Supplementary Material
Life Events and Personality Trait Change: A Coordinated Data Analysis
ReadMe
This document includes additional information and analysis code for the manuscript “Life Events and Personality Trait Change: A Coordinated Data Analysis”.
Tabs
To make the document easier to read, we use tabs. You find tabs in gray in many places, where you can choose between different information (e.g., tables or figures for different event characteristics). Note: Sometimes there are several levels of tabs, so for example, on the first level you decide whether you want to see tables or graphics and on the next level you choose an event characteristic.
Analysis Code
The document includes all R code used to create the graphs and tables. As some of the main analyses were tim comsuming the analysis code for these analyses is not included in this document but uploaded as separate scripts to OSF. The code to create the tables and plots is hidden by default to enhance readability. If you want to see the code, simply click on the respective CODE button on the right hand side just above the figure or table—you will then see the associated codechunk. If you want to show all codechunks by default, click CODE > Show all Code at the very top of the document.
Preregistration
The coordinated data analysis was preregistered at https://osf.io/f4cbd?view_only=bde5a94b7eee40439f44c9f9f5a6b455. Deviations from this preregistration are summarized in the following table:
deviations <- data.frame(
`Preregistered Plan` = c("..."),
"Deviation" = c("..."),
"Reason" = c("...")
)
kable(deviations
, caption="**Deviations From the Preregistration**"
, escape=FALSE
, label = NA
, col.names = c("Preregistered plan", "Deviation", "Reason")) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
fixed_thead = T, full_width = TRUE, position="left")| Preregistered plan | Deviation | Reason |
|---|---|---|
| … | … | … |
Section 1: Descriptive Statistics
Sample Sizes for Different Life Events
Number of Participants
parti <- as.data.frame(cbind(
rbind(
relbeg_hilda %>% filter(relbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
relbeg_hilda %>% filter(relbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_hilda %>% filter(marriage_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_hilda %>% filter(marriage_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
child_hilda %>% filter(child_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
child_hilda %>% filter(child_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
separat_hilda %>% filter(separat_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
separat_hilda %>% filter(separat_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
divor_hilda %>% filter(divor_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
divor_hilda %>% filter(divor_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
widow_hilda %>% filter(widow_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
widow_hilda %>% filter(widow_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
gradu_hilda %>% filter(gradu_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
gradu_hilda %>% filter(gradu_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
jobbeg_hilda %>% filter(jobbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
jobbeg_hilda %>% filter(jobbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_hilda %>% filter(unemploy_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_hilda %>% filter(unemploy_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
retire_hilda %>% filter(retire_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
retire_hilda %>% filter(retire_control == 0) %>% pull(ID_pers) %>% unique() %>% length()),
rbind(
relbeg_hrs %>% filter(relbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
relbeg_hrs %>% filter(relbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_hrs %>% filter(marriage_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_hrs %>% filter(marriage_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
child_hrs %>% filter(child_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
child_hrs %>% filter(child_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
separat_hrs %>% filter(separat_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
separat_hrs %>% filter(separat_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
divor_hrs %>% filter(divor_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
divor_hrs %>% filter(divor_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
widow_hrs %>% filter(widow_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
widow_hrs %>% filter(widow_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
NA,
NA,
jobbeg_hrs %>% filter(jobbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
jobbeg_hrs %>% filter(jobbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_hrs %>% filter(unemploy_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_hrs %>% filter(unemploy_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
retire_hrs %>% filter(retire_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
retire_hrs %>% filter(retire_control == 0) %>% pull(ID_pers) %>% unique() %>% length()),
rbind(
relbeg_liss %>% filter(relbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
relbeg_liss %>% filter(relbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_liss %>% filter(marriage_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_liss %>% filter(marriage_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
child_liss %>% filter(child_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
child_liss %>% filter(child_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
separat_liss %>% filter(separat_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
separat_liss %>% filter(separat_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
divor_liss %>% filter(divor_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
divor_liss %>% filter(divor_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
widow_liss %>% filter(widow_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
widow_liss %>% filter(widow_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
gradu_liss %>% filter(gradu_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
gradu_liss %>% filter(gradu_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
jobbeg_liss %>% filter(jobbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
jobbeg_liss %>% filter(jobbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_liss %>% filter(unemploy_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_liss %>% filter(unemploy_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
retire_liss %>% filter(retire_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
retire_liss %>% filter(retire_control == 0) %>% pull(ID_pers) %>% unique() %>% length()),
rbind(
relbeg_midus %>% filter(relbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
relbeg_midus %>% filter(relbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_midus %>% filter(marriage_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_midus %>% filter(marriage_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
child_midus %>% filter(child_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
child_midus %>% filter(child_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
NA,
NA,
divor_midus %>% filter(divor_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
divor_midus %>% filter(divor_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
widow_midus %>% filter(widow_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
widow_midus %>% filter(widow_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
NA,
NA,
NA,
NA,
NA,
NA,
retire_midus %>% filter(retire_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
retire_midus %>% filter(retire_control == 0) %>% pull(ID_pers) %>% unique() %>% length()),
rbind(
relbeg_nlsy %>% filter(relbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
relbeg_nlsy %>% filter(relbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_nlsy %>% filter(marriage_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_nlsy %>% filter(marriage_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
child_nlsy %>% filter(child_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
child_nlsy %>% filter(child_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
separat_nlsy %>% filter(separat_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
separat_nlsy %>% filter(separat_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
divor_nlsy %>% filter(divor_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
divor_nlsy %>% filter(divor_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
NA,
NA,
gradu_nlsy %>% filter(gradu_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
gradu_nlsy %>% filter(gradu_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
NA,
NA,
unemploy_nlsy %>% filter(unemploy_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_nlsy %>% filter(unemploy_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
NA,
NA),
rbind(
relbeg_pair %>% filter(relbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
relbeg_pair %>% filter(relbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_pair %>% filter(marriage_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_pair %>% filter(marriage_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
child_pair %>% filter(child_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
child_pair %>% filter(child_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
separat_pair %>% filter(separat_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
separat_pair %>% filter(separat_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
divor_pair %>% filter(divor_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
divor_pair %>% filter(divor_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
NA,
NA,
gradu_pair %>% filter(gradu_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
gradu_pair %>% filter(gradu_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
jobbeg_pair %>% filter(jobbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
jobbeg_pair %>% filter(jobbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_pair %>% filter(unemploy_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_pair %>% filter(unemploy_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
NA,
NA),
rbind(
relbeg_soep %>% filter(relbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
relbeg_soep %>% filter(relbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_soep %>% filter(marriage_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
marriage_soep %>% filter(marriage_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
child_soep %>% filter(child_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
child_soep %>% filter(child_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
separat_soep %>% filter(separat_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
separat_soep %>% filter(separat_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
divor_soep %>% filter(divor_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
divor_soep %>% filter(divor_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
widow_soep %>% filter(widow_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
widow_soep %>% filter(widow_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
gradu_soep %>% filter(gradu_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
gradu_soep %>% filter(gradu_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
jobbeg_soep %>% filter(jobbeg_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
jobbeg_soep %>% filter(jobbeg_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_soep %>% filter(unemploy_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
unemploy_soep %>% filter(unemploy_control == 0) %>% pull(ID_pers) %>% unique() %>% length(),
retire_soep %>% filter(retire_control == 1) %>% pull(ID_pers) %>% unique() %>% length(),
retire_soep %>% filter(retire_control == 0) %>% pull(ID_pers) %>% unique() %>% length())
))
names(parti) <- c("HILDA", "HRS", "LISS", "MIDUS", "NLSY", "PAIRFAM", "SOEP")
parti$Total <- rowSums(parti, na.rm = TRUE)
parti$Group <- rep(c("Event group", "Control group"), 10)
parti <- select(parti, Group, HILDA, HRS, LISS, MIDUS, NLSY, PAIRFAM, SOEP, Total)
kable(parti
, caption="**Sample size across different life events and panel studies**"
, escape=FALSE
, label = NA
, digits = 3) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
pack_rows(index = c("New relationship" = 2, "Marriage" = 2, "Childbirth" = 2,
"Separation" = 2, "Divorce" = 2, "Widowhood" = 2,
"Graduation" = 2, "New employment" = 2,
"Unemployment" = 2, "Retirement" = 2))| Group | HILDA | HRS | LISS | MIDUS | NLSY | PAIRFAM | SOEP | Total |
|---|---|---|---|---|---|---|---|---|
| New relationship | ||||||||
| Event group | 748 | 597 | 1533 | 178 | 1521 | 3204 | 8588 | 16369 |
| Control group | 33753 | 23249 | 14949 | 6274 | 7149 | 9194 | 85319 | 179887 |
| Marriage | ||||||||
| Event group | 3146 | 783 | 1056 | 321 | 2440 | 1145 | 3545 | 12436 |
| Control group | 31355 | 23063 | 15426 | 6131 | 6230 | 11253 | 90362 | 183820 |
| Childbirth | ||||||||
| Event group | 4136 | 1599 | 818 | 400 | 3554 | 1584 | 4328 | 16419 |
| Control group | 30365 | 22247 | 15664 | 6052 | 5116 | 10814 | 89579 | 179837 |
| Separation | ||||||||
| Event group | 4032 | 871 | 185 | 522 | 2723 | 1292 | 9625 | |
| Control group | 10684 | 1034 | 8155 | 2824 | 4936 | 32470 | 60103 | |
| Divorce | ||||||||
| Event group | 913 | 1054 | 369 | 349 | 742 | 291 | 1435 | 5153 |
| Control group | 11811 | 14802 | 8029 | 4482 | 2628 | 3272 | 32773 | 77797 |
| Widowhood | ||||||||
| Event group | 812 | 2968 | 271 | 346 | 1038 | 5435 | ||
| Control group | 11783 | 12822 | 8092 | 4442 | 32751 | 69890 | ||
| Graduation | ||||||||
| Event group | 3890 | 2345 | 4081 | 307 | 3744 | 14367 | ||
| Control group | 30611 | 14137 | 4589 | 12091 | 90163 | 151591 | ||
| New employment | ||||||||
| Event group | 10135 | 2345 | 2652 | 4043 | 14188 | 33363 | ||
| Control group | 24366 | 21501 | 13830 | 8355 | 79719 | 147771 | ||
| Unemployment | ||||||||
| Event group | 3920 | 840 | 1551 | 1122 | 1410 | 5536 | 14379 | |
| Control group | 15565 | 10944 | 10293 | 5228 | 7419 | 32921 | 82370 | |
| Retirement | ||||||||
| Event group | 3249 | 5298 | 1269 | 1088 | 3781 | 14685 | ||
| Control group | 17423 | 5506 | 11132 | 4184 | 51130 | 89375 | ||
Number of Personality Trait Assessments
assess <- as.data.frame(cbind(
rbind(
relbeg_hilda %>% filter(relbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
relbeg_hilda %>% filter(relbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_hilda %>% filter(marriage_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_hilda %>% filter(marriage_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_hilda %>% filter(child_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_hilda %>% filter(child_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_hilda %>% filter(separat_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_hilda %>% filter(separat_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_hilda %>% filter(divor_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_hilda %>% filter(divor_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
widow_hilda %>% filter(widow_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
widow_hilda %>% filter(widow_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
gradu_hilda %>% filter(gradu_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
gradu_hilda %>% filter(gradu_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
jobbeg_hilda %>% filter(jobbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
jobbeg_hilda %>% filter(jobbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_hilda %>% filter(unemploy_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_hilda %>% filter(unemploy_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
retire_hilda %>% filter(retire_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
retire_hilda %>% filter(retire_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow()),
rbind(
relbeg_hrs %>% filter(relbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
relbeg_hrs %>% filter(relbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_hrs %>% filter(marriage_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_hrs %>% filter(marriage_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_hrs %>% filter(child_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_hrs %>% filter(child_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_hrs %>% filter(separat_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_hrs %>% filter(separat_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_hrs %>% filter(divor_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_hrs %>% filter(divor_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
widow_hrs %>% filter(widow_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
widow_hrs %>% filter(widow_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
NA,
NA,
jobbeg_hrs %>% filter(jobbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
jobbeg_hrs %>% filter(jobbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_hrs %>% filter(unemploy_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_hrs %>% filter(unemploy_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
retire_hrs %>% filter(retire_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
retire_hrs %>% filter(retire_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow()),
rbind(
relbeg_liss %>% filter(relbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
relbeg_liss %>% filter(relbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_liss %>% filter(marriage_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_liss %>% filter(marriage_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_liss %>% filter(child_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_liss %>% filter(child_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_liss %>% filter(separat_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_liss %>% filter(separat_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_liss %>% filter(divor_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_liss %>% filter(divor_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
widow_liss %>% filter(widow_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
widow_liss %>% filter(widow_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
gradu_liss %>% filter(gradu_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
gradu_liss %>% filter(gradu_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
jobbeg_liss %>% filter(jobbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
jobbeg_liss %>% filter(jobbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_liss %>% filter(unemploy_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_liss %>% filter(unemploy_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
retire_liss %>% filter(retire_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
retire_liss %>% filter(retire_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow()),
rbind(
relbeg_midus %>% filter(relbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
relbeg_midus %>% filter(relbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_midus %>% filter(marriage_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_midus %>% filter(marriage_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_midus %>% filter(child_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_midus %>% filter(child_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
NA,
NA,
divor_midus %>% filter(divor_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_midus %>% filter(divor_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
widow_midus %>% filter(widow_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
widow_midus %>% filter(widow_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
NA,
NA,
NA,
NA,
NA,
NA,
retire_midus %>% filter(retire_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
retire_midus %>% filter(retire_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow()),
rbind(
relbeg_nlsy %>% filter(relbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
relbeg_nlsy %>% filter(relbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_nlsy %>% filter(marriage_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_nlsy %>% filter(marriage_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_nlsy %>% filter(child_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_nlsy %>% filter(child_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_nlsy %>% filter(separat_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_nlsy %>% filter(separat_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_nlsy %>% filter(divor_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_nlsy %>% filter(divor_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
NA,
NA,
gradu_nlsy %>% filter(gradu_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
gradu_nlsy %>% filter(gradu_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
NA,
NA,
unemploy_nlsy %>% filter(unemploy_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_nlsy %>% filter(unemploy_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
NA,
NA),
rbind(
relbeg_pair %>% filter(relbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
relbeg_pair %>% filter(relbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_pair %>% filter(marriage_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_pair %>% filter(marriage_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_pair %>% filter(child_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_pair %>% filter(child_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_pair %>% filter(separat_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_pair %>% filter(separat_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_pair %>% filter(divor_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_pair %>% filter(divor_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
NA,
NA,
gradu_pair %>% filter(gradu_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
gradu_pair %>% filter(gradu_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
jobbeg_pair %>% filter(jobbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
jobbeg_pair %>% filter(jobbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_pair %>% filter(unemploy_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_pair %>% filter(unemploy_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
NA,
NA),
rbind(
relbeg_soep %>% filter(relbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
relbeg_soep %>% filter(relbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_soep %>% filter(marriage_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
marriage_soep %>% filter(marriage_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_soep %>% filter(child_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
child_soep %>% filter(child_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_soep %>% filter(separat_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
separat_soep %>% filter(separat_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_soep %>% filter(divor_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
divor_soep %>% filter(divor_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
widow_soep %>% filter(widow_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
widow_soep %>% filter(widow_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
gradu_soep %>% filter(gradu_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
gradu_soep %>% filter(gradu_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
jobbeg_soep %>% filter(jobbeg_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
jobbeg_soep %>% filter(jobbeg_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_soep %>% filter(unemploy_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
unemploy_soep %>% filter(unemploy_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
retire_soep %>% filter(retire_control == 1) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow(),
retire_soep %>% filter(retire_control == 0) %>% filter(!is.na(agree.z) | !is.na(consc.z) | !is.na(emost.z) | !is.na(extra.z) | !is.na(open.z)) %>% nrow())
))
names(assess) <- c("HILDA", "HRS", "LISS", "MIDUS", "NLSY", "PAIRFAM", "SOEP")
assess$Total <- rowSums(assess, na.rm = TRUE)
assess$Group <- rep(c("Event group", "Control group"), 10)
assess <- select(assess, Group, HILDA, HRS, LISS, MIDUS, NLSY, PAIRFAM, SOEP, Total)
kable(assess
, caption="**Number of assessments across different life events and panel studies**"
, escape=FALSE
, label = NA
, digits = 3) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
pack_rows(index = c("New relationship" = 2, "Marriage" = 2, "Childbirth" = 2,
"Separation" = 2, "Divorce" = 2, "Widowhood" = 2,
"Graduation" = 2, "New employment" = 2,
"Unemployment" = 2, "Retirement" = 2))| Group | HILDA | HRS | LISS | MIDUS | NLSY | PAIRFAM | SOEP | Total |
|---|---|---|---|---|---|---|---|---|
| New relationship | ||||||||
| Event group | 2372 | 1594 | 9107 | 434 | 4637 | 7734 | 22910 | 48788 |
| Control group | 66042 | 52148 | 57789 | 12712 | 18922 | 10970 | 97779 | 316362 |
| Marriage | ||||||||
| Event group | 11134 | 2068 | 5952 | 887 | 7645 | 3073 | 11105 | 41864 |
| Control group | 57280 | 51674 | 60944 | 12259 | 15914 | 15631 | 109584 | 323286 |
| Childbirth | ||||||||
| Event group | 14278 | 4062 | 4628 | 1060 | 10962 | 4152 | 12249 | 51391 |
| Control group | 54136 | 49680 | 62268 | 12086 | 12597 | 14552 | 108440 | 313759 |
| Separation | ||||||||
| Event group | 13777 | 2206 | 1223 | 1611 | 6649 | 4159 | 29625 | |
| Control group | 34079 | 2014 | 38226 | 8532 | 9567 | 75302 | 167720 | |
| Divorce | ||||||||
| Event group | 3548 | 2649 | 2271 | 877 | 2283 | 783 | 4548 | 16959 |
| Control group | 38407 | 35206 | 37425 | 9543 | 7926 | 7026 | 76045 | 211578 |
| Widowhood | ||||||||
| Event group | 2977 | 8145 | 2081 | 913 | 3629 | 17745 | ||
| Control group | 38473 | 29633 | 37551 | 9404 | 75931 | 190992 | ||
| Graduation | ||||||||
| Event group | 12886 | 10730 | 11717 | 806 | 8996 | 45135 | ||
| Control group | 55528 | 56166 | 11842 | 17898 | 111693 | 253127 | ||
| New employment | ||||||||
| Event group | 33096 | 6584 | 13495 | 10333 | 38996 | 102504 | ||
| Control group | 35318 | 47158 | 53401 | 8371 | 81693 | 225941 | ||
| Unemployment | ||||||||
| Event group | 14118 | 2283 | 8995 | 3512 | 3554 | 16066 | 48528 | |
| Control group | 42208 | 26179 | 38228 | 15470 | 14539 | 70446 | 207070 | |
| Retirement | ||||||||
| Event group | 12760 | 15037 | 9864 | 2894 | 13868 | 54423 | ||
| Control group | 46471 | 10161 | 40428 | 8231 | 105718 | 211009 | ||
Number of Life Satisfaction Assessments
assess <- as.data.frame(cbind(
rbind(
relbeg_hilda %>% filter(relbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
relbeg_hilda %>% filter(relbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_hilda %>% filter(marriage_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_hilda %>% filter(marriage_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
child_hilda %>% filter(child_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
child_hilda %>% filter(child_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_hilda %>% filter(separat_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_hilda %>% filter(separat_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_hilda %>% filter(divor_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_hilda %>% filter(divor_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
widow_hilda %>% filter(widow_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
widow_hilda %>% filter(widow_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
gradu_hilda %>% filter(gradu_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
gradu_hilda %>% filter(gradu_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
jobbeg_hilda %>% filter(jobbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
jobbeg_hilda %>% filter(jobbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_hilda %>% filter(unemploy_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_hilda %>% filter(unemploy_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
retire_hilda %>% filter(retire_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
retire_hilda %>% filter(retire_control == 0) %>% filter(!is.na(ls.z)) %>% nrow()),
rbind(
relbeg_hrs %>% filter(relbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
relbeg_hrs %>% filter(relbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_hrs %>% filter(marriage_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_hrs %>% filter(marriage_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
child_hrs %>% filter(child_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
child_hrs %>% filter(child_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_hrs %>% filter(separat_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_hrs %>% filter(separat_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_hrs %>% filter(divor_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_hrs %>% filter(divor_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
widow_hrs %>% filter(widow_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
widow_hrs %>% filter(widow_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
NA,
NA,
jobbeg_hrs %>% filter(jobbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
jobbeg_hrs %>% filter(jobbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_hrs %>% filter(unemploy_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_hrs %>% filter(unemploy_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
retire_hrs %>% filter(retire_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
retire_hrs %>% filter(retire_control == 0) %>% filter(!is.na(ls.z)) %>% nrow()),
rbind(
relbeg_liss %>% filter(relbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
relbeg_liss %>% filter(relbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_liss %>% filter(marriage_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_liss %>% filter(marriage_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
child_liss %>% filter(child_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
child_liss %>% filter(child_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_liss %>% filter(separat_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_liss %>% filter(separat_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_liss %>% filter(divor_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_liss %>% filter(divor_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
widow_liss %>% filter(widow_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
widow_liss %>% filter(widow_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
gradu_liss %>% filter(gradu_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
gradu_liss %>% filter(gradu_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
jobbeg_liss %>% filter(jobbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
jobbeg_liss %>% filter(jobbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_liss %>% filter(unemploy_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_liss %>% filter(unemploy_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
retire_liss %>% filter(retire_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
retire_liss %>% filter(retire_control == 0) %>% filter(!is.na(ls.z)) %>% nrow()),
rbind(
relbeg_midus %>% filter(relbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
relbeg_midus %>% filter(relbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_midus %>% filter(marriage_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_midus %>% filter(marriage_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
child_midus %>% filter(child_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
child_midus %>% filter(child_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
NA,
NA,
divor_midus %>% filter(divor_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_midus %>% filter(divor_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
widow_midus %>% filter(widow_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
widow_midus %>% filter(widow_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
NA,
NA,
NA,
NA,
NA,
NA,
retire_midus %>% filter(retire_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
retire_midus %>% filter(retire_control == 0) %>% filter(!is.na(ls.z)) %>% nrow()),
rbind(
relbeg_nlsy %>% filter(relbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
relbeg_nlsy %>% filter(relbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_nlsy %>% filter(marriage_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_nlsy %>% filter(marriage_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
child_nlsy %>% filter(child_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
child_nlsy %>% filter(child_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_nlsy %>% filter(separat_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_nlsy %>% filter(separat_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_nlsy %>% filter(divor_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_nlsy %>% filter(divor_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
NA,
NA,
gradu_nlsy %>% filter(gradu_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
gradu_nlsy %>% filter(gradu_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
NA,
NA,
unemploy_nlsy %>% filter(unemploy_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_nlsy %>% filter(unemploy_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
NA,
NA),
rbind(
relbeg_pair %>% filter(relbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
relbeg_pair %>% filter(relbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_pair %>% filter(marriage_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_pair %>% filter(marriage_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
child_pair %>% filter(child_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
child_pair %>% filter(child_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_pair %>% filter(separat_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_pair %>% filter(separat_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_pair %>% filter(divor_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_pair %>% filter(divor_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
NA,
NA,
gradu_pair %>% filter(gradu_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
gradu_pair %>% filter(gradu_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
jobbeg_pair %>% filter(jobbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
jobbeg_pair %>% filter(jobbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_pair %>% filter(unemploy_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_pair %>% filter(unemploy_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
NA,
NA),
rbind(
relbeg_soep %>% filter(relbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
relbeg_soep %>% filter(relbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_soep %>% filter(marriage_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
marriage_soep %>% filter(marriage_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
child_soep %>% filter(child_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
child_soep %>% filter(child_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_soep %>% filter(separat_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
separat_soep %>% filter(separat_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_soep %>% filter(divor_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
divor_soep %>% filter(divor_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
widow_soep %>% filter(widow_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
widow_soep %>% filter(widow_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
gradu_soep %>% filter(gradu_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
gradu_soep %>% filter(gradu_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
jobbeg_soep %>% filter(jobbeg_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
jobbeg_soep %>% filter(jobbeg_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_soep %>% filter(unemploy_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
unemploy_soep %>% filter(unemploy_control == 0) %>% filter(!is.na(ls.z)) %>% nrow(),
retire_soep %>% filter(retire_control == 1) %>% filter(!is.na(ls.z)) %>% nrow(),
retire_soep %>% filter(retire_control == 0) %>% filter(!is.na(ls.z)) %>% nrow())
))
names(assess) <- c("HILDA", "HRS", "LISS", "MIDUS", "NLSY", "PAIRFAM", "SOEP")
assess$Total <- rowSums(assess, na.rm = TRUE)
assess$Group <- rep(c("Event group", "Control group"), 10)
assess <- select(assess, Group, HILDA, HRS, LISS, MIDUS, NLSY, PAIRFAM, SOEP, Total)
kable(assess
, caption="**Number of assessments across different life events and panel studies**"
, escape=FALSE
, label = NA
, digits = 3) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
pack_rows(index = c("New relationship" = 2, "Marriage" = 2, "Childbirth" = 2,
"Separation" = 2, "Divorce" = 2, "Widowhood" = 2,
"Graduation" = 2, "New employment" = 2,
"Unemployment" = 2, "Retirement" = 2))| Group | HILDA | HRS | LISS | MIDUS | NLSY | PAIRFAM | SOEP | Total |
|---|---|---|---|---|---|---|---|---|
| New relationship | ||||||||
| Event group | 11180 | 1374 | 12122 | 433 | 2866 | 26600 | 102867 | 157442 |
| Control group | 326236 | 43993 | 74678 | 12624 | 12363 | 40022 | 628877 | 1138793 |
| Marriage | ||||||||
| Event group | 49415 | 1795 | 7918 | 883 | 4440 | 10420 | 50083 | 124954 |
| Control group | 288001 | 43572 | 78882 | 12174 | 10789 | 56202 | 681661 | 1171281 |
| Childbirth | ||||||||
| Event group | 64337 | 3441 | 6125 | 1058 | 6158 | 14188 | 53868 | 149175 |
| Control group | 273079 | 41926 | 80675 | 11999 | 9071 | 52434 | 677876 | 1147060 |
| Separation | ||||||||
| Event group | 63333 | 1932 | 1646 | 843 | 22878 | 20311 | 110943 | |
| Control group | 158590 | 1795 | 50393 | 4748 | 32133 | 380296 | 627955 | |
| Divorce | ||||||||
| Event group | 16657 | 2266 | 3037 | 872 | 1177 | 2663 | 22246 | 48918 |
| Control group | 178496 | 29710 | 49322 | 9454 | 4435 | 23763 | 383905 | 679085 |
| Widowhood | ||||||||
| Event group | 14486 | 6579 | 2841 | 896 | 20236 | 45038 | ||
| Control group | 178370 | 25308 | 49452 | 9329 | 380984 | 643443 | ||
| Graduation | ||||||||
| Event group | 57393 | 14082 | 9080 | 2721 | 34404 | 117680 | ||
| Control group | 280023 | 72718 | 6149 | 63901 | 697340 | 1120131 | ||
| New employment | ||||||||
| Event group | 148103 | 5662 | 17857 | 35431 | 178202 | 385255 | ||
| Control group | 189313 | 39705 | 68943 | 31191 | 553542 | 882694 | ||
| Unemployment | ||||||||
| Event group | 63594 | 1988 | 12050 | 2315 | 12180 | 77329 | 169456 | |
| Control group | 191756 | 22871 | 48897 | 9423 | 48901 | 325616 | 647464 | |
| Retirement | ||||||||
| Event group | 58877 | 12931 | 13310 | 2866 | 74557 | 162541 | ||
| Control group | 210097 | 9250 | 51753 | 8168 | 506486 | 785754 | ||
Number of Self-Esteem Assessments
assess <- as.data.frame(cbind(
rbind(
relbeg_liss %>% filter(relbeg_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
relbeg_liss %>% filter(relbeg_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
marriage_liss %>% filter(marriage_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
marriage_liss %>% filter(marriage_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
child_liss %>% filter(child_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
child_liss %>% filter(child_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
separat_liss %>% filter(separat_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
separat_liss %>% filter(separat_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
divor_liss %>% filter(divor_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
divor_liss %>% filter(divor_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
widow_liss %>% filter(widow_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
widow_liss %>% filter(widow_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
gradu_liss %>% filter(gradu_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
