.character(modcat), "Continuous"), ")",
      "<br>b = ", round(b, 3),
      "<br>95% CI = [", round(ci.lb, 3), ", ", round(ci.ub, 3), "]",
      "<br>p = ", round(p, 3),
      "<br>ES = ", k
      ),
      data_id = modlabel
    ),
    position = position_dodge(width = 0.75),
    size = 2.5
  ) +

  geom_errorbarh(
    aes(
      colour = significance,
      xmin = ci.lb,
      xmax = ci.ub
      ),
    position = position_dodge(width = 0.75),
    height = 0.15,
    linewidth = .5
    ) +
geom_text(
  data = subset(comb_mods_plot_uplifts_within, modtype == "categorical"),
  aes(
    x = ci.ub + right_pad,
    label = modlabel
  ),
  position = position_dodge(width = 0.75),
  hjust = 0,
  size = 3
) +
geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
    ) +
  
  theme_pub() +

  labs(
    x = "Meta-regression coefficient (b)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +
  
  theme(
    axis.text.y = element_text(hjust = 1)
    )

girafe(
  ggobj = plot,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Hover over the points in the plot to display more information for the corresponding moderator. Significant moderation effects are depicted in black, non-significant moderation effects are depicted in grey.

Between-Person Associations

Regression Coefficient Table

# All Moderators Combined

comb_mods_between <- c("Female_c + AgeM_c + Year_c + WbReliability_c + SampleType + country_region +
                      StudyDesign + DeDomainAll + TypeDeMeasure + DeDataType + WbDimension")

comb_mods_uplifts_between <- setNames(
  lapply(
    comb_mods_between,
    run_ma_mod,
    data = uplifts_between,
    SamV = V_uplifts_between
  ),
  comb_mods_between
)

comb_mods_results_uplifts_between <- lapply(
  comb_mods_uplifts_between,
  extract_mod_results,
  data = uplifts_between,
  SamV = V_uplifts_between
) %>%
  bind_rows(.id = "moderator")


comb_mods_table_uplifts_between <- comb_mods_results_uplifts_between %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

comb_mods_table_uplifts_between$R2_Level1 <- ifelse(comb_mods_table_uplifts_between$R2_Level1 < 0, 0, comb_mods_table_uplifts_between$R2_Level1)
comb_mods_table_uplifts_between$R2_Level2 <- ifelse(comb_mods_table_uplifts_between$R2_Level2 < 0, 0, comb_mods_table_uplifts_between$R2_Level2)
comb_mods_table_uplifts_between$R2_Total <- ifelse(comb_mods_table_uplifts_between$R2_Total < 0, 0, comb_mods_table_uplifts_between$R2_Total)

names(comb_mods_table_uplifts_between) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                                            "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                                            "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(comb_mods_table_uplifts_between) <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                         "College", "Northern Europe", "Asia", "ESM",
                                         "Work/Achievement", "Social", "Other event", "Open-response", 
                                         "Interview", "Numeric count", "Scale score", "Domain satisfaction", "Positive affect", 
                                         "Negative affect")

order_comb_mods_uplifts_between <- c(
  "Publication year",
  "College",
  "% Female",
  "Mean age",
  "Northern Europe","Asia",
  "Work/Achievement", "Social", "Other event",
  "Open-response", "Interview",
  "Numeric count", "Scale score",
  "Domain satisfaction", "Positive affect", "Negative affect",
  "SWB reliability",
  "ESM"
)

comb_mods_table_uplifts_between <- comb_mods_table_uplifts_between[order_comb_mods_uplifts_between, ]

kable(round(comb_mods_table_uplifts_between, 3),
      caption = "**Results of Multivariate Moderator Analyses for the Between-Person Association Between Daily Uplifts and SWB**",
      escape = FALSE,
      label = NA,
      digits = 3) %>%
  pack_rows("Region", 5, 6) %>%
  pack_rows("Daily event type", 7, 9) %>%
  pack_rows("Daily event measurement", 10, 11) %>%
  pack_rows("Daily event data type", 12, 13) %>%
  pack_rows("SWB dimension", 14, 16) %>%
  column_spec(c(3:6), bold = ifelse(comb_mods_table_uplifts_between[,5] < 0.05, TRUE, FALSE)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 114: Results of Multivariate Moderator Analyses for the Between-Person Association Between Daily Uplifts and SWB

	ES	b	SE	t	p	τ2Level1	τ2Level2	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Publication year	164	0.004	0.004	0.923	0.380	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
College	164	-0.050	0.088	-0.567	0.578	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
% Female	164	0.001	0.001	0.822	0.451	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Mean age	164	-0.002	0.003	-0.759	0.468	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Region
Northern Europe	164	0.094	0.097	0.965	0.361	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Asia	164	-0.097	0.214	-0.455	0.693	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Daily event type
Work/Achievement	164	-0.095	0.073	-1.311	0.213	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Social	164	-0.005	0.079	-0.069	0.946	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Other event	164	-0.088	0.111	-0.792	0.541	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Daily event measurement
Open-response	164	-0.054	0.111	-0.486	0.667	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Interview	164	-0.186	0.107	-1.735	0.184	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Daily event data type
Numeric count	164	0.129	0.095	1.362	0.202	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Scale score	164	0.138	0.086	1.609	0.132	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
SWB dimension
Domain satisfaction	164	0.198	0.247	0.802	0.524	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Positive affect	164	0.029	0.065	0.449	0.680	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
Negative affect	164	-0.318	0.066	-4.789	0.006	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
SWB reliability	164	0.640	0.277	2.307	0.034	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71
ESM	164	-0.077	0.094	-0.821	0.442	0.022	0.014	54.453	35.499	89.952	68.346	0	47.71

Note. Level-specific \(R^2\) values were calculated as the proportional reduction in the estimated random-effect variance components relative to the corresponding null model fitted to the same observations (see the corresponding analysis code for more details). Because the addition of moderators changes how remaining residual heterogeneity is partitioned between the within- and between-study levels, individual variance components may increase or decrease after including a moderator. Consequently, \(R^2\) values can get negative. Negative \(R^2\) values were truncated to 0.000% for presentation.

