Longitudinal Assortative Mating EDA: Transition to Parenthood
Linh Nguyen
2024-08-30
I. Meta-data
In order to replicate this code file, two files are needed in the
directory data/ttp/ found within the project directory:
TTP Data Request.xlsx,
TTP_Measure Summary.xlsx, and
Phase 1 All Items.sav. R code associated with each code
chunk can be viewed and hidden by toggling on Code on the
top right of each output.
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
options(digits = 3)
libraries <- c("apaTables", # for cor table
"here", # directory access
"psych", # for scale alphas
"tidyr", # pivot_wider
"rio", # import export
"summarytools", # descriptives
"ggtext", # for custom css title
"ggplot2", # plots
"kableExtra", # scroll box
"forcats", # fct_reorder for barplot
"sjPlot", # plot_grid
"dplyr", # general wrangling
"lmerTest") # linear mixed models
lapply(libraries, require, character.only = TRUE)
source("00_helpers.R")# list of loaded packages and versions
si <- devtools::session_info()[[2]]
rownames(si) <- NULL
si %>%
select(package, loadedversion, date, source) %>%
#red bold the called packages
mutate(package =
cell_spec(package,
color = ifelse(package %in% libraries, "red", "black"),
bold = ifelse(package %in% libraries, TRUE, FALSE))) %>%
knitr::kable(escape = F, caption = "All loaded packages.
Bolded in red are those loaded explicitly
with <code>library()</code>") %>%
kable_styling() %>%
scroll_box(height = "300px")| package | loadedversion | date | source |
|---|---|---|---|
| apaTables | 2.0.8 | 2021-01-04 | CRAN (R 4.2.3) |
| backports | 1.4.1 | 2021-12-13 | CRAN (R 4.2.0) |
| base64enc | 0.1-3 | 2015-07-28 | CRAN (R 4.2.0) |
| bayestestR | 0.13.1 | 2023-04-07 | CRAN (R 4.2.3) |
| boot | 1.3-28.1 | 2022-11-22 | CRAN (R 4.2.3) |
| broom | 1.0.5 | 2023-06-09 | CRAN (R 4.2.3) |
| bslib | 0.6.1 | 2023-11-28 | CRAN (R 4.2.3) |
| cachem | 1.0.8 | 2023-05-01 | CRAN (R 4.2.3) |
| checkmate | 2.3.1 | 2023-12-04 | CRAN (R 4.2.3) |
| cli | 3.6.2 | 2023-12-11 | CRAN (R 4.2.3) |
| coda | 0.19-4 | 2020-09-30 | CRAN (R 4.2.3) |
| codetools | 0.2-18 | 2020-11-04 | CRAN (R 4.2.2) |
| colorspace | 2.1-0 | 2023-01-23 | CRAN (R 4.2.3) |
| devtools | 2.4.5 | 2022-10-11 | CRAN (R 4.2.1) |
| digest | 0.6.33 | 2023-07-07 | CRAN (R 4.2.3) |
| dplyr | 1.1.4 | 2023-11-17 | CRAN (R 4.2.3) |
| ellipsis | 0.3.2 | 2021-04-29 | CRAN (R 4.2.3) |
| emmeans | 1.8.9 | 2023-10-17 | CRAN (R 4.2.3) |
| estimability | 1.4.1 | 2022-08-05 | CRAN (R 4.2.1) |
| evaluate | 0.23 | 2023-11-01 | CRAN (R 4.2.3) |