gradu_liss %>% filter(gradu_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
jobbeg_liss %>% filter(jobbeg_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
jobbeg_liss %>% filter(jobbeg_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
unemploy_liss %>% filter(unemploy_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
unemploy_liss %>% filter(unemploy_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
retire_liss %>% filter(retire_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
retire_liss %>% filter(retire_control == 0) %>% filter(!is.na(se.z)) %>% nrow()),
rbind(
relbeg_midus %>% filter(relbeg_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
relbeg_midus %>% filter(relbeg_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
marriage_midus %>% filter(marriage_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
marriage_midus %>% filter(marriage_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
child_midus %>% filter(child_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
child_midus %>% filter(child_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
NA,
NA,
divor_midus %>% filter(divor_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
divor_midus %>% filter(divor_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
widow_midus %>% filter(widow_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
widow_midus %>% filter(widow_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
NA,
NA,
NA,
NA,
NA,
NA,
retire_midus %>% filter(retire_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
retire_midus %>% filter(retire_control == 0) %>% filter(!is.na(se.z)) %>% nrow()),
rbind(
relbeg_nlsy %>% filter(relbeg_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
relbeg_nlsy %>% filter(relbeg_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
marriage_nlsy %>% filter(marriage_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
marriage_nlsy %>% filter(marriage_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
child_nlsy %>% filter(child_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
child_nlsy %>% filter(child_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
separat_nlsy %>% filter(separat_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
separat_nlsy %>% filter(separat_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
divor_nlsy %>% filter(divor_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
divor_nlsy %>% filter(divor_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
NA,
NA,
gradu_nlsy %>% filter(gradu_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
gradu_nlsy %>% filter(gradu_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
NA,
NA,
unemploy_nlsy %>% filter(unemploy_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
unemploy_nlsy %>% filter(unemploy_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
NA,
NA),
rbind(
relbeg_pair %>% filter(relbeg_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
relbeg_pair %>% filter(relbeg_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
marriage_pair %>% filter(marriage_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
marriage_pair %>% filter(marriage_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
child_pair %>% filter(child_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
child_pair %>% filter(child_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
separat_pair %>% filter(separat_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
separat_pair %>% filter(separat_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
divor_pair %>% filter(divor_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
divor_pair %>% filter(divor_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
NA,
NA,
gradu_pair %>% filter(gradu_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
gradu_pair %>% filter(gradu_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
jobbeg_pair %>% filter(jobbeg_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
jobbeg_pair %>% filter(jobbeg_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
unemploy_pair %>% filter(unemploy_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
unemploy_pair %>% filter(unemploy_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
NA,
NA),
rbind(
relbeg_soep %>% filter(relbeg_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
relbeg_soep %>% filter(relbeg_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
marriage_soep %>% filter(marriage_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
marriage_soep %>% filter(marriage_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
child_soep %>% filter(child_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
child_soep %>% filter(child_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
separat_soep %>% filter(separat_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
separat_soep %>% filter(separat_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
divor_soep %>% filter(divor_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
divor_soep %>% filter(divor_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
widow_soep %>% filter(widow_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
widow_soep %>% filter(widow_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
gradu_soep %>% filter(gradu_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
gradu_soep %>% filter(gradu_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
jobbeg_soep %>% filter(jobbeg_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
jobbeg_soep %>% filter(jobbeg_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
unemploy_soep %>% filter(unemploy_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
unemploy_soep %>% filter(unemploy_control == 0) %>% filter(!is.na(se.z)) %>% nrow(),
retire_soep %>% filter(retire_control == 1) %>% filter(!is.na(se.z)) %>% nrow(),
retire_soep %>% filter(retire_control == 0) %>% filter(!is.na(se.z)) %>% nrow())
))
names(assess) <- c("LISS", "MIDUS", "NLSY", "PAIRFAM", "SOEP")
assess$Total <- rowSums(assess, na.rm = TRUE)
assess$Group <- rep(c("Event group", "Control group"), 10)
assess <- select(assess, Group, LISS, MIDUS, NLSY, PAIRFAM, SOEP, Total)
kable(assess
, caption="**Number of assessments across different life events and panel studies**"
, escape=FALSE
, label = NA
, digits = 3) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
pack_rows(index = c("New relationship" = 2, "Marriage" = 2, "Childbirth" = 2,
"Separation" = 2, "Divorce" = 2, "Widowhood" = 2,
"Graduation" = 2, "New employment" = 2,
"Unemployment" = 2, "Retirement" = 2))| Group | LISS | MIDUS | NLSY | PAIRFAM | SOEP | Total |
|---|---|---|---|---|---|---|
| New relationship | ||||||
| Event group | 9096 | 256 | 5134 | 23257 | 13305 | 51048 |
| Control group | 57734 | 6503 | 22391 | 30482 | 58454 | 175564 |
| Marriage | ||||||
| Event group | 5942 | 558 | 8702 | 9231 | 6583 | 31016 |
| Control group | 60888 | 6201 | 18823 | 44508 | 65176 | 195596 |
| Childbirth | ||||||
| Event group | 4624 | 661 | 12988 | 12538 | 7204 | 38015 |
| Control group | 62206 | 6098 | 14537 | 41201 | 64555 | 188597 |
| Separation | ||||||
| Event group | 1223 | 2209 | 20050 | 2388 | 25870 | |
| Control group | 38201 | 10437 | 26992 | 41637 | 117267 | |
| Divorce | ||||||
| Event group | 2271 | 528 | 3112 | 2359 | 2649 | 10919 |
| Control group | 37400 | 5060 | 9634 | 20345 | 42044 | 114483 |
| Widowhood | ||||||
| Event group | 2080 | 555 | 2076 | 4711 | ||
| Control group | 37527 | 4973 | 42011 | 84511 | ||
| Graduation | ||||||
| Event group | 10709 | 10764 | 2397 | 5306 | 29176 | |
| Control group | 56121 | 16761 | 51342 | 66453 | 190677 | |
| New employment | ||||||
| Event group | 13477 | 31211 | 22976 | 67664 | ||
| Control group | 53353 | 22528 | 48783 | 124664 | ||
| Unemployment | ||||||
| Event group | 8985 | 3625 | 10669 | 9344 | 32623 | |
| Control group | 38183 | 17818 | 41189 | 39361 | 136551 | |
| Retirement | ||||||
| Event group | 9862 | 1768 | 8051 | 19681 | ||
| Control group | 40374 | 4092 | 57533 | 101999 | ||
Sample Characteristics
Overall Sample
hilda_unique <- long_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE)
hrs_unique <- long_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE)
liss_unique <- long_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE)
midus_unique <- long_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE)
nlsy_unique <- long_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE)
pair_unique <- long_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE)
soep_unique <- long_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique), paste0(round(describe(hilda_unique$age)$mean, 2), " (",
round(describe(hilda_unique$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique$education))["higher"]),4)*100),
c(nrow(hrs_unique), paste0(round(describe(hrs_unique$age)$mean, 2), " (",
round(describe(hrs_unique$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique$education))["higher"]),4)*100),
c(nrow(liss_unique), paste0(round(describe(liss_unique$age)$mean, 2), " (",
round(describe(liss_unique$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique$education))["higher"]),4)*100),
c(nrow(midus_unique), paste0(round(describe(midus_unique$age)$mean, 2), " (",
round(describe(midus_unique$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique$education))["higher"]),4)*100),
c(nrow(nlsy_unique), paste0(round(describe(nlsy_unique$age)$mean, 2), " (",
round(describe(nlsy_unique$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique$high.edu))["higher"]),4)*100),
c(nrow(pair_unique), paste0(round(describe(pair_unique$age)$mean, 2), " (",
round(describe(pair_unique$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique$education))["higher"]),4)*100),
c(nrow(soep_unique), paste0(round(describe(soep_unique$age)$mean, 2), " (",
round(describe(soep_unique$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "HRS", "LISS", "MIDUS", "NLSY", "PAIRFAM", "SOEP")
names(desc) <- c("N", "Mean age (SD)", "% Female", "% Higher education")
kable(desc
, caption="**Sample characteristics across events**"
, escape=FALSE
, label = NA
, digits = 3) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left")| N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|
| HILDA | 34501 | 36.05 (18.2) | 51.49 | 42.51 |
| HRS | 23846 | 66.46 (11.68) | 58.35 | 47.64 |
| LISS | 16482 | 44.7 (18.1) | 54.05 | 44.5 |
| MIDUS | 6452 | 47.06 (12.97) | 52.46 | 39.34 |
| NLSY | 8670 | 19.08 (4.57) | 48.87 | 24.47 |
| PAIRFAM | 12398 | 25.86 (8.34) | 51.39 | 28.16 |
| SOEP | 93907 | 39.18 (17.36) | 51.3 | 28.12 |
Event Specific
New Relationship
hilda_unique_event <- relbeg_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 1)
hilda_unique_control <- relbeg_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 0)
hrs_unique_event <- relbeg_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 1)
hrs_unique_control <- relbeg_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 0)
liss_unique_event <- relbeg_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 1)
liss_unique_control <- relbeg_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 0)
midus_unique_event <- relbeg_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 1)
midus_unique_control <- relbeg_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 0)
nlsy_unique_event <- relbeg_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 1)
nlsy_unique_control <- relbeg_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 0)
pair_unique_event <- relbeg_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 1)
pair_unique_control <- relbeg_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 0)
soep_unique_event <- relbeg_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 1)
soep_unique_control <- relbeg_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(relbeg_control == 0)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique_event), paste0(round(describe(hilda_unique_event$age)$mean, 2), " (",
round(describe(hilda_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_event$education))["higher"]),4)*100,
nrow(hilda_unique_control), paste0(round(describe(hilda_unique_control$age)$mean, 2), " (",
round(describe(hilda_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_control$education))["higher"]),4)*100),
c(nrow(hrs_unique_event), paste0(round(describe(hrs_unique_event$age)$mean, 2), " (",
round(describe(hrs_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_event$education))["higher"]),4)*100,
nrow(hrs_unique_control), paste0(round(describe(hrs_unique_control$age)$mean, 2), " (",
round(describe(hrs_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_control$education))["higher"]),4)*100),
c(nrow(liss_unique_event), paste0(round(describe(liss_unique_event$age)$mean, 2), " (",
round(describe(liss_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_event$education))["higher"]),4)*100,
nrow(liss_unique_control), paste0(round(describe(liss_unique_control$age)$mean, 2), " (",
round(describe(liss_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_control$education))["higher"]),4)*100),
c(nrow(midus_unique_event), paste0(round(describe(midus_unique_event$age)$mean, 2), " (",
round(describe(midus_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_event$education))["higher"]),4)*100,
nrow(midus_unique_control), paste0(round(describe(midus_unique_control$age)$mean, 2), " (",
round(describe(midus_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_control$education))["higher"]),4)*100),
c(nrow(nlsy_unique_event), paste0(round(describe(nlsy_unique_event$age)$mean, 2), " (",
round(describe(nlsy_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_event$education))["higher"]),4)*100,
nrow(nlsy_unique_control), paste0(round(describe(nlsy_unique_control$age)$mean, 2), " (",
round(describe(nlsy_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_control$education))["higher"]),4)*100),
c(nrow(pair_unique_event), paste0(round(describe(pair_unique_event$age)$mean, 2), " (",
round(describe(pair_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_event$education))["higher"]),4)*100,
nrow(pair_unique_control), paste0(round(describe(pair_unique_control$age)$mean, 2), " (",
round(describe(pair_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_control$education))["higher"]),4)*100),
c(nrow(soep_unique_event), paste0(round(describe(soep_unique_event$age)$mean, 2), " (",
round(describe(soep_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_event$education))["higher"]),4)*100,
nrow(soep_unique_control), paste0(round(describe(soep_unique_control$age)$mean, 2), " (",
round(describe(soep_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_control$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "HRS", "LISS", "MIDUS", "NLSY", "PAIRFAM", "SOEP")
kable(desc
, caption="**Sample characteristics for event new relationship**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = rep(c("N", "Mean age (SD)", "% Female", "% Higher education"), 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
add_header_above(c(" " = 1, "Event group" = 4, "Control group" = 4))| N | Mean age (SD) | % Female | % Higher education | N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|---|---|---|---|
| HILDA | 748 | 23.39 (9.69) | 50.27 | 25.4 | 33753 | 36.33 (18.25) | 51.51 | 42.89 |
| HRS | 597 | 60.67 (9.65) | 50.92 | 51.59 | 23249 | 66.61 (11.69) | 58.54 | 47.54 |
| LISS | 1533 | 31.9 (15.73) | 58.77 | 49.05 | 14949 | 46.02 (17.81) | 53.56 | 44.03 |
| MIDUS | 178 | 39.58 (10.84) | 50.56 | 42.7 | 6274 | 47.28 (12.96) | 52.52 | 39.24 |
| NLSY | 1521 | 18.69 (3.98) | 52.14 | 7149 | 19.17 (4.69) | 48.17 | ||
| PAIRFAM | 3204 | 20.63 (7.13) | 50.75 | 27.34 | 9194 | 27.69 (7.95) | 51.61 | 28.29 |
| SOEP | 8588 | 27.77 (13.41) | 55.67 | 19.78 | 85319 | 40.33 (17.3) | 50.86 | 28.96 |
Marriage
hilda_unique_event <- marriage_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 1)
hilda_unique_control <- marriage_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 0)
hrs_unique_event <- marriage_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 1)
hrs_unique_control <- marriage_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 0)
liss_unique_event <- marriage_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 1)
liss_unique_control <- marriage_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 0)
midus_unique_event <- marriage_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 1)
midus_unique_control <- marriage_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 0)
nlsy_unique_event <- marriage_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 1)
nlsy_unique_control <- marriage_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 0)
pair_unique_event <- marriage_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 1)
pair_unique_control <- marriage_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 0)
soep_unique_event <- marriage_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 1)
soep_unique_control <- marriage_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(marriage_control == 0)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique_event), paste0(round(describe(hilda_unique_event$age)$mean, 2), " (",
round(describe(hilda_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_event$education))["higher"]),4)*100,
nrow(hilda_unique_control), paste0(round(describe(hilda_unique_control$age)$mean, 2), " (",
round(describe(hilda_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_control$education))["higher"]),4)*100),
c(nrow(hrs_unique_event), paste0(round(describe(hrs_unique_event$age)$mean, 2), " (",
round(describe(hrs_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_event$education))["higher"]),4)*100,
nrow(hrs_unique_control), paste0(round(describe(hrs_unique_control$age)$mean, 2), " (",
round(describe(hrs_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_control$education))["higher"]),4)*100),
c(nrow(liss_unique_event), paste0(round(describe(liss_unique_event$age)$mean, 2), " (",
round(describe(liss_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_event$education))["higher"]),4)*100,
nrow(liss_unique_control), paste0(round(describe(liss_unique_control$age)$mean, 2), " (",
round(describe(liss_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_control$education))["higher"]),4)*100),
c(nrow(midus_unique_event), paste0(round(describe(midus_unique_event$age)$mean, 2), " (",
round(describe(midus_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_event$education))["higher"]),4)*100,
nrow(midus_unique_control), paste0(round(describe(midus_unique_control$age)$mean, 2), " (",
round(describe(midus_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_control$education))["higher"]),4)*100),
c(nrow(nlsy_unique_event), paste0(round(describe(nlsy_unique_event$age)$mean, 2), " (",
round(describe(nlsy_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_event$education))["higher"]),4)*100,
nrow(nlsy_unique_control), paste0(round(describe(nlsy_unique_control$age)$mean, 2), " (",
round(describe(nlsy_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_control$education))["higher"]),4)*100),
c(nrow(pair_unique_event), paste0(round(describe(pair_unique_event$age)$mean, 2), " (",
round(describe(pair_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_event$education))["higher"]),4)*100,
nrow(pair_unique_control), paste0(round(describe(pair_unique_control$age)$mean, 2), " (",
round(describe(pair_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_control$education))["higher"]),4)*100),
c(nrow(soep_unique_event), paste0(round(describe(soep_unique_event$age)$mean, 2), " (",
round(describe(soep_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_event$education))["higher"]),4)*100,
nrow(soep_unique_control), paste0(round(describe(soep_unique_control$age)$mean, 2), " (",
round(describe(soep_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_control$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "HRS", "LISS", "MIDUS", "NLSY", "PAIRFAM", "SOEP")
kable(desc
, caption="**Sample characteristics for event marriage**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = rep(c("N", "Mean age (SD)", "% Female", "% Higher education"), 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
add_header_above(c(" " = 1, "Event group" = 4, "Control group" = 4))| N | Mean age (SD) | % Female | % Higher education | N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|---|---|---|---|
| HILDA | 3146 | 28.86 (12.52) | 52.03 | 46.76 | 31355 | 36.78 (18.52) | 51.43 | 42.08 |
| HRS | 783 | 60.7 (9.73) | 51.21 | 56.45 | 23063 | 66.65 (11.69) | 58.59 | 47.34 |
| LISS | 1056 | 33.7 (13.03) | 54.73 | 54.74 | 15426 | 45.46 (18.15) | 54 | 43.79 |
| MIDUS | 321 | 41.77 (9.83) | 53.89 | 44.86 | 6131 | 47.34 (13.05) | 52.39 | 39.05 |
| NLSY | 2440 | 19.11 (4.01) | 53.2 | 6230 | 19.07 (4.78) | 47.17 | ||
| PAIRFAM | 1145 | 25.28 (6.77) | 52.23 | 35.67 | 11253 | 25.92 (8.48) | 51.3 | 27.35 |
| SOEP | 3545 | 29.01 (11.59) | 55.09 | 28.58 | 90362 | 39.58 (17.43) | 51.15 | 28.1 |
Childbirth
hilda_unique_event <- child_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 1)
hilda_unique_control <- child_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 0)
hrs_unique_event <- child_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 1)
hrs_unique_control <- child_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 0)
liss_unique_event <- child_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 1)
liss_unique_control <- child_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 0)
midus_unique_event <- child_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 1)
midus_unique_control <- child_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 0)
nlsy_unique_event <- child_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 1)
nlsy_unique_control <- child_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 0)
pair_unique_event <- child_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 1)
pair_unique_control <- child_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 0)
soep_unique_event <- child_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 1)
soep_unique_control <- child_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(child_control == 0)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique_event), paste0(round(describe(hilda_unique_event$age)$mean, 2), " (",
round(describe(hilda_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_event$education))["higher"]),4)*100,
nrow(hilda_unique_control), paste0(round(describe(hilda_unique_control$age)$mean, 2), " (",
round(describe(hilda_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_control$education))["higher"]),4)*100),
c(nrow(hrs_unique_event), paste0(round(describe(hrs_unique_event$age)$mean, 2), " (",
round(describe(hrs_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_event$education))["higher"]),4)*100,
nrow(hrs_unique_control), paste0(round(describe(hrs_unique_control$age)$mean, 2), " (",
round(describe(hrs_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_control$education))["higher"]),4)*100),
c(nrow(liss_unique_event), paste0(round(describe(liss_unique_event$age)$mean, 2), " (",
round(describe(liss_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_event$education))["higher"]),4)*100,
nrow(liss_unique_control), paste0(round(describe(liss_unique_control$age)$mean, 2), " (",
round(describe(liss_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_control$education))["higher"]),4)*100),
c(nrow(midus_unique_event), paste0(round(describe(midus_unique_event$age)$mean, 2), " (",
round(describe(midus_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_event$education))["higher"]),4)*100,
nrow(midus_unique_control), paste0(round(describe(midus_unique_control$age)$mean, 2), " (",
round(describe(midus_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_control$education))["higher"]),4)*100),
c(nrow(nlsy_unique_event), paste0(round(describe(nlsy_unique_event$age)$mean, 2), " (",
round(describe(nlsy_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_event$education))["higher"]),4)*100,
nrow(nlsy_unique_control), paste0(round(describe(nlsy_unique_control$age)$mean, 2), " (",
round(describe(nlsy_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_control$education))["higher"]),4)*100),
c(nrow(pair_unique_event), paste0(round(describe(pair_unique_event$age)$mean, 2), " (",
round(describe(pair_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_event$education))["higher"]),4)*100,
nrow(pair_unique_control), paste0(round(describe(pair_unique_control$age)$mean, 2), " (",
round(describe(pair_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_control$education))["higher"]),4)*100),
c(nrow(soep_unique_event), paste0(round(describe(soep_unique_event$age)$mean, 2), " (",
round(describe(soep_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_event$education))["higher"]),4)*100,
nrow(soep_unique_control), paste0(round(describe(soep_unique_control$age)$mean, 2), " (",
round(describe(soep_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_control$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "HRS", "LISS", "MIDUS", "NLSY", "PAIRFAM", "SOEP")
kable(desc
, caption="**Sample characteristics for event childbirth**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = rep(c("N", "Mean age (SD)", "% Female", "% Higher education"), 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
add_header_above(c(" " = 1, "Event group" = 4, "Control group" = 4))| N | Mean age (SD) | % Female | % Higher education | N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|---|---|---|---|
| HILDA | 4136 | 25.4 (7.54) | 53.68 | 51.21 | 30365 | 37.5 (18.74) | 51.19 | 41.32 |
| HRS | 1599 | 63.77 (11.24) | 54.78 | 44.78 | 22247 | 66.65 (11.68) | 58.6 | 47.85 |
| LISS | 818 | 37.68 (11.26) | 31.17 | 56.37 | 15664 | 45.07 (18.31) | 55.24 | 43.88 |
| MIDUS | 400 | 32.58 (6.4) | 45 | 60.75 | 6052 | 48.02 (12.72) | 52.96 | 37.92 |
| NLSY | 3554 | 19.57 (3.99) | 54.98 | 5116 | 18.74 (4.91) | 44.62 | ||
| PAIRFAM | 1584 | 26.54 (6.48) | 53.54 | 36.99 | 10814 | 25.77 (8.58) | 51.07 | 26.55 |
| SOEP | 4328 | 25.94 (7.3) | 54.81 | 32.36 | 89579 | 39.82 (17.45) | 51.13 | 27.91 |
Separation
hilda_unique_event <- separat_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 1)
hilda_unique_control <- separat_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 0)
hrs_unique_event <- separat_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 1)
hrs_unique_control <- separat_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 0)
liss_unique_event <- separat_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 1)
liss_unique_control <- separat_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 0)
nlsy_unique_event <- separat_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 1)
nlsy_unique_control <- separat_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 0)
pair_unique_event <- separat_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 1)
pair_unique_control <- separat_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 0)
soep_unique_event <- separat_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 1)
soep_unique_control <- separat_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(separat_control == 0)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique_event), paste0(round(describe(hilda_unique_event$age)$mean, 2), " (",
round(describe(hilda_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_event$education))["higher"]),4)*100,
nrow(hilda_unique_control), paste0(round(describe(hilda_unique_control$age)$mean, 2), " (",
round(describe(hilda_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_control$education))["higher"]),4)*100),
c(nrow(hrs_unique_event), paste0(round(describe(hrs_unique_event$age)$mean, 2), " (",
round(describe(hrs_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_event$education))["higher"]),4)*100,
nrow(hrs_unique_control), paste0(round(describe(hrs_unique_control$age)$mean, 2), " (",
round(describe(hrs_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_control$education))["higher"]),4)*100),
c(nrow(liss_unique_event), paste0(round(describe(liss_unique_event$age)$mean, 2), " (",
round(describe(liss_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_event$education))["higher"]),4)*100,
nrow(liss_unique_control), paste0(round(describe(liss_unique_control$age)$mean, 2), " (",
round(describe(liss_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_control$education))["higher"]),4)*100),
c(nrow(nlsy_unique_event), paste0(round(describe(nlsy_unique_event$age)$mean, 2), " (",
round(describe(nlsy_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_event$education))["higher"]),4)*100,
nrow(nlsy_unique_control), paste0(round(describe(nlsy_unique_control$age)$mean, 2), " (",
round(describe(nlsy_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_control$education))["higher"]),4)*100),
c(nrow(pair_unique_event), paste0(round(describe(pair_unique_event$age)$mean, 2), " (",
round(describe(pair_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_event$education))["higher"]),4)*100,
nrow(pair_unique_control), paste0(round(describe(pair_unique_control$age)$mean, 2), " (",
round(describe(pair_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_control$education))["higher"]),4)*100),
c(nrow(soep_unique_event), paste0(round(describe(soep_unique_event$age)$mean, 2), " (",
round(describe(soep_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_event$education))["higher"]),4)*100,
nrow(soep_unique_control), paste0(round(describe(soep_unique_control$age)$mean, 2), " (",
round(describe(soep_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_control$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "HRS", "LISS", "NLSY", "PAIRFAM", "SOEP")
kable(desc
, caption="**Sample characteristics for event separation**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = rep(c("N", "Mean age (SD)", "% Female", "% Higher education"), 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
add_header_above(c(" " = 1, "Event group" = 4, "Control group" = 4))| N | Mean age (SD) | % Female | % Higher education | N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|---|---|---|---|
| HILDA | 4032 | 29.97 (13.95) | 55.28 | 35.96 | 10684 | 41.86 (16.13) | 50.65 | 56.23 |
| HRS | 871 | 60.8 (10.5) | 54.19 | 48.11 | 1034 | 60.29 (9.88) | 51.16 | 47.58 |
| LISS | 185 | 46.37 (13.82) | 52.43 | 48.11 | 8155 | 50.66 (14.74) | 53.03 | 43.05 |
| NLSY | 522 | 21.44 (4.28) | 58.05 | 2824 | 20.15 (4.48) | 53.54 | ||
| PAIRFAM | 2723 | 21 (7.35) | 51.23 | 26.25 | 4936 | 28.8 (7.74) | 56.56 | 32.3 |
| SOEP | 1292 | 33.15 (11.15) | 58.59 | 31.19 | 32470 | 43.53 (15.12) | 49.98 | 37.68 |
Divorce
hilda_unique_event <- divor_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 1)
hilda_unique_control <- divor_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 0)
hrs_unique_event <- divor_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 1)
hrs_unique_control <- divor_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 0)
liss_unique_event <- divor_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 1)
liss_unique_control <- divor_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 0)
midus_unique_event <- divor_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 1)
midus_unique_control <- divor_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 0)
nlsy_unique_event <- divor_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 1)
nlsy_unique_control <- divor_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 0)
pair_unique_event <- divor_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 1)
pair_unique_control <- divor_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 0)
soep_unique_event <- divor_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 1)
soep_unique_control <- divor_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(divor_control == 0)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique_event), paste0(round(describe(hilda_unique_event$age)$mean, 2), " (",
round(describe(hilda_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_event$education))["higher"]),4)*100,
nrow(hilda_unique_control), paste0(round(describe(hilda_unique_control$age)$mean, 2), " (",
round(describe(hilda_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_control$education))["higher"]),4)*100),
c(nrow(hrs_unique_event), paste0(round(describe(hrs_unique_event$age)$mean, 2), " (",
round(describe(hrs_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_event$education))["higher"]),4)*100,
nrow(hrs_unique_control), paste0(round(describe(hrs_unique_control$age)$mean, 2), " (",
round(describe(hrs_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_control$education))["higher"]),4)*100),
c(nrow(liss_unique_event), paste0(round(describe(liss_unique_event$age)$mean, 2), " (",
round(describe(liss_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_event$education))["higher"]),4)*100,
nrow(liss_unique_control), paste0(round(describe(liss_unique_control$age)$mean, 2), " (",
round(describe(liss_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_control$education))["higher"]),4)*100),
c(nrow(midus_unique_event), paste0(round(describe(midus_unique_event$age)$mean, 2), " (",
round(describe(midus_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_event$education))["higher"]),4)*100,
nrow(midus_unique_control), paste0(round(describe(midus_unique_control$age)$mean, 2), " (",
round(describe(midus_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_control$education))["higher"]),4)*100),
c(nrow(nlsy_unique_event), paste0(round(describe(nlsy_unique_event$age)$mean, 2), " (",
round(describe(nlsy_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_event$education))["higher"]),4)*100,
nrow(nlsy_unique_control), paste0(round(describe(nlsy_unique_control$age)$mean, 2), " (",
round(describe(nlsy_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_control$education))["higher"]),4)*100),
c(nrow(pair_unique_event), paste0(round(describe(pair_unique_event$age)$mean, 2), " (",
round(describe(pair_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_event$education))["higher"]),4)*100,
nrow(pair_unique_control), paste0(round(describe(pair_unique_control$age)$mean, 2), " (",
round(describe(pair_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_control$education))["higher"]),4)*100),
c(nrow(soep_unique_event), paste0(round(describe(soep_unique_event$age)$mean, 2), " (",
round(describe(soep_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_event$education))["higher"]),4)*100,
nrow(soep_unique_control), paste0(round(describe(soep_unique_control$age)$mean, 2), " (",
round(describe(soep_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_control$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "HRS", "LISS", "MIDUS", "NLSY", "PAIRFAM", "SOEP")
kable(desc
, caption="**Sample characteristics for event divorce**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = rep(c("N", "Mean age (SD)", "% Female", "% Higher education"), 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
add_header_above(c(" " = 1, "Event group" = 4, "Control group" = 4))| N | Mean age (SD) | % Female | % Higher education | N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|---|---|---|---|
| HILDA | 913 | 38.54 (13.81) | 59.69 | 51.04 | 11811 | 41.06 (16.17) | 50.82 | 54.88 |
| HRS | 1054 | 63.29 (10.53) | 63.76 | 47.06 | 14802 | 65.49 (11) | 50.84 | 50.96 |
| LISS | 369 | 42.33 (13) | 54.74 | 46.47 | 8029 | 50.9 (14.71) | 53.01 | 43.09 |
| MIDUS | 349 | 39.82 (9.76) | 57.88 | 36.96 | 4482 | 47.11 (12.61) | 48.24 | 41.06 |
| NLSY | 742 | 21.48 (4.26) | 57.95 | 2628 | 20.08 (4.5) | 52.78 | ||
| PAIRFAM | 291 | 30.42 (6.66) | 63.57 | 29.25 | 3272 | 31.76 (5.81) | 57.64 | 33.43 |
| SOEP | 1435 | 34.57 (11.47) | 60.11 | 32.04 | 32773 | 43.44 (15.12) | 50.05 | 37.61 |
Widowhood
hilda_unique_event <- widow_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(widow_control == 1)
hilda_unique_control <- widow_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(widow_control == 0)
hrs_unique_event <- widow_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(widow_control == 1)
hrs_unique_control <- widow_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(widow_control == 0)
liss_unique_event <- widow_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(widow_control == 1)
liss_unique_control <- widow_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(widow_control == 0)
midus_unique_event <- widow_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(widow_control == 1)
midus_unique_control <- widow_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(widow_control == 0)
soep_unique_event <- widow_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(widow_control == 1)
soep_unique_control <- widow_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(widow_control == 0)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique_event), paste0(round(describe(hilda_unique_event$age)$mean, 2), " (",
round(describe(hilda_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_event$education))["higher"]),4)*100,
nrow(hilda_unique_control), paste0(round(describe(hilda_unique_control$age)$mean, 2), " (",
round(describe(hilda_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_control$education))["higher"]),4)*100),
c(nrow(hrs_unique_event), paste0(round(describe(hrs_unique_event$age)$mean, 2), " (",
round(describe(hrs_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_event$education))["higher"]),4)*100,
nrow(hrs_unique_control), paste0(round(describe(hrs_unique_control$age)$mean, 2), " (",
round(describe(hrs_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_control$education))["higher"]),4)*100),
c(nrow(liss_unique_event), paste0(round(describe(liss_unique_event$age)$mean, 2), " (",
round(describe(liss_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_event$education))["higher"]),4)*100,
nrow(liss_unique_control), paste0(round(describe(liss_unique_control$age)$mean, 2), " (",
round(describe(liss_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_control$education))["higher"]),4)*100),
c(nrow(midus_unique_event), paste0(round(describe(midus_unique_event$age)$mean, 2), " (",
round(describe(midus_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_event$education))["higher"]),4)*100,
nrow(midus_unique_control), paste0(round(describe(midus_unique_control$age)$mean, 2), " (",
round(describe(midus_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_control$education))["higher"]),4)*100),
c(nrow(soep_unique_event), paste0(round(describe(soep_unique_event$age)$mean, 2), " (",
round(describe(soep_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_event$education))["higher"]),4)*100,
nrow(soep_unique_control), paste0(round(describe(soep_unique_control$age)$mean, 2), " (",
round(describe(soep_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_control$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "HRS", "LISS", "MIDUS", "SOEP")
kable(desc
, caption="**Sample characteristics for event widowhood**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = rep(c("N", "Mean age (SD)", "% Female", "% Higher education"), 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
add_header_above(c(" " = 1, "Event group" = 4, "Control group" = 4))| N | Mean age (SD) | % Female | % Higher education | N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|---|---|---|---|
| HILDA | 812 | 59.6 (13.21) | 74.51 | 36.25 | 11783 | 39.68 (15.51) | 49.85 | 55.7 |
| HRS | 2968 | 72.03 (10.04) | 72.04 | 38.95 | 12822 | 64.05 (10.7) | 47.4 | 52.96 |
| LISS | 271 | 66.17 (11.22) | 61.25 | 35.69 | 8092 | 50.09 (14.56) | 52.79 | 43.38 |
| MIDUS | 346 | 57.59 (10.12) | 76.3 | 34.78 | 4442 | 45.81 (12.36) | 46.78 | 41.38 |
| SOEP | 1038 | 54.96 (13.55) | 70.04 | 25.83 | 32751 | 42.77 (15.02) | 49.73 | 37.78 |
Graduation
hilda_unique_event <- gradu_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(gradu_control == 1)
hilda_unique_control <- gradu_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(gradu_control == 0)
liss_unique_event <- gradu_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(gradu_control == 1)
liss_unique_control <- gradu_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(gradu_control == 0)
nlsy_unique_event <- gradu_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(gradu_control == 1)