Regression Coefficient Plot

comb_mods_plot_uplifts_between <- comb_mods_results_uplifts_between

comb_mods_plot_uplifts_between$modlabel <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                            "College", "Northern Europe", "Asia", "ESM",
                                            "Work/Achievement", "Social", "Other event", "Open-response",
                                            "Interview", "Numeric count", "Scale score", "Domain satisfaction", 
                                            "Positive affect", "Negative affect")

comb_mods_plot_uplifts_between <- comb_mods_plot_uplifts_between %>%
  mutate(
    modcat = case_when(
      modlabel %in% c("Work/Achievement", "Social", "Other event") ~ "Daily event type",
      modlabel %in% c("Open-response", "Interview", "Other measure") ~ "Daily event measurement",
      modlabel %in% c("Numeric count", "Scale score") ~ "Daily event data type",
      modlabel %in% c("Domain satisfaction", "Positive affect", "Negative affect") ~ "SWB dimension",
      modlabel %in% c("College") ~ "Sample type",
      modlabel %in% c("Northern Europe", "Asia") ~ "Region",
      modlabel %in% c("ESM") ~ "Study design",
      modlabel %in% c("Thesis", "Unpublished") ~ "Publication type",
      TRUE ~ modlabel
    )
  ) %>%
  mutate(
    modtype = ifelse(
      term %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c"),
      "continuous",
      "categorical"
    ),
    significance = ifelse(
      p < .05,
      "significant",
      "non-significant"
    )
  )

comb_mods_plot_uplifts_between <- comb_mods_plot_uplifts_between %>%
  mutate(modcat = factor(modcat, levels = order_modcat_between)) %>%
  arrange(modcat)

comb_mods_plot_uplifts_between$modcat <- factor(
  comb_mods_plot_uplifts_between$modcat,
  levels = rev(unique(comb_mods_plot_uplifts_between$modcat))
)

comb_mods_plot_uplifts_between <- comb_mods_plot_uplifts_between %>%
  mutate(modlabel = factor(modlabel, levels = rev(order_comb_mods_uplifts_between)))

# Compute x-axis limits
x_min <- min(comb_mods_plot_uplifts_between$ci.lb)
x_max <- max(comb_mods_plot_uplifts_between$ci.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

plot <- ggplot(data = comb_mods_plot_uplifts_between,
       aes(
         x = b,
         y = modcat,
         group = modlabel
         )
       ) +
  
   geom_point_interactive(
    aes(
      colour = significance,
      tooltip = paste0(
      "<b>", modlabel, "</b>",
      " (",ifelse(modtype == "categorical", as.character(modcat), "Continuous"), ")",
      "<br>b = ", round(b, 3),
      "<br>95% CI = [", round(ci.lb, 3), ", ", round(ci.ub, 3), "]",
      "<br>p = ", round(p, 3),
      "<br>ES = ", k
      ),
      data_id = modlabel
    ),
    position = position_dodge(width = 0.75),
    size = 2.5
  ) +

  geom_errorbarh(
    aes(
      colour = significance,
      xmin = ci.lb,
      xmax = ci.ub
      ),
    position = position_dodge(width = 0.75),
    height = 0.15,
    linewidth = .5
    ) +
geom_text(
  data = subset(comb_mods_plot_uplifts_between, modtype == "categorical"),
  aes(
    x = ci.ub + right_pad,
    label = modlabel
  ),
  position = position_dodge(width = 0.75),
  hjust = 0,
  size = 3
) +
geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
    ) +
  
  theme_pub() +

  labs(
    x = "Meta-regression coefficient (b)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +
  
  theme(
    axis.text.y = element_text(hjust = 1)
    )

girafe(
  ggobj = plot,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Hover over the points in the plot to display more information for the corresponding moderator. Significant moderation effects are depicted in black, non-significant moderation effects are depicted in grey.

Hassles & SWB

Within-Person Associations

Regression Coefficient Table

# All Moderators Combined

comb_mods_hassles_within <- setNames(
  lapply(
    comb_mods_within,
    run_ma_mod,
    data = hassles_within,
    SamV = V_hassles_within
  ),
  comb_mods_within
)

comb_mods_results_hassles_within <- lapply(
  comb_mods_hassles_within,
  extract_mod_results,
  data = hassles_within,
  SamV = V_hassles_within
) %>%
  bind_rows(.id = "moderator")


comb_mods_table_hassles_within <- comb_mods_results_hassles_within %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

comb_mods_table_hassles_within$R2_Level1 <- ifelse(comb_mods_table_hassles_within$R2_Level1 < 0, 0, comb_mods_table_hassles_within$R2_Level1)
comb_mods_table_hassles_within$R2_Level2 <- ifelse(comb_mods_table_hassles_within$R2_Level2 < 0, 0, comb_mods_table_hassles_within$R2_Level2)
comb_mods_table_hassles_within$R2_Total <- ifelse(comb_mods_table_hassles_within$R2_Total < 0, 0, comb_mods_table_hassles_within$R2_Total)

names(comb_mods_table_hassles_within) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                                           "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                                           "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(comb_mods_table_hassles_within) <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                              "College", "Northern Europe", "Southern Europe", "Asia",
                                              "ESM", "Work/Achievement", "Social", "Health",
                                              "Other event ","Open-response", "Interview", "Numeric count", 
                                              "Scale score", "Positive affect", "Negative affect", "Global well-being",
                                              "Prospective")

order_comb_mods_hassles_within <- c(
  "Publication year",
  "College",
  "% Female",
  "Mean age",
  "Northern Europe", "Southern Europe", "Asia",
  "Work/Achievement", "Social", "Health", "Other event",
  "Open-response", "Interview",
  "Numeric count", "Scale score",
  "Positive affect", "Negative affect", "Global well-being",
  "SWB reliability",
  "ESM",
  "Prospective"
)

comb_mods_table_hassles_within <- comb_mods_table_hassles_within[order_comb_mods_hassles_within, ]

kable(round(comb_mods_table_hassles_within, 3),
      caption = "**Results of Multivariate Moderator Analyses for the Within-Person Association Between Daily Hassles and SWB**",
      escape = FALSE,
      label = NA,
      digits = 3) %>%
  pack_rows("Region", 5, 7) %>%
  pack_rows("Daily event type", 8, 11) %>%
  pack_rows("Daily event measurement", 12, 13) %>%
  pack_rows("Daily event data type", 14, 15) %>%
  pack_rows("SWB dimension", 16, 18) %>%
  column_spec(c(3:6), bold = ifelse(comb_mods_table_hassles_within[,5] < 0.05, TRUE, FALSE)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 115: Results of Multivariate Moderator Analyses for the Within-Person Association Between Daily Hassles and SWB

	ES	b	SE	t	p	τ2Level1	τ2Level2	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Publication year	362	0.002	0.002	1.188	0.257	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
College	362	-0.036	0.031	-1.164	0.252	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
% Female	362	0.000	0.000	-0.669	0.527	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Mean age	362	-0.001	0.001	-0.910	0.376	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Region
Northern Europe	362	-0.032	0.033	-0.970	0.342	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Southern Europe	362	-0.033	0.195	-0.168	0.890	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Asia	362	0.044	0.030	1.438	0.234	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Daily event type
Work/Achievement	362	0.052	0.020	2.669	0.013	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Social	362	0.087	0.021	4.166	0.000	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Health	362	0.037	0.064	0.581	0.589	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Other event	362	0.145	0.048	3.026	0.037	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Daily event measurement
Open-response	362	-0.015	0.051	-0.287	0.786	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Interview	362	0.086	0.049	1.759	0.131	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Daily event data type
Numeric count	362	0.045	0.036	1.246	0.223	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Scale score	362	-0.046	0.034	-1.376	0.181	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
SWB dimension
Positive affect	362	0.077	0.014	5.424	0.014	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Negative affect	362	-0.066	0.017	-3.913	0.019	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Global well-being	362	-0.048	0.022	-2.212	0.080	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
SWB reliability	362	-0.020	0.084	-0.234	0.817	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
ESM	362	-0.056	0.031	-1.824	0.079	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809
Prospective	362	0.208	0.020	10.131	0.000	0.008	0.004	63.188	34.667	97.855	64.719	0	51.809

Note. Level-specific \(R^2\) values were calculated as the proportional reduction in the estimated random-effect variance components relative to the corresponding null model fitted to the same observations (see the corresponding analysis code for more details). Because the addition of moderators changes how remaining residual heterogeneity is partitioned between the within- and between-study levels, individual variance components may increase or decrease after including a moderator. Consequently, \(R^2\) values can get negative. Negative \(R^2\) values were truncated to 0.000% for presentation.