| fansi | 1.0.6 | 2023-12-08 | CRAN (R 4.2.3) |
| fastmap | 1.1.1 | 2023-02-24 | CRAN (R 4.2.3) |
| forcats | 1.0.0 | 2023-01-29 | CRAN (R 4.2.3) |
| fs | 1.6.3 | 2023-07-20 | CRAN (R 4.2.3) |
| generics | 0.1.3 | 2022-07-05 | CRAN (R 4.2.3) |
| ggeffects | 1.3.2 | 2023-10-17 | CRAN (R 4.2.3) |
| ggplot2 | 3.4.4 | 2023-10-12 | CRAN (R 4.2.3) |
| ggtext | 0.1.2 | 2022-09-16 | CRAN (R 4.2.3) |
| glue | 1.6.2 | 2022-02-24 | CRAN (R 4.2.3) |
| gridtext | 0.1.5 | 2022-09-16 | CRAN (R 4.2.3) |
| gtable | 0.3.4 | 2023-08-21 | CRAN (R 4.2.3) |
| here | 1.0.1 | 2020-12-13 | CRAN (R 4.2.3) |
| htmltools | 0.5.7 | 2023-11-03 | CRAN (R 4.2.3) |
| htmlwidgets | 1.6.4 | 2023-12-06 | CRAN (R 4.2.3) |
| httpuv | 1.6.13 | 2023-12-06 | CRAN (R 4.2.3) |
| httr | 1.4.7 | 2023-08-15 | CRAN (R 4.2.3) |
| insight | 0.19.6 | 2023-10-12 | CRAN (R 4.2.3) |
| jquerylib | 0.1.4 | 2021-04-26 | CRAN (R 4.2.3) |
| jsonlite | 1.8.8 | 2023-12-04 | CRAN (R 4.2.3) |
| kableExtra | 1.3.4 | 2021-02-20 | CRAN (R 4.2.3) |
| knitr | 1.45 | 2023-10-30 | CRAN (R 4.2.3) |
| later | 1.3.2 | 2023-12-06 | CRAN (R 4.2.3) |
| lattice | 0.21-8 | 2023-04-05 | CRAN (R 4.2.2) |
| lifecycle | 1.0.4 | 2023-11-07 | CRAN (R 4.2.3) |
| lme4 | 1.1-35.1 | 2023-11-05 | CRAN (R 4.2.3) |
| lmerTest | 3.1-3 | 2020-10-23 | CRAN (R 4.2.3) |
| lubridate | 1.9.3 | 2023-09-27 | CRAN (R 4.2.3) |
| magick | 2.8.1 | 2023-10-22 | CRAN (R 4.2.3) |
| magrittr | 2.0.3 | 2022-03-30 | CRAN (R 4.2.3) |
| MASS | 7.3-60 | 2023-05-04 | CRAN (R 4.2.3) |
| Matrix | 1.6-4 | 2023-11-30 | CRAN (R 4.2.3) |
| matrixStats | 1.1.0 | 2023-11-07 | CRAN (R 4.2.3) |
| memoise | 2.0.1 | 2021-11-26 | CRAN (R 4.2.3) |
| mime | 0.12 | 2021-09-28 | CRAN (R 4.2.0) |
| miniUI | 0.1.1.1 | 2018-05-18 | CRAN (R 4.2.1) |
| minqa | 1.2.6 | 2023-09-11 | CRAN (R 4.2.3) |
| mnormt | 2.1.1 | 2022-09-26 | CRAN (R 4.2.1) |
| modelr | 0.1.11 | 2023-03-22 | CRAN (R 4.2.3) |
| munsell | 0.5.0 | 2018-06-12 | CRAN (R 4.2.1) |
| mvtnorm | 1.2-4 | 2023-11-27 | CRAN (R 4.2.3) |
| nlme | 3.1-162 | 2023-01-31 | CRAN (R 4.2.2) |
| nloptr | 2.0.3 | 2022-05-26 | CRAN (R 4.2.3) |
| numDeriv | 2016.8-1.1 | 2019-06-06 | CRAN (R 4.2.0) |
| pander | 0.6.5 | 2022-03-18 | CRAN (R 4.2.3) |
| performance | 0.10.8 | 2023-10-30 | CRAN (R 4.2.3) |
| pillar | 1.9.0 | 2023-03-22 | CRAN (R 4.2.3) |
| pkgbuild | 1.4.3 | 2023-12-10 | CRAN (R 4.2.3) |
| pkgconfig | 2.0.3 | 2019-09-22 | CRAN (R 4.2.3) |
| pkgload | 1.3.3 | 2023-09-22 | CRAN (R 4.2.3) |
| plyr | 1.8.9 | 2023-10-02 | CRAN (R 4.2.3) |
| profvis | 0.3.8 | 2023-05-02 | CRAN (R 4.2.3) |