nlsy_unique_control <- gradu_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(gradu_control == 0)
pair_unique_event <- gradu_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(gradu_control == 1)
pair_unique_control <- gradu_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(gradu_control == 0)
soep_unique_event <- gradu_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(gradu_control == 1)
soep_unique_control <- gradu_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(gradu_control == 0)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique_event), paste0(round(describe(hilda_unique_event$age)$mean, 2), " (",
round(describe(hilda_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_event$education))["higher"]),4)*100,
nrow(hilda_unique_control), paste0(round(describe(hilda_unique_control$age)$mean, 2), " (",
round(describe(hilda_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_control$education))["higher"]),4)*100),
c(nrow(liss_unique_event), paste0(round(describe(liss_unique_event$age)$mean, 2), " (",
round(describe(liss_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_event$education))["higher"]),4)*100,
nrow(liss_unique_control), paste0(round(describe(liss_unique_control$age)$mean, 2), " (",
round(describe(liss_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_control$education))["higher"]),4)*100),
c(nrow(nlsy_unique_event), paste0(round(describe(nlsy_unique_event$age)$mean, 2), " (",
round(describe(nlsy_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_event$education))["higher"]),4)*100,
nrow(nlsy_unique_control), paste0(round(describe(nlsy_unique_control$age)$mean, 2), " (",
round(describe(nlsy_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_control$education))["higher"]),4)*100),
c(nrow(pair_unique_event), paste0(round(describe(pair_unique_event$age)$mean, 2), " (",
round(describe(pair_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_event$education))["higher"]),4)*100,
nrow(pair_unique_control), paste0(round(describe(pair_unique_control$age)$mean, 2), " (",
round(describe(pair_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_control$education))["higher"]),4)*100),
c(nrow(soep_unique_event), paste0(round(describe(soep_unique_event$age)$mean, 2), " (",
round(describe(soep_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_event$education))["higher"]),4)*100,
nrow(soep_unique_control), paste0(round(describe(soep_unique_control$age)$mean, 2), " (",
round(describe(soep_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_control$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "LISS", "NLSY", "PAIRFAM", "SOEP")
kable(desc
, caption="**Sample characteristics for event graduation**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = rep(c("N", "Mean age (SD)", "% Female", "% Higher education"), 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
add_header_above(c(" " = 1, "Event group" = 4, "Control group" = 4))| N | Mean age (SD) | % Female | % Higher education | N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|---|---|---|---|
| HILDA | 3890 | 23.56 (9.58) | 59.69 | 25.66 | 30611 | 37.64 (18.42) | 50.44 | 44.65 |
| LISS | 2345 | 27.07 (15.4) | 56.2 | 38.09 | 14137 | 47.65 (16.8) | 53.69 | 45.57 |
| NLSY | 4081 | 16.58 (3) | 53.37 | 4589 | 21.32 (4.57) | 44.87 | ||
| PAIRFAM | 307 | 23.29 (6.8) | 45.93 | 9.73 | 12091 | 25.93 (8.37) | 51.53 | 28.62 |
| SOEP | 3744 | 19.18 (5.49) | 52.4 | 4.57 | 90163 | 40.02 (17.19) | 51.26 | 29.1 |
New Employment
hilda_unique_event <- jobbeg_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(jobbeg_control == 1)
hilda_unique_control <- jobbeg_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(jobbeg_control == 0)
hrs_unique_event <- jobbeg_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(jobbeg_control == 1)
hrs_unique_control <- jobbeg_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(jobbeg_control == 0)
liss_unique_event <- jobbeg_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(jobbeg_control == 1)
liss_unique_control <- jobbeg_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(jobbeg_control == 0)
pair_unique_event <- jobbeg_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(jobbeg_control == 1)
pair_unique_control <- jobbeg_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(jobbeg_control == 0)
soep_unique_event <- jobbeg_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(jobbeg_control == 1)
soep_unique_control <- jobbeg_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(jobbeg_control == 0)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique_event), paste0(round(describe(hilda_unique_event$age)$mean, 2), " (",
round(describe(hilda_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_event$education))["higher"]),4)*100,
nrow(hilda_unique_control), paste0(round(describe(hilda_unique_control$age)$mean, 2), " (",
round(describe(hilda_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_control$education))["higher"]),4)*100),
c(nrow(hrs_unique_event), paste0(round(describe(hrs_unique_event$age)$mean, 2), " (",
round(describe(hrs_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_event$education))["higher"]),4)*100,
nrow(hrs_unique_control), paste0(round(describe(hrs_unique_control$age)$mean, 2), " (",
round(describe(hrs_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_control$education))["higher"]),4)*100),
c(nrow(liss_unique_event), paste0(round(describe(liss_unique_event$age)$mean, 2), " (",
round(describe(liss_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_event$education))["higher"]),4)*100,
nrow(liss_unique_control), paste0(round(describe(liss_unique_control$age)$mean, 2), " (",
round(describe(liss_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_control$education))["higher"]),4)*100),
c(nrow(pair_unique_event), paste0(round(describe(pair_unique_event$age)$mean, 2), " (",
round(describe(pair_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_event$education))["higher"]),4)*100,
nrow(pair_unique_control), paste0(round(describe(pair_unique_control$age)$mean, 2), " (",
round(describe(pair_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_control$education))["higher"]),4)*100),
c(nrow(soep_unique_event), paste0(round(describe(soep_unique_event$age)$mean, 2), " (",
round(describe(soep_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_event$education))["higher"]),4)*100,
nrow(soep_unique_control), paste0(round(describe(soep_unique_control$age)$mean, 2), " (",
round(describe(soep_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_control$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "HRS", "LISS", "PAIRFAM", "SOEP")
kable(desc
, caption="**Sample characteristics for event new employment**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = rep(c("N", "Mean age (SD)", "% Female", "% Higher education"), 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
add_header_above(c(" " = 1, "Event group" = 4, "Control group" = 4))| N | Mean age (SD) | % Female | % Higher education | N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|---|---|---|---|
| HILDA | 10135 | 28.03 (11.56) | 52.3 | 43.06 | 24366 | 39.39 (19.38) | 51.15 | 42.28 |
| HRS | 2345 | 62.08 (9.36) | 56.42 | 53.9 | 21501 | 66.94 (11.8) | 58.56 | 46.96 |
| LISS | 2652 | 30.92 (13.43) | 58.9 | 49.28 | 13830 | 47.36 (17.67) | 53.12 | 43.58 |
| PAIRFAM | 4043 | 23.8 (8.14) | 56.34 | 29.9 | 8355 | 26.86 (8.25) | 48.99 | 27.55 |
| SOEP | 14188 | 29.99 (12.22) | 62.31 | 26.68 | 79719 | 40.82 (17.63) | 49.34 | 28.37 |
Unemployment
hilda_unique_event <- unemploy_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 1)
hilda_unique_control <- unemploy_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 0)
hrs_unique_event <- unemploy_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 1)
hrs_unique_control <- unemploy_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 0)
liss_unique_event <- unemploy_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 1)
liss_unique_control <- unemploy_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 0)
nlsy_unique_event <- unemploy_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 1)
nlsy_unique_control <- unemploy_nlsy %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 0)
pair_unique_event <- unemploy_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 1)
pair_unique_control <- unemploy_pair %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 0)
soep_unique_event <- unemploy_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 1)
soep_unique_control <- unemploy_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(unemploy_control == 0)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique_event), paste0(round(describe(hilda_unique_event$age)$mean, 2), " (",
round(describe(hilda_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_event$education))["higher"]),4)*100,
nrow(hilda_unique_control), paste0(round(describe(hilda_unique_control$age)$mean, 2), " (",
round(describe(hilda_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_control$education))["higher"]),4)*100),
c(nrow(hrs_unique_event), paste0(round(describe(hrs_unique_event$age)$mean, 2), " (",
round(describe(hrs_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_event$education))["higher"]),4)*100,
nrow(hrs_unique_control), paste0(round(describe(hrs_unique_control$age)$mean, 2), " (",
round(describe(hrs_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_control$education))["higher"]),4)*100),
c(nrow(liss_unique_event), paste0(round(describe(liss_unique_event$age)$mean, 2), " (",
round(describe(liss_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_event$education))["higher"]),4)*100,
nrow(liss_unique_control), paste0(round(describe(liss_unique_control$age)$mean, 2), " (",
round(describe(liss_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_control$education))["higher"]),4)*100),
c(nrow(nlsy_unique_event), paste0(round(describe(nlsy_unique_event$age)$mean, 2), " (",
round(describe(nlsy_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_event$education))["higher"]),4)*100,
nrow(nlsy_unique_control), paste0(round(describe(nlsy_unique_control$age)$mean, 2), " (",
round(describe(nlsy_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(nlsy_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(nlsy_unique_control$education))["higher"]),4)*100),
c(nrow(pair_unique_event), paste0(round(describe(pair_unique_event$age)$mean, 2), " (",
round(describe(pair_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_event$education))["higher"]),4)*100,
nrow(pair_unique_control), paste0(round(describe(pair_unique_control$age)$mean, 2), " (",
round(describe(pair_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(pair_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(pair_unique_control$education))["higher"]),4)*100),
c(nrow(soep_unique_event), paste0(round(describe(soep_unique_event$age)$mean, 2), " (",
round(describe(soep_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_event$education))["higher"]),4)*100,
nrow(soep_unique_control), paste0(round(describe(soep_unique_control$age)$mean, 2), " (",
round(describe(soep_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_control$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "HRS", "LISS", "NLSY", "PAIRFAM", "SOEP")
kable(desc
, caption="**Sample characteristics for event unemployment**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = rep(c("N", "Mean age (SD)", "% Female", "% Higher education"), 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
add_header_above(c(" " = 1, "Event group" = 4, "Control group" = 4))| N | Mean age (SD) | % Female | % Higher education | N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|---|---|---|---|
| HILDA | 3920 | 31.46 (13.11) | 44.49 | 43.67 | 15565 | 31.63 (13.84) | 52.09 | 47.73 |
| HRS | 840 | 58.76 (8.05) | 55 | 56.02 | 10944 | 60.74 (8.92) | 54.7 | 58.26 |
| LISS | 1551 | 35.86 (13.86) | 54.03 | 47.26 | 10293 | 38.2 (14.38) | 51.96 | 48.18 |
| NLSY | 1122 | 18.14 (3.67) | 60.07 | 5228 | 19.21 (4.4) | 48.74 | ||
| PAIRFAM | 1410 | 23.65 (7.9) | 49.43 | 19.12 | 7419 | 25.42 (8.61) | 51.14 | 32.78 |
| SOEP | 5536 | 30.01 (12.17) | 54.57 | 20.4 | 32921 | 35.91 (13.23) | 48.08 | 37.94 |
Retirement
hilda_unique_event <- retire_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(retire_control == 1)
hilda_unique_control <- retire_hilda %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(retire_control == 0)
hrs_unique_event <- retire_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(retire_control == 1)
hrs_unique_control <- retire_hrs %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(retire_control == 0)
liss_unique_event <- retire_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(retire_control == 1)
liss_unique_control <- retire_liss %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(retire_control == 0)
midus_unique_event <- retire_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(retire_control == 1)
midus_unique_control <- retire_midus %>% arrange(wave) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(retire_control == 0)
soep_unique_event <- retire_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(retire_control == 1)
soep_unique_control <- retire_soep %>% arrange(syear) %>% filter(duplicated(ID_pers) == FALSE) %>% filter(retire_control == 0)
desc <- as.data.frame(rbind(
c(nrow(hilda_unique_event), paste0(round(describe(hilda_unique_event$age)$mean, 2), " (",
round(describe(hilda_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_event$education))["higher"]),4)*100,
nrow(hilda_unique_control), paste0(round(describe(hilda_unique_control$age)$mean, 2), " (",
round(describe(hilda_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hilda_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hilda_unique_control$education))["higher"]),4)*100),
c(nrow(hrs_unique_event), paste0(round(describe(hrs_unique_event$age)$mean, 2), " (",
round(describe(hrs_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_event$education))["higher"]),4)*100,
nrow(hrs_unique_control), paste0(round(describe(hrs_unique_control$age)$mean, 2), " (",
round(describe(hrs_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(hrs_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(hrs_unique_control$education))["higher"]),4)*100),
c(nrow(liss_unique_event), paste0(round(describe(liss_unique_event$age)$mean, 2), " (",
round(describe(liss_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_event$education))["higher"]),4)*100,
nrow(liss_unique_control), paste0(round(describe(liss_unique_control$age)$mean, 2), " (",
round(describe(liss_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(liss_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(liss_unique_control$education))["higher"]),4)*100),
c(nrow(midus_unique_event), paste0(round(describe(midus_unique_event$age)$mean, 2), " (",
round(describe(midus_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_event$education))["higher"]),4)*100,
nrow(midus_unique_control), paste0(round(describe(midus_unique_control$age)$mean, 2), " (",
round(describe(midus_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(midus_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(midus_unique_control$education))["higher"]),4)*100),
c(nrow(soep_unique_event), paste0(round(describe(soep_unique_event$age)$mean, 2), " (",
round(describe(soep_unique_event$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_event$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_event$education))["higher"]),4)*100,
nrow(soep_unique_control), paste0(round(describe(soep_unique_control$age)$mean, 2), " (",
round(describe(soep_unique_control$age)$sd, 2), ")"),
round(as.numeric(prop.table(table(soep_unique_control$gender))["Female"]),4)*100,
round(as.numeric(prop.table(table(soep_unique_control$education))["higher"]),4)*100)
))
row.names(desc) <- c("HILDA", "HRS", "LISS", "MIDUS", "SOEP")
kable(desc
, caption="**Sample characteristics for event retirement**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = rep(c("N", "Mean age (SD)", "% Female", "% Higher education"), 2)) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
add_header_above(c(" " = 1, "Event group" = 4, "Control group" = 4))| N | Mean age (SD) | % Female | % Higher education | N | Mean age (SD) | % Female | % Higher education | |
|---|---|---|---|---|---|---|---|---|
| HILDA | 3249 | 51.14 (12.4) | 51.46 | 50.72 | 17423 | 28.89 (12.14) | 50.4 | 44.76 |
| HRS | 5298 | 61.95 (7.68) | 59.23 | 54.11 | 5506 | 55.61 (7.26) | 59.63 | 57.86 |
| LISS | 1269 | 60.53 (6.91) | 50.51 | 40.5 | 11132 | 36.28 (13.42) | 52.93 | 48.08 |
| MIDUS | 1088 | 53.7 (7.36) | 54.69 | 40.61 | 4184 | 41.56 (10.67) | 49.31 | 42.46 |
| SOEP | 3781 | 46.83 (10.4) | 52.18 | 35.25 | 51130 | 39.13 (17.35) | 51.91 | 30.9 |
Outcome Variables
HILDA
Table
pers_hilda <- long_hilda %>% ungroup() %>% dplyr::select(agree, consc, emost, extra,
open, ls) %>% describe() %>% dplyr::select(n, mean, sd, min, max)
row.names(pers_hilda) <- c("Agreeableness", "Conscientiousness", "Emotional stability",
"Extraversion", "Openness", "Life satisfaction")
kable(pers_hilda
, caption="**Descriptive statistics of our outcome variables across individuals and assessments**"
, escape=FALSE
, label = NA
, digits = 2
, col.names = c("*N*", "*M*", "*SD*", "*Min*", "*Max*")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left")| N | M | SD | Min | Max | |
|---|---|---|---|---|---|
| Agreeableness | 68162 | 5.17 | 0.92 | 1 | 7 |
| Conscientiousness | 67517 | 5.09 | 1.03 | 1 | 7 |
| Emotional stability | 67577 | 5.20 | 1.09 | 1 | 7 |
| Extraversion | 67292 | 4.42 | 1.08 | 1 | 7 |
| Openness | 67251 | 4.20 | 1.07 | 1 | 7 |
| Life satisfaction | 337416 | 7.92 | 1.47 | 0 | 10 |
Distribution
pers_hilda <- long_hilda %>% ungroup() %>%
select(ID_pers, extra.z, consc.z, agree.z,
emost.z, open.z, ls.z) %>%
pivot_longer(cols = -ID_pers)
pers_hilda$name <- factor(pers_hilda$name, levels = c("se.z", "ls.z", "open.z", "extra.z",
"emost.z","consc.z", "agree.z"),
labels = c("Self-esteem", "Life satisfaction","Openness",
"Extraversion", "Emotional stability",
"Conscientiousness", "Agreeableness"), ordered = TRUE)
ggplot(data = pers_hilda, aes(y = name, x = value, fill = name)) +
geom_density_ridges(scale = 0.9) +
labs(y = "Trait", x = "Score") +
theme_pub() +
theme(legend.position = "none")
HRS
Table
pers_hrs <- long_hrs %>% ungroup() %>% dplyr::select(agree, consc, emost, extra,
open, ls) %>% describe() %>% dplyr::select(n, mean, sd, min, max)
row.names(pers_hrs) <- c("Agreeableness", "Conscientiousness", "Emotional stability",
"Extraversion", "Openness", "Life satisfaction")
kable(pers_hrs
, caption="**Descriptive statistics of our outcome variables across individuals and assessments**"
, escape=FALSE
, label = NA
, digits = 2
, col.names = c("*N*", "*M*", "*SD*", "*Min*", "*Max*")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left")| N | M | SD | Min | Max | |
|---|---|---|---|---|---|
| Agreeableness | 52152 | 3.53 | 0.48 | 1 | 4 |
| Conscientiousness | 51521 | 3.36 | 0.49 | 1 | 4 |
| Emotional stability | 52578 | 2.99 | 0.62 | 1 | 4 |
| Extraversion | 52089 | 3.06 | 0.63 | 1 | 4 |
| Openness | 50538 | 2.93 | 0.57 | 1 | 4 |
| Life satisfaction | 45367 | 4.93 | 1.53 | 1 | 7 |
Distribution
pers_hrs <- long_hrs %>% ungroup() %>%
select(ID_pers, extra.z, consc.z, agree.z,
emost.z, open.z, ls.z) %>%
pivot_longer(cols = -ID_pers)
pers_hrs$name <- factor(pers_hrs$name, levels = c("se.z", "ls.z", "open.z", "extra.z",
"emost.z","consc.z", "agree.z"),
labels = c("Self-esteem", "Life satisfaction","Openness",
"Extraversion", "Emotional stability",
"Conscientiousness", "Agreeableness"), ordered = TRUE)
ggplot(data = pers_hrs, aes(y = name, x = value, fill = name)) +
geom_density_ridges(scale = 0.9) +
labs(y = "Trait", x = "Score") +
theme_pub() +
theme(legend.position = "none")
LISS
Table
pers_liss <- long_liss %>% ungroup() %>% dplyr::select(agree, consc, emost, extra,
open, ls, se) %>% describe() %>% dplyr::select(n, mean, sd, min, max)
row.names(pers_liss) <- c("Agreeableness", "Conscientiousness", "Emotional stability",
"Extraversion", "Openness", "Life satisfaction", "Self-esteem")
kable(pers_liss
, caption="**Descriptive statistics of our outcome variables across individuals and assessments**"
, escape=FALSE
, label = NA
, digits = 2
, col.names = c("*N*", "*M*", "*SD*", "*Min*", "*Max*")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left")| N | M | SD | Min | Max | |
|---|---|---|---|---|---|
| Agreeableness | 66896 | 3.86 | 0.51 | 1.2 | 5 |
| Conscientiousness | 66896 | 3.71 | 0.53 | 1.1 | 5 |
| Emotional stability | 66896 | 3.45 | 0.70 | 1.0 | 5 |
| Extraversion | 66896 | 3.24 | 0.66 | 1.0 | 5 |
| Openness | 66896 | 3.49 | 0.50 | 1.0 | 5 |
| Life satisfaction | 86800 | 5.06 | 1.11 | 1.0 | 7 |
| Self-esteem | 66830 | 5.54 | 1.02 | 1.0 | 7 |
Distribution
pers_liss <- long_liss %>% ungroup() %>%
select(ID_pers, extra.z, consc.z, agree.z,
emost.z, open.z, ls.z, se.z) %>%
pivot_longer(cols = -ID_pers)
pers_liss$name <- factor(pers_liss$name, levels = c("se.z", "ls.z", "open.z", "extra.z",
"emost.z","consc.z", "agree.z"),
labels = c("Self-esteem", "Life satisfaction","Openness",
"Extraversion", "Emotional stability",
"Conscientiousness", "Agreeableness"), ordered = TRUE)
ggplot(data = pers_liss, aes(y = name, x = value, fill = name)) +
geom_density_ridges(scale = 0.9) +
labs(y = "Trait", x = "Score") +
theme_pub() +
theme(legend.position = "none")
MIDUS
Table
pers_midus <- long_midus %>% ungroup() %>% dplyr::select(agree, consc, emost, extra,
open, ls, se) %>% describe() %>% dplyr::select(n, mean, sd, min, max)
row.names(pers_midus) <- c("Agreeableness", "Conscientiousness", "Emotional stability",
"Extraversion", "Openness", "Life satisfaction", "Self-esteem")
kable(pers_midus
, caption="**Descriptive statistics of our outcome variables across individuals and assessments**"
, escape=FALSE
, label = NA
, digits = 2
, col.names = c("*N*", "*M*", "*SD*", "*Min*", "*Max*")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left")| N | M | SD | Min | Max | |
|---|---|---|---|---|---|
| Agreeableness | 12966 | 3.49 | 0.48 | 1.00 | 4 |
| Conscientiousness | 12985 | 3.44 | 0.45 | 1.00 | 4 |
| Emotional stability | 12934 | 2.85 | 0.65 | 1.00 | 4 |
| Extraversion | 12970 | 3.03 | 0.63 | 1.00 | 4 |
| Openness | 12807 | 2.96 | 0.54 | 1.00 | 4 |
| Life satisfaction | 13057 | 7.83 | 1.59 | 0.00 | 10 |
| Self-esteem | 6759 | 5.39 | 1.04 | 1.57 | 7 |
Distribution
pers_midus <- long_midus %>% ungroup() %>%
select(ID_pers, extra.z, consc.z, agree.z,
emost.z, open.z, ls.z, se.z) %>%
pivot_longer(cols = -ID_pers)
pers_midus$name <- factor(pers_midus$name, levels = c("se.z", "ls.z", "open.z", "extra.z",
"emost.z","consc.z", "agree.z"),
labels = c("Self-esteem", "Life satisfaction","Openness",
"Extraversion", "Emotional stability",
"Conscientiousness", "Agreeableness"), ordered = TRUE)
ggplot(data = pers_midus, aes(y = name, x = value, fill = name)) +
geom_density_ridges(scale = 0.9) +
labs(y = "Trait", x = "Score") +
theme_pub() +
theme(legend.position = "none")
NLSY
Table
pers_nlsy <- long_nlsy %>% ungroup() %>% dplyr::select(agree, consc, emost, extra,
open, ls, se) %>% describe() %>% dplyr::select(n, mean, sd, min, max)
row.names(pers_nlsy) <- c("Agreeableness", "Conscientiousness", "Emotional stability",
"Extraversion", "Openness", "Life satisfaction", "Self-esteem")
kable(pers_nlsy
, caption="**Descriptive statistics of our outcome variables across individuals and assessments**"
, escape=FALSE
, label = NA
, digits = 2
, col.names = c("*N*", "*M*", "*SD*", "*Min*", "*Max*")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left")| N | M | SD | Min | Max | |
|---|---|---|---|---|---|
| Agreeableness | 23152 | 5.04 | 1.18 | 1 | 7 |
| Conscientiousness | 23479 | 5.69 | 1.19 | 1 | 7 |
| Emotional stability | 23468 | 5.06 | 1.33 | 1 | 7 |
| Extraversion | 23347 | 4.61 | 1.40 | 1 | 7 |
| Openness | 23439 | 5.43 | 1.18 | 1 | 7 |
| Life satisfaction | 15229 | 4.09 | 0.91 | 1 | 5 |
| Self-esteem | 27525 | 3.15 | 0.51 | 1 | 4 |
Distribution
pers_nlsy <- long_nlsy %>% ungroup() %>%
select(ID_pers, extra.z, consc.z, agree.z,
emost.z, open.z, ls.z, se.z) %>%
pivot_longer(cols = -ID_pers)
pers_nlsy$name <- factor(pers_nlsy$name, levels = c("se.z", "ls.z", "open.z", "extra.z",
"emost.z","consc.z", "agree.z"),
labels = c("Self-esteem", "Life satisfaction","Openness",
"Extraversion", "Emotional stability",
"Conscientiousness", "Agreeableness"), ordered = TRUE)
ggplot(data = pers_nlsy, aes(y = name, x = value, fill = name)) +
geom_density_ridges(scale = 0.9) +
labs(y = "Trait", x = "Score") +
theme_pub() +
theme(legend.position = "none")
PAIRFAM
Table
pers_pair <- long_pair %>% ungroup() %>% dplyr::select(agree, consc, emost, extra,
open, ls, se) %>% describe() %>% dplyr::select(n, mean, sd, min, max)
row.names(pers_pair) <- c("Agreeableness", "Conscientiousness", "Emotional stability",
"Extraversion", "Openness", "Life satisfaction", "Self-esteem")
kable(pers_pair
, caption="**Descriptive statistics of our outcome variables across individuals and assessments**"
, escape=FALSE
, label = NA
, digits = 2
, col.names = c("*N*", "*M*", "*SD*", "*Min*", "*Max*")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left")| N | M | SD | Min | Max | |
|---|---|---|---|---|---|
| Agreeableness | 18191 | 3.25 | 0.72 | 1 | 5 |
| Conscientiousness | 18345 | 3.79 | 0.66 | 1 | 5 |
| Emotional stability | 18368 | 3.30 | 0.80 | 1 | 5 |
| Extraversion | 18203 | 3.53 | 0.82 | 1 | 5 |
| Openness | 18131 | 3.64 | 0.69 | 1 | 5 |
| Life satisfaction | 66622 | 7.58 | 1.68 | 0 | 10 |
| Self-esteem | 53739 | 3.89 | 0.84 | 1 | 5 |
Distribution
pers_pair <- long_pair %>% ungroup() %>%
select(ID_pers, extra.z, consc.z, agree.z,
emost.z, open.z, ls.z, se.z) %>%
pivot_longer(cols = -ID_pers)
pers_pair$name <- factor(pers_pair$name, levels = c("se.z", "ls.z", "open.z", "extra.z",
"emost.z","consc.z", "agree.z"),
labels = c("Self-esteem", "Life satisfaction","Openness",
"Extraversion", "Emotional stability",
"Conscientiousness", "Agreeableness"), ordered = TRUE)
ggplot(data = pers_pair, aes(y = name, x = value, fill = name)) +
geom_density_ridges(scale = 0.9) +
labs(y = "Trait", x = "Score") +
theme_pub() +
theme(legend.position = "none")
SOEP
Table
pers_soep <- long_soep %>% ungroup() %>% dplyr::select(agree, consc, emost, extra,
open, ls, se) %>% describe() %>% dplyr::select(n, mean, sd, min, max)
row.names(pers_soep) <- c("Agreeableness", "Conscientiousness", "Emotional stability",
"Extraversion", "Openness", "Life satisfaction", "Self-esteem")
kable(pers_soep
, caption="**Descriptive statistics of our outcome variables across individuals and assessments**"
, escape=FALSE
, label = NA
, digits = 2
, col.names = c("*N*", "*M*", "*SD*", "*Min*", "*Max*")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left")| N | M | SD | Min | Max | |
|---|---|---|---|---|---|
| Agreeableness | 120074 | 5.40 | 0.97 | 1 | 7 |
| Conscientiousness | 119746 | 5.81 | 0.94 | 1 | 7 |
| Emotional stability | 120129 | 4.21 | 1.24 | 1 | 7 |
| Extraversion | 120070 | 4.87 | 1.14 | 1 | 7 |
| Openness | 119287 | 4.56 | 1.20 | 1 | 7 |
| Life satisfaction | 731744 | 7.15 | 1.79 | 0 | 10 |
| Self-esteem | 71759 | 5.62 | 1.29 | 1 | 7 |
Distribution
pers_soep <- long_soep %>% ungroup() %>%
select(ID_pers, extra.z, consc.z, agree.z,
emost.z, open.z, ls.z, se.z) %>%
pivot_longer(cols = -ID_pers)
pers_soep$name <- factor(pers_soep$name, levels = c("se.z", "ls.z", "open.z", "extra.z",
"emost.z","consc.z", "agree.z"),
labels = c("Self-esteem", "Life satisfaction","Openness",
"Extraversion", "Emotional stability",
"Conscientiousness", "Agreeableness"), ordered = TRUE)
ggplot(data = pers_soep, aes(y = name, x = value, fill = name)) +
geom_density_ridges(scale = 0.9) +
labs(y = "Trait", x = "Score") +
theme_pub() +
theme(legend.position = "none")
Section 2: Model With Five Dummies
We used fixed-effect regression models to examine event-related changes in the seven outcome variables. With fixed-effect models, a dummy variable for each person is included in the model that accounts for stable between-person differences. Standardized Big Five trait scores, self-esteem, or life satisfaction for person i at time point t severed as dependent variables . As predictors, we included five mutually exclusive dummy variables that quantified event-related changes in the outcome variables at different time points before and after the event occurrence:
- D-2Y = 1 for assessments that took place up to 2 years prior to the occurrence of the target event
- D-1Y = 1 for assessments that took place up to 1 year prior to the occurrence of the target event
- D+1Y = 1 for assessments that took place up to 1 year after the occurrence of the target event
- D+2Y = 1 for assessments that took place up to 2 years after the occurrence of the target event
- D>+2Y = 1 for assessments that took place more than 2 years after the occurrence of the target event.
## Results across datasets
res_ma_5dumm_long <- res_ma_5dumm %>%
pivot_longer(-c("event", "trait", "DIFFR2_avg"),
names_sep = "_",
names_to = c("Effect", "Names")) %>%
pivot_wider(names_from = ("Names"),
values_from = "value")
## Results across datasets and events
res_ma_event_5dumm_long <- res_ma_event_5dumm %>%
pivot_longer(-c("EventType", "trait", "DIFFR2_avg"),
names_sep = "_",
names_to = c("Effect", "Names")) %>%
pivot_wider(names_from = ("Names"),
values_from = "value")New Relationship
relbeg_res_ma_5dumm <- filter(res_ma_5dumm_long, event == "New relationship")
relbeg_res_ma_5dumm <- dplyr::select(relbeg_res_ma_5dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the five event-related dummy variables. These effects describe within-person changes in our outcome variables between assessments at a certain time point before/after the event occurrence and assessments unrelated to the event occurrence. Significant effects (p < .01) are depicted in bold.
relbeg_res_ma_5dumm_dummies <- filter(relbeg_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2") %>%
dplyr::select(-DIFFR2_avg)
relbeg_res_ma_5dumm_dummies$Effect <- recode(relbeg_res_ma_5dumm_dummies$Effect,
"DM2" = "2 years before event",
"DM1" = "1 year before event",
"DP1" = "1 year after event",
"DP2" = "2 years after event",
"DA2" = "> 2 years after event")
## Create table
kable(relbeg_res_ma_5dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(relbeg_res_ma_5dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 2 years before event | 7 | -0.021 | 0.019 | -1.120 | 0.263 | -0.069 | 0.027 |
| Agreeableness | 1 year before event | 7 | 0.020 | 0.042 | 0.468 | 0.639 | -0.090 | 0.129 |
| Agreeableness | 1 year after event | 7 | 0.042 | 0.039 | 1.056 | 0.291 | -0.060 | 0.143 |
| Agreeableness | 2 years after event | 7 | 0.011 | 0.015 | 0.688 | 0.492 | -0.029 | 0.050 |
| Agreeableness | > 2 years after event | 7 | 0.001 | 0.003 | 0.190 | 0.849 | -0.007 | 0.008 |
| Conscientiousness | 2 years before event | 7 | 0.001 | 0.024 | 0.047 | 0.963 | -0.062 | 0.064 |
| Conscientiousness | 1 year before event | 7 | -0.028 | 0.020 | -1.392 | 0.164 | -0.080 | 0.024 |
| Conscientiousness | 1 year after event | 7 | 0.001 | 0.022 | 0.029 | 0.977 | -0.055 | 0.056 |
| Conscientiousness | 2 years after event | 7 | 0.025 | 0.015 | 1.679 | 0.093 | -0.013 | 0.063 |
| Conscientiousness | > 2 years after event | 7 | 0.006 | 0.003 | 2.096 | 0.036 | -0.001 | 0.013 |
| Extraversion | 2 years before event | 7 | 0.019 | 0.013 | 1.464 | 0.143 | -0.014 | 0.051 |
| Extraversion | 1 year before event | 7 | 0.036 | 0.013 | 2.872 | 0.004 | 0.004 | 0.069 |
| Extraversion | 1 year after event | 7 | 0.012 | 0.014 | 0.867 | 0.386 | -0.024 | 0.047 |
| Extraversion | 2 years after event | 7 | 0.018 | 0.020 | 0.906 | 0.365 | -0.033 | 0.068 |
| Extraversion | > 2 years after event | 7 | -0.001 | 0.002 | -0.760 | 0.447 | -0.005 | 0.003 |
| Emotional stability | 2 years before event | 7 | -0.001 | 0.018 | -0.051 | 0.960 | -0.047 | 0.045 |
| Emotional stability | 1 year before event | 7 | 0.010 | 0.015 | 0.677 | 0.499 | -0.029 | 0.050 |
| Emotional stability | 1 year after event | 7 | 0.015 | 0.028 | 0.523 | 0.601 | -0.057 | 0.087 |
| Emotional stability | 2 years after event | 7 | 0.022 | 0.015 | 1.495 | 0.135 | -0.016 | 0.060 |
| Emotional stability | > 2 years after event | 7 | 0.002 | 0.002 | 1.031 | 0.303 | -0.003 | 0.006 |
| Openness | 2 years before event | 7 | 0.030 | 0.019 | 1.624 | 0.104 | -0.018 | 0.078 |
| Openness | 1 year before event | 7 | 0.060 | 0.025 | 2.430 | 0.015 | -0.004 | 0.123 |
| Openness | 1 year after event | 7 | 0.007 | 0.025 | 0.290 | 0.772 | -0.056 | 0.070 |
| Openness | 2 years after event | 7 | -0.007 | 0.017 | -0.419 | 0.675 | -0.052 | 0.037 |
| Openness | > 2 years after event | 7 | 0.002 | 0.003 | 0.897 | 0.370 | -0.004 | 0.009 |
| Life satisfaction | 2 years before event | 7 | -0.060 | 0.008 | -7.013 | 0.000 | -0.081 | -0.038 |
| Life satisfaction | 1 year before event | 7 | -0.041 | 0.038 | -1.074 | 0.283 | -0.139 | 0.057 |
| Life satisfaction | 1 year after event | 7 | 0.116 | 0.064 | 1.802 | 0.072 | -0.050 | 0.282 |
| Life satisfaction | 2 years after event | 7 | 0.068 | 0.044 | 1.539 | 0.124 | -0.046 | 0.182 |
| Life satisfaction | > 2 years after event | 7 | 0.018 | 0.006 | 3.119 | 0.002 | 0.003 | 0.033 |
| Self-esteem | 2 years before event | 5 | 0.012 | 0.055 | 0.224 | 0.822 | -0.130 | 0.155 |
| Self-esteem | 1 year before event | 5 | 0.045 | 0.060 | 0.748 | 0.455 | -0.110 | 0.200 |
| Self-esteem | 1 year after event | 5 | 0.061 | 0.025 | 2.424 | 0.015 | -0.004 | 0.126 |
| Self-esteem | 2 years after event | 5 | 0.061 | 0.016 | 3.898 | 0.000 | 0.021 | 0.101 |
| Self-esteem | > 2 years after event | 5 | 0.008 | 0.004 | 2.002 | 0.045 | -0.002 | 0.019 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the six linear contrasts that we used to examine personality changes occurring from specific pre-event to specific post-event assessments. Significant effects (p < .01) are depicted in bold.
relbeg_res_ma_5dumm_contrast <- filter(relbeg_res_ma_5dumm, Effect == "CONTRM2P1" |
Effect == "CONTRM2P2" | Effect == "CONTRM2A2" |
Effect == "CONTRM1P1" | Effect == "CONTRM1P2" |
Effect == "CONTRM1A2") %>%
dplyr::select(-DIFFR2_avg)
relbeg_res_ma_5dumm_contrast$Effect <- recode(relbeg_res_ma_5dumm_contrast$Effect,
"CONTRM2P1" = "1Y After - 2Y Before",
"CONTRM2P2" = "2Y After - 2Y Before",
"CONTRM2A2" = ">2Y After - 2Y Before",
"CONTRM1P1" = "1Y After - 1Y Before",
"CONTRM1P2" = "2Y After - 1Y Before",
"CONTRM1A2" = ">2Y After - 1Y Before")
## Create table
kable(relbeg_res_ma_5dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(relbeg_res_ma_5dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 1Y After - 2Y Before | 7 | 0.050 | 0.068 | 0.734 | 0.463 | -0.126 | 0.226 |
| Agreeableness | 2Y After - 2Y Before | 7 | 0.034 | 0.019 | 1.748 | 0.080 | -0.016 | 0.084 |
| Agreeableness | >2Y After - 2Y Before | 7 | 0.022 | 0.014 | 1.560 | 0.119 | -0.014 | 0.058 |
| Agreeableness | 1Y After - 1Y Before | 7 | 0.021 | 0.069 | 0.308 | 0.758 | -0.156 | 0.198 |
| Agreeableness | 2Y After - 1Y Before | 7 | 0.001 | 0.044 | 0.023 | 0.981 | -0.111 | 0.113 |
| Agreeableness | >2Y After - 1Y Before | 7 | -0.019 | 0.039 | -0.481 | 0.630 | -0.118 | 0.081 |
| Conscientiousness | 1Y After - 2Y Before | 7 | 0.022 | 0.018 | 1.182 | 0.237 | -0.026 | 0.069 |
| Conscientiousness | 2Y After - 2Y Before | 7 | 0.021 | 0.028 | 0.756 | 0.450 | -0.051 | 0.094 |
| Conscientiousness | >2Y After - 2Y Before | 7 | 0.006 | 0.025 | 0.242 | 0.809 | -0.058 | 0.070 |
| Conscientiousness | 1Y After - 1Y Before | 7 | 0.023 | 0.018 | 1.271 | 0.204 | -0.023 | 0.069 |
| Conscientiousness | 2Y After - 1Y Before | 7 | 0.046 | 0.023 | 1.995 | 0.046 | -0.013 | 0.105 |
| Conscientiousness | >2Y After - 1Y Before | 7 | 0.038 | 0.021 | 1.815 | 0.070 | -0.016 | 0.092 |
| Extraversion | 1Y After - 2Y Before | 7 | -0.005 | 0.019 | -0.288 | 0.774 | -0.053 | 0.043 |
| Extraversion | 2Y After - 2Y Before | 7 | 0.000 | 0.022 | 0.003 | 0.998 | -0.058 | 0.058 |
| Extraversion | >2Y After - 2Y Before | 7 | -0.019 | 0.012 | -1.540 | 0.124 | -0.051 | 0.013 |
| Extraversion | 1Y After - 1Y Before | 7 | -0.019 | 0.016 | -1.186 | 0.236 | -0.061 | 0.023 |
| Extraversion | 2Y After - 1Y Before | 7 | -0.022 | 0.025 | -0.870 | 0.385 | -0.087 | 0.043 |
| Extraversion | >2Y After - 1Y Before | 7 | -0.037 | 0.012 | -3.039 | 0.002 | -0.068 | -0.006 |
| Emotional stability | 1Y After - 2Y Before | 7 | 0.023 | 0.029 | 0.798 | 0.425 | -0.051 | 0.097 |
| Emotional stability | 2Y After - 2Y Before | 7 | 0.020 | 0.018 | 1.092 | 0.275 | -0.027 | 0.066 |
| Emotional stability | >2Y After - 2Y Before | 7 | 0.006 | 0.018 | 0.307 | 0.759 | -0.042 | 0.053 |
| Emotional stability | 1Y After - 1Y Before | 7 | 0.013 | 0.025 | 0.534 | 0.593 | -0.051 | 0.078 |
| Emotional stability | 2Y After - 1Y Before | 7 | 0.008 | 0.017 | 0.458 | 0.647 | -0.036 | 0.051 |
| Emotional stability | >2Y After - 1Y Before | 7 | -0.005 | 0.016 | -0.302 | 0.763 | -0.047 | 0.037 |
| Openness | 1Y After - 2Y Before | 7 | -0.029 | 0.027 | -1.053 | 0.292 | -0.099 | 0.041 |
| Openness | 2Y After - 2Y Before | 7 | -0.032 | 0.030 | -1.064 | 0.287 | -0.109 | 0.045 |
| Openness | >2Y After - 2Y Before | 7 | -0.028 | 0.018 | -1.515 | 0.130 | -0.076 | 0.020 |
| Openness | 1Y After - 1Y Before | 7 | -0.049 | 0.019 | -2.523 | 0.012 | -0.099 | 0.001 |
| Openness | 2Y After - 1Y Before | 7 | -0.064 | 0.033 | -1.940 | 0.052 | -0.150 | 0.021 |
| Openness | >2Y After - 1Y Before | 7 | -0.057 | 0.024 | -2.367 | 0.018 | -0.119 | 0.005 |
| Life satisfaction | 1Y After - 2Y Before | 7 | 0.150 | 0.060 | 2.524 | 0.012 | -0.003 | 0.304 |
| Life satisfaction | 2Y After - 2Y Before | 7 | 0.125 | 0.036 | 3.532 | 0.000 | 0.034 | 0.217 |
| Life satisfaction | >2Y After - 2Y Before | 7 | 0.087 | 0.008 | 10.654 | 0.000 | 0.066 | 0.108 |
| Life satisfaction | 1Y After - 1Y Before | 7 | 0.126 | 0.032 | 3.936 | 0.000 | 0.044 | 0.209 |
| Life satisfaction | 2Y After - 1Y Before | 7 | 0.131 | 0.018 | 7.109 | 0.000 | 0.084 | 0.179 |
| Life satisfaction | >2Y After - 1Y Before | 7 | 0.063 | 0.036 | 1.786 | 0.074 | -0.028 | 0.155 |
| Self-esteem | 1Y After - 2Y Before | 5 | 0.097 | 0.018 | 5.476 | 0.000 | 0.051 | 0.142 |
| Self-esteem | 2Y After - 2Y Before | 5 | 0.087 | 0.026 | 3.331 | 0.001 | 0.020 | 0.155 |
| Self-esteem | >2Y After - 2Y Before | 5 | -0.022 | 0.072 | -0.308 | 0.758 | -0.207 | 0.163 |
| Self-esteem | 1Y After - 1Y Before | 5 | 0.081 | 0.019 | 4.368 | 0.000 | 0.033 | 0.129 |
| Self-esteem | 2Y After - 1Y Before | 5 | 0.072 | 0.023 | 3.152 | 0.002 | 0.013 | 0.130 |
| Self-esteem | >2Y After - 1Y Before | 5 | -0.051 | 0.073 | -0.691 | 0.490 | -0.240 | 0.139 |
Graph
This graph illustrates the meta-analytic estimates of the five event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the five event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
relbeg_res_ma_5dumm_dummies <- filter(relbeg_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
## Create variable on significance
relbeg_res_ma_5dumm_dummies$sig <- ifelse(relbeg_res_ma_5dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
relbeg_res_ma_5dumm_dummies$trait <- factor(relbeg_res_ma_5dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
relbeg_res_ma_5dumm_dummies$Effect <- factor(relbeg_res_ma_5dumm_dummies$Effect,
levels = c("DM2", "DM1", "DP1", "DP2", "DA2"),
labels = c("-2 Years", "-1 Year", "+1 Year",
"+2 Years", ">2 Years"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- relbeg_res_ma_5dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
relbeg_res_ma_5dumm_dummies$ID <- 1:nrow(relbeg_res_ma_5dumm_dummies)
plot <- ggplot(data = relbeg_res_ma_5dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 2.5, y = 0.28), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 2.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 4.5, y = 0.28),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Marriage
marriage_res_ma_5dumm <- filter(res_ma_5dumm_long, event == "Marriage")
marriage_res_ma_5dumm <- dplyr::select(marriage_res_ma_5dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the five event-related dummy variables. These effects describe within-person changes in our outcome variables between assessments at a certain time point before/after the event occurrence and assessments unrelated to the event occurrence. Significant effects (p < .01) are depicted in bold.