Regression Coefficient Plot

comb_mods_plot_hassles_within <- comb_mods_results_hassles_within

comb_mods_plot_hassles_within$modlabel <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                          "College", "Northern Europe", "Southern Europe", "Asia",
                                          "ESM", "Work/Achievement", "Social", "Health",
                                          "Other event", "Open-response", "Interview", "Numeric count",
                                          "Scale score", "Positive affect", "Negative affect", "Global well-being",
                                          "Prospective")

comb_mods_plot_hassles_within <- comb_mods_plot_hassles_within %>%
  mutate(
    modcat = case_when(
      modlabel %in% c("Work/Achievement", "Social", "Health", "Other event") ~ "Daily event type",
      modlabel %in% c("Open-response", "Interview", "Other measure") ~ "Daily event measurement",
      modlabel %in% c("Numeric count", "Scale score", "Other type") ~ "Daily event data type",
      modlabel %in% c("Domain satisfaction", "Positive affect", "Negative affect", "Global well-being") ~ "SWB dimension",
      modlabel %in% c("College") ~ "Sample type",
      modlabel %in% c("Northern Europe", "Southern Europe", "Asia", "Australia") ~ "Region",
      modlabel %in% c("Thesis", "Unpublished") ~ "Publication type",
      modlabel %in% c("ESM") ~ "Study design",
      modlabel == "Prospective" ~ "Correlation lag",
      TRUE ~ modlabel
    )
  ) %>%
  mutate(
    modtype = ifelse(
      term %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c"),
      "continuous",
      "categorical"
    ),
    significance = ifelse(
      p < .05,
      "significant",
      "non-significant"
    )
  )

comb_mods_plot_hassles_within <- comb_mods_plot_hassles_within %>%
  mutate(modcat = factor(modcat, levels = order_modcat_within)) %>%
  arrange(modcat)

comb_mods_plot_hassles_within$modcat <- factor(
  comb_mods_plot_hassles_within$modcat,
  levels = rev(unique(comb_mods_plot_hassles_within$modcat))
)

comb_mods_plot_hassles_within <- comb_mods_plot_hassles_within %>%
  mutate(modlabel = factor(modlabel, levels = rev(order_comb_mods_hassles_within)))

# Compute x-axis limits
x_min <- min(comb_mods_plot_hassles_within$ci.lb)
x_max <- max(comb_mods_plot_hassles_within$ci.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

plot <- ggplot(data = comb_mods_plot_hassles_within,
       aes(
         x = b,
         y = modcat,
         group = modlabel
         )
       ) +
  
   geom_point_interactive(
    aes(
      colour = significance,
      tooltip = paste0(
      "<b>", modlabel, "</b>",
      " (",ifelse(modtype == "categorical", as.character(modcat), "Continuous"), ")",
      "<br>b = ", round(b, 3),
      "<br>95% CI = [", round(ci.lb, 3), ", ", round(ci.ub, 3), "]",
      "<br>p = ", round(p, 3),
      "<br>ES = ", k
      ),
      data_id = modlabel
    ),
    position = position_dodge(width = 0.75),
    size = 2.5
  ) +

  geom_errorbarh(
    aes(
      colour = significance,
      xmin = ci.lb,
      xmax = ci.ub
      ),
    position = position_dodge(width = 0.75),
    height = 0.15,
    linewidth = .5
    ) +
geom_text(
  data = subset(comb_mods_plot_hassles_within, modtype == "categorical"),
  aes(
    x = ci.ub + right_pad,
    label = modlabel
  ),
  position = position_dodge(width = 0.75),
  hjust = 0,
  size = 3
) +
geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
    ) +
  
  theme_pub() +

  labs(
    x = "Meta-regression coefficient (b)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +
  
  theme(
    axis.text.y = element_text(hjust = 1)
    )

girafe(
  ggobj = plot,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Between-Person Associations

Regression Coefficient Table

# All Moderators Combined

comb_mods_hassles_between <- setNames(
  lapply(
    comb_mods_within,
    run_ma_mod,
    data = hassles_between,
    SamV = V_hassles_between
  ),
  comb_mods_within
)

comb_mods_results_hassles_between <- lapply(
  comb_mods_hassles_between,
  extract_mod_results,
  data = hassles_between,
  SamV = V_hassles_between
) %>%
  bind_rows(.id = "moderator")


comb_mods_table_hassles_between <- comb_mods_results_hassles_between %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

comb_mods_table_hassles_between$R2_Level1 <- ifelse(comb_mods_table_hassles_between$R2_Level1 < 0, 0, comb_mods_table_hassles_between$R2_Level1)
comb_mods_table_hassles_between$R2_Level2 <- ifelse(comb_mods_table_hassles_between$R2_Level2 < 0, 0, comb_mods_table_hassles_between$R2_Level2)
comb_mods_table_hassles_between$R2_Total <- ifelse(comb_mods_table_hassles_between$R2_Total < 0, 0, comb_mods_table_hassles_between$R2_Total)

names(comb_mods_table_hassles_between) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                                            "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                                            "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(comb_mods_table_hassles_between) <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                                "College", "Northern Europe", "Southern Europe", "Asia",
                                                "Australia", "ESM", "Work/Achievement", "Social",
                                                "Health", "Other event", "Open-response", "Interview",
                                                "Numeric count", "Domain satisfaction", "Scale score", 
                                                "Positive affect", "Negative affect",
                                                "Global well-being")

order_comb_mods_hassles_between <- c(
  "Publication year",
  "College",
  "% Female",
  "Mean age",
  "Northern Europe","Southern Europe", "Asia", "Australia",
  "Work/Achievement", "Social", "Health", "Other event",
  "Open-response", "Interview",
  "Numeric count", "Scale score",
  "Positive affect", "Negative affect", "Global well-being",
  "SWB reliability",
  "ESM"
)

comb_mods_table_hassles_between <- comb_mods_table_hassles_between[order_comb_mods_hassles_between, ]

kable(round(comb_mods_table_hassles_between, 3),
      caption = "**Results of Multivariate Moderator Analyses for the Between-Person Association Between Daily Hassles and SWB**",
      escape = FALSE,
      label = NA,
      digits = 3) %>%
  pack_rows("Region", 5, 8) %>%
  pack_rows("Daily event type", 9, 12) %>%
  pack_rows("Daily event measurement", 13, 14) %>%
  pack_rows("Daily event data type", 15, 16) %>%
  pack_rows("SWB dimension", 17, 19) %>%
  column_spec(c(3:6), bold = ifelse(comb_mods_table_hassles_between[,5] < 0.05, TRUE, FALSE)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 116: Results of Multivariate Moderator Analyses for the Between-Person Association Between Daily Hassles and SWB