| promises | 1.2.1 | 2023-08-10 | CRAN (R 4.2.3) |
| pryr | 0.1.6 | 2023-01-17 | CRAN (R 4.2.3) |
| psych | 2.3.12 | 2023-12-20 | CRAN (R 4.2.2) |
| purrr | 1.0.2 | 2023-08-10 | CRAN (R 4.2.3) |
| R6 | 2.5.1 | 2021-08-19 | CRAN (R 4.2.3) |
| rapportools | 1.1 | 2022-03-22 | CRAN (R 4.2.3) |
| Rcpp | 1.0.11 | 2023-07-06 | CRAN (R 4.2.3) |
| remotes | 2.4.2.1 | 2023-07-18 | CRAN (R 4.2.3) |
| reshape2 | 1.4.4 | 2020-04-09 | CRAN (R 4.2.3) |
| rio | 1.0.1 | 2023-09-19 | CRAN (R 4.2.3) |
| rlang | 1.1.4 | 2024-06-04 | CRAN (R 4.2.2) |
| rmarkdown | 2.25 | 2023-09-18 | CRAN (R 4.2.3) |
| rprojroot | 2.0.4 | 2023-11-05 | CRAN (R 4.2.3) |
| rstudioapi | 0.15.0 | 2023-07-07 | CRAN (R 4.2.3) |
| rvest | 1.0.3 | 2022-08-19 | CRAN (R 4.2.3) |
| sass | 0.4.8 | 2023-12-06 | CRAN (R 4.2.3) |
| scales | 1.3.0 | 2023-11-28 | CRAN (R 4.2.3) |
| sessioninfo | 1.2.2 | 2021-12-06 | CRAN (R 4.2.3) |
| shiny | 1.8.0 | 2023-11-17 | CRAN (R 4.2.3) |
| sjlabelled | 1.2.0 | 2022-04-10 | CRAN (R 4.2.3) |
| sjmisc | 2.8.9 | 2021-12-03 | CRAN (R 4.2.3) |
| sjPlot | 2.8.15 | 2023-08-17 | CRAN (R 4.2.3) |
| sjstats | 0.18.2 | 2022-11-19 | CRAN (R 4.2.3) |
| stringi | 1.8.3 | 2023-12-11 | CRAN (R 4.2.3) |
| stringr | 1.5.1 | 2023-11-14 | CRAN (R 4.2.3) |
| summarytools | 1.0.1 | 2022-05-20 | CRAN (R 4.2.3) |
| svglite | 2.1.3 | 2023-12-08 | CRAN (R 4.2.3) |
| systemfonts | 1.0.5 | 2023-10-09 | CRAN (R 4.2.3) |
| tibble | 3.2.1 | 2023-03-20 | CRAN (R 4.2.3) |
| tidyr | 1.3.0 | 2023-01-24 | CRAN (R 4.2.3) |
| tidyselect | 1.2.0 | 2022-10-10 | CRAN (R 4.2.1) |
| timechange | 0.2.0 | 2023-01-11 | CRAN (R 4.2.3) |
| urlchecker | 1.0.1 | 2021-11-30 | CRAN (R 4.2.3) |
| usethis | 2.2.2 | 2023-07-06 | CRAN (R 4.2.3) |
| utf8 | 1.2.4 | 2023-10-22 | CRAN (R 4.2.3) |
| vctrs | 0.6.5 | 2023-12-01 | CRAN (R 4.2.3) |
| viridisLite | 0.4.2 | 2023-05-02 | CRAN (R 4.2.3) |
| webshot | 0.5.5 | 2023-06-26 | CRAN (R 4.2.3) |
| withr | 2.5.2 | 2023-10-30 | CRAN (R 4.2.3) |
| xfun | 0.41 | 2023-11-01 | CRAN (R 4.2.3) |
| xml2 | 1.3.6 | 2023-12-04 | CRAN (R 4.2.3) |
| xtable | 1.8-4 | 2019-04-21 | CRAN (R 4.2.3) |
| yaml | 2.3.8 | 2023-12-11 | CRAN (R 4.2.3) |
II. Basic Descriptives
# data and dictionary
data <- rio::import(file = paste0(here(), "/data/ttp/TTP Data Request.xlsx"))
wave_1 <- rio::import(file = paste0(here(), "/data/ttp/Phase 1 All Items.sav")) %>%
filter(ID %in% data$ID) %>%
mutate(ID = as.character(ID))
dict <- rio::import(file = paste0(here(), "/data/ttp/TTP_Measure Summary.xlsx"),
sheet = "Scale Specfications") %>%