marriage_res_ma_5dumm_dummies <- filter(marriage_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2") %>%
dplyr::select(-DIFFR2_avg)
marriage_res_ma_5dumm_dummies$Effect <- recode(marriage_res_ma_5dumm_dummies$Effect,
"DM2" = "2 years before event",
"DM1" = "1 year before event",
"DP1" = "1 year after event",
"DP2" = "2 years after event",
"DA2" = "> 2 years after event")
## Create table
kable(marriage_res_ma_5dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(marriage_res_ma_5dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 2 years before event | 7 | 0.049 | 0.015 | 3.330 | 0.001 | 0.011 | 0.088 |
| Agreeableness | 1 year before event | 7 | 0.045 | 0.021 | 2.147 | 0.032 | -0.009 | 0.099 |
| Agreeableness | 1 year after event | 7 | -0.009 | 0.016 | -0.556 | 0.578 | -0.052 | 0.033 |
| Agreeableness | 2 years after event | 7 | -0.003 | 0.020 | -0.175 | 0.861 | -0.054 | 0.047 |
| Agreeableness | > 2 years after event | 7 | -0.002 | 0.003 | -0.583 | 0.560 | -0.009 | 0.005 |
| Conscientiousness | 2 years before event | 7 | 0.064 | 0.014 | 4.589 | 0.000 | 0.028 | 0.099 |
| Conscientiousness | 1 year before event | 7 | 0.021 | 0.015 | 1.391 | 0.164 | -0.018 | 0.059 |
| Conscientiousness | 1 year after event | 7 | 0.002 | 0.020 | 0.094 | 0.925 | -0.051 | 0.055 |
| Conscientiousness | 2 years after event | 7 | 0.025 | 0.021 | 1.158 | 0.247 | -0.030 | 0.080 |
| Conscientiousness | > 2 years after event | 7 | -0.001 | 0.002 | -0.727 | 0.467 | -0.006 | 0.003 |
| Extraversion | 2 years before event | 7 | 0.010 | 0.015 | 0.680 | 0.497 | -0.028 | 0.048 |
| Extraversion | 1 year before event | 7 | 0.003 | 0.013 | 0.245 | 0.806 | -0.029 | 0.035 |
| Extraversion | 1 year after event | 7 | 0.010 | 0.014 | 0.738 | 0.460 | -0.026 | 0.046 |
| Extraversion | 2 years after event | 7 | -0.022 | 0.014 | -1.571 | 0.116 | -0.057 | 0.014 |
| Extraversion | > 2 years after event | 7 | -0.003 | 0.001 | -2.340 | 0.019 | -0.007 | 0.000 |
| Emotional stability | 2 years before event | 7 | 0.018 | 0.014 | 1.243 | 0.214 | -0.019 | 0.055 |
| Emotional stability | 1 year before event | 7 | 0.013 | 0.014 | 0.910 | 0.363 | -0.024 | 0.050 |
| Emotional stability | 1 year after event | 7 | 0.028 | 0.016 | 1.726 | 0.084 | -0.014 | 0.070 |
| Emotional stability | 2 years after event | 7 | 0.028 | 0.016 | 1.739 | 0.082 | -0.013 | 0.070 |
| Emotional stability | > 2 years after event | 7 | -0.002 | 0.002 | -1.454 | 0.146 | -0.007 | 0.002 |
| Openness | 2 years before event | 7 | 0.018 | 0.014 | 1.257 | 0.209 | -0.019 | 0.055 |
| Openness | 1 year before event | 7 | 0.015 | 0.024 | 0.628 | 0.530 | -0.047 | 0.078 |
| Openness | 1 year after event | 7 | -0.046 | 0.015 | -2.981 | 0.003 | -0.085 | -0.006 |
| Openness | 2 years after event | 7 | -0.075 | 0.029 | -2.570 | 0.010 | -0.151 | 0.000 |
| Openness | > 2 years after event | 7 | -0.003 | 0.004 | -0.823 | 0.411 | -0.013 | 0.007 |
| Life satisfaction | 2 years before event | 7 | 0.097 | 0.009 | 11.250 | 0.000 | 0.075 | 0.119 |
| Life satisfaction | 1 year before event | 7 | 0.166 | 0.022 | 7.718 | 0.000 | 0.111 | 0.221 |
| Life satisfaction | 1 year after event | 7 | 0.219 | 0.023 | 9.356 | 0.000 | 0.159 | 0.279 |
| Life satisfaction | 2 years after event | 7 | 0.189 | 0.021 | 9.101 | 0.000 | 0.136 | 0.243 |
| Life satisfaction | > 2 years after event | 7 | 0.017 | 0.005 | 3.661 | 0.000 | 0.005 | 0.028 |
| Self-esteem | 2 years before event | 5 | 0.083 | 0.037 | 2.234 | 0.026 | -0.013 | 0.179 |
| Self-esteem | 1 year before event | 5 | 0.051 | 0.019 | 2.756 | 0.006 | 0.003 | 0.099 |
| Self-esteem | 1 year after event | 5 | 0.060 | 0.020 | 3.022 | 0.003 | 0.009 | 0.111 |
| Self-esteem | 2 years after event | 5 | 0.045 | 0.020 | 2.228 | 0.026 | -0.007 | 0.098 |
| Self-esteem | > 2 years after event | 5 | 0.003 | 0.003 | 1.149 | 0.250 | -0.004 | 0.010 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the six linear contrasts that we used to examine personality changes occurring from specific pre-event to specific post-event assessments. Significant effects (p < .01) are depicted in bold.
marriage_res_ma_5dumm_contrast <- filter(marriage_res_ma_5dumm, Effect == "CONTRM2P1" |
Effect == "CONTRM2P2" | Effect == "CONTRM2A2" |
Effect == "CONTRM1P1" | Effect == "CONTRM1P2" |
Effect == "CONTRM1A2") %>%
dplyr::select(-DIFFR2_avg)
marriage_res_ma_5dumm_contrast$Effect <- recode(marriage_res_ma_5dumm_contrast$Effect,
"CONTRM2P1" = "1Y After - 2Y Before",
"CONTRM2P2" = "2Y After - 2Y Before",
"CONTRM2A2" = ">2Y After - 2Y Before",
"CONTRM1P1" = "1Y After - 1Y Before",
"CONTRM1P2" = "2Y After - 1Y Before",
"CONTRM1A2" = ">2Y After - 1Y Before")
## Create table
kable(marriage_res_ma_5dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(marriage_res_ma_5dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 1Y After - 2Y Before | 7 | -0.056 | 0.021 | -2.712 | 0.007 | -0.109 | -0.003 |
| Agreeableness | 2Y After - 2Y Before | 7 | -0.055 | 0.020 | -2.729 | 0.006 | -0.106 | -0.003 |
| Agreeableness | >2Y After - 2Y Before | 7 | -0.051 | 0.014 | -3.523 | 0.000 | -0.088 | -0.014 |
| Agreeableness | 1Y After - 1Y Before | 7 | -0.056 | 0.020 | -2.765 | 0.006 | -0.109 | -0.004 |
| Agreeableness | 2Y After - 1Y Before | 7 | -0.045 | 0.035 | -1.279 | 0.201 | -0.136 | 0.046 |
| Agreeableness | >2Y After - 1Y Before | 7 | -0.046 | 0.024 | -1.884 | 0.060 | -0.109 | 0.017 |
| Conscientiousness | 1Y After - 2Y Before | 7 | -0.058 | 0.019 | -3.057 | 0.002 | -0.106 | -0.009 |
| Conscientiousness | 2Y After - 2Y Before | 7 | -0.048 | 0.019 | -2.558 | 0.011 | -0.097 | 0.000 |
| Conscientiousness | >2Y After - 2Y Before | 7 | -0.065 | 0.013 | -4.851 | 0.000 | -0.099 | -0.030 |
| Conscientiousness | 1Y After - 1Y Before | 7 | -0.015 | 0.019 | -0.771 | 0.440 | -0.063 | 0.034 |
| Conscientiousness | 2Y After - 1Y Before | 7 | -0.010 | 0.019 | -0.559 | 0.576 | -0.058 | 0.037 |
| Conscientiousness | >2Y After - 1Y Before | 7 | -0.022 | 0.014 | -1.575 | 0.115 | -0.058 | 0.014 |
| Extraversion | 1Y After - 2Y Before | 7 | 0.002 | 0.022 | 0.098 | 0.922 | -0.055 | 0.059 |
| Extraversion | 2Y After - 2Y Before | 7 | -0.036 | 0.024 | -1.476 | 0.140 | -0.097 | 0.026 |
| Extraversion | >2Y After - 2Y Before | 7 | -0.013 | 0.014 | -0.931 | 0.352 | -0.049 | 0.023 |
| Extraversion | 1Y After - 1Y Before | 7 | 0.008 | 0.017 | 0.473 | 0.636 | -0.035 | 0.051 |
| Extraversion | 2Y After - 1Y Before | 7 | -0.034 | 0.021 | -1.632 | 0.103 | -0.088 | 0.020 |
| Extraversion | >2Y After - 1Y Before | 7 | -0.006 | 0.012 | -0.530 | 0.596 | -0.037 | 0.025 |
| Emotional stability | 1Y After - 2Y Before | 7 | 0.007 | 0.020 | 0.362 | 0.717 | -0.043 | 0.057 |
| Emotional stability | 2Y After - 2Y Before | 7 | 0.013 | 0.020 | 0.631 | 0.528 | -0.039 | 0.064 |
| Emotional stability | >2Y After - 2Y Before | 7 | -0.022 | 0.014 | -1.553 | 0.120 | -0.058 | 0.014 |
| Emotional stability | 1Y After - 1Y Before | 7 | 0.016 | 0.019 | 0.810 | 0.418 | -0.034 | 0.065 |
| Emotional stability | 2Y After - 1Y Before | 7 | 0.013 | 0.019 | 0.660 | 0.509 | -0.037 | 0.063 |
| Emotional stability | >2Y After - 1Y Before | 7 | -0.016 | 0.014 | -1.194 | 0.232 | -0.052 | 0.019 |
| Openness | 1Y After - 2Y Before | 7 | -0.059 | 0.023 | -2.561 | 0.010 | -0.119 | 0.000 |
| Openness | 2Y After - 2Y Before | 7 | -0.097 | 0.034 | -2.895 | 0.004 | -0.184 | -0.011 |
| Openness | >2Y After - 2Y Before | 7 | -0.023 | 0.013 | -1.711 | 0.087 | -0.056 | 0.011 |
| Openness | 1Y After - 1Y Before | 7 | -0.054 | 0.020 | -2.679 | 0.007 | -0.106 | -0.002 |
| Openness | 2Y After - 1Y Before | 7 | -0.087 | 0.029 | -2.996 | 0.003 | -0.163 | -0.012 |
| Openness | >2Y After - 1Y Before | 7 | -0.018 | 0.019 | -0.964 | 0.335 | -0.067 | 0.031 |
| Life satisfaction | 1Y After - 2Y Before | 7 | 0.126 | 0.026 | 4.921 | 0.000 | 0.060 | 0.192 |
| Life satisfaction | 2Y After - 2Y Before | 7 | 0.090 | 0.020 | 4.636 | 0.000 | 0.040 | 0.141 |
| Life satisfaction | >2Y After - 2Y Before | 7 | -0.076 | 0.010 | -7.625 | 0.000 | -0.101 | -0.050 |
| Life satisfaction | 1Y After - 1Y Before | 7 | 0.051 | 0.011 | 4.581 | 0.000 | 0.022 | 0.079 |
| Life satisfaction | 2Y After - 1Y Before | 7 | 0.013 | 0.010 | 1.199 | 0.231 | -0.014 | 0.039 |
| Life satisfaction | >2Y After - 1Y Before | 7 | -0.147 | 0.015 | -10.125 | 0.000 | -0.185 | -0.110 |
| Self-esteem | 1Y After - 2Y Before | 5 | -0.013 | 0.035 | -0.382 | 0.702 | -0.103 | 0.076 |
| Self-esteem | 2Y After - 2Y Before | 5 | -0.023 | 0.035 | -0.654 | 0.513 | -0.115 | 0.068 |
| Self-esteem | >2Y After - 2Y Before | 5 | -0.081 | 0.035 | -2.334 | 0.020 | -0.170 | 0.008 |
| Self-esteem | 1Y After - 1Y Before | 5 | 0.008 | 0.021 | 0.355 | 0.723 | -0.047 | 0.062 |
| Self-esteem | 2Y After - 1Y Before | 5 | 0.003 | 0.023 | 0.131 | 0.896 | -0.055 | 0.061 |
| Self-esteem | >2Y After - 1Y Before | 5 | -0.051 | 0.018 | -2.904 | 0.004 | -0.096 | -0.006 |
Graph
This graph illustrates the meta-analytic estimates of the five event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the five event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
marriage_res_ma_5dumm_dummies <- filter(marriage_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
## Create variable on significance
marriage_res_ma_5dumm_dummies$sig <- ifelse(marriage_res_ma_5dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
marriage_res_ma_5dumm_dummies$trait <- factor(marriage_res_ma_5dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
marriage_res_ma_5dumm_dummies$Effect <- factor(marriage_res_ma_5dumm_dummies$Effect,
levels = c("DM2", "DM1", "DP1", "DP2", "DA2"),
labels = c("-2 Years", "-1 Year", "+1 Year",
"+2 Years", ">2 Years"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- marriage_res_ma_5dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
marriage_res_ma_5dumm_dummies$ID <- 1:nrow(marriage_res_ma_5dumm_dummies)
plot <- ggplot(data = marriage_res_ma_5dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 2.5, y = 0.28), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 2.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 4.5, y = 0.28),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Childbirth
child_res_ma_5dumm <- filter(res_ma_5dumm_long, event == "Childbirth")
child_res_ma_5dumm <- dplyr::select(child_res_ma_5dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the five event-related dummy variables. These effects describe within-person changes in our outcome variables between assessments at a certain time point before/after the event occurrence and assessments unrelated to the event occurrence. Significant effects (p < .01) are depicted in bold.
child_res_ma_5dumm_dummies <- filter(child_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2") %>%
dplyr::select(-DIFFR2_avg)
child_res_ma_5dumm_dummies$Effect <- recode(child_res_ma_5dumm_dummies$Effect,
"DM2" = "2 years before event",
"DM1" = "1 year before event",
"DP1" = "1 year after event",
"DP2" = "2 years after event",
"DA2" = "> 2 years after event")
## Create table
kable(child_res_ma_5dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(child_res_ma_5dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 2 years before event | 7 | 0.024 | 0.018 | 1.301 | 0.193 | -0.024 | 0.072 |
| Agreeableness | 1 year before event | 7 | 0.015 | 0.013 | 1.112 | 0.266 | -0.020 | 0.050 |
| Agreeableness | 1 year after event | 7 | -0.004 | 0.022 | -0.195 | 0.845 | -0.061 | 0.052 |
| Agreeableness | 2 years after event | 7 | -0.030 | 0.015 | -1.968 | 0.049 | -0.069 | 0.009 |
| Agreeableness | > 2 years after event | 7 | -0.003 | 0.004 | -0.952 | 0.341 | -0.012 | 0.006 |
| Conscientiousness | 2 years before event | 7 | 0.015 | 0.023 | 0.654 | 0.513 | -0.044 | 0.073 |
| Conscientiousness | 1 year before event | 7 | 0.015 | 0.017 | 0.885 | 0.376 | -0.029 | 0.060 |
| Conscientiousness | 1 year after event | 7 | -0.021 | 0.015 | -1.333 | 0.182 | -0.061 | 0.019 |
| Conscientiousness | 2 years after event | 7 | -0.042 | 0.023 | -1.833 | 0.067 | -0.101 | 0.017 |
| Conscientiousness | > 2 years after event | 7 | -0.006 | 0.002 | -3.058 | 0.002 | -0.012 | -0.001 |
| Extraversion | 2 years before event | 7 | 0.022 | 0.012 | 1.796 | 0.073 | -0.010 | 0.054 |
| Extraversion | 1 year before event | 7 | -0.013 | 0.011 | -1.204 | 0.229 | -0.042 | 0.015 |
| Extraversion | 1 year after event | 7 | -0.008 | 0.013 | -0.616 | 0.538 | -0.041 | 0.025 |
| Extraversion | 2 years after event | 7 | -0.027 | 0.013 | -2.031 | 0.042 | -0.060 | 0.007 |
| Extraversion | > 2 years after event | 7 | -0.001 | 0.002 | -0.523 | 0.601 | -0.006 | 0.004 |
| Emotional stability | 2 years before event | 7 | 0.009 | 0.014 | 0.643 | 0.520 | -0.026 | 0.044 |
| Emotional stability | 1 year before event | 7 | 0.016 | 0.019 | 0.819 | 0.413 | -0.033 | 0.065 |
| Emotional stability | 1 year after event | 7 | 0.043 | 0.025 | 1.715 | 0.086 | -0.021 | 0.107 |
| Emotional stability | 2 years after event | 7 | 0.033 | 0.017 | 1.908 | 0.056 | -0.011 | 0.077 |
| Emotional stability | > 2 years after event | 7 | -0.001 | 0.004 | -0.167 | 0.868 | -0.012 | 0.010 |
| Openness | 2 years before event | 7 | 0.027 | 0.013 | 2.104 | 0.035 | -0.006 | 0.061 |
| Openness | 1 year before event | 7 | -0.020 | 0.012 | -1.639 | 0.101 | -0.052 | 0.011 |
| Openness | 1 year after event | 7 | -0.085 | 0.019 | -4.421 | 0.000 | -0.135 | -0.036 |
| Openness | 2 years after event | 7 | -0.074 | 0.022 | -3.299 | 0.001 | -0.131 | -0.016 |
| Openness | > 2 years after event | 7 | -0.001 | 0.002 | -0.416 | 0.678 | -0.007 | 0.005 |
| Life satisfaction | 2 years before event | 7 | 0.053 | 0.014 | 3.864 | 0.000 | 0.018 | 0.088 |
| Life satisfaction | 1 year before event | 7 | 0.105 | 0.038 | 2.787 | 0.005 | 0.008 | 0.203 |
| Life satisfaction | 1 year after event | 7 | 0.159 | 0.044 | 3.589 | 0.000 | 0.045 | 0.274 |
| Life satisfaction | 2 years after event | 7 | 0.080 | 0.018 | 4.388 | 0.000 | 0.033 | 0.126 |
| Life satisfaction | > 2 years after event | 7 | 0.008 | 0.005 | 1.539 | 0.124 | -0.005 | 0.021 |
| Self-esteem | 2 years before event | 5 | 0.033 | 0.017 | 1.869 | 0.062 | -0.012 | 0.078 |
| Self-esteem | 1 year before event | 5 | 0.078 | 0.026 | 3.018 | 0.003 | 0.011 | 0.145 |
| Self-esteem | 1 year after event | 5 | 0.069 | 0.018 | 3.878 | 0.000 | 0.023 | 0.115 |
| Self-esteem | 2 years after event | 5 | 0.033 | 0.019 | 1.757 | 0.079 | -0.015 | 0.081 |
| Self-esteem | > 2 years after event | 5 | 0.001 | 0.003 | 0.334 | 0.738 | -0.008 | 0.010 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the six linear contrasts that we used to examine personality changes occurring from specific pre-event to specific post-event assessments. Significant effects (p < .01) are depicted in bold.
child_res_ma_5dumm_contrast <- filter(child_res_ma_5dumm, Effect == "CONTRM2P1" |
Effect == "CONTRM2P2" | Effect == "CONTRM2A2" |
Effect == "CONTRM1P1" | Effect == "CONTRM1P2" |
Effect == "CONTRM1A2") %>%
dplyr::select(-DIFFR2_avg)
child_res_ma_5dumm_contrast$Effect <- recode(child_res_ma_5dumm_contrast$Effect,
"CONTRM2P1" = "1Y After - 2Y Before",
"CONTRM2P2" = "2Y After - 2Y Before",
"CONTRM2A2" = ">2Y After - 2Y Before",
"CONTRM1P1" = "1Y After - 1Y Before",
"CONTRM1P2" = "2Y After - 1Y Before",
"CONTRM1A2" = ">2Y After - 1Y Before")
## Create table
kable(child_res_ma_5dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(child_res_ma_5dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 1Y After - 2Y Before | 7 | -0.026 | 0.036 | -0.738 | 0.461 | -0.118 | 0.066 |
| Agreeableness | 2Y After - 2Y Before | 7 | -0.038 | 0.035 | -1.079 | 0.280 | -0.129 | 0.053 |
| Agreeableness | >2Y After - 2Y Before | 7 | -0.024 | 0.020 | -1.242 | 0.214 | -0.075 | 0.026 |
| Agreeableness | 1Y After - 1Y Before | 7 | -0.026 | 0.024 | -1.112 | 0.266 | -0.087 | 0.034 |
| Agreeableness | 2Y After - 1Y Before | 7 | -0.049 | 0.018 | -2.648 | 0.008 | -0.096 | -0.001 |
| Agreeableness | >2Y After - 1Y Before | 7 | -0.017 | 0.013 | -1.330 | 0.184 | -0.050 | 0.016 |
| Conscientiousness | 1Y After - 2Y Before | 7 | -0.038 | 0.025 | -1.512 | 0.131 | -0.104 | 0.027 |
| Conscientiousness | 2Y After - 2Y Before | 7 | -0.058 | 0.034 | -1.695 | 0.090 | -0.145 | 0.030 |
| Conscientiousness | >2Y After - 2Y Before | 7 | -0.020 | 0.023 | -0.878 | 0.380 | -0.080 | 0.039 |
| Conscientiousness | 1Y After - 1Y Before | 7 | -0.034 | 0.018 | -1.951 | 0.051 | -0.080 | 0.011 |
| Conscientiousness | 2Y After - 1Y Before | 7 | -0.060 | 0.017 | -3.528 | 0.000 | -0.104 | -0.016 |
| Conscientiousness | >2Y After - 1Y Before | 7 | -0.021 | 0.017 | -1.227 | 0.220 | -0.065 | 0.023 |
| Extraversion | 1Y After - 2Y Before | 7 | -0.027 | 0.020 | -1.379 | 0.168 | -0.077 | 0.023 |
| Extraversion | 2Y After - 2Y Before | 7 | -0.050 | 0.017 | -2.968 | 0.003 | -0.092 | -0.007 |
| Extraversion | >2Y After - 2Y Before | 7 | -0.022 | 0.012 | -1.848 | 0.065 | -0.053 | 0.009 |
| Extraversion | 1Y After - 1Y Before | 7 | 0.002 | 0.015 | 0.152 | 0.879 | -0.037 | 0.042 |
| Extraversion | 2Y After - 1Y Before | 7 | -0.016 | 0.019 | -0.855 | 0.392 | -0.064 | 0.032 |
| Extraversion | >2Y After - 1Y Before | 7 | 0.014 | 0.011 | 1.302 | 0.193 | -0.014 | 0.041 |
| Emotional stability | 1Y After - 2Y Before | 7 | 0.030 | 0.029 | 1.037 | 0.300 | -0.045 | 0.106 |
| Emotional stability | 2Y After - 2Y Before | 7 | 0.021 | 0.021 | 1.002 | 0.316 | -0.033 | 0.074 |
| Emotional stability | >2Y After - 2Y Before | 7 | -0.007 | 0.013 | -0.543 | 0.587 | -0.041 | 0.027 |
| Emotional stability | 1Y After - 1Y Before | 7 | 0.025 | 0.029 | 0.881 | 0.378 | -0.049 | 0.100 |
| Emotional stability | 2Y After - 1Y Before | 7 | 0.021 | 0.029 | 0.722 | 0.471 | -0.054 | 0.097 |
| Emotional stability | >2Y After - 1Y Before | 7 | -0.014 | 0.019 | -0.748 | 0.454 | -0.062 | 0.034 |
| Openness | 1Y After - 2Y Before | 7 | -0.113 | 0.018 | -6.376 | 0.000 | -0.158 | -0.067 |
| Openness | 2Y After - 2Y Before | 7 | -0.105 | 0.018 | -5.943 | 0.000 | -0.150 | -0.059 |
| Openness | >2Y After - 2Y Before | 7 | -0.028 | 0.013 | -2.232 | 0.026 | -0.060 | 0.004 |
| Openness | 1Y After - 1Y Before | 7 | -0.066 | 0.017 | -3.786 | 0.000 | -0.111 | -0.021 |
| Openness | 2Y After - 1Y Before | 7 | -0.059 | 0.018 | -3.265 | 0.001 | -0.106 | -0.012 |
| Openness | >2Y After - 1Y Before | 7 | 0.019 | 0.012 | 1.601 | 0.109 | -0.011 | 0.049 |
| Life satisfaction | 1Y After - 2Y Before | 7 | 0.105 | 0.042 | 2.523 | 0.012 | -0.002 | 0.213 |
| Life satisfaction | 2Y After - 2Y Before | 7 | 0.025 | 0.023 | 1.100 | 0.271 | -0.033 | 0.083 |
| Life satisfaction | >2Y After - 2Y Before | 7 | -0.041 | 0.015 | -2.806 | 0.005 | -0.079 | -0.003 |
| Life satisfaction | 1Y After - 1Y Before | 7 | 0.047 | 0.031 | 1.514 | 0.130 | -0.033 | 0.126 |
| Life satisfaction | 2Y After - 1Y Before | 7 | -0.029 | 0.038 | -0.757 | 0.449 | -0.128 | 0.070 |
| Life satisfaction | >2Y After - 1Y Before | 7 | -0.096 | 0.036 | -2.692 | 0.007 | -0.188 | -0.004 |
| Self-esteem | 1Y After - 2Y Before | 5 | 0.029 | 0.032 | 0.917 | 0.359 | -0.052 | 0.110 |
| Self-esteem | 2Y After - 2Y Before | 5 | -0.002 | 0.022 | -0.072 | 0.943 | -0.058 | 0.055 |
| Self-esteem | >2Y After - 2Y Before | 5 | -0.029 | 0.017 | -1.676 | 0.094 | -0.072 | 0.015 |
| Self-esteem | 1Y After - 1Y Before | 5 | -0.006 | 0.022 | -0.287 | 0.774 | -0.064 | 0.051 |
| Self-esteem | 2Y After - 1Y Before | 5 | -0.053 | 0.021 | -2.518 | 0.012 | -0.107 | 0.001 |
| Self-esteem | >2Y After - 1Y Before | 5 | -0.074 | 0.025 | -2.937 | 0.003 | -0.140 | -0.009 |
Graph
This graph illustrates the meta-analytic estimates of the five event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the five event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
child_res_ma_5dumm_dummies <- filter(child_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
## Create variable on significance
child_res_ma_5dumm_dummies$sig <- ifelse(child_res_ma_5dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
child_res_ma_5dumm_dummies$trait <- factor(child_res_ma_5dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
child_res_ma_5dumm_dummies$Effect <- factor(child_res_ma_5dumm_dummies$Effect,
levels = c("DM2", "DM1", "DP1", "DP2", "DA2"),
labels = c("-2 Years", "-1 Year", "+1 Year",
"+2 Years", ">2 Years"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- child_res_ma_5dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
child_res_ma_5dumm_dummies$ID <- 1:nrow(child_res_ma_5dumm_dummies)
plot <- ggplot(data = child_res_ma_5dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 2.5, y = 0.28), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 2.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 4.5, y = 0.28),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Separation
separat_res_ma_5dumm <- filter(res_ma_5dumm_long, event == "Separation")
separat_res_ma_5dumm <- dplyr::select(separat_res_ma_5dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the five event-related dummy variables. These effects describe within-person changes in our outcome variables between assessments at a certain time point before/after the event occurrence and assessments unrelated to the event occurrence. Significant effects (p < .01) are depicted in bold.
separat_res_ma_5dumm_dummies <- filter(separat_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2") %>%
dplyr::select(-DIFFR2_avg)
separat_res_ma_5dumm_dummies$Effect <- recode(separat_res_ma_5dumm_dummies$Effect,
"DM2" = "2 years before event",
"DM1" = "1 year before event",
"DP1" = "1 year after event",
"DP2" = "2 years after event",
"DA2" = "> 2 years after event")
## Create table
kable(separat_res_ma_5dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(separat_res_ma_5dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 2 years before event | 6 | -0.027 | 0.029 | -0.942 | 0.346 | -0.102 | 0.047 |
| Agreeableness | 1 year before event | 6 | -0.041 | 0.019 | -2.213 | 0.027 | -0.090 | 0.007 |
| Agreeableness | 1 year after event | 6 | 0.042 | 0.022 | 1.874 | 0.061 | -0.016 | 0.099 |
| Agreeableness | 2 years after event | 6 | 0.081 | 0.028 | 2.901 | 0.004 | 0.009 | 0.152 |
| Agreeableness | > 2 years after event | 6 | 0.000 | 0.005 | 0.010 | 0.992 | -0.013 | 0.013 |
| Conscientiousness | 2 years before event | 6 | 0.001 | 0.019 | 0.066 | 0.947 | -0.048 | 0.051 |
| Conscientiousness | 1 year before event | 6 | -0.011 | 0.017 | -0.636 | 0.525 | -0.055 | 0.033 |
| Conscientiousness | 1 year after event | 6 | -0.016 | 0.024 | -0.651 | 0.515 | -0.078 | 0.047 |
| Conscientiousness | 2 years after event | 6 | -0.010 | 0.019 | -0.513 | 0.608 | -0.058 | 0.039 |
| Conscientiousness | > 2 years after event | 6 | 0.001 | 0.004 | 0.245 | 0.806 | -0.009 | 0.011 |
| Extraversion | 2 years before event | 6 | -0.018 | 0.016 | -1.156 | 0.248 | -0.059 | 0.022 |
| Extraversion | 1 year before event | 6 | -0.015 | 0.020 | -0.733 | 0.464 | -0.067 | 0.037 |
| Extraversion | 1 year after event | 6 | 0.019 | 0.029 | 0.680 | 0.497 | -0.054 | 0.093 |
| Extraversion | 2 years after event | 6 | 0.002 | 0.017 | 0.128 | 0.899 | -0.042 | 0.046 |
| Extraversion | > 2 years after event | 6 | 0.001 | 0.003 | 0.393 | 0.694 | -0.006 | 0.009 |
| Emotional stability | 2 years before event | 6 | -0.014 | 0.019 | -0.754 | 0.451 | -0.063 | 0.035 |
| Emotional stability | 1 year before event | 6 | -0.074 | 0.048 | -1.542 | 0.123 | -0.197 | 0.049 |
| Emotional stability | 1 year after event | 6 | -0.051 | 0.021 | -2.413 | 0.016 | -0.106 | 0.003 |
| Emotional stability | 2 years after event | 6 | 0.007 | 0.021 | 0.336 | 0.737 | -0.046 | 0.060 |
| Emotional stability | > 2 years after event | 6 | 0.007 | 0.002 | 3.351 | 0.001 | 0.002 | 0.012 |
| Openness | 2 years before event | 6 | -0.027 | 0.022 | -1.274 | 0.203 | -0.083 | 0.028 |
| Openness | 1 year before event | 6 | 0.014 | 0.027 | 0.510 | 0.610 | -0.055 | 0.082 |
| Openness | 1 year after event | 6 | 0.037 | 0.018 | 2.030 | 0.042 | -0.010 | 0.085 |
| Openness | 2 years after event | 6 | 0.010 | 0.019 | 0.525 | 0.599 | -0.038 | 0.058 |
| Openness | > 2 years after event | 6 | 0.005 | 0.002 | 2.705 | 0.007 | 0.000 | 0.010 |
| Life satisfaction | 2 years before event | 6 | -0.046 | 0.023 | -1.962 | 0.050 | -0.105 | 0.014 |
| Life satisfaction | 1 year before event | 6 | -0.169 | 0.082 | -2.069 | 0.039 | -0.380 | 0.041 |
| Life satisfaction | 1 year after event | 6 | -0.260 | 0.061 | -4.293 | 0.000 | -0.416 | -0.104 |
| Life satisfaction | 2 years after event | 6 | -0.144 | 0.041 | -3.532 | 0.000 | -0.248 | -0.039 |
| Life satisfaction | > 2 years after event | 6 | 0.006 | 0.005 | 1.079 | 0.281 | -0.008 | 0.019 |
| Self-esteem | 2 years before event | 4 | -0.081 | 0.062 | -1.305 | 0.192 | -0.240 | 0.078 |
| Self-esteem | 1 year before event | 4 | -0.121 | 0.077 | -1.570 | 0.116 | -0.320 | 0.078 |
| Self-esteem | 1 year after event | 4 | -0.116 | 0.070 | -1.657 | 0.097 | -0.297 | 0.064 |
| Self-esteem | 2 years after event | 4 | -0.050 | 0.062 | -0.805 | 0.421 | -0.210 | 0.110 |
| Self-esteem | > 2 years after event | 4 | 0.003 | 0.004 | 0.808 | 0.419 | -0.007 | 0.013 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the six linear contrasts that we used to examine personality changes occurring from specific pre-event to specific post-event assessments. Significant effects (p < .01) are depicted in bold.
separat_res_ma_5dumm_contrast <- filter(separat_res_ma_5dumm, Effect == "CONTRM2P1" |
Effect == "CONTRM2P2" | Effect == "CONTRM2A2" |
Effect == "CONTRM1P1" | Effect == "CONTRM1P2" |
Effect == "CONTRM1A2") %>%
dplyr::select(-DIFFR2_avg)
separat_res_ma_5dumm_contrast$Effect <- recode(separat_res_ma_5dumm_contrast$Effect,
"CONTRM2P1" = "1Y After - 2Y Before",
"CONTRM2P2" = "2Y After - 2Y Before",
"CONTRM2A2" = ">2Y After - 2Y Before",
"CONTRM1P1" = "1Y After - 1Y Before",
"CONTRM1P2" = "2Y After - 1Y Before",
"CONTRM1A2" = ">2Y After - 1Y Before")
## Create table
kable(separat_res_ma_5dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(separat_res_ma_5dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 1Y After - 2Y Before | 6 | 0.071 | 0.028 | 2.540 | 0.011 | -0.001 | 0.142 |
| Agreeableness | 2Y After - 2Y Before | 6 | 0.098 | 0.051 | 1.914 | 0.056 | -0.034 | 0.230 |
| Agreeableness | >2Y After - 2Y Before | 6 | 0.028 | 0.026 | 1.061 | 0.289 | -0.040 | 0.095 |
| Agreeableness | 1Y After - 1Y Before | 6 | 0.086 | 0.028 | 3.072 | 0.002 | 0.014 | 0.157 |
| Agreeableness | 2Y After - 1Y Before | 6 | 0.120 | 0.038 | 3.160 | 0.002 | 0.022 | 0.217 |
| Agreeableness | >2Y After - 1Y Before | 6 | 0.041 | 0.018 | 2.257 | 0.024 | -0.006 | 0.087 |
| Conscientiousness | 1Y After - 2Y Before | 6 | -0.013 | 0.034 | -0.372 | 0.710 | -0.099 | 0.074 |
| Conscientiousness | 2Y After - 2Y Before | 6 | -0.014 | 0.025 | -0.577 | 0.564 | -0.077 | 0.049 |
| Conscientiousness | >2Y After - 2Y Before | 6 | 0.002 | 0.020 | 0.100 | 0.921 | -0.048 | 0.052 |
| Conscientiousness | 1Y After - 1Y Before | 6 | -0.009 | 0.025 | -0.357 | 0.721 | -0.073 | 0.055 |
| Conscientiousness | 2Y After - 1Y Before | 6 | -0.003 | 0.026 | -0.099 | 0.921 | -0.069 | 0.064 |
| Conscientiousness | >2Y After - 1Y Before | 6 | 0.013 | 0.017 | 0.772 | 0.440 | -0.030 | 0.055 |
| Extraversion | 1Y After - 2Y Before | 6 | 0.045 | 0.036 | 1.258 | 0.208 | -0.047 | 0.137 |
| Extraversion | 2Y After - 2Y Before | 6 | 0.020 | 0.022 | 0.895 | 0.371 | -0.037 | 0.077 |
| Extraversion | >2Y After - 2Y Before | 6 | 0.018 | 0.015 | 1.162 | 0.245 | -0.022 | 0.057 |
| Extraversion | 1Y After - 1Y Before | 6 | 0.036 | 0.032 | 1.124 | 0.261 | -0.046 | 0.118 |
| Extraversion | 2Y After - 1Y Before | 6 | 0.018 | 0.022 | 0.823 | 0.410 | -0.038 | 0.074 |
| Extraversion | >2Y After - 1Y Before | 6 | 0.014 | 0.019 | 0.722 | 0.470 | -0.035 | 0.063 |
| Emotional stability | 1Y After - 2Y Before | 6 | -0.034 | 0.028 | -1.249 | 0.212 | -0.105 | 0.037 |
| Emotional stability | 2Y After - 2Y Before | 6 | 0.019 | 0.027 | 0.712 | 0.477 | -0.050 | 0.087 |
| Emotional stability | >2Y After - 2Y Before | 6 | 0.021 | 0.019 | 1.134 | 0.257 | -0.027 | 0.069 |
| Emotional stability | 1Y After - 1Y Before | 6 | 0.003 | 0.027 | 0.106 | 0.915 | -0.067 | 0.073 |
| Emotional stability | 2Y After - 1Y Before | 6 | 0.066 | 0.040 | 1.624 | 0.104 | -0.038 | 0.170 |
| Emotional stability | >2Y After - 1Y Before | 6 | 0.080 | 0.048 | 1.660 | 0.097 | -0.044 | 0.205 |
| Openness | 1Y After - 2Y Before | 6 | 0.060 | 0.024 | 2.468 | 0.014 | -0.003 | 0.123 |
| Openness | 2Y After - 2Y Before | 6 | 0.034 | 0.025 | 1.370 | 0.171 | -0.030 | 0.097 |
| Openness | >2Y After - 2Y Before | 6 | 0.033 | 0.021 | 1.598 | 0.110 | -0.020 | 0.086 |
| Openness | 1Y After - 1Y Before | 6 | 0.014 | 0.050 | 0.278 | 0.781 | -0.114 | 0.142 |
| Openness | 2Y After - 1Y Before | 6 | 0.000 | 0.027 | 0.007 | 0.994 | -0.069 | 0.069 |
| Openness | >2Y After - 1Y Before | 6 | -0.008 | 0.026 | -0.313 | 0.755 | -0.075 | 0.058 |
| Life satisfaction | 1Y After - 2Y Before | 6 | -0.237 | 0.039 | -6.051 | 0.000 | -0.338 | -0.136 |
| Life satisfaction | 2Y After - 2Y Before | 6 | -0.099 | 0.014 | -6.889 | 0.000 | -0.136 | -0.062 |
| Life satisfaction | >2Y After - 2Y Before | 6 | 0.048 | 0.030 | 1.631 | 0.103 | -0.028 | 0.124 |
| Life satisfaction | 1Y After - 1Y Before | 6 | -0.139 | 0.017 | -7.981 | 0.000 | -0.184 | -0.094 |
| Life satisfaction | 2Y After - 1Y Before | 6 | 0.021 | 0.043 | 0.496 | 0.620 | -0.090 | 0.133 |
| Life satisfaction | >2Y After - 1Y Before | 6 | 0.173 | 0.088 | 1.978 | 0.048 | -0.052 | 0.399 |
| Self-esteem | 1Y After - 2Y Before | 4 | -0.004 | 0.022 | -0.199 | 0.842 | -0.062 | 0.053 |
| Self-esteem | 2Y After - 2Y Before | 4 | 0.030 | 0.023 | 1.301 | 0.193 | -0.029 | 0.090 |
| Self-esteem | >2Y After - 2Y Before | 4 | 0.083 | 0.061 | 1.361 | 0.173 | -0.074 | 0.240 |
| Self-esteem | 1Y After - 1Y Before | 4 | 0.012 | 0.031 | 0.387 | 0.699 | -0.068 | 0.092 |
| Self-esteem | 2Y After - 1Y Before | 4 | 0.070 | 0.054 | 1.299 | 0.194 | -0.068 | 0.208 |
| Self-esteem | >2Y After - 1Y Before | 4 | 0.123 | 0.076 | 1.622 | 0.105 | -0.073 | 0.320 |
Graph
This graph illustrates the meta-analytic estimates of the five event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the five event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
separat_res_ma_5dumm_dummies <- filter(separat_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
## Create variable on significance
separat_res_ma_5dumm_dummies$sig <- ifelse(separat_res_ma_5dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
separat_res_ma_5dumm_dummies$trait <- factor(separat_res_ma_5dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
separat_res_ma_5dumm_dummies$Effect <- factor(separat_res_ma_5dumm_dummies$Effect,
levels = c("DM2", "DM1", "DP1", "DP2", "DA2"),
labels = c("-2 Years", "-1 Year", "+1 Year",
"+2 Years", ">2 Years"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- separat_res_ma_5dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
separat_res_ma_5dumm_dummies$ID <- 1:nrow(separat_res_ma_5dumm_dummies)
plot <- ggplot(data = separat_res_ma_5dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 2.5, y = 0.28), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 2.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 4.5, y = 0.28),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Divorce
divor_res_ma_5dumm <- filter(res_ma_5dumm_long, event == "Divorce")
divor_res_ma_5dumm <- dplyr::select(divor_res_ma_5dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the five event-related dummy variables. These effects describe within-person changes in our outcome variables between assessments at a certain time point before/after the event occurrence and assessments unrelated to the event occurrence. Significant effects (p < .01) are depicted in bold.