	ES	b	SE	t	p	τ2Level1	τ2Level2	I2Level1	I2Level2	I2Total	R2Level1	R2Level2	R2Total
Publication year	375	-0.006	0.002	-2.352	0.026	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
College	375	0.065	0.041	1.565	0.123	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
% Female	375	0.000	0.000	0.827	0.426	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Mean age	375	0.001	0.001	1.201	0.240	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Region
Northern Europe	375	-0.052	0.041	-1.247	0.224	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Southern Europe	375	-0.154	0.226	-0.682	0.605	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Asia	375	0.078	0.072	1.082	0.300	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Australia	375	-0.121	0.067	-1.814	0.134	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Daily event type
Work/Achievement	375	0.019	0.025	0.785	0.438	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Social	375	0.056	0.025	2.246	0.029	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Health	375	0.092	0.045	2.052	0.071	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Other event	375	0.105	0.051	2.047	0.070	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Daily event measurement
Open-response	375	-0.031	0.048	-0.640	0.543	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Interview	375	0.053	0.093	0.576	0.611	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Daily event data type
Numeric count	375	0.025	0.041	0.614	0.543	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Scale score	375	0.084	0.054	1.566	0.195	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
SWB dimension
Positive affect	375	0.129	0.031	4.168	0.011	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Negative affect	375	-0.182	0.031	-5.850	0.002	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
Global well-being	375	0.057	0.044	1.300	0.338	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
SWB reliability	375	0.286	0.166	1.719	0.094	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519
ESM	375	0.042	0.035	1.202	0.238	0.019	0.01	57.661	29.786	87.447	61.631	0	45.519

Note. Level-specific \(R^2\) values were calculated as the proportional reduction in the estimated random-effect variance components relative to the corresponding null model fitted to the same observations (see the corresponding analysis code for more details). Because the addition of moderators changes how remaining residual heterogeneity is partitioned between the within- and between-study levels, individual variance components may increase or decrease after including a moderator. Consequently, \(R^2\) values can get negative. Negative \(R^2\) values were truncated to 0.000% for presentation.

Regression Coefficient Plot

comb_mods_plot_hassles_between <- comb_mods_results_hassles_between

comb_mods_plot_hassles_between$modlabel <- c("% Female", "Mean age", "Publication year", "SWB reliability",
                                            "College", "Northern Europe", "Southern Europe", "Asia",
                                            "Australia", "ESM", "Work/Achievement", "Social",
                                            "Health", "Other event", "Open-response", "Interview", "Numeric count",
                                            "Scale score", "Domain satisfaction", "Positive affect", "Negative affect", "Global well-being")

comb_mods_plot_hassles_between <- comb_mods_plot_hassles_between %>%
  mutate(
    modcat = case_when(
      modlabel %in% c("Work/Achievement", "Social", "Health", "Other event") ~ "Daily event type",
      modlabel %in% c("Open-response", "Interview", "Other measure") ~ "Daily event measurement",
      modlabel %in% c("Numeric count", "Scale score", "Other type") ~ "Daily event data type",
      modlabel %in% c("Domain satisfaction", "Positive affect", "Negative affect", "Global well-being") ~ "SWB dimension",
      modlabel %in% c("College") ~ "Sample type",
      modlabel %in% c("Northern Europe", "Southern Europe", "Asia", "Australia") ~ "Region",
      modlabel %in% c("ESM") ~ "Study design",
      modlabel %in% c("Thesis", "Unpublished") ~ "Publication type",
      TRUE ~ modlabel
    )
  ) %>%
  mutate(
    modtype = ifelse(
      term %in% c("Female_c", "AgeM_c", "Year_c", "WbReliability_c"),
      "continuous",
      "categorical"
    ),
    significance = ifelse(
      p < .05,
      "significant",
      "non-significant"
    )
  )

comb_mods_plot_hassles_between <- comb_mods_plot_hassles_between %>%
  mutate(modcat = factor(modcat, levels = order_modcat_between)) %>%
  arrange(modcat)

comb_mods_plot_hassles_between$modcat <- factor(
  comb_mods_plot_hassles_between$modcat,
  levels = rev(unique(comb_mods_plot_hassles_between$modcat))
)

comb_mods_plot_hassles_between <- comb_mods_plot_hassles_between %>%
  mutate(modlabel = factor(modlabel, levels = rev(order_comb_mods_hassles_between)))

# Compute x-axis limits
x_min <- min(comb_mods_plot_hassles_between$ci.lb)
x_max <- max(comb_mods_plot_hassles_between$ci.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.05

plot <- ggplot(data = comb_mods_plot_hassles_between,
       aes(
         x = b,
         y = modcat,
         group = modlabel
         )
       ) +
  
   geom_point_interactive(
    aes(
      colour = significance,
      tooltip = paste0(
      "<b>", modlabel, "</b>",
      " (",ifelse(modtype == "categorical", as.character(modcat), "Continuous"), ")",
      "<br>b = ", round(b, 3),
      "<br>95% CI = [", round(ci.lb, 3), ", ", round(ci.ub, 3), "]",
      "<br>p = ", round(p, 3),
      "<br>ES = ", k
      ),
      data_id = modlabel
    ),
    position = position_dodge(width = 0.75),
    size = 2.5
  ) +

  geom_errorbarh(
    aes(
      colour = significance,
      xmin = ci.lb,
      xmax = ci.ub
      ),
    position = position_dodge(width = 0.75),
    height = 0.15,
    linewidth = .5
    ) +
geom_text(
  data = subset(comb_mods_plot_hassles_between, modtype == "categorical"),
  aes(
    x = ci.ub + right_pad,
    label = modlabel
  ),
  position = position_dodge(width = 0.75),
  hjust = 0,
  size = 3
) +
geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
    ) +
  
  theme_pub() +

  labs(
    x = "Meta-regression coefficient (b)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +
  
  theme(
    axis.text.y = element_text(hjust = 1)
    )

girafe(
  ggobj = plot,
  height_svg = 10,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Hover over the points in the plot to display more information for the corresponding moderator. Significant moderation effects are depicted in black, non-significant moderation effects are depicted in grey.