select(c("Measure", "Variable Name", "Scaling")) %>%
filter(!is.na(Measure))
codebook <- rio::import(file = paste0(here(), "/data/ttp/Dictionary.xlsx"))
# variable type
data <- data %>%
# each ID refers to a dyad. 5 first rows are P1 then P2
mutate(ID = as.character(ID),
Asex = as.factor(Asex),
Ababysex = as.factor(Ababysex),
P1Arace = as.factor(P1Arace),
P1Aeducate = as.factor(P1Aeducate),
P1Aincome = as.factor(P1Aincome))
# add time variable
data <- data %>%
# time in months from first assessment
# pre-natal is roughly 6 weeks prior
# add 1 month to each subsequent time
mutate(time = rep(c(0,7,13,19,25),
times = 168*2)) %>%
relocate(time, .after = ID)
# check var names in dictionary
var_name <- names(data)[-c(1,2)]
var_missing <- c()
for (var in var_name){
if(!var %in% dict$`Variable Name`){
var_missing <- append(x = var_missing, var)
}
}
# save only variables of interest in dict
dict <- dict %>%
filter(`Variable Name` %in% var_name)
# difference between P1Ammonths and P1Amtotalmo
# P1Ammonths includes additional months beyond P1Amyears
# same for P1Altmon1 and P1Altotalmo
# for relationship length: only use P1Amtotalmo and P1Altotalmo
data %>%
filter(time == 0) %>%
select(P1Ammonths, P1Amtotalmo) %>%
summary()
# add a unique variable per participant = ID + Sex (0)
data <- data %>%
mutate(IDg = paste0(ID, "_", as.numeric(Asex == 1)))
# wide-ish data with 1 row representing 1 couple at 1 timepoint
# needed for analyses
data_wide <- data %>%
# remove unnecessary variables
select(-c("IDg", "Ababysex", matches("LA"))) %>%
# rename variables for more readability
rename(couple = ID,
ecr_avoid = ABAvoid,
ecr_anx = ABAnx,
ss_partner = Assp,
ss_self = Assind,
sp_partner = Aspsp,
sp_self = Aisps,
ks_prox_partner = ApProx,
ks_sens_partner = Appsens,
ks_coop_partner = ApCoop,
ks_comp_partner = ApComp,
ks_prox_self = AiProx,
ks_sens_self = AiSens,
ks_coop_self = AiCoop,
ks_comp_self = AiComp,
con_collab_partner = Apkrcll,
con_capit_partner = Apkrac,
con_stale_partner = Apkrs,
con_verba_partner = Apkrva,
con_collab_self = Aikrcll,
con_capit_self = Aikrac,
con_stale_self = Aikrs,
con_verba_self = Aikrva,
das_satis = Asatis,
das_coh = Acohes,
bfi_e = P1AbfiE,
bfi_a = P1AbfiA,
bfi_c = P1AbfiC,
bfi_n = P1AbfiN,
bfi_o = P1AbfiO) %>%
# old code: male = 1, female = -1
# new code: male = 0, female = 1
mutate(Asex = ifelse(Asex == 1, 2, 1))
data_wide <- data_wide %>%
pivot_wider(id_cols = c("couple", "time"),
names_from = "Asex",
values_from = names(data_wide)[!names(data_wide)%in%
c("couple", "Asex", "time")])