divor_res_ma_5dumm_dummies <- filter(divor_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2") %>%
dplyr::select(-DIFFR2_avg)
divor_res_ma_5dumm_dummies$Effect <- recode(divor_res_ma_5dumm_dummies$Effect,
"DM2" = "2 years before event",
"DM1" = "1 year before event",
"DP1" = "1 year after event",
"DP2" = "2 years after event",
"DA2" = "> 2 years after event")
## Create table
kable(divor_res_ma_5dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(divor_res_ma_5dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 2 years before event | 7 | 0.001 | 0.038 | 0.034 | 0.973 | -0.096 | 0.099 |
| Agreeableness | 1 year before event | 7 | 0.003 | 0.024 | 0.115 | 0.908 | -0.059 | 0.065 |
| Agreeableness | 1 year after event | 7 | 0.078 | 0.027 | 2.865 | 0.004 | 0.008 | 0.148 |
| Agreeableness | 2 years after event | 7 | 0.065 | 0.036 | 1.833 | 0.067 | -0.027 | 0.157 |
| Agreeableness | > 2 years after event | 7 | 0.001 | 0.004 | 0.238 | 0.812 | -0.010 | 0.012 |
| Conscientiousness | 2 years before event | 7 | 0.002 | 0.025 | 0.080 | 0.936 | -0.062 | 0.066 |
| Conscientiousness | 1 year before event | 7 | -0.086 | 0.024 | -3.582 | 0.000 | -0.148 | -0.024 |
| Conscientiousness | 1 year after event | 7 | -0.032 | 0.026 | -1.228 | 0.219 | -0.098 | 0.035 |
| Conscientiousness | 2 years after event | 7 | -0.027 | 0.024 | -1.123 | 0.261 | -0.090 | 0.035 |
| Conscientiousness | > 2 years after event | 7 | -0.003 | 0.002 | -1.153 | 0.249 | -0.009 | 0.004 |
| Extraversion | 2 years before event | 7 | 0.012 | 0.022 | 0.564 | 0.573 | -0.044 | 0.069 |
| Extraversion | 1 year before event | 7 | -0.033 | 0.022 | -1.511 | 0.131 | -0.090 | 0.023 |
| Extraversion | 1 year after event | 7 | 0.005 | 0.024 | 0.203 | 0.839 | -0.056 | 0.066 |
| Extraversion | 2 years after event | 7 | 0.023 | 0.040 | 0.578 | 0.563 | -0.081 | 0.127 |
| Extraversion | > 2 years after event | 7 | 0.001 | 0.002 | 0.269 | 0.788 | -0.005 | 0.006 |
| Emotional stability | 2 years before event | 7 | -0.046 | 0.024 | -1.894 | 0.058 | -0.108 | 0.016 |
| Emotional stability | 1 year before event | 7 | -0.080 | 0.036 | -2.225 | 0.026 | -0.173 | 0.013 |
| Emotional stability | 1 year after event | 7 | -0.048 | 0.071 | -0.686 | 0.493 | -0.230 | 0.133 |
| Emotional stability | 2 years after event | 7 | 0.057 | 0.032 | 1.774 | 0.076 | -0.026 | 0.141 |
| Emotional stability | > 2 years after event | 7 | 0.005 | 0.002 | 2.079 | 0.038 | -0.001 | 0.011 |
| Openness | 2 years before event | 7 | 0.035 | 0.028 | 1.269 | 0.204 | -0.036 | 0.107 |
| Openness | 1 year before event | 7 | 0.005 | 0.022 | 0.249 | 0.803 | -0.050 | 0.061 |
| Openness | 1 year after event | 7 | 0.034 | 0.025 | 1.367 | 0.172 | -0.030 | 0.097 |
| Openness | 2 years after event | 7 | 0.008 | 0.023 | 0.350 | 0.726 | -0.051 | 0.067 |
| Openness | > 2 years after event | 7 | 0.006 | 0.002 | 2.768 | 0.006 | 0.000 | 0.012 |
| Life satisfaction | 2 years before event | 7 | -0.223 | 0.019 | -11.600 | 0.000 | -0.272 | -0.173 |
| Life satisfaction | 1 year before event | 7 | -0.246 | 0.072 | -3.401 | 0.001 | -0.432 | -0.060 |
| Life satisfaction | 1 year after event | 7 | -0.118 | 0.087 | -1.365 | 0.172 | -0.341 | 0.105 |
| Life satisfaction | 2 years after event | 7 | -0.035 | 0.059 | -0.597 | 0.550 | -0.188 | 0.117 |
| Life satisfaction | > 2 years after event | 7 | 0.016 | 0.006 | 2.643 | 0.008 | 0.000 | 0.031 |
| Self-esteem | 2 years before event | 5 | -0.070 | 0.039 | -1.796 | 0.072 | -0.170 | 0.030 |
| Self-esteem | 1 year before event | 5 | -0.113 | 0.049 | -2.318 | 0.020 | -0.238 | 0.013 |
| Self-esteem | 1 year after event | 5 | -0.047 | 0.050 | -0.929 | 0.353 | -0.176 | 0.083 |
| Self-esteem | 2 years after event | 5 | 0.062 | 0.086 | 0.719 | 0.472 | -0.159 | 0.282 |
| Self-esteem | > 2 years after event | 5 | 0.001 | 0.004 | 0.208 | 0.835 | -0.010 | 0.011 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the six linear contrasts that we used to examine personality changes occurring from specific pre-event to specific post-event assessments. Significant effects (p < .01) are depicted in bold.
divor_res_ma_5dumm_contrast <- filter(divor_res_ma_5dumm, Effect == "CONTRM2P1" |
Effect == "CONTRM2P2" | Effect == "CONTRM2A2" |
Effect == "CONTRM1P1" | Effect == "CONTRM1P2" |
Effect == "CONTRM1A2") %>%
dplyr::select(-DIFFR2_avg)
divor_res_ma_5dumm_contrast$Effect <- recode(divor_res_ma_5dumm_contrast$Effect,
"CONTRM2P1" = "1Y After - 2Y Before",
"CONTRM2P2" = "2Y After - 2Y Before",
"CONTRM2A2" = ">2Y After - 2Y Before",
"CONTRM1P1" = "1Y After - 1Y Before",
"CONTRM1P2" = "2Y After - 1Y Before",
"CONTRM1A2" = ">2Y After - 1Y Before")
## Create table
kable(divor_res_ma_5dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(divor_res_ma_5dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 1Y After - 2Y Before | 7 | 0.067 | 0.039 | 1.720 | 0.085 | -0.033 | 0.167 |
| Agreeableness | 2Y After - 2Y Before | 7 | 0.066 | 0.054 | 1.218 | 0.223 | -0.073 | 0.205 |
| Agreeableness | >2Y After - 2Y Before | 7 | -0.002 | 0.035 | -0.054 | 0.957 | -0.092 | 0.088 |
| Agreeableness | 1Y After - 1Y Before | 7 | 0.081 | 0.033 | 2.454 | 0.014 | -0.004 | 0.166 |
| Agreeableness | 2Y After - 1Y Before | 7 | 0.047 | 0.031 | 1.534 | 0.125 | -0.032 | 0.126 |
| Agreeableness | >2Y After - 1Y Before | 7 | -0.003 | 0.023 | -0.127 | 0.899 | -0.063 | 0.057 |
| Conscientiousness | 1Y After - 2Y Before | 7 | -0.038 | 0.040 | -0.938 | 0.348 | -0.141 | 0.066 |
| Conscientiousness | 2Y After - 2Y Before | 7 | -0.034 | 0.031 | -1.076 | 0.282 | -0.114 | 0.047 |
| Conscientiousness | >2Y After - 2Y Before | 7 | -0.005 | 0.024 | -0.192 | 0.848 | -0.067 | 0.058 |
| Conscientiousness | 1Y After - 1Y Before | 7 | 0.052 | 0.032 | 1.638 | 0.101 | -0.030 | 0.135 |
| Conscientiousness | 2Y After - 1Y Before | 7 | 0.062 | 0.038 | 1.648 | 0.099 | -0.035 | 0.159 |
| Conscientiousness | >2Y After - 1Y Before | 7 | 0.083 | 0.023 | 3.693 | 0.000 | 0.025 | 0.141 |
| Extraversion | 1Y After - 2Y Before | 7 | -0.015 | 0.030 | -0.496 | 0.620 | -0.093 | 0.063 |
| Extraversion | 2Y After - 2Y Before | 7 | 0.003 | 0.048 | 0.068 | 0.946 | -0.121 | 0.128 |
| Extraversion | >2Y After - 2Y Before | 7 | -0.012 | 0.021 | -0.545 | 0.586 | -0.067 | 0.044 |
| Extraversion | 1Y After - 1Y Before | 7 | 0.031 | 0.029 | 1.070 | 0.285 | -0.044 | 0.107 |
| Extraversion | 2Y After - 1Y Before | 7 | 0.051 | 0.049 | 1.031 | 0.302 | -0.076 | 0.177 |
| Extraversion | >2Y After - 1Y Before | 7 | 0.033 | 0.021 | 1.541 | 0.123 | -0.022 | 0.088 |
| Emotional stability | 1Y After - 2Y Before | 7 | 0.025 | 0.038 | 0.661 | 0.509 | -0.072 | 0.122 |
| Emotional stability | 2Y After - 2Y Before | 7 | 0.100 | 0.034 | 2.985 | 0.003 | 0.014 | 0.187 |
| Emotional stability | >2Y After - 2Y Before | 7 | 0.049 | 0.023 | 2.098 | 0.036 | -0.011 | 0.110 |
| Emotional stability | 1Y After - 1Y Before | 7 | 0.036 | 0.063 | 0.564 | 0.573 | -0.127 | 0.198 |
| Emotional stability | 2Y After - 1Y Before | 7 | 0.131 | 0.036 | 3.626 | 0.000 | 0.038 | 0.224 |
| Emotional stability | >2Y After - 1Y Before | 7 | 0.083 | 0.035 | 2.366 | 0.018 | -0.007 | 0.174 |
| Openness | 1Y After - 2Y Before | 7 | -0.011 | 0.042 | -0.251 | 0.802 | -0.120 | 0.099 |
| Openness | 2Y After - 2Y Before | 7 | -0.012 | 0.041 | -0.285 | 0.776 | -0.118 | 0.094 |
| Openness | >2Y After - 2Y Before | 7 | -0.029 | 0.027 | -1.077 | 0.281 | -0.099 | 0.041 |
| Openness | 1Y After - 1Y Before | 7 | 0.026 | 0.039 | 0.656 | 0.512 | -0.076 | 0.128 |
| Openness | 2Y After - 1Y Before | 7 | -0.003 | 0.029 | -0.098 | 0.922 | -0.077 | 0.071 |
| Openness | >2Y After - 1Y Before | 7 | 0.001 | 0.021 | 0.028 | 0.978 | -0.053 | 0.055 |
| Life satisfaction | 1Y After - 2Y Before | 7 | 0.050 | 0.101 | 0.498 | 0.619 | -0.209 | 0.309 |
| Life satisfaction | 2Y After - 2Y Before | 7 | 0.141 | 0.078 | 1.813 | 0.070 | -0.059 | 0.342 |
| Life satisfaction | >2Y After - 2Y Before | 7 | 0.245 | 0.019 | 13.095 | 0.000 | 0.197 | 0.293 |
| Life satisfaction | 1Y After - 1Y Before | 7 | 0.134 | 0.021 | 6.428 | 0.000 | 0.081 | 0.188 |
| Life satisfaction | 2Y After - 1Y Before | 7 | 0.219 | 0.047 | 4.650 | 0.000 | 0.098 | 0.340 |
| Life satisfaction | >2Y After - 1Y Before | 7 | 0.266 | 0.069 | 3.872 | 0.000 | 0.089 | 0.442 |
| Self-esteem | 1Y After - 2Y Before | 5 | 0.023 | 0.058 | 0.402 | 0.688 | -0.126 | 0.172 |
| Self-esteem | 2Y After - 2Y Before | 5 | 0.117 | 0.084 | 1.400 | 0.162 | -0.099 | 0.334 |
| Self-esteem | >2Y After - 2Y Before | 5 | 0.070 | 0.038 | 1.837 | 0.066 | -0.028 | 0.168 |
| Self-esteem | 1Y After - 1Y Before | 5 | 0.062 | 0.040 | 1.536 | 0.125 | -0.042 | 0.166 |
| Self-esteem | 2Y After - 1Y Before | 5 | 0.174 | 0.043 | 4.031 | 0.000 | 0.063 | 0.286 |
| Self-esteem | >2Y After - 1Y Before | 5 | 0.112 | 0.048 | 2.311 | 0.021 | -0.013 | 0.236 |
Graph
This graph illustrates the meta-analytic estimates of the five event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the five event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
divor_res_ma_5dumm_dummies <- filter(divor_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
## Create variable on significance
divor_res_ma_5dumm_dummies$sig <- ifelse(divor_res_ma_5dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
divor_res_ma_5dumm_dummies$trait <- factor(divor_res_ma_5dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
divor_res_ma_5dumm_dummies$Effect <- factor(divor_res_ma_5dumm_dummies$Effect,
levels = c("DM2", "DM1", "DP1", "DP2", "DA2"),
labels = c("-2 Years", "-1 Year", "+1 Year",
"+2 Years", ">2 Years"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- divor_res_ma_5dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
divor_res_ma_5dumm_dummies$ID <- 1:nrow(divor_res_ma_5dumm_dummies)
plot <- ggplot(data = divor_res_ma_5dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 2.5, y = 0.28), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 2.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 4.5, y = 0.28),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Widowhood
widow_res_ma_5dumm <- filter(res_ma_5dumm_long, event == "Widowhood")
widow_res_ma_5dumm <- dplyr::select(widow_res_ma_5dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the five event-related dummy variables. These effects describe within-person changes in our outcome variables between assessments at a certain time point before/after the event occurrence and assessments unrelated to the event occurrence. Significant effects (p < .01) are depicted in bold.
widow_res_ma_5dumm_dummies <- filter(widow_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2") %>%
dplyr::select(-DIFFR2_avg)
widow_res_ma_5dumm_dummies$Effect <- recode(widow_res_ma_5dumm_dummies$Effect,
"DM2" = "2 years before event",
"DM1" = "1 year before event",
"DP1" = "1 year after event",
"DP2" = "2 years after event",
"DA2" = "> 2 years after event")
## Create table
kable(widow_res_ma_5dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(widow_res_ma_5dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 2 years before event | 5 | 0.007 | 0.027 | 0.239 | 0.811 | -0.064 | 0.077 |
| Agreeableness | 1 year before event | 5 | 0.002 | 0.021 | 0.097 | 0.922 | -0.053 | 0.057 |
| Agreeableness | 1 year after event | 5 | 0.011 | 0.047 | 0.226 | 0.821 | -0.111 | 0.133 |
| Agreeableness | 2 years after event | 5 | 0.044 | 0.024 | 1.856 | 0.063 | -0.017 | 0.106 |
| Agreeableness | > 2 years after event | 5 | 0.000 | 0.005 | 0.044 | 0.965 | -0.014 | 0.014 |
| Conscientiousness | 2 years before event | 5 | 0.020 | 0.033 | 0.615 | 0.538 | -0.064 | 0.105 |
| Conscientiousness | 1 year before event | 5 | -0.026 | 0.030 | -0.858 | 0.391 | -0.102 | 0.051 |
| Conscientiousness | 1 year after event | 5 | -0.066 | 0.041 | -1.590 | 0.112 | -0.172 | 0.041 |
| Conscientiousness | 2 years after event | 5 | -0.026 | 0.023 | -1.141 | 0.254 | -0.085 | 0.033 |
| Conscientiousness | > 2 years after event | 5 | -0.007 | 0.003 | -2.487 | 0.013 | -0.014 | 0.000 |
| Extraversion | 2 years before event | 5 | 0.032 | 0.044 | 0.719 | 0.472 | -0.082 | 0.146 |
| Extraversion | 1 year before event | 5 | 0.009 | 0.042 | 0.216 | 0.829 | -0.100 | 0.118 |
| Extraversion | 1 year after event | 5 | -0.083 | 0.021 | -3.996 | 0.000 | -0.137 | -0.030 |
| Extraversion | 2 years after event | 5 | -0.025 | 0.021 | -1.220 | 0.222 | -0.079 | 0.028 |
| Extraversion | > 2 years after event | 5 | -0.002 | 0.005 | -0.294 | 0.769 | -0.015 | 0.012 |
| Emotional stability | 2 years before event | 5 | -0.066 | 0.021 | -3.146 | 0.002 | -0.120 | -0.012 |
| Emotional stability | 1 year before event | 5 | -0.106 | 0.021 | -4.953 | 0.000 | -0.161 | -0.051 |
| Emotional stability | 1 year after event | 5 | -0.057 | 0.056 | -1.019 | 0.308 | -0.202 | 0.088 |
| Emotional stability | 2 years after event | 5 | -0.001 | 0.047 | -0.016 | 0.988 | -0.122 | 0.121 |
| Emotional stability | > 2 years after event | 5 | 0.008 | 0.005 | 1.693 | 0.090 | -0.004 | 0.020 |
| Openness | 2 years before event | 5 | 0.011 | 0.033 | 0.336 | 0.737 | -0.074 | 0.096 |
| Openness | 1 year before event | 5 | -0.030 | 0.028 | -1.069 | 0.285 | -0.102 | 0.042 |
| Openness | 1 year after event | 5 | -0.049 | 0.023 | -2.097 | 0.036 | -0.109 | 0.011 |
| Openness | 2 years after event | 5 | -0.001 | 0.022 | -0.037 | 0.971 | -0.057 | 0.055 |
| Openness | > 2 years after event | 5 | -0.001 | 0.002 | -0.316 | 0.752 | -0.007 | 0.006 |
| Life satisfaction | 2 years before event | 5 | -0.087 | 0.027 | -3.184 | 0.001 | -0.158 | -0.017 |
| Life satisfaction | 1 year before event | 5 | -0.218 | 0.036 | -6.075 | 0.000 | -0.311 | -0.126 |
| Life satisfaction | 1 year after event | 5 | -0.470 | 0.113 | -4.169 | 0.000 | -0.760 | -0.180 |
| Life satisfaction | 2 years after event | 5 | -0.264 | 0.049 | -5.388 | 0.000 | -0.390 | -0.138 |
| Life satisfaction | > 2 years after event | 5 | 0.005 | 0.006 | 0.819 | 0.413 | -0.011 | 0.021 |
| Self-esteem | 2 years before event | 3 | 0.006 | 0.041 | 0.135 | 0.893 | -0.100 | 0.111 |
| Self-esteem | 1 year before event | 3 | -0.037 | 0.070 | -0.527 | 0.598 | -0.216 | 0.143 |
| Self-esteem | 1 year after event | 3 | -0.098 | 0.039 | -2.476 | 0.013 | -0.199 | 0.004 |
| Self-esteem | 2 years after event | 3 | -0.079 | 0.050 | -1.601 | 0.109 | -0.207 | 0.048 |
| Self-esteem | > 2 years after event | 3 | 0.010 | 0.009 | 1.138 | 0.255 | -0.013 | 0.034 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the six linear contrasts that we used to examine personality changes occurring from specific pre-event to specific post-event assessments. Significant effects (p < .01) are depicted in bold.
widow_res_ma_5dumm_contrast <- filter(widow_res_ma_5dumm, Effect == "CONTRM2P1" |
Effect == "CONTRM2P2" | Effect == "CONTRM2A2" |
Effect == "CONTRM1P1" | Effect == "CONTRM1P2" |
Effect == "CONTRM1A2") %>%
dplyr::select(-DIFFR2_avg)
widow_res_ma_5dumm_contrast$Effect <- recode(widow_res_ma_5dumm_contrast$Effect,
"CONTRM2P1" = "1Y After - 2Y Before",
"CONTRM2P2" = "2Y After - 2Y Before",
"CONTRM2A2" = ">2Y After - 2Y Before",
"CONTRM1P1" = "1Y After - 1Y Before",
"CONTRM1P2" = "2Y After - 1Y Before",
"CONTRM1A2" = ">2Y After - 1Y Before")
## Create table
kable(widow_res_ma_5dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(widow_res_ma_5dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 1Y After - 2Y Before | 5 | 0.009 | 0.072 | 0.130 | 0.897 | -0.176 | 0.194 |
| Agreeableness | 2Y After - 2Y Before | 5 | 0.047 | 0.048 | 0.972 | 0.331 | -0.078 | 0.171 |
| Agreeableness | >2Y After - 2Y Before | 5 | -0.004 | 0.033 | -0.128 | 0.898 | -0.089 | 0.081 |
| Agreeableness | 1Y After - 1Y Before | 5 | 0.010 | 0.031 | 0.337 | 0.736 | -0.068 | 0.089 |
| Agreeableness | 2Y After - 1Y Before | 5 | 0.044 | 0.029 | 1.494 | 0.135 | -0.032 | 0.120 |
| Agreeableness | >2Y After - 1Y Before | 5 | 0.000 | 0.021 | 0.019 | 0.985 | -0.053 | 0.053 |
| Conscientiousness | 1Y After - 2Y Before | 5 | -0.083 | 0.048 | -1.721 | 0.085 | -0.207 | 0.041 |
| Conscientiousness | 2Y After - 2Y Before | 5 | -0.044 | 0.038 | -1.173 | 0.241 | -0.141 | 0.053 |
| Conscientiousness | >2Y After - 2Y Before | 5 | -0.028 | 0.034 | -0.824 | 0.410 | -0.116 | 0.060 |
| Conscientiousness | 1Y After - 1Y Before | 5 | -0.034 | 0.040 | -0.858 | 0.391 | -0.136 | 0.068 |
| Conscientiousness | 2Y After - 1Y Before | 5 | 0.002 | 0.031 | 0.079 | 0.937 | -0.077 | 0.082 |
| Conscientiousness | >2Y After - 1Y Before | 5 | 0.019 | 0.029 | 0.654 | 0.513 | -0.056 | 0.095 |
| Extraversion | 1Y After - 2Y Before | 5 | -0.104 | 0.042 | -2.463 | 0.014 | -0.212 | 0.005 |
| Extraversion | 2Y After - 2Y Before | 5 | -0.074 | 0.059 | -1.260 | 0.208 | -0.225 | 0.077 |
| Extraversion | >2Y After - 2Y Before | 5 | -0.034 | 0.044 | -0.778 | 0.436 | -0.146 | 0.078 |
| Extraversion | 1Y After - 1Y Before | 5 | -0.080 | 0.032 | -2.521 | 0.012 | -0.161 | 0.002 |
| Extraversion | 2Y After - 1Y Before | 5 | -0.039 | 0.031 | -1.250 | 0.211 | -0.118 | 0.041 |
| Extraversion | >2Y After - 1Y Before | 5 | -0.009 | 0.037 | -0.249 | 0.804 | -0.103 | 0.085 |
| Emotional stability | 1Y After - 2Y Before | 5 | -0.003 | 0.063 | -0.054 | 0.957 | -0.165 | 0.159 |
| Emotional stability | 2Y After - 2Y Before | 5 | 0.049 | 0.052 | 0.942 | 0.346 | -0.085 | 0.183 |
| Emotional stability | >2Y After - 2Y Before | 5 | 0.077 | 0.021 | 3.764 | 0.000 | 0.024 | 0.130 |
| Emotional stability | 1Y After - 1Y Before | 5 | 0.056 | 0.070 | 0.802 | 0.423 | -0.125 | 0.238 |
| Emotional stability | 2Y After - 1Y Before | 5 | 0.111 | 0.063 | 1.749 | 0.080 | -0.052 | 0.274 |
| Emotional stability | >2Y After - 1Y Before | 5 | 0.117 | 0.021 | 5.593 | 0.000 | 0.063 | 0.170 |
| Openness | 1Y After - 2Y Before | 5 | -0.055 | 0.032 | -1.711 | 0.087 | -0.137 | 0.028 |
| Openness | 2Y After - 2Y Before | 5 | 0.000 | 0.029 | 0.005 | 0.996 | -0.074 | 0.074 |
| Openness | >2Y After - 2Y Before | 5 | -0.013 | 0.033 | -0.383 | 0.702 | -0.097 | 0.072 |
| Openness | 1Y After - 1Y Before | 5 | -0.015 | 0.029 | -0.527 | 0.598 | -0.091 | 0.060 |
| Openness | 2Y After - 1Y Before | 5 | 0.038 | 0.028 | 1.396 | 0.163 | -0.033 | 0.109 |
| Openness | >2Y After - 1Y Before | 5 | 0.029 | 0.027 | 1.043 | 0.297 | -0.042 | 0.099 |
| Life satisfaction | 1Y After - 2Y Before | 5 | -0.411 | 0.106 | -3.893 | 0.000 | -0.684 | -0.139 |
| Life satisfaction | 2Y After - 2Y Before | 5 | -0.185 | 0.049 | -3.775 | 0.000 | -0.311 | -0.059 |
| Life satisfaction | >2Y After - 2Y Before | 5 | 0.089 | 0.030 | 2.907 | 0.004 | 0.010 | 0.167 |
| Life satisfaction | 1Y After - 1Y Before | 5 | -0.256 | 0.088 | -2.903 | 0.004 | -0.482 | -0.029 |
| Life satisfaction | 2Y After - 1Y Before | 5 | -0.041 | 0.025 | -1.620 | 0.105 | -0.106 | 0.024 |
| Life satisfaction | >2Y After - 1Y Before | 5 | 0.223 | 0.033 | 6.746 | 0.000 | 0.138 | 0.308 |
| Self-esteem | 1Y After - 2Y Before | 3 | -0.096 | 0.047 | -2.034 | 0.042 | -0.218 | 0.026 |
| Self-esteem | 2Y After - 2Y Before | 3 | -0.091 | 0.058 | -1.571 | 0.116 | -0.241 | 0.059 |
| Self-esteem | >2Y After - 2Y Before | 3 | -0.005 | 0.039 | -0.126 | 0.900 | -0.106 | 0.096 |
| Self-esteem | 1Y After - 1Y Before | 3 | -0.025 | 0.052 | -0.473 | 0.637 | -0.159 | 0.109 |
| Self-esteem | 2Y After - 1Y Before | 3 | -0.019 | 0.057 | -0.335 | 0.738 | -0.165 | 0.127 |
| Self-esteem | >2Y After - 1Y Before | 3 | 0.048 | 0.057 | 0.838 | 0.402 | -0.099 | 0.195 |
Graph
This graph illustrates the meta-analytic estimates of the five event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the five event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
widow_res_ma_5dumm_dummies <- filter(widow_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
## Create variable on significance
widow_res_ma_5dumm_dummies$sig <- ifelse(widow_res_ma_5dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
widow_res_ma_5dumm_dummies$trait <- factor(widow_res_ma_5dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
widow_res_ma_5dumm_dummies$Effect <- factor(widow_res_ma_5dumm_dummies$Effect,
levels = c("DM2", "DM1", "DP1", "DP2", "DA2"),
labels = c("-2 Years", "-1 Year", "+1 Year",
"+2 Years", ">2 Years"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- widow_res_ma_5dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
widow_res_ma_5dumm_dummies$ID <- 1:nrow(widow_res_ma_5dumm_dummies)
plot <- ggplot(data = widow_res_ma_5dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 2.5, y = 0.28), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 2.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 4.5, y = 0.28),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Graduation
gradu_res_ma_5dumm <- filter(res_ma_5dumm_long, event == "Graduation")
gradu_res_ma_5dumm <- dplyr::select(gradu_res_ma_5dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the five event-related dummy variables. These effects describe within-person changes in our outcome variables between assessments at a certain time point before/after the event occurrence and assessments unrelated to the event occurrence. Significant effects (p < .01) are depicted in bold.
gradu_res_ma_5dumm_dummies <- filter(gradu_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2") %>%
dplyr::select(-DIFFR2_avg)
gradu_res_ma_5dumm_dummies$Effect <- recode(gradu_res_ma_5dumm_dummies$Effect,
"DM2" = "2 years before event",
"DM1" = "1 year before event",
"DP1" = "1 year after event",
"DP2" = "2 years after event",
"DA2" = "> 2 years after event")
## Create table
kable(gradu_res_ma_5dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(gradu_res_ma_5dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 2 years before event | 5 | -0.004 | 0.022 | -0.171 | 0.864 | -0.061 | 0.053 |
| Agreeableness | 1 year before event | 5 | 0.010 | 0.015 | 0.647 | 0.518 | -0.028 | 0.048 |
| Agreeableness | 1 year after event | 5 | 0.013 | 0.015 | 0.845 | 0.398 | -0.026 | 0.052 |
| Agreeableness | 2 years after event | 5 | 0.003 | 0.016 | 0.203 | 0.839 | -0.037 | 0.043 |
| Agreeableness | > 2 years after event | 5 | 0.003 | 0.002 | 1.392 | 0.164 | -0.002 | 0.007 |
| Conscientiousness | 2 years before event | 5 | 0.004 | 0.026 | 0.158 | 0.874 | -0.062 | 0.070 |
| Conscientiousness | 1 year before event | 5 | -0.014 | 0.015 | -0.943 | 0.346 | -0.054 | 0.025 |
| Conscientiousness | 1 year after event | 5 | 0.042 | 0.040 | 1.067 | 0.286 | -0.060 | 0.145 |
| Conscientiousness | 2 years after event | 5 | 0.030 | 0.041 | 0.736 | 0.462 | -0.075 | 0.136 |
| Conscientiousness | > 2 years after event | 5 | 0.009 | 0.006 | 1.392 | 0.164 | -0.007 | 0.024 |
| Extraversion | 2 years before event | 5 | 0.003 | 0.013 | 0.253 | 0.800 | -0.030 | 0.037 |
| Extraversion | 1 year before event | 5 | 0.011 | 0.021 | 0.539 | 0.590 | -0.042 | 0.065 |
| Extraversion | 1 year after event | 5 | -0.015 | 0.019 | -0.778 | 0.436 | -0.063 | 0.034 |
| Extraversion | 2 years after event | 5 | -0.016 | 0.013 | -1.189 | 0.235 | -0.050 | 0.018 |
| Extraversion | > 2 years after event | 5 | -0.001 | 0.003 | -0.358 | 0.720 | -0.008 | 0.006 |
| Emotional stability | 2 years before event | 5 | -0.005 | 0.016 | -0.328 | 0.743 | -0.045 | 0.035 |
| Emotional stability | 1 year before event | 5 | -0.035 | 0.014 | -2.554 | 0.011 | -0.071 | 0.000 |
| Emotional stability | 1 year after event | 5 | 0.042 | 0.027 | 1.546 | 0.122 | -0.028 | 0.112 |
| Emotional stability | 2 years after event | 5 | 0.003 | 0.015 | 0.184 | 0.854 | -0.037 | 0.042 |
| Emotional stability | > 2 years after event | 5 | -0.003 | 0.004 | -0.700 | 0.484 | -0.014 | 0.008 |
| Openness | 2 years before event | 5 | -0.018 | 0.019 | -0.986 | 0.324 | -0.066 | 0.030 |
| Openness | 1 year before event | 5 | 0.054 | 0.013 | 4.050 | 0.000 | 0.020 | 0.088 |
| Openness | 1 year after event | 5 | 0.013 | 0.014 | 0.976 | 0.329 | -0.022 | 0.049 |
| Openness | 2 years after event | 5 | -0.007 | 0.015 | -0.449 | 0.653 | -0.046 | 0.032 |
| Openness | > 2 years after event | 5 | 0.002 | 0.002 | 0.902 | 0.367 | -0.004 | 0.008 |
| Life satisfaction | 2 years before event | 5 | -0.029 | 0.022 | -1.319 | 0.187 | -0.087 | 0.028 |
| Life satisfaction | 1 year before event | 5 | 0.027 | 0.041 | 0.647 | 0.518 | -0.080 | 0.133 |
| Life satisfaction | 1 year after event | 5 | -0.007 | 0.012 | -0.611 | 0.541 | -0.038 | 0.024 |
| Life satisfaction | 2 years after event | 5 | -0.012 | 0.009 | -1.266 | 0.205 | -0.036 | 0.012 |
| Life satisfaction | > 2 years after event | 5 | 0.008 | 0.004 | 1.981 | 0.048 | -0.002 | 0.017 |
| Self-esteem | 2 years before event | 4 | -0.009 | 0.023 | -0.388 | 0.698 | -0.067 | 0.050 |
| Self-esteem | 1 year before event | 4 | 0.017 | 0.019 | 0.915 | 0.360 | -0.031 | 0.066 |
| Self-esteem | 1 year after event | 4 | 0.001 | 0.021 | 0.061 | 0.951 | -0.053 | 0.055 |
| Self-esteem | 2 years after event | 4 | -0.017 | 0.022 | -0.799 | 0.424 | -0.073 | 0.038 |
| Self-esteem | > 2 years after event | 4 | -0.001 | 0.007 | -0.174 | 0.862 | -0.021 | 0.018 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the six linear contrasts that we used to examine personality changes occurring from specific pre-event to specific post-event assessments. Significant effects (p < .01) are depicted in bold.