Exploratory Moderator Analyses

Correlation Lag Across Daily Diary & ESM

Daily Diary

Uplifts & SWB

# Create subsets of the data and the variance/covariance-matrix
daily_diary <- uplifts_within$StudyDesign == "daily_diary"

uplifts_within_dd <- uplifts_within[daily_diary, ]

V_uplifts_within_dd <- V_uplifts_within[daily_diary, daily_diary]

# Run moderator analysis
corr_mod_uplifts_within_dd <- setNames(
  lapply(
    corr_mods,
    run_ma_mod,
    data = uplifts_within_dd,
    SamV = V_uplifts_within_dd
  ),
  corr_mods
)

corr_mod_results_uplifts_within_dd <- lapply(
  corr_mod_uplifts_within_dd,
  extract_mod_results,
  data = uplifts_within_dd,
  SamV = V_uplifts_within_dd
) %>%
  bind_rows(.id = "moderator")

# Create table

corr_mod_results_uplifts_within_dd <- corr_mod_results_uplifts_within_dd %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

corr_mod_results_uplifts_within_dd$R2_Level1 <- ifelse(corr_mod_results_uplifts_within_dd$R2_Level1 < 0, 0, corr_mod_results_uplifts_within_dd$R2_Level1)
corr_mod_results_uplifts_within_dd$R2_Level2 <- ifelse(corr_mod_results_uplifts_within_dd$R2_Level2 < 0, 0, corr_mod_results_uplifts_within_dd$R2_Level2)
corr_mod_results_uplifts_within_dd$R2_Total <- ifelse(corr_mod_results_uplifts_within_dd$R2_Total < 0, 0, corr_mod_results_uplifts_within_dd$R2_Total)

names(corr_mod_results_uplifts_within_dd) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                               "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                               "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(corr_mod_results_uplifts_within_dd) <- c("Prospective")

# Set moderator levels
corr_cats_uplifts_within_dd <- data.frame(
  CorrLag = factor(
    c("concurrent", "prospective"),
    levels = c("concurrent", "prospective")
  )
)

# Create model matrix for the moderator
corr_mods_uplifts_within_dd <- 
  model.matrix(~ CorrLag, data = corr_cats_uplifts_within_dd)[, -1]

# Get predictions
pred_corr_mods_uplifts_within_dd <- predict(
  corr_mod_uplifts_within_dd$CorrLag$robust,
  newmods = corr_mods_uplifts_within_dd
  )

# Create plotting data
df_pred_corr_mods_uplifts_within_dd <- data.frame(
  label = c("Concurrent", "Prospective"),
  r = tanh(pred_corr_mods_uplifts_within_dd$pred),
  r.lb = tanh(pred_corr_mods_uplifts_within_dd$ci.lb),
  r.ub = tanh(pred_corr_mods_uplifts_within_dd$ci.ub),
  k = corr_mod_uplifts_within_dd$CorrLag$robust$k) %>%
  mutate(
    significance = if_else(
      (r.lb > 0 & r.ub > 0) | (r.lb < 0 & r.ub < 0),
      "significant",
      "non-significant"
    ),
    tooltip = paste0(
      "<b>", label, "</b>",
      "<br>Predicted r = ", round(r, 3),
      "<br>95% CI = [", round(r.lb, 3), ", ",
      round(r.ub, 3), "]",
      "<br>ES =", k
    )
  )

# Compute x-axis limits
x_min <- min(df_pred_corr_mods_uplifts_within_dd$r.lb)
x_max <- max(df_pred_corr_mods_uplifts_within_dd$r.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.20

# Create plot
simple_slopes_uplifts_within_dd <-
  ggplot(
    df_pred_corr_mods_uplifts_within_dd,
    aes(
      x = r,
      y = label,
      color = significance
      )
  ) +

  geom_errorbarh(
    aes(
      xmin = r.lb,
      xmax = r.ub
    ),
    height = 0.1,
    linewidth = 0.5
  ) +

  geom_point_interactive(
    aes(
      tooltip = tooltip,
      data_id = label
    ),
    size = 3,
  ) +

  geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
  ) +

  coord_cartesian(
    xlim = c(
      x_min,
      x_max + right_pad
    ),
    clip = "off"
  ) +

  labs(
    x = "Predicted correlation (r)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +

  theme_pub() +

  theme(
    axis.text.y = element_text(hjust = 1),
    panel.grid.major.y = element_blank()
  )

# Interactive plot
girafe(
  ggobj = simple_slopes_uplifts_within_dd,
  height_svg = 3,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Points represent model-predicted correlations. Hover over the points in the plot to display more information for the corresponding moderator. Significant predicted correlations are depicted in black, non-significant correlations are depicted in grey.

Hassles & SWB

# Create subsets of the data and the variance/covariance-matrix
daily_diary <- hassles_within$StudyDesign == "daily_diary"

hassles_within_dd <- hassles_within[daily_diary, ]

V_hassles_within_dd <- V_hassles_within[daily_diary, daily_diary]

# Run moderator analysis
corr_mod_hassles_within_dd <- setNames(
  lapply(
    corr_mods,
    run_ma_mod,
    data = hassles_within_dd,
    SamV = V_hassles_within_dd
  ),
  corr_mods
)

corr_mod_results_hassles_within_dd <- lapply(
  corr_mod_hassles_within_dd,
  extract_mod_results,
  data = hassles_within_dd,
  SamV = V_hassles_within_dd
) %>%
  bind_rows(.id = "moderator")

# Create table

corr_mod_results_hassles_within_dd <- corr_mod_results_hassles_within_dd %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

corr_mod_results_hassles_within_dd$R2_Level1 <- ifelse(corr_mod_results_hassles_within_dd$R2_Level1 < 0, 0, corr_mod_results_hassles_within_dd$R2_Level1)
corr_mod_results_hassles_within_dd$R2_Level2 <- ifelse(corr_mod_results_hassles_within_dd$R2_Level2 < 0, 0, corr_mod_results_hassles_within_dd$R2_Level2)
corr_mod_results_hassles_within_dd$R2_Total <- ifelse(corr_mod_results_hassles_within_dd$R2_Total < 0, 0, corr_mod_results_hassles_within_dd$R2_Total)

names(corr_mod_results_hassles_within_dd) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                               "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                               "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(corr_mod_results_hassles_within_dd) <- c("Prospective")

# Set moderator levels
corr_cats_hassles_within_dd <- data.frame(
  CorrLag = factor(
    c("concurrent", "prospective"),
    levels = c("concurrent", "prospective")
  )
)

# Create model matrix for the moderator
corr_mods_hassles_within_dd <- 
  model.matrix(~ CorrLag, data = corr_cats_hassles_within_dd)[, -1]

# Get predictions
pred_corr_mods_hassles_within_dd <- predict(
  corr_mod_hassles_within_dd$CorrLag$robust,
  newmods = corr_mods_hassles_within_dd
  )

# Create plotting data
df_pred_corr_mods_hassles_within_dd <- data.frame(
  label = c("Concurrent", "Prospective"),
  r = tanh(pred_corr_mods_hassles_within_dd$pred),
  r.lb = tanh(pred_corr_mods_hassles_within_dd$ci.lb),
  r.ub = tanh(pred_corr_mods_hassles_within_dd$ci.ub),
  k = corr_mod_hassles_within_dd$CorrLag$robust$k) %>%
  mutate(
    significance = if_else(
      (r.lb > 0 & r.ub > 0) | (r.lb < 0 & r.ub < 0),
      "significant",
      "non-significant"
    ),
    tooltip = paste0(
      "<b>", label, "</b>",
      "<br>Predicted r = ", round(r, 3),
      "<br>95% CI = [", round(r.lb, 3), ", ",
      round(r.ub, 3), "]",
      "<br>ES =", k
    )
  )