# check that all IDs appear exactly 5 times
#sum(table(data$couple)!=5)
# impute ss variables for female at wave 1 from raw data
# sort wave_1 ID using data_wide$couple to cbind easily
order_ID <- match(data_wide[data_wide$time==0,]$couple, wave_1$ID)
wave_1 <- wave_1[order_ID, ]
# grab items specific to ss_self_1
ss_self_1_items <- wave_1 %>%
select(names(wave_1)[grepl("^P1Fssind[0-9]+$", names(wave_1))])
# grab items specific to ss_partner_2
ss_partner_1_items <- wave_1 %>%
select(names(wave_1)[grepl("^P1Fssp[0-9]+$", names(wave_1))])
# store scale scores in data_wide for time 1
data_wide[data_wide$time == 0,]$ss_self_1 <- rowMeans(ss_self_1_items, na.rm = T)
data_wide[data_wide$time == 0,]$ss_partner_1 <- rowMeans(ss_partner_1_items, na.rm = T)
# store scale scores in data for time 1
data[data$time == 0 & data$Asex == -1,]$Assind <- rowMeans(ss_self_1_items, na.rm = T)
data[data$time == 0 & data$Asex == -1,]$Assp <- rowMeans(ss_partner_1_items, na.rm = T)
# fix character ID and standardize 1 -> length
data_wide <- data_wide %>%
mutate(couple = as.numeric(couple)) %>%
arrange(couple)
data_wide$couple <- rep(1:length(unique(data_wide$couple)), each = 5)
# save data files
# to be used in the analyses
#rio::export(data_wide,
# paste0(here(), "/data/cleaned_data_ttp.RDS"))# print dictionary for easy viewing
dict %>%
knitr::kable(escape = F, caption = "All variables in analytic dataset") %>%
kable_styling() %>%
scroll_box(height = "300px")| Measure | Variable Name | Scaling |
|---|---|---|
| Experienes in Close Relationships - Global Avoidance | ABAvoid | 1 to 7 |
| Experienes in Close Relationships - Global Anxiety | ABAnx | 1 to 7 |
| Sarason’s Social Support - Perceived Partner | Assp | 1 to 7 |
| Sarason’s Social Support - Self-Reported Given to Partner | Assind | 1 to 7 |
| Cutrona’s Social Provisions - Perceived from Partner | Aspsp | -1 to +1 |
| Cutrona’s Social Provisions - Self-Reported Given to Partner | Aisps | -1 to +1 |
| Kunce & Shaver - Perceived Partner Proximity v. Distance | ApProx | 1 to 7 |
| Kunce & Shaver - Perceived Partner Sensitivity v. Insensitivity | Appsens | 1 to 7 |
| Kunce & Shaver - Perceived Partner Cooperation v. Control | ApCoop | 1 to 7 |
| Kunce & Shaver - Perceived Partner Compulsive Caregiving | ApComp | 1 to 7 |
| Kunce & Shaver - Self-Reported Proximity v. Distance | AiProx | 1 to 7 |
| Kunce & Shaver - Self-Reported Sensitivity v. Insensitivity | AiSens | 1 to 7 |
| Kunce & Shaver - Self-Reported Cooperation v. Control | AiCoop | 1 to 7 |
| Kunce & Shaver - Self-Reported Compulsive Caregiving | AiComp | 1 to 7 |