gradu_res_ma_5dumm_contrast <- filter(gradu_res_ma_5dumm, Effect == "CONTRM2P1" |
Effect == "CONTRM2P2" | Effect == "CONTRM2A2" |
Effect == "CONTRM1P1" | Effect == "CONTRM1P2" |
Effect == "CONTRM1A2") %>%
dplyr::select(-DIFFR2_avg)
gradu_res_ma_5dumm_contrast$Effect <- recode(gradu_res_ma_5dumm_contrast$Effect,
"CONTRM2P1" = "1Y After - 2Y Before",
"CONTRM2P2" = "2Y After - 2Y Before",
"CONTRM2A2" = ">2Y After - 2Y Before",
"CONTRM1P1" = "1Y After - 1Y Before",
"CONTRM1P2" = "2Y After - 1Y Before",
"CONTRM1A2" = ">2Y After - 1Y Before")
## Create table
kable(gradu_res_ma_5dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(gradu_res_ma_5dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 1Y After - 2Y Before | 5 | 0.017 | 0.030 | 0.579 | 0.562 | -0.059 | 0.094 |
| Agreeableness | 2Y After - 2Y Before | 5 | 0.007 | 0.026 | 0.281 | 0.779 | -0.059 | 0.074 |
| Agreeableness | >2Y After - 2Y Before | 5 | 0.008 | 0.022 | 0.341 | 0.733 | -0.049 | 0.064 |
| Agreeableness | 1Y After - 1Y Before | 5 | 0.001 | 0.018 | 0.063 | 0.950 | -0.046 | 0.048 |
| Agreeableness | 2Y After - 1Y Before | 5 | -0.006 | 0.025 | -0.243 | 0.808 | -0.070 | 0.058 |
| Agreeableness | >2Y After - 1Y Before | 5 | -0.006 | 0.014 | -0.415 | 0.678 | -0.043 | 0.031 |
| Conscientiousness | 1Y After - 2Y Before | 5 | 0.058 | 0.020 | 2.922 | 0.003 | 0.007 | 0.109 |
| Conscientiousness | 2Y After - 2Y Before | 5 | 0.027 | 0.039 | 0.697 | 0.486 | -0.074 | 0.129 |
| Conscientiousness | >2Y After - 2Y Before | 5 | 0.003 | 0.025 | 0.134 | 0.893 | -0.061 | 0.068 |
| Conscientiousness | 1Y After - 1Y Before | 5 | 0.057 | 0.037 | 1.563 | 0.118 | -0.037 | 0.151 |
| Conscientiousness | 2Y After - 1Y Before | 5 | 0.045 | 0.042 | 1.062 | 0.288 | -0.064 | 0.154 |
| Conscientiousness | >2Y After - 1Y Before | 5 | 0.022 | 0.013 | 1.634 | 0.102 | -0.013 | 0.057 |
| Extraversion | 1Y After - 2Y Before | 5 | -0.014 | 0.016 | -0.892 | 0.373 | -0.055 | 0.027 |
| Extraversion | 2Y After - 2Y Before | 5 | -0.020 | 0.017 | -1.227 | 0.220 | -0.064 | 0.023 |
| Extraversion | >2Y After - 2Y Before | 5 | -0.005 | 0.013 | -0.367 | 0.713 | -0.037 | 0.028 |
| Extraversion | 1Y After - 1Y Before | 5 | -0.024 | 0.015 | -1.564 | 0.118 | -0.064 | 0.016 |
| Extraversion | 2Y After - 1Y Before | 5 | -0.027 | 0.016 | -1.733 | 0.083 | -0.068 | 0.013 |
| Extraversion | >2Y After - 1Y Before | 5 | -0.009 | 0.016 | -0.594 | 0.553 | -0.050 | 0.031 |
| Emotional stability | 1Y After - 2Y Before | 5 | 0.043 | 0.018 | 2.455 | 0.014 | -0.002 | 0.089 |
| Emotional stability | 2Y After - 2Y Before | 5 | 0.010 | 0.019 | 0.556 | 0.578 | -0.038 | 0.059 |
| Emotional stability | >2Y After - 2Y Before | 5 | 0.000 | 0.016 | -0.006 | 0.995 | -0.041 | 0.041 |
| Emotional stability | 1Y After - 1Y Before | 5 | 0.069 | 0.030 | 2.308 | 0.021 | -0.008 | 0.147 |
| Emotional stability | 2Y After - 1Y Before | 5 | 0.034 | 0.019 | 1.757 | 0.079 | -0.016 | 0.083 |
| Emotional stability | >2Y After - 1Y Before | 5 | 0.030 | 0.013 | 2.294 | 0.022 | -0.004 | 0.064 |
| Openness | 1Y After - 2Y Before | 5 | 0.033 | 0.017 | 1.949 | 0.051 | -0.011 | 0.076 |
| Openness | 2Y After - 2Y Before | 5 | 0.014 | 0.027 | 0.532 | 0.595 | -0.055 | 0.083 |
| Openness | >2Y After - 2Y Before | 5 | 0.022 | 0.020 | 1.106 | 0.269 | -0.029 | 0.073 |
| Openness | 1Y After - 1Y Before | 5 | -0.039 | 0.016 | -2.381 | 0.017 | -0.081 | 0.003 |
| Openness | 2Y After - 1Y Before | 5 | -0.060 | 0.017 | -3.559 | 0.000 | -0.104 | -0.017 |
| Openness | >2Y After - 1Y Before | 5 | -0.051 | 0.013 | -3.907 | 0.000 | -0.085 | -0.017 |
| Life satisfaction | 1Y After - 2Y Before | 5 | 0.018 | 0.015 | 1.213 | 0.225 | -0.021 | 0.057 |
| Life satisfaction | 2Y After - 2Y Before | 5 | 0.011 | 0.014 | 0.762 | 0.446 | -0.026 | 0.047 |
| Life satisfaction | >2Y After - 2Y Before | 5 | 0.036 | 0.017 | 2.060 | 0.039 | -0.009 | 0.080 |
| Life satisfaction | 1Y After - 1Y Before | 5 | -0.029 | 0.037 | -0.785 | 0.432 | -0.124 | 0.066 |
| Life satisfaction | 2Y After - 1Y Before | 5 | -0.038 | 0.032 | -1.205 | 0.228 | -0.120 | 0.044 |
| Life satisfaction | >2Y After - 1Y Before | 5 | -0.021 | 0.040 | -0.521 | 0.602 | -0.125 | 0.083 |
| Self-esteem | 1Y After - 2Y Before | 4 | 0.014 | 0.022 | 0.621 | 0.535 | -0.044 | 0.071 |
| Self-esteem | 2Y After - 2Y Before | 4 | 0.001 | 0.025 | 0.034 | 0.973 | -0.064 | 0.065 |
| Self-esteem | >2Y After - 2Y Before | 4 | 0.005 | 0.027 | 0.204 | 0.839 | -0.064 | 0.075 |
| Self-esteem | 1Y After - 1Y Before | 4 | -0.024 | 0.020 | -1.199 | 0.231 | -0.076 | 0.028 |
| Self-esteem | 2Y After - 1Y Before | 4 | -0.035 | 0.023 | -1.515 | 0.130 | -0.095 | 0.025 |
| Self-esteem | >2Y After - 1Y Before | 4 | -0.017 | 0.018 | -0.941 | 0.347 | -0.063 | 0.029 |
Graph
This graph illustrates the meta-analytic estimates of the five event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the five event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
gradu_res_ma_5dumm_dummies <- filter(gradu_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
## Create variable on significance
gradu_res_ma_5dumm_dummies$sig <- ifelse(gradu_res_ma_5dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
gradu_res_ma_5dumm_dummies$trait <- factor(gradu_res_ma_5dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
gradu_res_ma_5dumm_dummies$Effect <- factor(gradu_res_ma_5dumm_dummies$Effect,
levels = c("DM2", "DM1", "DP1", "DP2", "DA2"),
labels = c("-2 Years", "-1 Year", "+1 Year",
"+2 Years", ">2 Years"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- gradu_res_ma_5dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
gradu_res_ma_5dumm_dummies$ID <- 1:nrow(gradu_res_ma_5dumm_dummies)
plot <- ggplot(data = gradu_res_ma_5dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 2.5, y = 0.28), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 2.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 4.5, y = 0.28),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
New Employment
jobbeg_res_ma_5dumm <- filter(res_ma_5dumm_long, event == "New employment")
jobbeg_res_ma_5dumm <- dplyr::select(jobbeg_res_ma_5dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the five event-related dummy variables. These effects describe within-person changes in our outcome variables between assessments at a certain time point before/after the event occurrence and assessments unrelated to the event occurrence. Significant effects (p < .01) are depicted in bold.
jobbeg_res_ma_5dumm_dummies <- filter(jobbeg_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2") %>%
dplyr::select(-DIFFR2_avg)
jobbeg_res_ma_5dumm_dummies$Effect <- recode(jobbeg_res_ma_5dumm_dummies$Effect,
"DM2" = "2 years before event",
"DM1" = "1 year before event",
"DP1" = "1 year after event",
"DP2" = "2 years after event",
"DA2" = "> 2 years after event")
## Create table
kable(jobbeg_res_ma_5dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(jobbeg_res_ma_5dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 2 years before event | 5 | 0.016 | 0.011 | 1.437 | 0.151 | -0.013 | 0.045 |
| Agreeableness | 1 year before event | 5 | 0.022 | 0.016 | 1.421 | 0.155 | -0.018 | 0.063 |
| Agreeableness | 1 year after event | 5 | 0.033 | 0.011 | 3.082 | 0.002 | 0.005 | 0.061 |
| Agreeableness | 2 years after event | 5 | 0.026 | 0.013 | 1.993 | 0.046 | -0.008 | 0.059 |
| Agreeableness | > 2 years after event | 5 | 0.002 | 0.002 | 0.718 | 0.473 | -0.005 | 0.008 |
| Conscientiousness | 2 years before event | 5 | -0.006 | 0.021 | -0.274 | 0.784 | -0.060 | 0.049 |
| Conscientiousness | 1 year before event | 5 | 0.023 | 0.017 | 1.368 | 0.171 | -0.021 | 0.068 |
| Conscientiousness | 1 year after event | 5 | 0.051 | 0.016 | 3.179 | 0.001 | 0.010 | 0.093 |
| Conscientiousness | 2 years after event | 5 | 0.039 | 0.010 | 3.943 | 0.000 | 0.014 | 0.065 |
| Conscientiousness | > 2 years after event | 5 | 0.003 | 0.002 | 1.702 | 0.089 | -0.002 | 0.008 |
| Extraversion | 2 years before event | 5 | 0.019 | 0.008 | 2.237 | 0.025 | -0.003 | 0.040 |
| Extraversion | 1 year before event | 5 | -0.004 | 0.008 | -0.473 | 0.636 | -0.023 | 0.016 |
| Extraversion | 1 year after event | 5 | 0.011 | 0.009 | 1.176 | 0.240 | -0.013 | 0.035 |
| Extraversion | 2 years after event | 5 | -0.001 | 0.009 | -0.163 | 0.870 | -0.025 | 0.022 |
| Extraversion | > 2 years after event | 5 | 0.002 | 0.002 | 1.444 | 0.149 | -0.002 | 0.006 |
| Emotional stability | 2 years before event | 5 | -0.005 | 0.009 | -0.518 | 0.604 | -0.029 | 0.019 |
| Emotional stability | 1 year before event | 5 | -0.028 | 0.018 | -1.550 | 0.121 | -0.073 | 0.018 |
| Emotional stability | 1 year after event | 5 | 0.031 | 0.011 | 2.843 | 0.004 | 0.003 | 0.058 |
| Emotional stability | 2 years after event | 5 | 0.033 | 0.010 | 3.279 | 0.001 | 0.007 | 0.059 |
| Emotional stability | > 2 years after event | 5 | 0.003 | 0.001 | 2.871 | 0.004 | 0.000 | 0.006 |
| Openness | 2 years before event | 5 | 0.019 | 0.011 | 1.636 | 0.102 | -0.011 | 0.048 |
| Openness | 1 year before event | 5 | 0.023 | 0.012 | 1.886 | 0.059 | -0.008 | 0.055 |
| Openness | 1 year after event | 5 | 0.003 | 0.016 | 0.212 | 0.832 | -0.038 | 0.045 |
| Openness | 2 years after event | 5 | 0.001 | 0.011 | 0.081 | 0.935 | -0.027 | 0.029 |
| Openness | > 2 years after event | 5 | 0.004 | 0.002 | 1.772 | 0.076 | -0.002 | 0.010 |
| Life satisfaction | 2 years before event | 5 | -0.008 | 0.012 | -0.651 | 0.515 | -0.037 | 0.022 |
| Life satisfaction | 1 year before event | 5 | -0.058 | 0.019 | -2.970 | 0.003 | -0.107 | -0.008 |
| Life satisfaction | 1 year after event | 5 | 0.020 | 0.005 | 3.810 | 0.000 | 0.007 | 0.034 |
| Life satisfaction | 2 years after event | 5 | 0.020 | 0.006 | 3.375 | 0.001 | 0.005 | 0.035 |
| Life satisfaction | > 2 years after event | 5 | 0.011 | 0.004 | 2.684 | 0.007 | 0.000 | 0.022 |
| Self-esteem | 2 years before event | 3 | -0.004 | 0.018 | -0.199 | 0.843 | -0.050 | 0.043 |
| Self-esteem | 1 year before event | 3 | -0.014 | 0.037 | -0.385 | 0.700 | -0.110 | 0.081 |
| Self-esteem | 1 year after event | 3 | 0.001 | 0.023 | 0.061 | 0.952 | -0.057 | 0.060 |
| Self-esteem | 2 years after event | 3 | 0.008 | 0.022 | 0.365 | 0.715 | -0.050 | 0.066 |
| Self-esteem | > 2 years after event | 3 | 0.007 | 0.002 | 3.306 | 0.001 | 0.002 | 0.012 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the six linear contrasts that we used to examine personality changes occurring from specific pre-event to specific post-event assessments. Significant effects (p < .01) are depicted in bold.
jobbeg_res_ma_5dumm_contrast <- filter(jobbeg_res_ma_5dumm, Effect == "CONTRM2P1" |
Effect == "CONTRM2P2" | Effect == "CONTRM2A2" |
Effect == "CONTRM1P1" | Effect == "CONTRM1P2" |
Effect == "CONTRM1A2") %>%
dplyr::select(-DIFFR2_avg)
jobbeg_res_ma_5dumm_contrast$Effect <- recode(jobbeg_res_ma_5dumm_contrast$Effect,
"CONTRM2P1" = "1Y After - 2Y Before",
"CONTRM2P2" = "2Y After - 2Y Before",
"CONTRM2A2" = ">2Y After - 2Y Before",
"CONTRM1P1" = "1Y After - 1Y Before",
"CONTRM1P2" = "2Y After - 1Y Before",
"CONTRM1A2" = ">2Y After - 1Y Before")
## Create table
kable(jobbeg_res_ma_5dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(jobbeg_res_ma_5dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 1Y After - 2Y Before | 5 | 0.019 | 0.018 | 1.064 | 0.287 | -0.028 | 0.067 |
| Agreeableness | 2Y After - 2Y Before | 5 | 0.007 | 0.013 | 0.517 | 0.605 | -0.027 | 0.041 |
| Agreeableness | >2Y After - 2Y Before | 5 | -0.015 | 0.011 | -1.379 | 0.168 | -0.042 | 0.013 |
| Agreeableness | 1Y After - 1Y Before | 5 | 0.016 | 0.023 | 0.695 | 0.487 | -0.044 | 0.077 |
| Agreeableness | 2Y After - 1Y Before | 5 | 0.007 | 0.023 | 0.308 | 0.758 | -0.052 | 0.067 |
| Agreeableness | >2Y After - 1Y Before | 5 | -0.022 | 0.013 | -1.632 | 0.103 | -0.056 | 0.012 |
| Conscientiousness | 1Y After - 2Y Before | 5 | 0.061 | 0.017 | 3.486 | 0.000 | 0.016 | 0.106 |
| Conscientiousness | 2Y After - 2Y Before | 5 | 0.049 | 0.022 | 2.201 | 0.028 | -0.008 | 0.107 |
| Conscientiousness | >2Y After - 2Y Before | 5 | 0.010 | 0.020 | 0.494 | 0.621 | -0.041 | 0.061 |
| Conscientiousness | 1Y After - 1Y Before | 5 | 0.030 | 0.017 | 1.708 | 0.088 | -0.015 | 0.075 |
| Conscientiousness | 2Y After - 1Y Before | 5 | 0.018 | 0.018 | 0.993 | 0.321 | -0.029 | 0.065 |
| Conscientiousness | >2Y After - 1Y Before | 5 | -0.020 | 0.016 | -1.247 | 0.212 | -0.060 | 0.021 |
| Extraversion | 1Y After - 2Y Before | 5 | -0.006 | 0.012 | -0.491 | 0.623 | -0.038 | 0.026 |
| Extraversion | 2Y After - 2Y Before | 5 | -0.021 | 0.011 | -1.898 | 0.058 | -0.050 | 0.008 |
| Extraversion | >2Y After - 2Y Before | 5 | -0.016 | 0.008 | -2.029 | 0.042 | -0.037 | 0.004 |
| Extraversion | 1Y After - 1Y Before | 5 | 0.018 | 0.013 | 1.369 | 0.171 | -0.016 | 0.053 |
| Extraversion | 2Y After - 1Y Before | 5 | 0.002 | 0.010 | 0.171 | 0.864 | -0.025 | 0.028 |
| Extraversion | >2Y After - 1Y Before | 5 | 0.006 | 0.007 | 0.846 | 0.398 | -0.013 | 0.025 |
| Emotional stability | 1Y After - 2Y Before | 5 | 0.039 | 0.013 | 3.073 | 0.002 | 0.006 | 0.072 |
| Emotional stability | 2Y After - 2Y Before | 5 | 0.039 | 0.013 | 3.026 | 0.002 | 0.006 | 0.072 |
| Emotional stability | >2Y After - 2Y Before | 5 | 0.008 | 0.009 | 0.912 | 0.362 | -0.015 | 0.032 |
| Emotional stability | 1Y After - 1Y Before | 5 | 0.057 | 0.017 | 3.320 | 0.001 | 0.013 | 0.101 |
| Emotional stability | 2Y After - 1Y Before | 5 | 0.059 | 0.018 | 3.328 | 0.001 | 0.013 | 0.104 |
| Emotional stability | >2Y After - 1Y Before | 5 | 0.032 | 0.018 | 1.849 | 0.064 | -0.013 | 0.078 |
| Openness | 1Y After - 2Y Before | 5 | -0.016 | 0.024 | -0.681 | 0.496 | -0.078 | 0.045 |
| Openness | 2Y After - 2Y Before | 5 | -0.016 | 0.023 | -0.694 | 0.488 | -0.074 | 0.043 |
| Openness | >2Y After - 2Y Before | 5 | -0.015 | 0.012 | -1.249 | 0.212 | -0.046 | 0.016 |
| Openness | 1Y After - 1Y Before | 5 | -0.020 | 0.013 | -1.521 | 0.128 | -0.053 | 0.014 |
| Openness | 2Y After - 1Y Before | 5 | -0.021 | 0.011 | -1.922 | 0.055 | -0.049 | 0.007 |
| Openness | >2Y After - 1Y Before | 5 | -0.019 | 0.011 | -1.712 | 0.087 | -0.047 | 0.010 |
| Life satisfaction | 1Y After - 2Y Before | 5 | 0.025 | 0.009 | 2.860 | 0.004 | 0.003 | 0.048 |
| Life satisfaction | 2Y After - 2Y Before | 5 | 0.024 | 0.010 | 2.346 | 0.019 | -0.002 | 0.051 |
| Life satisfaction | >2Y After - 2Y Before | 5 | 0.021 | 0.013 | 1.606 | 0.108 | -0.013 | 0.055 |
| Life satisfaction | 1Y After - 1Y Before | 5 | 0.067 | 0.013 | 5.095 | 0.000 | 0.033 | 0.100 |
| Life satisfaction | 2Y After - 1Y Before | 5 | 0.057 | 0.008 | 7.081 | 0.000 | 0.036 | 0.078 |
| Life satisfaction | >2Y After - 1Y Before | 5 | 0.068 | 0.016 | 4.188 | 0.000 | 0.026 | 0.110 |
| Self-esteem | 1Y After - 2Y Before | 3 | 0.019 | 0.014 | 1.316 | 0.188 | -0.018 | 0.055 |
| Self-esteem | 2Y After - 2Y Before | 3 | 0.009 | 0.015 | 0.586 | 0.558 | -0.030 | 0.048 |
| Self-esteem | >2Y After - 2Y Before | 3 | 0.013 | 0.016 | 0.803 | 0.422 | -0.028 | 0.053 |
| Self-esteem | 1Y After - 1Y Before | 3 | 0.016 | 0.038 | 0.431 | 0.667 | -0.081 | 0.113 |
| Self-esteem | 2Y After - 1Y Before | 3 | 0.018 | 0.015 | 1.212 | 0.225 | -0.020 | 0.056 |
| Self-esteem | >2Y After - 1Y Before | 3 | 0.023 | 0.036 | 0.633 | 0.527 | -0.070 | 0.115 |
Graph
This graph illustrates the meta-analytic estimates of the five event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the five event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
jobbeg_res_ma_5dumm_dummies <- filter(jobbeg_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
## Create variable on significance
jobbeg_res_ma_5dumm_dummies$sig <- ifelse(jobbeg_res_ma_5dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
jobbeg_res_ma_5dumm_dummies$trait <- factor(jobbeg_res_ma_5dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
jobbeg_res_ma_5dumm_dummies$Effect <- factor(jobbeg_res_ma_5dumm_dummies$Effect,
levels = c("DM2", "DM1", "DP1", "DP2", "DA2"),
labels = c("-2 Years", "-1 Year", "+1 Year",
"+2 Years", ">2 Years"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- jobbeg_res_ma_5dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
jobbeg_res_ma_5dumm_dummies$ID <- 1:nrow(jobbeg_res_ma_5dumm_dummies)
plot <- ggplot(data = jobbeg_res_ma_5dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 2.5, y = 0.28), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 2.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 4.5, y = 0.28),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Unemployment
unemploy_res_ma_5dumm <- filter(res_ma_5dumm_long, event == "Unemployment")
unemploy_res_ma_5dumm <- dplyr::select(unemploy_res_ma_5dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the five event-related dummy variables. These effects describe within-person changes in our outcome variables between assessments at a certain time point before/after the event occurrence and assessments unrelated to the event occurrence. Significant effects (p < .01) are depicted in bold.
unemploy_res_ma_5dumm_dummies <- filter(unemploy_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2") %>%
dplyr::select(-DIFFR2_avg)
unemploy_res_ma_5dumm_dummies$Effect <- recode(unemploy_res_ma_5dumm_dummies$Effect,
"DM2" = "2 years before event",
"DM1" = "1 year before event",
"DP1" = "1 year after event",
"DP2" = "2 years after event",
"DA2" = "> 2 years after event")
## Create table
kable(unemploy_res_ma_5dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(unemploy_res_ma_5dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 2 years before event | 6 | 0.011 | 0.042 | 0.274 | 0.784 | -0.096 | 0.119 |
| Agreeableness | 1 year before event | 6 | -0.016 | 0.033 | -0.476 | 0.634 | -0.102 | 0.070 |
| Agreeableness | 1 year after event | 6 | 0.013 | 0.018 | 0.735 | 0.463 | -0.033 | 0.059 |
| Agreeableness | 2 years after event | 6 | 0.015 | 0.020 | 0.741 | 0.459 | -0.037 | 0.067 |
| Agreeableness | > 2 years after event | 6 | -0.004 | 0.005 | -0.888 | 0.375 | -0.016 | 0.008 |
| Conscientiousness | 2 years before event | 6 | -0.010 | 0.018 | -0.574 | 0.566 | -0.056 | 0.036 |
| Conscientiousness | 1 year before event | 6 | -0.019 | 0.023 | -0.839 | 0.402 | -0.079 | 0.040 |
| Conscientiousness | 1 year after event | 6 | -0.011 | 0.020 | -0.554 | 0.580 | -0.064 | 0.041 |
| Conscientiousness | 2 years after event | 6 | -0.010 | 0.022 | -0.474 | 0.636 | -0.067 | 0.046 |
| Conscientiousness | > 2 years after event | 6 | -0.004 | 0.002 | -1.594 | 0.111 | -0.010 | 0.002 |
| Extraversion | 2 years before event | 6 | 0.015 | 0.012 | 1.203 | 0.229 | -0.017 | 0.047 |
| Extraversion | 1 year before event | 6 | 0.000 | 0.012 | -0.037 | 0.971 | -0.031 | 0.030 |
| Extraversion | 1 year after event | 6 | 0.002 | 0.013 | 0.138 | 0.890 | -0.031 | 0.035 |
| Extraversion | 2 years after event | 6 | -0.001 | 0.013 | -0.064 | 0.949 | -0.033 | 0.032 |
| Extraversion | > 2 years after event | 6 | 0.001 | 0.003 | 0.548 | 0.584 | -0.006 | 0.009 |
| Emotional stability | 2 years before event | 6 | -0.006 | 0.014 | -0.410 | 0.682 | -0.041 | 0.030 |
| Emotional stability | 1 year before event | 6 | -0.055 | 0.021 | -2.647 | 0.008 | -0.109 | -0.001 |
| Emotional stability | 1 year after event | 6 | -0.034 | 0.016 | -2.107 | 0.035 | -0.075 | 0.008 |
| Emotional stability | 2 years after event | 6 | -0.008 | 0.018 | -0.465 | 0.642 | -0.055 | 0.038 |
| Emotional stability | > 2 years after event | 6 | -0.001 | 0.002 | -0.604 | 0.546 | -0.005 | 0.003 |
| Openness | 2 years before event | 6 | 0.013 | 0.020 | 0.642 | 0.521 | -0.039 | 0.064 |
| Openness | 1 year before event | 6 | -0.004 | 0.013 | -0.311 | 0.756 | -0.036 | 0.028 |
| Openness | 1 year after event | 6 | 0.004 | 0.018 | 0.201 | 0.840 | -0.042 | 0.050 |
| Openness | 2 years after event | 6 | 0.007 | 0.034 | 0.216 | 0.829 | -0.081 | 0.096 |
| Openness | > 2 years after event | 6 | 0.002 | 0.001 | 1.637 | 0.102 | -0.001 | 0.006 |
| Life satisfaction | 2 years before event | 6 | -0.026 | 0.010 | -2.556 | 0.011 | -0.052 | 0.000 |
| Life satisfaction | 1 year before event | 6 | -0.076 | 0.027 | -2.806 | 0.005 | -0.146 | -0.006 |
| Life satisfaction | 1 year after event | 6 | -0.151 | 0.021 | -7.272 | 0.000 | -0.205 | -0.098 |
| Life satisfaction | 2 years after event | 6 | -0.081 | 0.015 | -5.251 | 0.000 | -0.121 | -0.041 |
| Life satisfaction | > 2 years after event | 6 | 0.004 | 0.003 | 1.485 | 0.137 | -0.003 | 0.012 |
| Self-esteem | 2 years before event | 4 | 0.004 | 0.018 | 0.201 | 0.841 | -0.044 | 0.051 |
| Self-esteem | 1 year before event | 4 | -0.032 | 0.018 | -1.734 | 0.083 | -0.079 | 0.015 |
| Self-esteem | 1 year after event | 4 | -0.056 | 0.019 | -2.936 | 0.003 | -0.105 | -0.007 |
| Self-esteem | 2 years after event | 4 | -0.063 | 0.037 | -1.719 | 0.086 | -0.158 | 0.031 |
| Self-esteem | > 2 years after event | 4 | -0.009 | 0.009 | -1.006 | 0.314 | -0.032 | 0.014 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the six linear contrasts that we used to examine personality changes occurring from specific pre-event to specific post-event assessments. Significant effects (p < .01) are depicted in bold.
unemploy_res_ma_5dumm_contrast <- filter(unemploy_res_ma_5dumm, Effect == "CONTRM2P1" |
Effect == "CONTRM2P2" | Effect == "CONTRM2A2" |
Effect == "CONTRM1P1" | Effect == "CONTRM1P2" |
Effect == "CONTRM1A2") %>%
dplyr::select(-DIFFR2_avg)
unemploy_res_ma_5dumm_contrast$Effect <- recode(unemploy_res_ma_5dumm_contrast$Effect,
"CONTRM2P1" = "1Y After - 2Y Before",
"CONTRM2P2" = "2Y After - 2Y Before",
"CONTRM2A2" = ">2Y After - 2Y Before",
"CONTRM1P1" = "1Y After - 1Y Before",
"CONTRM1P2" = "2Y After - 1Y Before",
"CONTRM1A2" = ">2Y After - 1Y Before")
## Create table
kable(unemploy_res_ma_5dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(unemploy_res_ma_5dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 1Y After - 2Y Before | 6 | 0.000 | 0.043 | 0.004 | 0.997 | -0.112 | 0.112 |
| Agreeableness | 2Y After - 2Y Before | 6 | -0.006 | 0.031 | -0.187 | 0.852 | -0.084 | 0.073 |
| Agreeableness | >2Y After - 2Y Before | 6 | -0.017 | 0.036 | -0.465 | 0.642 | -0.111 | 0.077 |
| Agreeableness | 1Y After - 1Y Before | 6 | 0.007 | 0.026 | 0.278 | 0.781 | -0.060 | 0.075 |
| Agreeableness | 2Y After - 1Y Before | 6 | 0.013 | 0.018 | 0.724 | 0.469 | -0.034 | 0.060 |
| Agreeableness | >2Y After - 1Y Before | 6 | 0.007 | 0.028 | 0.258 | 0.796 | -0.064 | 0.079 |
| Conscientiousness | 1Y After - 2Y Before | 6 | 0.007 | 0.034 | 0.203 | 0.839 | -0.080 | 0.094 |
| Conscientiousness | 2Y After - 2Y Before | 6 | 0.002 | 0.031 | 0.080 | 0.936 | -0.078 | 0.083 |
| Conscientiousness | >2Y After - 2Y Before | 6 | 0.007 | 0.017 | 0.400 | 0.689 | -0.037 | 0.050 |
| Conscientiousness | 1Y After - 1Y Before | 6 | 0.004 | 0.017 | 0.234 | 0.815 | -0.041 | 0.049 |
| Conscientiousness | 2Y After - 1Y Before | 6 | 0.008 | 0.027 | 0.302 | 0.763 | -0.061 | 0.077 |
| Conscientiousness | >2Y After - 1Y Before | 6 | 0.015 | 0.022 | 0.698 | 0.485 | -0.042 | 0.072 |
| Extraversion | 1Y After - 2Y Before | 6 | -0.014 | 0.025 | -0.561 | 0.575 | -0.077 | 0.050 |
| Extraversion | 2Y After - 2Y Before | 6 | -0.015 | 0.017 | -0.854 | 0.393 | -0.060 | 0.030 |
| Extraversion | >2Y After - 2Y Before | 6 | -0.013 | 0.012 | -1.062 | 0.288 | -0.044 | 0.018 |
| Extraversion | 1Y After - 1Y Before | 6 | 0.001 | 0.028 | 0.038 | 0.970 | -0.071 | 0.073 |
| Extraversion | 2Y After - 1Y Before | 6 | 0.005 | 0.015 | 0.311 | 0.756 | -0.034 | 0.044 |
| Extraversion | >2Y After - 1Y Before | 6 | 0.003 | 0.011 | 0.303 | 0.762 | -0.026 | 0.032 |
| Emotional stability | 1Y After - 2Y Before | 6 | -0.035 | 0.018 | -1.934 | 0.053 | -0.082 | 0.012 |
| Emotional stability | 2Y After - 2Y Before | 6 | 0.001 | 0.018 | 0.030 | 0.976 | -0.047 | 0.048 |
| Emotional stability | >2Y After - 2Y Before | 6 | 0.004 | 0.013 | 0.302 | 0.762 | -0.030 | 0.038 |
| Emotional stability | 1Y After - 1Y Before | 6 | 0.012 | 0.021 | 0.548 | 0.584 | -0.043 | 0.066 |
| Emotional stability | 2Y After - 1Y Before | 6 | 0.050 | 0.029 | 1.753 | 0.080 | -0.024 | 0.124 |
| Emotional stability | >2Y After - 1Y Before | 6 | 0.054 | 0.020 | 2.673 | 0.008 | 0.002 | 0.105 |
| Openness | 1Y After - 2Y Before | 6 | -0.004 | 0.037 | -0.116 | 0.907 | -0.100 | 0.091 |
| Openness | 2Y After - 2Y Before | 6 | -0.001 | 0.044 | -0.027 | 0.979 | -0.113 | 0.111 |
| Openness | >2Y After - 2Y Before | 6 | -0.012 | 0.020 | -0.578 | 0.563 | -0.063 | 0.040 |
| Openness | 1Y After - 1Y Before | 6 | 0.008 | 0.020 | 0.414 | 0.679 | -0.044 | 0.060 |
| Openness | 2Y After - 1Y Before | 6 | 0.007 | 0.037 | 0.191 | 0.849 | -0.089 | 0.103 |
| Openness | >2Y After - 1Y Before | 6 | 0.006 | 0.012 | 0.505 | 0.614 | -0.025 | 0.037 |
| Life satisfaction | 1Y After - 2Y Before | 6 | -0.133 | 0.022 | -5.929 | 0.000 | -0.190 | -0.075 |
| Life satisfaction | 2Y After - 2Y Before | 6 | -0.061 | 0.019 | -3.221 | 0.001 | -0.110 | -0.012 |
| Life satisfaction | >2Y After - 2Y Before | 6 | 0.027 | 0.013 | 2.069 | 0.038 | -0.007 | 0.061 |
| Life satisfaction | 1Y After - 1Y Before | 6 | -0.070 | 0.026 | -2.733 | 0.006 | -0.136 | -0.004 |
| Life satisfaction | 2Y After - 1Y Before | 6 | 0.006 | 0.020 | 0.287 | 0.774 | -0.045 | 0.056 |
| Life satisfaction | >2Y After - 1Y Before | 6 | 0.077 | 0.030 | 2.600 | 0.009 | 0.001 | 0.153 |
| Self-esteem | 1Y After - 2Y Before | 4 | -0.054 | 0.022 | -2.446 | 0.014 | -0.111 | 0.003 |
| Self-esteem | 2Y After - 2Y Before | 4 | -0.054 | 0.022 | -2.391 | 0.017 | -0.112 | 0.004 |
| Self-esteem | >2Y After - 2Y Before | 4 | -0.008 | 0.018 | -0.448 | 0.654 | -0.053 | 0.037 |
| Self-esteem | 1Y After - 1Y Before | 4 | -0.028 | 0.030 | -0.930 | 0.352 | -0.106 | 0.050 |
| Self-esteem | 2Y After - 1Y Before | 4 | -0.025 | 0.037 | -0.670 | 0.503 | -0.122 | 0.071 |
| Self-esteem | >2Y After - 1Y Before | 4 | 0.028 | 0.017 | 1.609 | 0.108 | -0.017 | 0.073 |
Graph
This graph illustrates the meta-analytic estimates of the five event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the five event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
unemploy_res_ma_5dumm_dummies <- filter(unemploy_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
## Create variable on significance
unemploy_res_ma_5dumm_dummies$sig <- ifelse(unemploy_res_ma_5dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
unemploy_res_ma_5dumm_dummies$trait <- factor(unemploy_res_ma_5dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
unemploy_res_ma_5dumm_dummies$Effect <- factor(unemploy_res_ma_5dumm_dummies$Effect,
levels = c("DM2", "DM1", "DP1", "DP2", "DA2"),
labels = c("-2 Years", "-1 Year", "+1 Year",
"+2 Years", ">2 Years"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- unemploy_res_ma_5dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
unemploy_res_ma_5dumm_dummies$ID <- 1:nrow(unemploy_res_ma_5dumm_dummies)
plot <- ggplot(data = unemploy_res_ma_5dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 2.5, y = 0.28), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 2.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 4.5, y = 0.28),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Retirement
retire_res_ma_5dumm <- filter(res_ma_5dumm_long, event == "Retirement")
retire_res_ma_5dumm <- dplyr::select(retire_res_ma_5dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the five event-related dummy variables. These effects describe within-person changes in our outcome variables between assessments at a certain time point before/after the event occurrence and assessments unrelated to the event occurrence. Significant effects (p < .01) are depicted in bold.