# Compute x-axis limits
x_min <- min(df_pred_corr_mods_hassles_within_dd$r.lb)
x_max <- max(df_pred_corr_mods_hassles_within_dd$r.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.20

# Create plot
simple_slopes_hassles_within_dd <-
  ggplot(
    df_pred_corr_mods_hassles_within_dd,
    aes(
      x = r,
      y = label,
      color = significance
      )
  ) +

  geom_errorbarh(
    aes(
      xmin = r.lb,
      xmax = r.ub
    ),
    height = 0.1,
    linewidth = 0.5
  ) +

  geom_point_interactive(
    aes(
      tooltip = tooltip,
      data_id = label
    ),
    size = 3,
  ) +

  geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
  ) +

  coord_cartesian(
    xlim = c(
      x_min,
      x_max + right_pad
    ),
    clip = "off"
  ) +

  labs(
    x = "Predicted correlation (r)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +

  theme_pub() +

  theme(
    axis.text.y = element_text(hjust = 1),
    panel.grid.major.y = element_blank()
  )

# Interactive plot
girafe(
  ggobj = simple_slopes_hassles_within_dd,
  height_svg = 3,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Points represent model-predicted correlations. Hover over the points in the plot to display more information for the corresponding moderator. Significant predicted correlations are depicted in black, non-significant correlations are depicted in grey.

ESM

Uplifts & SWB

# Create subsets of the data and the variance/covariance-matrix
esm <- uplifts_within$StudyDesign == "esm"

uplifts_within_esm <- uplifts_within[esm, ]

V_uplifts_within_esm <- V_uplifts_within[esm, esm]

# Run moderator analysis
corr_mod_uplifts_within_esm <- setNames(
  lapply(
    corr_mods,
    run_ma_mod,
    data = uplifts_within_esm,
    SamV = V_uplifts_within_esm
  ),
  corr_mods
)

corr_mod_results_uplifts_within_esm <- lapply(
  corr_mod_uplifts_within_esm,
  extract_mod_results,
  data = uplifts_within_esm,
  SamV = V_uplifts_within_esm
) %>%
  bind_rows(.id = "moderator")

# Create table

corr_mod_results_uplifts_within_esm <- corr_mod_results_uplifts_within_esm %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

corr_mod_results_uplifts_within_esm$R2_Level1 <- ifelse(corr_mod_results_uplifts_within_esm$R2_Level1 < 0, 0, corr_mod_results_uplifts_within_esm$R2_Level1)
corr_mod_results_uplifts_within_esm$R2_Level2 <- ifelse(corr_mod_results_uplifts_within_esm$R2_Level2 < 0, 0, corr_mod_results_uplifts_within_esm$R2_Level2)
corr_mod_results_uplifts_within_esm$R2_Total <- ifelse(corr_mod_results_uplifts_within_esm$R2_Total < 0, 0, corr_mod_results_uplifts_within_esm$R2_Total)

names(corr_mod_results_uplifts_within_esm) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                               "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                               "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(corr_mod_results_uplifts_within_esm) <- c("Prospective")

# Set moderator levels
corr_cats_uplifts_within_esm <- data.frame(
  CorrLag = factor(
    c("concurrent", "prospective"),
    levels = c("concurrent", "prospective")
  )
)

# Create model matrix for the moderator
corr_mods_uplifts_within_esm <- 
  model.matrix(~ CorrLag, data = corr_cats_uplifts_within_esm)[, -1]

# Get predictions
pred_corr_mods_uplifts_within_esm <- predict(
  corr_mod_uplifts_within_esm$CorrLag$robust,
  newmods = corr_mods_uplifts_within_esm
  )

# Create plotting data
df_pred_corr_mods_uplifts_within_esm <- data.frame(
  label = c("Concurrent", "Prospective"),
  r = tanh(pred_corr_mods_uplifts_within_esm$pred),
  r.lb = tanh(pred_corr_mods_uplifts_within_esm$ci.lb),
  r.ub = tanh(pred_corr_mods_uplifts_within_esm$ci.ub),
  k = corr_mod_uplifts_within_esm$CorrLag$robust$k) %>%
  mutate(
    significance = if_else(
      (r.lb > 0 & r.ub > 0) | (r.lb < 0 & r.ub < 0),
      "significant",
      "non-significant"
    ),
    tooltip = paste0(
      "<b>", label, "</b>",
      "<br>Predicted r = ", round(r, 3),
      "<br>95% CI = [", round(r.lb, 3), ", ",
      round(r.ub, 3), "]",
      "<br>ES =", k
    )
  )

# Compute x-axis limits
x_min <- min(df_pred_corr_mods_uplifts_within_esm$r.lb)
x_max <- max(df_pred_corr_mods_uplifts_within_esm$r.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.20

# Create plot
simple_slopes_uplifts_within_esm <-
  ggplot(
    df_pred_corr_mods_uplifts_within_esm,
    aes(
      x = r,
      y = label,
      color = significance
      )
  ) +

  geom_errorbarh(
    aes(
      xmin = r.lb,
      xmax = r.ub
    ),
    height = 0.1,
    linewidth = 0.5
  ) +

  geom_point_interactive(
    aes(
      tooltip = tooltip,
      data_id = label
    ),
    size = 3,
  ) +

  geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
  ) +

  coord_cartesian(
    xlim = c(
      x_min,
      x_max + right_pad
    ),
    clip = "off"
  ) +

  labs(
    x = "Predicted correlation (r)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +

  theme_pub() +

  theme(
    axis.text.y = element_text(hjust = 1),
    panel.grid.major.y = element_blank()
  )

# Interactive plot
girafe(
  ggobj = simple_slopes_uplifts_within_esm,
  height_svg = 3,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Points represent model-predicted correlations. Hover over the points in the plot to display more information for the corresponding moderator. Significant predicted correlations are depicted in black, non-significant correlations are depicted in grey.

Hassles & SWB

# Create subsets of the data and the variance/covariance-matrix
esm <- hassles_within$StudyDesign == "esm"

hassles_within_esm <- hassles_within[esm, ]

V_hassles_within_esm <- V_hassles_within[esm, esm]

# Run moderator analysis
corr_mod_hassles_within_esm <- setNames(
  lapply(
    corr_mods,
    run_ma_mod,
    data = hassles_within_esm,
    SamV = V_hassles_within_esm
  ),
  corr_mods
)

corr_mod_results_hassles_within_esm <- lapply(
  corr_mod_hassles_within_esm,
  extract_mod_results,
  data = hassles_within_esm,
  SamV = V_hassles_within_esm
) %>%
  bind_rows(.id = "moderator")