| Kerig Conflict Strategies - Perceived Partner Collaboration | Apkrcll | 1 to 7 |
| Kerig Conflict Strategies - Perceived Partner Avoidance - Capitulation | Apkrac | 1 to 7 |
| Kerig Conflict Strategies - Perceived Partner Stalemate | Apkrs | 1 to 7 |
| Kerig Conflict Strategies - Perceived Partner Verbal Aggression | Apkrva | 1 to 7 |
| Kerig Conflict Strategies - Self-Reported Collaboration | Aikrcll | 1 to 7 |
| Kerig Conflict Strategies - Self-Reported Avoidance - Capitulation | Aikrac | 1 to 7 |
| Kerig Conflict Strategies - Self-Reported Stalemate | Aikrs | 1 to 7 |
| Kerig Conflict Strategies - Self-Reported Verbal Aggression | Aikrva | 1 to 7 |
| Dyadic Satisfaction (DAS) | Asatis | 1 to 50 |
| Dyadic Cohesion (DAS) | Acohes | 1 to 25 |
| Marriage Length in Years | P1Amyears | in years |
| Marriage Length in Months | P1Ammonths | in months |
| Marriage Length in Months Total | P1Amtotalmo | total months |
| Relationship Length in Years | P1Altyears | in years |
| Relationship Length in Months | P1Altmon1 | in months |
| Relationship Length in Months Total | P1Altotalmo | total months |
| Age | P1Aage | in years |
| Race reported | P1Arace | categorical |
| Level of Education | P1Aeducate | categorical |
| Current Household Yearly Income | P1Aincome | categorical |
| Big Five Inventory (BFI) - Extraversion | P1AbfiE | 1 to 5 |
| Big Five Inventory (BFI) - Agreeableness | P1AbfiA | 1 to 5 |
| Big Five Inventory (BFI) - Conscientiousness | P1AbfiC | 1 to 5 |
| Big Five Inventory (BFI) - Neuroticism | P1AbfiN | 1 to 5 |
| Big Five Inventory (BFI) - Openness | P1AbfiO | 1 to 5 |
| Lab - Mood Measure A - Positive | LAmoodAP | 0 to 120 |
| Lab - Mood Measure A - Negative | LAmoodAN | 0 to 100 |
| Lab - Mood Measure B - Positive | LAmoodBP | 0 to 120 |
| Lab - Mood Measure B - Negative | LAmoodBN | 0 to 100 |
| Lab - Mood Measure C - Positive | LAmoodCP | 0 to 120 |
| Lab - Mood Measure C - Negative | LAmoodCN | 0 to 100 |
| Lab - Postive Social Exchange - Partner acted towards me | LAppse | 1 to 7 |
| Lab - Negative Social Exchange - Partner acted towards me | LApnse | 1 to 7 |
| Lab - Kerig Collaboration | LApkrcll | 1 to 7 |
| Lab - Kerig Avoidance-Capitulation | LApkrac | 1 to 7 |
| Lab - Kerig Stalemate | LApkrs | 1 to 7 |
| Lab - Kerig Verbal Aggression | LApkrva | 1 to 7 |
There are a total of 168 couples at time 1, 153 couples at time 2, 144 couples at time 3, 142 couples at time 4, and 129 couples at time 5, using the ECR Avoidance variable to assess missingness.