retire_res_ma_5dumm_dummies <- filter(retire_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2") %>%
dplyr::select(-DIFFR2_avg)
retire_res_ma_5dumm_dummies$Effect <- recode(retire_res_ma_5dumm_dummies$Effect,
"DM2" = "2 years before event",
"DM1" = "1 year before event",
"DP1" = "1 year after event",
"DP2" = "2 years after event",
"DA2" = "> 2 years after event")
## Create table
kable(retire_res_ma_5dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(retire_res_ma_5dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 2 years before event | 5 | 0.022 | 0.019 | 1.168 | 0.243 | -0.027 | 0.072 |
| Agreeableness | 1 year before event | 5 | -0.008 | 0.012 | -0.683 | 0.495 | -0.040 | 0.023 |
| Agreeableness | 1 year after event | 5 | 0.013 | 0.016 | 0.810 | 0.418 | -0.028 | 0.054 |
| Agreeableness | 2 years after event | 5 | 0.017 | 0.021 | 0.805 | 0.421 | -0.036 | 0.069 |
| Agreeableness | > 2 years after event | 5 | -0.001 | 0.003 | -0.342 | 0.732 | -0.008 | 0.006 |
| Conscientiousness | 2 years before event | 5 | 0.023 | 0.011 | 1.969 | 0.049 | -0.007 | 0.052 |
| Conscientiousness | 1 year before event | 5 | 0.017 | 0.011 | 1.482 | 0.138 | -0.012 | 0.046 |
| Conscientiousness | 1 year after event | 5 | -0.014 | 0.017 | -0.778 | 0.436 | -0.058 | 0.031 |
| Conscientiousness | 2 years after event | 5 | -0.037 | 0.026 | -1.414 | 0.157 | -0.104 | 0.030 |
| Conscientiousness | > 2 years after event | 5 | -0.001 | 0.002 | -0.369 | 0.712 | -0.005 | 0.004 |
| Extraversion | 2 years before event | 5 | -0.010 | 0.015 | -0.695 | 0.487 | -0.048 | 0.028 |
| Extraversion | 1 year before event | 5 | 0.008 | 0.012 | 0.668 | 0.504 | -0.022 | 0.038 |
| Extraversion | 1 year after event | 5 | 0.008 | 0.011 | 0.679 | 0.497 | -0.022 | 0.037 |
| Extraversion | 2 years after event | 5 | -0.006 | 0.017 | -0.327 | 0.744 | -0.049 | 0.038 |
| Extraversion | > 2 years after event | 5 | 0.001 | 0.003 | 0.363 | 0.717 | -0.007 | 0.010 |
| Emotional stability | 2 years before event | 5 | -0.007 | 0.012 | -0.617 | 0.537 | -0.038 | 0.023 |
| Emotional stability | 1 year before event | 5 | -0.026 | 0.017 | -1.554 | 0.120 | -0.068 | 0.017 |
| Emotional stability | 1 year after event | 5 | 0.002 | 0.023 | 0.100 | 0.921 | -0.056 | 0.061 |
| Emotional stability | 2 years after event | 5 | 0.023 | 0.016 | 1.401 | 0.161 | -0.019 | 0.064 |
| Emotional stability | > 2 years after event | 5 | -0.004 | 0.002 | -1.729 | 0.084 | -0.009 | 0.002 |
| Openness | 2 years before event | 5 | 0.008 | 0.011 | 0.702 | 0.483 | -0.021 | 0.038 |
| Openness | 1 year before event | 5 | 0.010 | 0.011 | 0.943 | 0.346 | -0.018 | 0.039 |
| Openness | 1 year after event | 5 | 0.015 | 0.013 | 1.082 | 0.279 | -0.020 | 0.049 |
| Openness | 2 years after event | 5 | 0.012 | 0.012 | 1.011 | 0.312 | -0.019 | 0.043 |
| Openness | > 2 years after event | 5 | 0.003 | 0.003 | 1.084 | 0.278 | -0.004 | 0.010 |
| Life satisfaction | 2 years before event | 5 | -0.014 | 0.019 | -0.706 | 0.480 | -0.063 | 0.036 |
| Life satisfaction | 1 year before event | 5 | -0.026 | 0.022 | -1.187 | 0.235 | -0.084 | 0.031 |
| Life satisfaction | 1 year after event | 5 | 0.045 | 0.028 | 1.636 | 0.102 | -0.026 | 0.117 |
| Life satisfaction | 2 years after event | 5 | 0.038 | 0.021 | 1.784 | 0.074 | -0.017 | 0.093 |
| Life satisfaction | > 2 years after event | 5 | 0.004 | 0.008 | 0.530 | 0.596 | -0.017 | 0.026 |
| Self-esteem | 2 years before event | 3 | -0.004 | 0.064 | -0.060 | 0.952 | -0.169 | 0.161 |
| Self-esteem | 1 year before event | 3 | 0.011 | 0.019 | 0.572 | 0.567 | -0.039 | 0.061 |
| Self-esteem | 1 year after event | 3 | 0.010 | 0.034 | 0.289 | 0.773 | -0.078 | 0.097 |
| Self-esteem | 2 years after event | 3 | 0.015 | 0.020 | 0.727 | 0.467 | -0.037 | 0.066 |
| Self-esteem | > 2 years after event | 3 | -0.002 | 0.003 | -0.575 | 0.565 | -0.009 | 0.005 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the six linear contrasts that we used to examine personality changes occurring from specific pre-event to specific post-event assessments. Significant effects (p < .01) are depicted in bold.
retire_res_ma_5dumm_contrast <- filter(retire_res_ma_5dumm, Effect == "CONTRM2P1" |
Effect == "CONTRM2P2" | Effect == "CONTRM2A2" |
Effect == "CONTRM1P1" | Effect == "CONTRM1P2" |
Effect == "CONTRM1A2") %>%
dplyr::select(-DIFFR2_avg)
retire_res_ma_5dumm_contrast$Effect <- recode(retire_res_ma_5dumm_contrast$Effect,
"CONTRM2P1" = "1Y After - 2Y Before",
"CONTRM2P2" = "2Y After - 2Y Before",
"CONTRM2A2" = ">2Y After - 2Y Before",
"CONTRM1P1" = "1Y After - 1Y Before",
"CONTRM1P2" = "2Y After - 1Y Before",
"CONTRM1A2" = ">2Y After - 1Y Before")
## Create table
kable(retire_res_ma_5dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(retire_res_ma_5dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | 1Y After - 2Y Before | 5 | -0.010 | 0.031 | -0.326 | 0.744 | -0.089 | 0.069 |
| Agreeableness | 2Y After - 2Y Before | 5 | -0.006 | 0.023 | -0.252 | 0.801 | -0.066 | 0.054 |
| Agreeableness | >2Y After - 2Y Before | 5 | -0.022 | 0.018 | -1.171 | 0.241 | -0.069 | 0.026 |
| Agreeableness | 1Y After - 1Y Before | 5 | 0.022 | 0.021 | 1.054 | 0.292 | -0.032 | 0.077 |
| Agreeableness | 2Y After - 1Y Before | 5 | 0.023 | 0.015 | 1.492 | 0.136 | -0.017 | 0.063 |
| Agreeableness | >2Y After - 1Y Before | 5 | 0.009 | 0.012 | 0.811 | 0.417 | -0.020 | 0.039 |
| Conscientiousness | 1Y After - 2Y Before | 5 | -0.036 | 0.022 | -1.662 | 0.097 | -0.092 | 0.020 |
| Conscientiousness | 2Y After - 2Y Before | 5 | -0.055 | 0.028 | -1.927 | 0.054 | -0.128 | 0.018 |
| Conscientiousness | >2Y After - 2Y Before | 5 | -0.022 | 0.011 | -1.960 | 0.050 | -0.050 | 0.007 |
| Conscientiousness | 1Y After - 1Y Before | 5 | -0.037 | 0.025 | -1.506 | 0.132 | -0.101 | 0.026 |
| Conscientiousness | 2Y After - 1Y Before | 5 | -0.062 | 0.036 | -1.723 | 0.085 | -0.154 | 0.031 |
| Conscientiousness | >2Y After - 1Y Before | 5 | -0.016 | 0.011 | -1.461 | 0.144 | -0.044 | 0.012 |
| Extraversion | 1Y After - 2Y Before | 5 | 0.022 | 0.016 | 1.374 | 0.170 | -0.019 | 0.062 |
| Extraversion | 2Y After - 2Y Before | 5 | 0.008 | 0.014 | 0.534 | 0.593 | -0.029 | 0.044 |
| Extraversion | >2Y After - 2Y Before | 5 | 0.012 | 0.014 | 0.842 | 0.400 | -0.024 | 0.048 |
| Extraversion | 1Y After - 1Y Before | 5 | 0.002 | 0.013 | 0.149 | 0.881 | -0.032 | 0.036 |
| Extraversion | 2Y After - 1Y Before | 5 | -0.013 | 0.013 | -1.026 | 0.305 | -0.047 | 0.020 |
| Extraversion | >2Y After - 1Y Before | 5 | -0.006 | 0.010 | -0.579 | 0.562 | -0.031 | 0.019 |
| Emotional stability | 1Y After - 2Y Before | 5 | 0.011 | 0.016 | 0.716 | 0.474 | -0.029 | 0.052 |
| Emotional stability | 2Y After - 2Y Before | 5 | 0.031 | 0.016 | 1.954 | 0.051 | -0.010 | 0.073 |
| Emotional stability | >2Y After - 2Y Before | 5 | 0.004 | 0.011 | 0.360 | 0.719 | -0.025 | 0.034 |
| Emotional stability | 1Y After - 1Y Before | 5 | 0.023 | 0.021 | 1.083 | 0.279 | -0.032 | 0.078 |
| Emotional stability | 2Y After - 1Y Before | 5 | 0.047 | 0.020 | 2.377 | 0.017 | -0.004 | 0.098 |
| Emotional stability | >2Y After - 1Y Before | 5 | 0.022 | 0.017 | 1.317 | 0.188 | -0.021 | 0.066 |
| Openness | 1Y After - 2Y Before | 5 | 0.008 | 0.017 | 0.451 | 0.652 | -0.037 | 0.053 |
| Openness | 2Y After - 2Y Before | 5 | 0.005 | 0.015 | 0.350 | 0.726 | -0.033 | 0.043 |
| Openness | >2Y After - 2Y Before | 5 | -0.004 | 0.011 | -0.336 | 0.737 | -0.032 | 0.025 |
| Openness | 1Y After - 1Y Before | 5 | 0.007 | 0.015 | 0.479 | 0.632 | -0.032 | 0.047 |
| Openness | 2Y After - 1Y Before | 5 | 0.004 | 0.014 | 0.284 | 0.777 | -0.032 | 0.040 |
| Openness | >2Y After - 1Y Before | 5 | -0.006 | 0.011 | -0.557 | 0.578 | -0.033 | 0.021 |
| Life satisfaction | 1Y After - 2Y Before | 5 | 0.055 | 0.035 | 1.556 | 0.120 | -0.036 | 0.146 |
| Life satisfaction | 2Y After - 2Y Before | 5 | 0.050 | 0.032 | 1.558 | 0.119 | -0.033 | 0.133 |
| Life satisfaction | >2Y After - 2Y Before | 5 | 0.020 | 0.025 | 0.800 | 0.423 | -0.044 | 0.083 |
| Life satisfaction | 1Y After - 1Y Before | 5 | 0.059 | 0.021 | 2.779 | 0.005 | 0.004 | 0.115 |
| Life satisfaction | 2Y After - 1Y Before | 5 | 0.064 | 0.025 | 2.586 | 0.010 | 0.000 | 0.129 |
| Life satisfaction | >2Y After - 1Y Before | 5 | 0.035 | 0.024 | 1.464 | 0.143 | -0.026 | 0.096 |
| Self-esteem | 1Y After - 2Y Before | 3 | 0.034 | 0.023 | 1.473 | 0.141 | -0.026 | 0.094 |
| Self-esteem | 2Y After - 2Y Before | 3 | 0.029 | 0.028 | 1.061 | 0.288 | -0.042 | 0.100 |
| Self-esteem | >2Y After - 2Y Before | 3 | -0.001 | 0.069 | -0.018 | 0.985 | -0.180 | 0.177 |
| Self-esteem | 1Y After - 1Y Before | 3 | 0.049 | 0.103 | 0.479 | 0.632 | -0.215 | 0.314 |
| Self-esteem | 2Y After - 1Y Before | 3 | 0.004 | 0.023 | 0.164 | 0.869 | -0.056 | 0.064 |
| Self-esteem | >2Y After - 1Y Before | 3 | -0.013 | 0.018 | -0.722 | 0.470 | -0.060 | 0.034 |
Graph
This graph illustrates the meta-analytic estimates of the five event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the five event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
retire_res_ma_5dumm_dummies <- filter(retire_res_ma_5dumm, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
## Create variable on significance
retire_res_ma_5dumm_dummies$sig <- ifelse(retire_res_ma_5dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
retire_res_ma_5dumm_dummies$trait <- factor(retire_res_ma_5dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
retire_res_ma_5dumm_dummies$Effect <- factor(retire_res_ma_5dumm_dummies$Effect,
levels = c("DM2", "DM1", "DP1", "DP2", "DA2"),
labels = c("-2 Years", "-1 Year", "+1 Year",
"+2 Years", ">2 Years"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- retire_res_ma_5dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
retire_res_ma_5dumm_dummies$ID <- 1:nrow(retire_res_ma_5dumm_dummies)
plot <- ggplot(data = retire_res_ma_5dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 2.5, y = 0.28), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 2.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 4.5, y = 0.28),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Patterns Across Life Events
Proportion of Significant Effects
res_ma_5dumm_long_dummmies <- filter(res_ma_5dumm_long, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
res_ma_5dumm_long_dummmies$sig <- ifelse(res_ma_5dumm_long_dummmies$p < 0.01, 1, 0)
dummies.sig <- as.data.frame(prop.table(table(res_ma_5dumm_long_dummmies$sig, res_ma_5dumm_long_dummmies$trait), margin = 2))
dummies.sig <- filter(dummies.sig, Var1 == 1) %>% select(-Var1)
kable(dummies.sig
, caption="**Proportion of significant effects across outcomes**"
, escape=FALSE
, label = NA
, digits = 2
, row.names = FALSE
, col.names = c("Trait", "Relative frequency")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") | Trait | Relative frequency |
|---|---|
| Agreeableness | 0.08 |
| Conscientiousness | 0.10 |
| Emotional stability | 0.14 |
| Extraversion | 0.04 |
| Life satisfaction | 0.54 |
| Openness | 0.12 |
| Self-esteem | 0.14 |
Average Effect Size of Significant Effects
res_ma_5dumm_long_dummmies <- filter(res_ma_5dumm_long, Effect == "DM2" |
Effect == "DM1" | Effect == "DP1" |
Effect == "DP2" | Effect == "DA2")
res_ma_5dumm_long_dummmies$sig <- ifelse(res_ma_5dumm_long_dummmies$p < 0.01, 1, 0)
dummies.sig <- filter(res_ma_5dumm_long_dummmies, sig == 1)
dummies.sig$ES <- abs(dummies.sig$ES)
es <- describeBy(dummies.sig$ES, group = dummies.sig$trait, mat = TRUE) %>% select(Trait = group1, Mean = mean, SD = sd)
kable(es
, caption="**Average effect size of significant effects**"
, escape=FALSE
, label = NA
, digits = 2
, row.names = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") | Trait | Mean | SD |
|---|---|---|
| Agreeableness | 0.06 | 0.02 |
| Conscientiousness | 0.05 | 0.03 |
| Emotional stability | 0.04 | 0.04 |
| Extraversion | 0.06 | 0.03 |
| Life satisfaction | 0.13 | 0.11 |
| Openness | 0.04 | 0.03 |
| Self-esteem | 0.05 | 0.02 |
R2 Values
r2.values <- describeBy(res_ma_5dumm_long$DIFFR2_avg, group = res_ma_5dumm_long$trait, mat = TRUE) %>% select(Trait = group1, Mean = mean, SD = sd)
kable(r2.values
, caption="**Average R^2^ values across outcomes**"
, escape=FALSE
, label = NA
, digits = 2
, row.names = FALSE) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") | Trait | Mean | SD |
|---|---|---|
| Agreeableness | 0.04 | 0.01 |
| Conscientiousness | 0.05 | 0.02 |
| Emotional stability | 0.05 | 0.03 |
| Extraversion | 0.03 | 0.01 |
| Life satisfaction | 0.17 | 0.09 |
| Openness | 0.04 | 0.01 |
| Self-esteem | 0.06 | 0.02 |
These R2 values indicate how much incremental variance the event-related dummies explain in the respective outcome beyond stable between-person differences and age-related changes.
Section 3: Model With Two Dummies
As an additional analysis, we explored the consistency of our findings when using a different way to specify the event-related dummies. To do so, we estimated fixed-effects the just two event-related dummies:
- DPre = 1 for all assessments that took place up to 2 years prior to the occurrence of the target event
- DPost = 1 for all assessments that took place after the occurrence of the target event.
## Results across datasets
res_ma_2dumm_long <- res_ma_2dumm %>%
pivot_longer(-c("event", "trait", "DIFFR2_avg"),
names_sep = "_",
names_to = c("Effect", "Names")) %>%
pivot_wider(names_from = ("Names"),
values_from = "value")
## Results across datasets and events
res_ma_event_2dumm_long <- res_ma_event_2dumm %>%
pivot_longer(-c("EventType", "trait", "DIFFR2_avg"),
names_sep = "_",
names_to = c("Effect", "Names")) %>%
pivot_wider(names_from = ("Names"),
values_from = "value")New Relationship
relbeg_res_ma_2dumm <- filter(res_ma_2dumm_long, event == "New relationship")
relbeg_res_ma_2dumm <- dplyr::select(relbeg_res_ma_2dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the two event-related dummy variables. These dummies quantify changes in our outcome variables across assessments occurring up to 2 years prior to or any time after the occurrence of a life event. Significant effects (p < .01) are depicted in bold.
relbeg_res_ma_2dumm_dummies <- filter(relbeg_res_ma_2dumm, Effect == "Pre" |
Effect == "Post") %>%
dplyr::select(-DIFFR2_avg)
relbeg_res_ma_2dumm_dummies$Effect <- recode(relbeg_res_ma_2dumm_dummies$Effect,
"Pre" = "Changes before event",
"Post" = "Changes after event")
## Create table
kable(relbeg_res_ma_2dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(relbeg_res_ma_2dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | Changes before event | 7 | 0.011 | 0.025 | 0.452 | 0.651 | -0.054 | 0.076 |
| Agreeableness | Changes after event | 7 | 0.011 | 0.012 | 0.974 | 0.330 | -0.019 | 0.041 |
| Conscientiousness | Changes before event | 7 | -0.015 | 0.022 | -0.676 | 0.499 | -0.071 | 0.042 |
| Conscientiousness | Changes after event | 7 | 0.019 | 0.020 | 0.924 | 0.356 | -0.033 | 0.071 |
| Extraversion | Changes before event | 7 | 0.032 | 0.011 | 2.903 | 0.004 | 0.004 | 0.061 |
| Extraversion | Changes after event | 7 | 0.000 | 0.010 | 0.004 | 0.997 | -0.027 | 0.027 |
| Emotional stability | Changes before event | 7 | -0.014 | 0.017 | -0.825 | 0.410 | -0.057 | 0.029 |
| Emotional stability | Changes after event | 7 | -0.005 | 0.013 | -0.389 | 0.697 | -0.039 | 0.029 |
| Openness | Changes before event | 7 | 0.043 | 0.019 | 2.263 | 0.024 | -0.006 | 0.093 |
| Openness | Changes after event | 7 | 0.009 | 0.018 | 0.517 | 0.605 | -0.038 | 0.057 |
| Life satisfaction | Changes before event | 7 | -0.024 | 0.040 | -0.596 | 0.551 | -0.128 | 0.080 |
| Life satisfaction | Changes after event | 7 | 0.121 | 0.038 | 3.175 | 0.001 | 0.023 | 0.220 |
| Self-esteem | Changes before event | 5 | 0.123 | 0.135 | 0.915 | 0.360 | -0.224 | 0.471 |
| Self-esteem | Changes after event | 5 | 0.060 | 0.014 | 4.383 | 0.000 | 0.025 | 0.095 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the linear contrast DPre – DPost = 0 that allowed us to examine whether there were any personality trait changes from pre-event to post-event personality assessments. Significant effects (p < .01) are depicted in bold.
relbeg_res_ma_2dumm_contrast <- filter(relbeg_res_ma_2dumm, Effect == "CONTRPrePost") %>%
dplyr::select(-DIFFR2_avg)
relbeg_res_ma_2dumm_contrast$Effect <- recode(relbeg_res_ma_2dumm_contrast$Effect,
"CONTRPrePost" = "After - Before")
## Create table
kable(relbeg_res_ma_2dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(relbeg_res_ma_2dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | After - Before | 7 | 0.003 | 0.023 | 0.141 | 0.888 | -0.057 | 0.064 |
| Conscientiousness | After - Before | 7 | 0.039 | 0.022 | 1.790 | 0.073 | -0.017 | 0.096 |
| Extraversion | After - Before | 7 | -0.026 | 0.011 | -2.385 | 0.017 | -0.054 | 0.002 |
| Emotional stability | After - Before | 7 | 0.013 | 0.018 | 0.741 | 0.459 | -0.032 | 0.058 |
| Openness | After - Before | 7 | -0.038 | 0.020 | -1.867 | 0.062 | -0.090 | 0.014 |
| Life satisfaction | After - Before | 7 | 0.152 | 0.023 | 6.763 | 0.000 | 0.094 | 0.210 |
| Self-esteem | After - Before | 5 | -0.049 | 0.112 | -0.437 | 0.662 | -0.338 | 0.240 |
Graph
This graph illustrates the meta-analytic estimates of the two event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the two event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
relbeg_res_ma_2dumm_dummies <- filter(relbeg_res_ma_2dumm, Effect == "Pre" |
Effect == "Post")
## Create variable on significance
relbeg_res_ma_2dumm_dummies$sig <- ifelse(relbeg_res_ma_2dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
relbeg_res_ma_2dumm_dummies$trait <- factor(relbeg_res_ma_2dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
relbeg_res_ma_2dumm_dummies$Effect <- factor(relbeg_res_ma_2dumm_dummies$Effect,
levels = c("Pre", "Post"),
labels = c("Before", "After"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- relbeg_res_ma_2dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
relbeg_res_ma_2dumm_dummies$ID <- 1:nrow(relbeg_res_ma_2dumm_dummies)
plot <- ggplot(data = relbeg_res_ma_2dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 1.5, y = 0.29), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 1.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 2.1, y = -0.25),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Marriage
marriage_res_ma_2dumm <- filter(res_ma_2dumm_long, event == "Marriage")
marriage_res_ma_2dumm <- dplyr::select(marriage_res_ma_2dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the two event-related dummy variables. These dummies quantify changes in our outcome variables across assessments occurring up to 2 years prior to or any time after the occurrence of a life event. Significant effects (p < .01) are depicted in bold.
marriage_res_ma_2dumm_dummies <- filter(marriage_res_ma_2dumm, Effect == "Pre" |
Effect == "Post") %>%
dplyr::select(-DIFFR2_avg)
marriage_res_ma_2dumm_dummies$Effect <- recode(marriage_res_ma_2dumm_dummies$Effect,
"Pre" = "Changes before event",
"Post" = "Changes after event")
## Create table
kable(marriage_res_ma_2dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(marriage_res_ma_2dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | Changes before event | 7 | 0.041 | 0.015 | 2.830 | 0.005 | 0.004 | 0.079 |
| Agreeableness | Changes after event | 7 | -0.020 | 0.015 | -1.302 | 0.193 | -0.059 | 0.019 |
| Conscientiousness | Changes before event | 7 | 0.048 | 0.013 | 3.750 | 0.000 | 0.015 | 0.081 |
| Conscientiousness | Changes after event | 7 | 0.005 | 0.011 | 0.485 | 0.628 | -0.023 | 0.034 |
| Extraversion | Changes before event | 7 | 0.005 | 0.010 | 0.448 | 0.654 | -0.022 | 0.031 |
| Extraversion | Changes after event | 7 | -0.023 | 0.009 | -2.490 | 0.013 | -0.048 | 0.001 |
| Emotional stability | Changes before event | 7 | 0.029 | 0.012 | 2.434 | 0.015 | -0.002 | 0.059 |
| Emotional stability | Changes after event | 7 | 0.018 | 0.011 | 1.660 | 0.097 | -0.010 | 0.046 |
| Openness | Changes before event | 7 | -0.001 | 0.020 | -0.061 | 0.952 | -0.052 | 0.050 |
| Openness | Changes after event | 7 | -0.057 | 0.031 | -1.822 | 0.068 | -0.137 | 0.023 |
| Life satisfaction | Changes before event | 7 | 0.161 | 0.014 | 11.263 | 0.000 | 0.124 | 0.198 |
| Life satisfaction | Changes after event | 7 | 0.204 | 0.020 | 10.023 | 0.000 | 0.151 | 0.256 |
| Self-esteem | Changes before event | 5 | 0.067 | 0.026 | 2.558 | 0.011 | 0.000 | 0.135 |
| Self-esteem | Changes after event | 5 | 0.028 | 0.015 | 1.891 | 0.059 | -0.010 | 0.067 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the linear contrast DPre – DPost = 0 that allowed us to examine whether there were any personality trait changes from pre-event to post-event personality assessments. Significant effects (p < .01) are depicted in bold.
marriage_res_ma_2dumm_contrast <- filter(marriage_res_ma_2dumm, Effect == "CONTRPrePost") %>%
dplyr::select(-DIFFR2_avg)
marriage_res_ma_2dumm_contrast$Effect <- recode(marriage_res_ma_2dumm_contrast$Effect,
"CONTRPrePost" = "After - Before")
## Create table
kable(marriage_res_ma_2dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(marriage_res_ma_2dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | After - Before | 7 | -0.065 | 0.022 | -2.960 | 0.003 | -0.121 | -0.008 |
| Conscientiousness | After - Before | 7 | -0.045 | 0.010 | -4.373 | 0.000 | -0.072 | -0.019 |
| Extraversion | After - Before | 7 | -0.029 | 0.009 | -3.119 | 0.002 | -0.053 | -0.005 |
| Emotional stability | After - Before | 7 | -0.013 | 0.011 | -1.256 | 0.209 | -0.041 | 0.014 |
| Openness | After - Before | 7 | -0.065 | 0.015 | -4.342 | 0.000 | -0.104 | -0.026 |
| Life satisfaction | After - Before | 7 | 0.034 | 0.016 | 2.111 | 0.035 | -0.007 | 0.075 |
| Self-esteem | After - Before | 5 | -0.035 | 0.018 | -2.002 | 0.045 | -0.080 | 0.010 |
Graph
This graph illustrates the meta-analytic estimates of the two event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the two event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
marriage_res_ma_2dumm_dummies <- filter(marriage_res_ma_2dumm, Effect == "Pre" |
Effect == "Post")
## Create variable on significance
marriage_res_ma_2dumm_dummies$sig <- ifelse(marriage_res_ma_2dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
marriage_res_ma_2dumm_dummies$trait <- factor(marriage_res_ma_2dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
marriage_res_ma_2dumm_dummies$Effect <- factor(marriage_res_ma_2dumm_dummies$Effect,
levels = c("Pre", "Post"),
labels = c("Before", "After"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- marriage_res_ma_2dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
marriage_res_ma_2dumm_dummies$ID <- 1:nrow(marriage_res_ma_2dumm_dummies)
plot <- ggplot(data = marriage_res_ma_2dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 1.5, y = 0.29), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 1.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 2.1, y = -0.25),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Childbirth
child_res_ma_2dumm <- filter(res_ma_2dumm_long, event == "Childbirth")
child_res_ma_2dumm <- dplyr::select(child_res_ma_2dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the two event-related dummy variables. These dummies quantify changes in our outcome variables across assessments occurring up to 2 years prior to or any time after the occurrence of a life event. Significant effects (p < .01) are depicted in bold.
child_res_ma_2dumm_dummies <- filter(child_res_ma_2dumm, Effect == "Pre" |
Effect == "Post") %>%
dplyr::select(-DIFFR2_avg)
child_res_ma_2dumm_dummies$Effect <- recode(child_res_ma_2dumm_dummies$Effect,
"Pre" = "Changes before event",
"Post" = "Changes after event")
## Create table
kable(child_res_ma_2dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(child_res_ma_2dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | Changes before event | 7 | 0.004 | 0.013 | 0.341 | 0.733 | -0.029 | 0.038 |
| Agreeableness | Changes after event | 7 | -0.056 | 0.037 | -1.493 | 0.135 | -0.152 | 0.040 |
| Conscientiousness | Changes before event | 7 | 0.004 | 0.020 | 0.213 | 0.832 | -0.048 | 0.056 |
| Conscientiousness | Changes after event | 7 | -0.050 | 0.018 | -2.710 | 0.007 | -0.097 | -0.002 |
| Extraversion | Changes before event | 7 | -0.015 | 0.010 | -1.501 | 0.133 | -0.040 | 0.011 |
| Extraversion | Changes after event | 7 | -0.030 | 0.009 | -3.152 | 0.002 | -0.054 | -0.005 |
| Emotional stability | Changes before event | 7 | 0.027 | 0.014 | 1.905 | 0.057 | -0.010 | 0.064 |
| Emotional stability | Changes after event | 7 | 0.015 | 0.035 | 0.424 | 0.672 | -0.075 | 0.105 |
| Openness | Changes before event | 7 | -0.038 | 0.015 | -2.517 | 0.012 | -0.078 | 0.001 |
| Openness | Changes after event | 7 | -0.075 | 0.023 | -3.240 | 0.001 | -0.135 | -0.015 |
| Life satisfaction | Changes before event | 7 | 0.104 | 0.017 | 6.146 | 0.000 | 0.060 | 0.147 |
| Life satisfaction | Changes after event | 7 | 0.081 | 0.034 | 2.353 | 0.019 | -0.008 | 0.169 |
| Self-esteem | Changes before event | 5 | 0.054 | 0.017 | 3.198 | 0.001 | 0.011 | 0.098 |
| Self-esteem | Changes after event | 5 | 0.022 | 0.014 | 1.548 | 0.122 | -0.015 | 0.059 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the linear contrast DPre – DPost = 0 that allowed us to examine whether there were any personality trait changes from pre-event to post-event personality assessments. Significant effects (p < .01) are depicted in bold.
child_res_ma_2dumm_contrast <- filter(child_res_ma_2dumm, Effect == "CONTRPrePost") %>%
dplyr::select(-DIFFR2_avg)
child_res_ma_2dumm_contrast$Effect <- recode(child_res_ma_2dumm_contrast$Effect,
"CONTRPrePost" = "After - Before")
## Create table
kable(child_res_ma_2dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(child_res_ma_2dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | After - Before | 7 | -0.045 | 0.021 | -2.204 | 0.028 | -0.099 | 0.008 |
| Conscientiousness | After - Before | 7 | -0.055 | 0.021 | -2.571 | 0.010 | -0.109 | 0.000 |
| Extraversion | After - Before | 7 | -0.022 | 0.017 | -1.254 | 0.210 | -0.066 | 0.023 |
| Emotional stability | After - Before | 7 | -0.006 | 0.025 | -0.241 | 0.810 | -0.069 | 0.057 |
| Openness | After - Before | 7 | -0.037 | 0.009 | -4.121 | 0.000 | -0.060 | -0.014 |
| Life satisfaction | After - Before | 7 | -0.016 | 0.027 | -0.606 | 0.545 | -0.086 | 0.053 |
| Self-esteem | After - Before | 5 | -0.031 | 0.013 | -2.295 | 0.022 | -0.065 | 0.004 |
Graph
This graph illustrates the meta-analytic estimates of the two event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the two event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
child_res_ma_2dumm_dummies <- filter(child_res_ma_2dumm, Effect == "Pre" |
Effect == "Post")
## Create variable on significance
child_res_ma_2dumm_dummies$sig <- ifelse(child_res_ma_2dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
child_res_ma_2dumm_dummies$trait <- factor(child_res_ma_2dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
child_res_ma_2dumm_dummies$Effect <- factor(child_res_ma_2dumm_dummies$Effect,
levels = c("Pre", "Post"),
labels = c("Before", "After"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- child_res_ma_2dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
child_res_ma_2dumm_dummies$ID <- 1:nrow(child_res_ma_2dumm_dummies)
plot <- ggplot(data = child_res_ma_2dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 1.5, y = 0.29), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 1.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 2.1, y = -0.25),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Separation
separat_res_ma_2dumm <- filter(res_ma_2dumm_long, event == "Separation")
separat_res_ma_2dumm <- dplyr::select(separat_res_ma_2dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the two event-related dummy variables. These dummies quantify changes in our outcome variables across assessments occurring up to 2 years prior to or any time after the occurrence of a life event. Significant effects (p < .01) are depicted in bold.
separat_res_ma_2dumm_dummies <- filter(separat_res_ma_2dumm, Effect == "Pre" |
Effect == "Post") %>%
dplyr::select(-DIFFR2_avg)
separat_res_ma_2dumm_dummies$Effect <- recode(separat_res_ma_2dumm_dummies$Effect,
"Pre" = "Changes before event",
"Post" = "Changes after event")
## Create table
kable(separat_res_ma_2dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(separat_res_ma_2dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | Changes before event | 6 | -0.002 | 0.016 | -0.127 | 0.899 | -0.044 | 0.040 |
| Agreeableness | Changes after event | 6 | 0.059 | 0.017 | 3.552 | 0.000 | 0.016 | 0.102 |
| Conscientiousness | Changes before event | 6 | -0.014 | 0.014 | -1.012 | 0.312 | -0.050 | 0.022 |
| Conscientiousness | Changes after event | 6 | -0.015 | 0.012 | -1.217 | 0.224 | -0.047 | 0.017 |
| Extraversion | Changes before event | 6 | -0.007 | 0.021 | -0.330 | 0.742 | -0.062 | 0.048 |
| Extraversion | Changes after event | 6 | 0.019 | 0.018 | 1.085 | 0.278 | -0.026 | 0.065 |
| Emotional stability | Changes before event | 6 | -0.053 | 0.034 | -1.549 | 0.121 | -0.142 | 0.035 |
| Emotional stability | Changes after event | 6 | 0.010 | 0.013 | 0.754 | 0.451 | -0.024 | 0.044 |
| Openness | Changes before event | 6 | 0.001 | 0.014 | 0.087 | 0.931 | -0.036 | 0.038 |
| Openness | Changes after event | 6 | 0.042 | 0.013 | 3.132 | 0.002 | 0.007 | 0.077 |
| Life satisfaction | Changes before event | 6 | -0.145 | 0.058 | -2.497 | 0.013 | -0.295 | 0.005 |
| Life satisfaction | Changes after event | 6 | -0.123 | 0.016 | -7.643 | 0.000 | -0.165 | -0.082 |
| Self-esteem | Changes before event | 4 | -0.119 | 0.069 | -1.725 | 0.085 | -0.296 | 0.059 |
| Self-esteem | Changes after event | 4 | -0.026 | 0.035 | -0.729 | 0.466 | -0.116 | 0.065 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the linear contrast DPre – DPost = 0 that allowed us to examine whether there were any personality trait changes from pre-event to post-event personality assessments. Significant effects (p < .01) are depicted in bold.