# Create table

corr_mod_results_hassles_within_esm <- corr_mod_results_hassles_within_esm %>%
  select(
    -moderator, -term, -ci.lb, -ci.ub
  )

corr_mod_results_hassles_within_esm$R2_Level1 <- ifelse(corr_mod_results_hassles_within_esm$R2_Level1 < 0, 0, corr_mod_results_hassles_within_esm$R2_Level1)
corr_mod_results_hassles_within_esm$R2_Level2 <- ifelse(corr_mod_results_hassles_within_esm$R2_Level2 < 0, 0, corr_mod_results_hassles_within_esm$R2_Level2)
corr_mod_results_hassles_within_esm$R2_Total <- ifelse(corr_mod_results_hassles_within_esm$R2_Total < 0, 0, corr_mod_results_hassles_within_esm$R2_Total)

names(corr_mod_results_hassles_within_esm) <- c("ES", "*b*", "*SE*", "*t*", "*p*", "τ^2^~Level1~", "τ^2^~Level2~",
                               "*I*^2^~Level1~","*I*^2^~Level2~", "*I*^2^~Total~",
                               "R^2^~Level1~","R^2^~Level2~", "R^2^~Total~")

row.names(corr_mod_results_hassles_within_esm) <- c("Prospective")

# Set moderator levels
corr_cats_hassles_within_esm <- data.frame(
  CorrLag = factor(
    c("concurrent", "prospective"),
    levels = c("concurrent", "prospective")
  )
)

# Create model matrix for the moderator
corr_mods_hassles_within_esm <- 
  model.matrix(~ CorrLag, data = corr_cats_hassles_within_esm)[, -1]

# Get predictions
pred_corr_mods_hassles_within_esm <- predict(
  corr_mod_hassles_within_esm$CorrLag$robust,
  newmods = corr_mods_hassles_within_esm
  )

# Create plotting data
df_pred_corr_mods_hassles_within_esm <- data.frame(
  label = c("Concurrent", "Prospective"),
  r = tanh(pred_corr_mods_hassles_within_esm$pred),
  r.lb = tanh(pred_corr_mods_hassles_within_esm$ci.lb),
  r.ub = tanh(pred_corr_mods_hassles_within_esm$ci.ub),
  k = corr_mod_hassles_within_esm$CorrLag$robust$k) %>%
  mutate(
    significance = if_else(
      (r.lb > 0 & r.ub > 0) | (r.lb < 0 & r.ub < 0),
      "significant",
      "non-significant"
    ),
    tooltip = paste0(
      "<b>", label, "</b>",
      "<br>Predicted r = ", round(r, 3),
      "<br>95% CI = [", round(r.lb, 3), ", ",
      round(r.ub, 3), "]</b>",
      "<br>ES =", k
    )
  )

# Compute x-axis limits
x_min <- min(df_pred_corr_mods_hassles_within_esm$r.lb)
x_max <- max(df_pred_corr_mods_hassles_within_esm$r.ub)
x_range <- x_max - x_min
right_pad <- x_range * 0.20

# Create plot
simple_slopes_hassles_within_esm <-
  ggplot(
    df_pred_corr_mods_hassles_within_esm,
    aes(
      x = r,
      y = label,
      color = significance
      )
  ) +

  geom_errorbarh(
    aes(
      xmin = r.lb,
      xmax = r.ub
    ),
    height = 0.1,
    linewidth = 0.5
  ) +

  geom_point_interactive(
    aes(
      tooltip = tooltip,
      data_id = label
    ),
    size = 3,
  ) +

  geom_vline(
    xintercept = 0,
    linetype = "dashed",
    colour = "grey75"
  ) +

  coord_cartesian(
    xlim = c(
      x_min,
      x_max + right_pad
    ),
    clip = "off"
  ) +

  labs(
    x = "Predicted correlation (r)",
    y = "Moderator category"
  ) +
  
  scale_colour_manual(
  values = c(
    "significant" = "black",
    "non-significant" = "grey50"
  ),
  guide = "none"
  ) +

  theme_pub() +

  theme(
    axis.text.y = element_text(hjust = 1),
    panel.grid.major.y = element_blank()
  )

# Interactive plot
girafe(
  ggobj = simple_slopes_hassles_within_esm,
  height_svg = 3,
  width_svg = 8,
  options = list(
    opts_hover(css = "fill:#D55E00;r:3.5pt;"),
    opts_tooltip(
      css = "
        background-color: white;
        color: black;
        font-family: Arial;
        font-size: 14px;
        padding: 8px;
        border: 1px solid grey;
        border-radius: 5px;
        box-shadow: 2px 2px 6px rgba(0,0,0,0.2);
      "
    )
  )
)

Note. Points represent model-predicted correlations. Hover over the points in the plot to display more information for the corresponding moderator. Significant predicted correlations are depicted in black, non-significant correlations are depicted in grey.

Section 5: Publication Bias

Funnel Plots

funnel_uplifts_within <- funnel(
ma_uplifts_within[["model"]],
refline = ma_uplifts_within[["model"]]$b[1],
atransf = transf.ztor,
level=c(90, 95, 99),
shade=c("white", "red", "orange"), 
main="Daily Uplifts & SWB (Within-Person)", 
xlab = "Correlation (r)",
ylab = "Standard error"
)

funnel_uplifts_between <- funnel(
ma_uplifts_between[["model"]],
refline = ma_uplifts_between[["model"]]$b[1],
atransf = transf.ztor,
level=c(90, 95, 99),
shade=c("white", "red", "orange"), 
main="Daily Uplifts & SWB (Between-Person)", 
xlab = "Correlation (r)",
ylab = "Standard error"
)

funnel_hassles_within <- funnel(
ma_hassles_within[["model"]],
refline = ma_hassles_within[["model"]]$b[1],
atransf = transf.ztor,
level=c(90, 95, 99),
shade=c("white", "red", "orange"), 
main="Daily Hassles & SWB (Within-Person)", 
xlab = "Correlation (r)",
ylab = "Standard error"
)

funnel_hassles_between <- funnel(
ma_hassles_between[["model"]],
atransf = transf.ztor,
refline = ma_hassles_between[["model"]]$b[1],
level=c(90, 95, 99),
shade=c("white", "red", "orange"), 
main="Daily Hassles & SWB (Between-Person)", 
xlab = "Correlation (r)",
ylab = "Standard error"
)

Egger’s Regression Test

# Define Function for Regression Test

run_ma_egger <- function(data, SamV) {
  
  ma_egger <- rma.mv(
    yi = ES,
    V = SamV,
    mods = ~ sqrt(SamVar),
    random = list(~ 1 | EffSizeID,
                  ~ 1 | StudyID),
    data = data,
    sparse = TRUE
  )
  
  robust_ma_egger <- robust(ma_egger, cluster = data$StudyID, clubSandwich = TRUE)
  
  list(
    model = ma_egger,
    robust = robust_ma_egger
  )
}

# Define Function to Extract Results from Regression Test

extract_egger <- function(results) {
  as.data.frame(
    round(
      cbind(
        b  = results[["robust"]]$b[2],
        se = results[["robust"]]$se[2],
        z  = results[["robust"]]$zval[2],
        p  = results[["robust"]]$pval[2]
      ),
      3
    )
  )
}