separat_res_ma_2dumm_contrast <- filter(separat_res_ma_2dumm, Effect == "CONTRPrePost") %>%
dplyr::select(-DIFFR2_avg)
separat_res_ma_2dumm_contrast$Effect <- recode(separat_res_ma_2dumm_contrast$Effect,
"CONTRPrePost" = "After - Before")
## Create table
kable(separat_res_ma_2dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(separat_res_ma_2dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | After - Before | 6 | 0.052 | 0.026 | 1.999 | 0.046 | -0.015 | 0.118 |
| Conscientiousness | After - Before | 6 | 0.003 | 0.013 | 0.199 | 0.842 | -0.031 | 0.036 |
| Extraversion | After - Before | 6 | 0.022 | 0.012 | 1.888 | 0.059 | -0.008 | 0.052 |
| Emotional stability | After - Before | 6 | 0.057 | 0.024 | 2.412 | 0.016 | -0.004 | 0.117 |
| Openness | After - Before | 6 | 0.035 | 0.018 | 1.982 | 0.047 | -0.011 | 0.081 |
| Life satisfaction | After - Before | 6 | 0.005 | 0.051 | 0.094 | 0.925 | -0.126 | 0.135 |
| Self-esteem | After - Before | 4 | 0.086 | 0.055 | 1.562 | 0.118 | -0.056 | 0.227 |
Graph
This graph illustrates the meta-analytic estimates of the two event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the two event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
separat_res_ma_2dumm_dummies <- filter(separat_res_ma_2dumm, Effect == "Pre" |
Effect == "Post")
## Create variable on significance
separat_res_ma_2dumm_dummies$sig <- ifelse(separat_res_ma_2dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
separat_res_ma_2dumm_dummies$trait <- factor(separat_res_ma_2dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
separat_res_ma_2dumm_dummies$Effect <- factor(separat_res_ma_2dumm_dummies$Effect,
levels = c("Pre", "Post"),
labels = c("Before", "After"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- separat_res_ma_2dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
separat_res_ma_2dumm_dummies$ID <- 1:nrow(separat_res_ma_2dumm_dummies)
plot <- ggplot(data = separat_res_ma_2dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 1.5, y = 0.29), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 1.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 2.1, y = -0.25),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Divorce
divor_res_ma_2dumm <- filter(res_ma_2dumm_long, event == "Divorce")
divor_res_ma_2dumm <- dplyr::select(divor_res_ma_2dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the two event-related dummy variables. These dummies quantify changes in our outcome variables across assessments occurring up to 2 years prior to or any time after the occurrence of a life event. Significant effects (p < .01) are depicted in bold.
divor_res_ma_2dumm_dummies <- filter(divor_res_ma_2dumm, Effect == "Pre" |
Effect == "Post") %>%
dplyr::select(-DIFFR2_avg)
divor_res_ma_2dumm_dummies$Effect <- recode(divor_res_ma_2dumm_dummies$Effect,
"Pre" = "Changes before event",
"Post" = "Changes after event")
## Create table
kable(divor_res_ma_2dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(divor_res_ma_2dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | Changes before event | 7 | 0.019 | 0.028 | 0.679 | 0.497 | -0.053 | 0.092 |
| Agreeableness | Changes after event | 7 | 0.039 | 0.025 | 1.563 | 0.118 | -0.025 | 0.103 |
| Conscientiousness | Changes before event | 7 | -0.050 | 0.018 | -2.792 | 0.005 | -0.096 | -0.004 |
| Conscientiousness | Changes after event | 7 | -0.035 | 0.015 | -2.242 | 0.025 | -0.074 | 0.005 |
| Extraversion | Changes before event | 7 | -0.001 | 0.017 | -0.054 | 0.957 | -0.045 | 0.043 |
| Extraversion | Changes after event | 7 | 0.026 | 0.021 | 1.238 | 0.216 | -0.028 | 0.081 |
| Emotional stability | Changes before event | 7 | -0.058 | 0.025 | -2.314 | 0.021 | -0.123 | 0.007 |
| Emotional stability | Changes after event | 7 | 0.033 | 0.019 | 1.801 | 0.072 | -0.014 | 0.081 |
| Openness | Changes before event | 7 | 0.026 | 0.024 | 1.092 | 0.275 | -0.035 | 0.087 |
| Openness | Changes after event | 7 | 0.045 | 0.014 | 3.142 | 0.002 | 0.008 | 0.082 |
| Life satisfaction | Changes before event | 7 | -0.236 | 0.037 | -6.293 | 0.000 | -0.332 | -0.139 |
| Life satisfaction | Changes after event | 7 | 0.003 | 0.042 | 0.062 | 0.951 | -0.107 | 0.112 |
| Self-esteem | Changes before event | 5 | -0.079 | 0.033 | -2.359 | 0.018 | -0.165 | 0.007 |
| Self-esteem | Changes after event | 5 | 0.010 | 0.040 | 0.253 | 0.800 | -0.093 | 0.113 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the linear contrast DPre – DPost = 0 that allowed us to examine whether there were any personality trait changes from pre-event to post-event personality assessments. Significant effects (p < .01) are depicted in bold.
divor_res_ma_2dumm_contrast <- filter(divor_res_ma_2dumm, Effect == "CONTRPrePost") %>%
dplyr::select(-DIFFR2_avg)
divor_res_ma_2dumm_contrast$Effect <- recode(divor_res_ma_2dumm_contrast$Effect,
"CONTRPrePost" = "After - Before")
## Create table
kable(divor_res_ma_2dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(divor_res_ma_2dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | After - Before | 7 | 0.021 | 0.018 | 1.165 | 0.244 | -0.025 | 0.066 |
| Conscientiousness | After - Before | 7 | 0.020 | 0.017 | 1.167 | 0.243 | -0.024 | 0.063 |
| Extraversion | After - Before | 7 | 0.022 | 0.021 | 1.072 | 0.284 | -0.031 | 0.076 |
| Emotional stability | After - Before | 7 | 0.079 | 0.018 | 4.457 | 0.000 | 0.033 | 0.125 |
| Openness | After - Before | 7 | 0.018 | 0.016 | 1.139 | 0.255 | -0.023 | 0.059 |
| Life satisfaction | After - Before | 7 | 0.262 | 0.021 | 12.288 | 0.000 | 0.207 | 0.317 |
| Self-esteem | After - Before | 5 | 0.098 | 0.030 | 3.306 | 0.001 | 0.022 | 0.175 |
Graph
This graph illustrates the meta-analytic estimates of the two event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the two event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
divor_res_ma_2dumm_dummies <- filter(divor_res_ma_2dumm, Effect == "Pre" |
Effect == "Post")
## Create variable on significance
divor_res_ma_2dumm_dummies$sig <- ifelse(divor_res_ma_2dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
divor_res_ma_2dumm_dummies$trait <- factor(divor_res_ma_2dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
divor_res_ma_2dumm_dummies$Effect <- factor(divor_res_ma_2dumm_dummies$Effect,
levels = c("Pre", "Post"),
labels = c("Before", "After"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- divor_res_ma_2dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
divor_res_ma_2dumm_dummies$ID <- 1:nrow(divor_res_ma_2dumm_dummies)
plot <- ggplot(data = divor_res_ma_2dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 1.5, y = 0.29), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 1.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 2.1, y = -0.25),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Widowhood
widow_res_ma_2dumm <- filter(res_ma_2dumm_long, event == "Widowhood")
widow_res_ma_2dumm <- dplyr::select(widow_res_ma_2dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the two event-related dummy variables. These dummies quantify changes in our outcome variables across assessments occurring up to 2 years prior to or any time after the occurrence of a life event. Significant effects (p < .01) are depicted in bold.
widow_res_ma_2dumm_dummies <- filter(widow_res_ma_2dumm, Effect == "Pre" |
Effect == "Post") %>%
dplyr::select(-DIFFR2_avg)
widow_res_ma_2dumm_dummies$Effect <- recode(widow_res_ma_2dumm_dummies$Effect,
"Pre" = "Changes before event",
"Post" = "Changes after event")
## Create table
kable(widow_res_ma_2dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(widow_res_ma_2dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | Changes before event | 5 | 0.006 | 0.017 | 0.341 | 0.733 | -0.037 | 0.048 |
| Agreeableness | Changes after event | 5 | 0.021 | 0.035 | 0.612 | 0.540 | -0.069 | 0.112 |
| Conscientiousness | Changes before event | 5 | -0.019 | 0.027 | -0.709 | 0.478 | -0.088 | 0.050 |
| Conscientiousness | Changes after event | 5 | -0.059 | 0.018 | -3.308 | 0.001 | -0.105 | -0.013 |
| Extraversion | Changes before event | 5 | 0.009 | 0.041 | 0.210 | 0.834 | -0.098 | 0.115 |
| Extraversion | Changes after event | 5 | -0.042 | 0.027 | -1.565 | 0.118 | -0.112 | 0.027 |
| Emotional stability | Changes before event | 5 | -0.089 | 0.017 | -5.385 | 0.000 | -0.132 | -0.046 |
| Emotional stability | Changes after event | 5 | 0.028 | 0.024 | 1.187 | 0.235 | -0.033 | 0.089 |
| Openness | Changes before event | 5 | -0.015 | 0.031 | -0.474 | 0.635 | -0.094 | 0.065 |
| Openness | Changes after event | 5 | -0.027 | 0.022 | -1.259 | 0.208 | -0.083 | 0.029 |
| Life satisfaction | Changes before event | 5 | -0.188 | 0.023 | -8.194 | 0.000 | -0.248 | -0.129 |
| Life satisfaction | Changes after event | 5 | -0.189 | 0.051 | -3.689 | 0.000 | -0.321 | -0.057 |
| Self-esteem | Changes before event | 3 | -0.046 | 0.048 | -0.956 | 0.339 | -0.169 | 0.077 |
| Self-esteem | Changes after event | 3 | -0.064 | 0.061 | -1.048 | 0.295 | -0.223 | 0.094 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the linear contrast DPre – DPost = 0 that allowed us to examine whether there were any personality trait changes from pre-event to post-event personality assessments. Significant effects (p < .01) are depicted in bold.
widow_res_ma_2dumm_contrast <- filter(widow_res_ma_2dumm, Effect == "CONTRPrePost") %>%
dplyr::select(-DIFFR2_avg)
widow_res_ma_2dumm_contrast$Effect <- recode(widow_res_ma_2dumm_contrast$Effect,
"CONTRPrePost" = "After - Before")
## Create table
kable(widow_res_ma_2dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(widow_res_ma_2dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | After - Before | 5 | 0.017 | 0.038 | 0.436 | 0.663 | -0.082 | 0.116 |
| Conscientiousness | After - Before | 5 | -0.039 | 0.031 | -1.272 | 0.203 | -0.118 | 0.040 |
| Extraversion | After - Before | 5 | -0.050 | 0.029 | -1.687 | 0.092 | -0.126 | 0.026 |
| Emotional stability | After - Before | 5 | 0.110 | 0.033 | 3.323 | 0.001 | 0.025 | 0.195 |
| Openness | After - Before | 5 | -0.012 | 0.028 | -0.418 | 0.676 | -0.083 | 0.060 |
| Life satisfaction | After - Before | 5 | -0.001 | 0.038 | -0.029 | 0.977 | -0.100 | 0.098 |
| Self-esteem | After - Before | 3 | -0.012 | 0.033 | -0.367 | 0.714 | -0.097 | 0.073 |
Graph
This graph illustrates the meta-analytic estimates of the two event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the two event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
widow_res_ma_2dumm_dummies <- filter(widow_res_ma_2dumm, Effect == "Pre" |
Effect == "Post")
## Create variable on significance
widow_res_ma_2dumm_dummies$sig <- ifelse(widow_res_ma_2dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
widow_res_ma_2dumm_dummies$trait <- factor(widow_res_ma_2dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
widow_res_ma_2dumm_dummies$Effect <- factor(widow_res_ma_2dumm_dummies$Effect,
levels = c("Pre", "Post"),
labels = c("Before", "After"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- widow_res_ma_2dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
widow_res_ma_2dumm_dummies$ID <- 1:nrow(widow_res_ma_2dumm_dummies)
plot <- ggplot(data = widow_res_ma_2dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 1.5, y = 0.29), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 1.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 2.1, y = -0.25),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Graduation
gradu_res_ma_2dumm <- filter(res_ma_2dumm_long, event == "Graduation")
gradu_res_ma_2dumm <- dplyr::select(gradu_res_ma_2dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the two event-related dummy variables. These dummies quantify changes in our outcome variables across assessments occurring up to 2 years prior to or any time after the occurrence of a life event. Significant effects (p < .01) are depicted in bold.
gradu_res_ma_2dumm_dummies <- filter(gradu_res_ma_2dumm, Effect == "Pre" |
Effect == "Post") %>%
dplyr::select(-DIFFR2_avg)
gradu_res_ma_2dumm_dummies$Effect <- recode(gradu_res_ma_2dumm_dummies$Effect,
"Pre" = "Changes before event",
"Post" = "Changes after event")
## Create table
kable(gradu_res_ma_2dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(gradu_res_ma_2dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | Changes before event | 5 | 0.013 | 0.020 | 0.666 | 0.506 | -0.038 | 0.065 |
| Agreeableness | Changes after event | 5 | 0.036 | 0.012 | 3.081 | 0.002 | 0.006 | 0.066 |
| Conscientiousness | Changes before event | 5 | 0.021 | 0.012 | 1.722 | 0.085 | -0.011 | 0.053 |
| Conscientiousness | Changes after event | 5 | 0.066 | 0.032 | 2.035 | 0.042 | -0.017 | 0.149 |
| Extraversion | Changes before event | 5 | -0.003 | 0.012 | -0.286 | 0.775 | -0.034 | 0.027 |
| Extraversion | Changes after event | 5 | -0.023 | 0.016 | -1.503 | 0.133 | -0.064 | 0.017 |
| Emotional stability | Changes before event | 5 | -0.007 | 0.023 | -0.308 | 0.758 | -0.066 | 0.052 |
| Emotional stability | Changes after event | 5 | 0.004 | 0.022 | 0.180 | 0.857 | -0.054 | 0.062 |
| Openness | Changes before event | 5 | 0.027 | 0.020 | 1.387 | 0.165 | -0.024 | 0.078 |
| Openness | Changes after event | 5 | 0.022 | 0.021 | 1.031 | 0.302 | -0.033 | 0.076 |
| Life satisfaction | Changes before event | 5 | -0.028 | 0.018 | -1.576 | 0.115 | -0.074 | 0.018 |
| Life satisfaction | Changes after event | 5 | -0.009 | 0.016 | -0.571 | 0.568 | -0.051 | 0.032 |
| Self-esteem | Changes before event | 4 | 0.006 | 0.017 | 0.345 | 0.730 | -0.037 | 0.049 |
| Self-esteem | Changes after event | 4 | -0.035 | 0.024 | -1.481 | 0.139 | -0.096 | 0.026 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the linear contrast DPre – DPost = 0 that allowed us to examine whether there were any personality trait changes from pre-event to post-event personality assessments. Significant effects (p < .01) are depicted in bold.
gradu_res_ma_2dumm_contrast <- filter(gradu_res_ma_2dumm, Effect == "CONTRPrePost") %>%
dplyr::select(-DIFFR2_avg)
gradu_res_ma_2dumm_contrast$Effect <- recode(gradu_res_ma_2dumm_contrast$Effect,
"CONTRPrePost" = "After - Before")
## Create table
kable(gradu_res_ma_2dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(gradu_res_ma_2dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | After - Before | 5 | 0.018 | 0.017 | 1.094 | 0.274 | -0.025 | 0.061 |
| Conscientiousness | After - Before | 5 | 0.056 | 0.020 | 2.823 | 0.005 | 0.005 | 0.108 |
| Extraversion | After - Before | 5 | -0.019 | 0.010 | -1.995 | 0.046 | -0.044 | 0.006 |
| Emotional stability | After - Before | 5 | 0.015 | 0.011 | 1.386 | 0.166 | -0.013 | 0.042 |
| Openness | After - Before | 5 | -0.009 | 0.017 | -0.525 | 0.600 | -0.051 | 0.034 |
| Life satisfaction | After - Before | 5 | -0.002 | 0.036 | -0.052 | 0.959 | -0.094 | 0.090 |
| Self-esteem | After - Before | 4 | -0.033 | 0.031 | -1.059 | 0.290 | -0.112 | 0.047 |
Graph
This graph illustrates the meta-analytic estimates of the two event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the two event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
gradu_res_ma_2dumm_dummies <- filter(gradu_res_ma_2dumm, Effect == "Pre" |
Effect == "Post")
## Create variable on significance
gradu_res_ma_2dumm_dummies$sig <- ifelse(gradu_res_ma_2dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
gradu_res_ma_2dumm_dummies$trait <- factor(gradu_res_ma_2dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
gradu_res_ma_2dumm_dummies$Effect <- factor(gradu_res_ma_2dumm_dummies$Effect,
levels = c("Pre", "Post"),
labels = c("Before", "After"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- gradu_res_ma_2dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
gradu_res_ma_2dumm_dummies$ID <- 1:nrow(gradu_res_ma_2dumm_dummies)
plot <- ggplot(data = gradu_res_ma_2dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 1.5, y = 0.29), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 1.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 2.1, y = -0.25),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
New Employment
jobbeg_res_ma_2dumm <- filter(res_ma_2dumm_long, event == "New employment")
jobbeg_res_ma_2dumm <- dplyr::select(jobbeg_res_ma_2dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the two event-related dummy variables. These dummies quantify changes in our outcome variables across assessments occurring up to 2 years prior to or any time after the occurrence of a life event. Significant effects (p < .01) are depicted in bold.
jobbeg_res_ma_2dumm_dummies <- filter(jobbeg_res_ma_2dumm, Effect == "Pre" |
Effect == "Post") %>%
dplyr::select(-DIFFR2_avg)
jobbeg_res_ma_2dumm_dummies$Effect <- recode(jobbeg_res_ma_2dumm_dummies$Effect,
"Pre" = "Changes before event",
"Post" = "Changes after event")
## Create table
kable(jobbeg_res_ma_2dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(jobbeg_res_ma_2dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | Changes before event | 5 | 0.037 | 0.008 | 4.620 | 0.000 | 0.016 | 0.057 |
| Agreeableness | Changes after event | 5 | 0.038 | 0.010 | 3.820 | 0.000 | 0.012 | 0.063 |
| Conscientiousness | Changes before event | 5 | 0.028 | 0.016 | 1.719 | 0.086 | -0.014 | 0.069 |
| Conscientiousness | Changes after event | 5 | 0.049 | 0.013 | 3.655 | 0.000 | 0.014 | 0.084 |
| Extraversion | Changes before event | 5 | 0.006 | 0.010 | 0.647 | 0.518 | -0.019 | 0.032 |
| Extraversion | Changes after event | 5 | 0.006 | 0.007 | 0.835 | 0.404 | -0.012 | 0.023 |
| Emotional stability | Changes before event | 5 | -0.011 | 0.011 | -0.977 | 0.329 | -0.039 | 0.017 |
| Emotional stability | Changes after event | 5 | 0.026 | 0.008 | 3.227 | 0.001 | 0.005 | 0.047 |
| Openness | Changes before event | 5 | 0.020 | 0.009 | 2.318 | 0.020 | -0.002 | 0.042 |
| Openness | Changes after event | 5 | 0.017 | 0.013 | 1.276 | 0.202 | -0.017 | 0.051 |
| Life satisfaction | Changes before event | 5 | -0.045 | 0.013 | -3.456 | 0.001 | -0.079 | -0.012 |
| Life satisfaction | Changes after event | 5 | 0.022 | 0.016 | 1.371 | 0.170 | -0.019 | 0.063 |
| Self-esteem | Changes before event | 3 | -0.022 | 0.021 | -1.063 | 0.288 | -0.076 | 0.031 |
| Self-esteem | Changes after event | 3 | 0.002 | 0.012 | 0.175 | 0.861 | -0.029 | 0.033 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the linear contrast DPre – DPost = 0 that allowed us to examine whether there were any personality trait changes from pre-event to post-event personality assessments. Significant effects (p < .01) are depicted in bold.
jobbeg_res_ma_2dumm_contrast <- filter(jobbeg_res_ma_2dumm, Effect == "CONTRPrePost") %>%
dplyr::select(-DIFFR2_avg)
jobbeg_res_ma_2dumm_contrast$Effect <- recode(jobbeg_res_ma_2dumm_contrast$Effect,
"CONTRPrePost" = "After - Before")
## Create table
kable(jobbeg_res_ma_2dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(jobbeg_res_ma_2dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | After - Before | 5 | 0.000 | 0.009 | 0.003 | 0.998 | -0.022 | 0.022 |
| Conscientiousness | After - Before | 5 | 0.022 | 0.016 | 1.365 | 0.172 | -0.020 | 0.064 |
| Extraversion | After - Before | 5 | -0.002 | 0.009 | -0.214 | 0.830 | -0.025 | 0.021 |
| Emotional stability | After - Before | 5 | 0.037 | 0.010 | 3.576 | 0.000 | 0.010 | 0.063 |
| Openness | After - Before | 5 | -0.006 | 0.014 | -0.447 | 0.655 | -0.041 | 0.029 |
| Life satisfaction | After - Before | 5 | 0.068 | 0.014 | 4.772 | 0.000 | 0.031 | 0.105 |
| Self-esteem | After - Before | 3 | 0.024 | 0.026 | 0.934 | 0.350 | -0.043 | 0.091 |
Graph
This graph illustrates the meta-analytic estimates of the two event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the two event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
jobbeg_res_ma_2dumm_dummies <- filter(jobbeg_res_ma_2dumm, Effect == "Pre" |
Effect == "Post")
## Create variable on significance
jobbeg_res_ma_2dumm_dummies$sig <- ifelse(jobbeg_res_ma_2dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
jobbeg_res_ma_2dumm_dummies$trait <- factor(jobbeg_res_ma_2dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
jobbeg_res_ma_2dumm_dummies$Effect <- factor(jobbeg_res_ma_2dumm_dummies$Effect,
levels = c("Pre", "Post"),
labels = c("Before", "After"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- jobbeg_res_ma_2dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
jobbeg_res_ma_2dumm_dummies$ID <- 1:nrow(jobbeg_res_ma_2dumm_dummies)
plot <- ggplot(data = jobbeg_res_ma_2dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 1.5, y = 0.29), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 1.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 2.1, y = -0.25),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Unemployment
unemploy_res_ma_2dumm <- filter(res_ma_2dumm_long, event == "Unemployment")
unemploy_res_ma_2dumm <- dplyr::select(unemploy_res_ma_2dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the two event-related dummy variables. These dummies quantify changes in our outcome variables across assessments occurring up to 2 years prior to or any time after the occurrence of a life event. Significant effects (p < .01) are depicted in bold.
unemploy_res_ma_2dumm_dummies <- filter(unemploy_res_ma_2dumm, Effect == "Pre" |
Effect == "Post") %>%
dplyr::select(-DIFFR2_avg)
unemploy_res_ma_2dumm_dummies$Effect <- recode(unemploy_res_ma_2dumm_dummies$Effect,
"Pre" = "Changes before event",
"Post" = "Changes after event")
## Create table
kable(unemploy_res_ma_2dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(unemploy_res_ma_2dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | Changes before event | 6 | 0.019 | 0.034 | 0.556 | 0.579 | -0.070 | 0.108 |
| Agreeableness | Changes after event | 6 | 0.007 | 0.023 | 0.300 | 0.764 | -0.053 | 0.067 |
| Conscientiousness | Changes before event | 6 | -0.011 | 0.011 | -0.974 | 0.330 | -0.039 | 0.018 |
| Conscientiousness | Changes after event | 6 | -0.014 | 0.010 | -1.427 | 0.154 | -0.039 | 0.011 |
| Extraversion | Changes before event | 6 | 0.003 | 0.010 | 0.327 | 0.744 | -0.022 | 0.029 |
| Extraversion | Changes after event | 6 | 0.000 | 0.009 | 0.049 | 0.961 | -0.022 | 0.023 |
| Emotional stability | Changes before event | 6 | -0.039 | 0.015 | -2.600 | 0.009 | -0.077 | 0.000 |
| Emotional stability | Changes after event | 6 | -0.020 | 0.010 | -2.007 | 0.045 | -0.046 | 0.006 |
| Openness | Changes before event | 6 | 0.005 | 0.010 | 0.504 | 0.614 | -0.022 | 0.032 |
| Openness | Changes after event | 6 | 0.012 | 0.014 | 0.845 | 0.398 | -0.025 | 0.049 |
| Life satisfaction | Changes before event | 6 | -0.082 | 0.016 | -5.249 | 0.000 | -0.122 | -0.042 |
| Life satisfaction | Changes after event | 6 | -0.087 | 0.019 | -4.452 | 0.000 | -0.137 | -0.037 |
| Self-esteem | Changes before event | 4 | -0.035 | 0.017 | -2.114 | 0.034 | -0.078 | 0.008 |
| Self-esteem | Changes after event | 4 | -0.091 | 0.046 | -1.994 | 0.046 | -0.208 | 0.026 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the linear contrast DPre – DPost = 0 that allowed us to examine whether there were any personality trait changes from pre-event to post-event personality assessments. Significant effects (p < .01) are depicted in bold.
unemploy_res_ma_2dumm_contrast <- filter(unemploy_res_ma_2dumm, Effect == "CONTRPrePost") %>%
dplyr::select(-DIFFR2_avg)
unemploy_res_ma_2dumm_contrast$Effect <- recode(unemploy_res_ma_2dumm_contrast$Effect,
"CONTRPrePost" = "After - Before")
## Create table
kable(unemploy_res_ma_2dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(unemploy_res_ma_2dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | After - Before | 6 | -0.012 | 0.017 | -0.712 | 0.476 | -0.056 | 0.032 |
| Conscientiousness | After - Before | 6 | 0.000 | 0.010 | 0.045 | 0.964 | -0.025 | 0.025 |
| Extraversion | After - Before | 6 | -0.001 | 0.012 | -0.119 | 0.905 | -0.032 | 0.029 |
| Emotional stability | After - Before | 6 | 0.015 | 0.011 | 1.386 | 0.166 | -0.013 | 0.042 |
| Openness | After - Before | 6 | 0.005 | 0.015 | 0.348 | 0.728 | -0.034 | 0.045 |
| Life satisfaction | After - Before | 6 | -0.017 | 0.027 | -0.641 | 0.522 | -0.086 | 0.052 |
| Self-esteem | After - Before | 4 | -0.066 | 0.068 | -0.971 | 0.332 | -0.240 | 0.109 |
Graph
This graph illustrates the meta-analytic estimates of the two event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the two event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
unemploy_res_ma_2dumm_dummies <- filter(unemploy_res_ma_2dumm, Effect == "Pre" |
Effect == "Post")
## Create variable on significance
unemploy_res_ma_2dumm_dummies$sig <- ifelse(unemploy_res_ma_2dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
unemploy_res_ma_2dumm_dummies$trait <- factor(unemploy_res_ma_2dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
unemploy_res_ma_2dumm_dummies$Effect <- factor(unemploy_res_ma_2dumm_dummies$Effect,
levels = c("Pre", "Post"),
labels = c("Before", "After"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- unemploy_res_ma_2dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
unemploy_res_ma_2dumm_dummies$ID <- 1:nrow(unemploy_res_ma_2dumm_dummies)
plot <- ggplot(data = unemploy_res_ma_2dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 1.5, y = 0.29), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 1.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 2.1, y = -0.25),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.
Retirement
retire_res_ma_2dumm <- filter(res_ma_2dumm_long, event == "Retirement")
retire_res_ma_2dumm <- dplyr::select(retire_res_ma_2dumm, -c(event))Table
Dummy Variables
This table includes results of the meta-analytic aggregations across panel studies for the two event-related dummy variables. These dummies quantify changes in our outcome variables across assessments occurring up to 2 years prior to or any time after the occurrence of a life event. Significant effects (p < .01) are depicted in bold.
retire_res_ma_2dumm_dummies <- filter(retire_res_ma_2dumm, Effect == "Pre" |
Effect == "Post") %>%
dplyr::select(-DIFFR2_avg)
retire_res_ma_2dumm_dummies$Effect <- recode(retire_res_ma_2dumm_dummies$Effect,
"Pre" = "Changes before event",
"Post" = "Changes after event")
## Create table
kable(retire_res_ma_2dumm_dummies
, caption="**Meta-Analytic Results on Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(retire_res_ma_2dumm_dummies[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | Changes before event | 5 | 0.018 | 0.010 | 1.855 | 0.064 | -0.007 | 0.043 |
| Agreeableness | Changes after event | 5 | 0.022 | 0.009 | 2.392 | 0.017 | -0.002 | 0.045 |
| Conscientiousness | Changes before event | 5 | 0.010 | 0.009 | 1.151 | 0.250 | -0.013 | 0.034 |
| Conscientiousness | Changes after event | 5 | -0.024 | 0.025 | -0.936 | 0.349 | -0.089 | 0.041 |
| Extraversion | Changes before event | 5 | 0.003 | 0.010 | 0.348 | 0.728 | -0.022 | 0.028 |
| Extraversion | Changes after event | 5 | 0.011 | 0.009 | 1.256 | 0.209 | -0.011 | 0.033 |
| Emotional stability | Changes before event | 5 | -0.001 | 0.015 | -0.042 | 0.966 | -0.039 | 0.038 |
| Emotional stability | Changes after event | 5 | 0.025 | 0.024 | 1.049 | 0.294 | -0.036 | 0.085 |
| Openness | Changes before event | 5 | 0.011 | 0.009 | 1.282 | 0.200 | -0.012 | 0.034 |
| Openness | Changes after event | 5 | 0.019 | 0.016 | 1.154 | 0.248 | -0.023 | 0.060 |
| Life satisfaction | Changes before event | 5 | 0.000 | 0.018 | 0.019 | 0.984 | -0.045 | 0.046 |
| Life satisfaction | Changes after event | 5 | 0.069 | 0.033 | 2.056 | 0.040 | -0.017 | 0.155 |
| Self-esteem | Changes before event | 3 | 0.012 | 0.028 | 0.415 | 0.678 | -0.060 | 0.083 |
| Self-esteem | Changes after event | 3 | 0.058 | 0.045 | 1.285 | 0.199 | -0.058 | 0.174 |
Linear Contrasts
This table includes results of the meta-analytic aggregations across panel studies for the linear contrast DPre – DPost = 0 that allowed us to examine whether there were any personality trait changes from pre-event to post-event personality assessments. Significant effects (p < .01) are depicted in bold.
retire_res_ma_2dumm_contrast <- filter(retire_res_ma_2dumm, Effect == "CONTRPrePost") %>%
dplyr::select(-DIFFR2_avg)
retire_res_ma_2dumm_contrast$Effect <- recode(retire_res_ma_2dumm_contrast$Effect,
"CONTRPrePost" = "After - Before")
## Create table
kable(retire_res_ma_2dumm_contrast
, caption="**Meta-Analytic Results on Linear Contrasts Across Event-Related Dummies Aggregated Across Datasets**"
, escape=FALSE
, label = NA
, digits = 3
, col.names = c("Trait", "Dummy", "*k*", "*ES*", "*SE*", "*z*", "*p*",
"99%-CI lower", "99%-CI upper")) %>%
kable_styling(bootstrap_options = c("striped", "hover"),
fixed_thead = T, full_width = TRUE, position="left") %>%
column_spec(c(4:9), bold = ifelse(retire_res_ma_2dumm_contrast[,7] < 0.01, TRUE, FALSE))| Trait | Dummy | k | ES | SE | z | p | 99%-CI lower | 99%-CI upper |
|---|---|---|---|---|---|---|---|---|
| Agreeableness | After - Before | 5 | 0.005 | 0.015 | 0.332 | 0.740 | -0.033 | 0.042 |
| Conscientiousness | After - Before | 5 | -0.040 | 0.025 | -1.623 | 0.105 | -0.105 | 0.024 |
| Extraversion | After - Before | 5 | 0.005 | 0.013 | 0.401 | 0.688 | -0.028 | 0.038 |
| Emotional stability | After - Before | 5 | 0.019 | 0.016 | 1.171 | 0.242 | -0.023 | 0.060 |
| Openness | After - Before | 5 | 0.011 | 0.015 | 0.698 | 0.485 | -0.028 | 0.050 |
| Life satisfaction | After - Before | 5 | 0.067 | 0.041 | 1.617 | 0.106 | -0.039 | 0.173 |
| Self-esteem | After - Before | 3 | 0.018 | 0.016 | 1.111 | 0.267 | -0.024 | 0.059 |
Graph
This graph illustrates the meta-analytic estimates of the two event-related dummy variables. Error bars indicate 99% confidence intervals. Significant effects (p < .01) are depicted in black. You can hover over the data points to receive more information on the effect size estimates. Furthermore, the graph includes the weighted average of the R2 difference scores that describe how much incremental variance the two event-related dummies explain in the respective outcome variable (beyond stable between-person differences and age-related changes).
## Create dataset
retire_res_ma_2dumm_dummies <- filter(retire_res_ma_2dumm, Effect == "Pre" |
Effect == "Post")
## Create variable on significance
retire_res_ma_2dumm_dummies$sig <- ifelse(retire_res_ma_2dumm_dummies$p < 0.01, 1, 0) %>% as.factor()
retire_res_ma_2dumm_dummies$trait <- factor(retire_res_ma_2dumm_dummies$trait,
levels = c("Agreeableness", "Conscientiousness",
"Emotional stability", "Extraversion",
"Openness", "Life satisfaction",
"Self-esteem"),
ordered = TRUE)
## Recode effect variables
retire_res_ma_2dumm_dummies$Effect <- factor(retire_res_ma_2dumm_dummies$Effect,
levels = c("Pre", "Post"),
labels = c("Before", "After"), ordered = TRUE)
## Calculate R2 trait means
trait_means <- retire_res_ma_2dumm_dummies %>%
group_by(trait) %>%
summarise(mean_r2 = round(mean(DIFFR2_avg, na.rm = TRUE), 2))
## Create ID variable for interactive plot
retire_res_ma_2dumm_dummies$ID <- 1:nrow(retire_res_ma_2dumm_dummies)
plot <- ggplot(data = retire_res_ma_2dumm_dummies, aes(x = Effect, y = ES)) +
geom_hline(yintercept = 0) +
geom_point_interactive(aes(tooltip =
paste0("b = ", round(ES,2),
", 99% CI = [", round(lower,2),
", ", round(higher,2), "]"),
data_id = ID, colour = sig), size = 1.8) +
geom_text(aes(x = 1.5, y = 0.29), label = "Event") +
geom_errorbar(aes(ymin = lower, ymax = higher, colour = sig), width = 0) +
geom_segment(aes(x = 1.5, y = -0.25, yend = 0.25)) +
scale_y_continuous(limits = c(-0.30, 0.30),
breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30),
oob = scales::oob_squish) +
scale_colour_manual(values = c("0" = "grey", "1" = "black")) +
facet_wrap(trait ~ ., ncol = 2, switch = "y") +
geom_text(data = trait_means, aes(x = 2.1, y = -0.25),
label = paste0("R", "\u00B2", " = ", trait_means$mean_r2, "%")) +
labs(y = "Effect size", x = "Event-related changes") +
theme_pub() +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45,
vjust = 1, hjust = 1))
ggiraph(ggobj = plot, height_svg = 8)Note. If a confidence intervals seems to be asymmetrical, this can be explained by truncating estimates going beyond the axis limits to the highest/lowest depictable value.