# Run Models for Egger's Regression Test and Extract Results

ma_egger_uplifts_within <- run_ma_egger(uplifts_within, V_uplifts_within)
ma_egger_results_uplifts_within <- extract_egger(ma_egger_uplifts_within)

ma_egger_uplifts_between <- run_ma_egger(uplifts_between, V_uplifts_between)
ma_egger_results_uplifts_between <- extract_egger(ma_egger_uplifts_between)

ma_egger_hassles_within <- run_ma_egger(hassles_within, V_hassles_within)
ma_egger_results_hassles_within <- extract_egger(ma_egger_hassles_within)

ma_egger_hassles_between <- run_ma_egger(hassles_between, V_hassles_between)
ma_egger_results_hassles_between <- extract_egger(ma_egger_hassles_between)

# Create table

egger_results <- as.data.frame(
  rbind(
    ma_egger_results_uplifts_within,
    ma_egger_results_uplifts_between,
    ma_egger_results_hassles_within,
    ma_egger_results_hassles_between
  )
)

egger_results <- cbind(
  corrtype = c(
    "Within-person",
    "Between-person",
    "Within-person",
    "Between-person"
  ),
  egger_results
)

names(egger_results) <- c("", "*b*", "*SE*", "*t*", "*p*")

kable(egger_results,
      caption="**Egger's Regression Test of Publication Bias**",
      escape=FALSE,
      label = NA) %>% 
  pack_rows("Daily Uplifts & SWB", 1, 2) %>%
  pack_rows("Daily Hassles & SWB", 3, 4) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left")

Table 117: Egger’s Regression Test of Publication Bias

	b	SE	t	p
Daily Uplifts & SWB
Within-person	-1.802	1.035	-1.741	0.110
Between-person	-0.162	0.487	-0.332	0.745
Daily Hassles & SWB
Within-person	1.691	0.802	2.108	0.055
Between-person	-0.073	0.307	-0.239	0.812

Note. Across all four meta-analytic models, the standard error was not a significant moderator of the effect sizes, providing no evidence for publication bias or small-study effects.

PET-PEESE

# The PET models correspond to the previously estimated models for Egger's Regression Test
# Consequently, no separate model computation is required.

# Define Function for PEESE

run_ma_peese <- function(data, SamV) {
  
  ma_peese <- rma.mv(
    yi = ES,
    V = SamV,
    mods = ~ SamVar,
    random = list(~ 1 | EffSizeID,
                  ~ 1 | StudyID),
    data = data,
    sparse = TRUE
  )
  
  robust_ma_peese <- robust(ma_peese, cluster = data$StudyID, clubSandwich = TRUE)
  
  list(
    model = ma_peese,
    robust = robust_ma_peese
  )
}

# Run Models for PEESE

ma_peese_uplifts_within <- run_ma_peese(uplifts_within, V_uplifts_within)

ma_peese_uplifts_between <- run_ma_peese(uplifts_between, V_uplifts_between)

ma_peese_hassles_within <- run_ma_peese(hassles_within, V_hassles_within)

ma_peese_hassles_between <- run_ma_peese(hassles_between, V_hassles_between)


# Define Function to Decide Between PET/PEESE

decide_petpeese <- function(pet, peese) {
  
  if(pet[["robust"]]$pval[1] < 0.05){
    
    output <- back_transform(peese)
    output$Method <- "PEESE"
    
  } else {
    
    output <- back_transform(pet)
    output$Method <- "PET"
  }
  
  output
  
}

# Extract Resuts PET/PEESE

corrected_results <- rbind(

  decide_petpeese(
    ma_egger_uplifts_within,
    ma_peese_uplifts_within
  ),

  decide_petpeese(
    ma_egger_uplifts_between,
    ma_peese_uplifts_between
  ),

  decide_petpeese(
    ma_egger_hassles_within,
    ma_peese_hassles_within
  ),

  decide_petpeese(
    ma_egger_hassles_between,
    ma_peese_hassles_between
  )
) %>%
  select(-ES)
  

# Create table

original_results <- rbind(
  results_ma_uplifts_within,
  results_ma_uplifts_between,
  results_ma_hassles_within,
  results_ma_hassles_between
) %>%
  select(-ES)

corrected_results_table <- corrected_results %>%
  mutate(across(where(is.numeric), \(x) round(x, 3))) %>%
  mutate('CI (95%)' = paste0("[", ci.lb, ", ", ci.ub, "]")) %>%
  select(-ci.lb, -ci.ub) %>%
  setNames(c("Correlation (*r*)", "*t*", "*p*", "Method", "CI (95%)"))

original_results_table <- original_results %>%
  select(-"I2_Level1", -"I2_Level2", -"I2_Total") %>%
  mutate(across(where(is.numeric), \(x) round(x, 3))) %>%
  mutate('CI (95%)' = paste0("[", ci.lb, ", ", ci.ub, "]")) %>%
  select(-ci.lb, -ci.ub) %>%
  setNames(c("Correlation (*r*)", "*t*", "*p*", "CI (95%)"))

petpeese_table <- cbind(
    corrected_results_table,
    original_results_table
)

petpeese_table <- petpeese_table %>%
  relocate(Method, .after = last_col())

petpeese_table <- cbind(
  " " = c(
    "Within-person",
    "Between-person",
    "Within-person",
    "Between-person"
  ),
  petpeese_table
)


kable(petpeese_table,
      caption="**PET-PEESE Bias-Corrected Meta-Analysis Results**",
      escape = FALSE,
      label = NA,
      row.names = FALSE
      ) %>%
  pack_rows("Daily Uplifts & SWB", 1, 2) %>%
  pack_rows("Daily Uplifts & SWB", 3, 4) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                fixed_thead = T, full_width = TRUE, position="left") %>% 
  add_header_above(c(" " = 1, "Adjusted Estimates" = 4, "Original Estimates" = 4, " " = 1))

Table 118: PET-PEESE Bias-Corrected Meta-Analysis Results

	Adjusted Estimates				Original Estimates
	Correlation (r)	t	p	CI (95%)	Correlation (r)	t	p	CI (95%)	Method
Daily Uplifts & SWB
Within-person	0.180	9.977	0	[0.143, 0.216]	0.160	9.517	0	[0.127, 0.193]	PEESE
Between-person	0.219	9.172	0	[0.169, 0.268]	0.209	13.726	0	[0.179, 0.239]	PEESE
Daily Uplifts & SWB
Within-person	-0.197	-18.292	0	[-0.218, -0.176]	-0.183	-19.962	0	[-0.201, -0.165]	PEESE
Between-person	-0.275	-17.126	0	[-0.305, -0.244]	-0.279	-25.719	0	[-0.299, -0.259]	PEESE

Note. The table presents the results of the PET-PEESE approach (Stanley and Doucouliagos, 2014) conducted to assess the potential influence of publication bias. Because the PET intercepts were statistically significant for all models, the corresponding PEESE models were estimated. Comparison of the PEESE-adjusted correlations with the original meta-analytic correlations revealed only negligible differences across all models (absolute differences ≤ .020). Therefore, the correction for potential small-study effects had a negligible impact on the estimated correlations, providing no evidence for significant publication bias.