Baseline demographics
for(trait in c("Agreeableness", "Conscientiousness", "Extraversion",
"Neuroticism", "Openness",
"Attachment Anxiety", "Attachment Avoidance",
"Social Support - Self", "Social Support - Partner",
"Social Provision - Self", "Social Provision - Partner",
"Sensitivity Caregiving - Self", "Sensitivity Caregiving - Partner",
"Proximity Caregiving - Self", "Proximity Caregiving - Partner",
"Cooperation Caregiving - Self", "Cooperation Caregiving - Partner",
"Compulsive Caregiving - Self", "Compulsive Caregiving - Partner",
"Collaboration - Self", "Collaboration - Partner",
"Stalemate - Self", "Stalemate - Partner",
"Avoidance-Capitulation - Self", "Avoidance-Capitulation - Partner",
"Verbal Aggression - Self", "Verbal Aggression - Partner",
"Dyadic Satisfaction", "Dyadic Cohesion")) {
# extract male and female items in codebook
items_male <- codebook %>%
filter(scale == trait & partner == "male") %>%
pull(variable)
items_female <- codebook %>%
filter(scale == trait & partner == "female") %>%
pull(variable)
# extract dataframes and combine
df_male <- wave_1 %>%
select(all_of(items_male))
names(df_male) <- paste0("item_", 1:ncol(df_male))
df_female <- wave_1 %>%
select(all_of(items_female))
names(df_female) <- paste0("item_", 1:ncol(df_female))
df_trait <- rbind(df_male, df_female)
# reverse items
reverse_items <- codebook %>%
filter(scale == trait & partner == "male")
reverse_items <- which(reverse_items$key == -1)
df_trait[, reverse_items] <- codebook[codebook$scale == trait, "max"][1] + 1 -
df_trait[, reverse_items]
# output alphas
cat(paste(trait, "Cronbach's alpha =",
round(psych::alpha(df_trait)$total["raw_alpha"], 2), "\n"))
}## Agreeableness Cronbach's alpha = 0.68
## Conscientiousness Cronbach's alpha = 0.73
## Extraversion Cronbach's alpha = 0.86
## Neuroticism Cronbach's alpha = 0.85
## Openness Cronbach's alpha = 0.7
## Attachment Anxiety Cronbach's alpha = 0.9
## Attachment Avoidance Cronbach's alpha = 0.91
## Social Support - Self Cronbach's alpha = 0.88
## Social Support - Partner Cronbach's alpha = 0.9
## Social Provision - Self Cronbach's alpha = 0.69
## Some items ( item_11 ) were negatively correlated with the first principal component and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' optionSocial Provision - Partner Cronbach's alpha = 0.59
## Sensitivity Caregiving - Self Cronbach's alpha = 0.85
## Sensitivity Caregiving - Partner Cronbach's alpha = 0.86
## Proximity Caregiving - Self Cronbach's alpha = 0.9
## Proximity Caregiving - Partner Cronbach's alpha = 0.92
## Cooperation Caregiving - Self Cronbach's alpha = 0.88
## Cooperation Caregiving - Partner Cronbach's alpha = 0.9
## Compulsive Caregiving - Self Cronbach's alpha = 0.83
## Compulsive Caregiving - Partner Cronbach's alpha = 0.76
## Collaboration - Self Cronbach's alpha = 0.9
## Collaboration - Partner Cronbach's alpha = 0.89
## Stalemate - Self Cronbach's alpha = 0.72
## Stalemate - Partner Cronbach's alpha = 0.72
## Avoidance-Capitulation - Self Cronbach's alpha = 0.82
## Avoidance-Capitulation - Partner Cronbach's alpha = 0.72
## Verbal Aggression - Self Cronbach's alpha = 0.86
## Verbal Aggression - Partner Cronbach's alpha = 0.89
## Dyadic Satisfaction Cronbach's alpha = 0.81
## Dyadic Cohesion Cronbach's alpha = 0.72data %>%
filter(time == 0) %>%
select("P1Aage", "P1Amtotalmo", "P1Altotalmo") %>%
descr(stats = "common", order = "p")Descriptive Statistics
| P1Aage | P1Amtotalmo | P1Altotalmo | |
|---|---|---|---|
| Mean | 27.67 | 39.52 | 42.21 |
| Std.Dev | 4.26 | 31.48 | 32.95 |
| Min | 19.00 | 0.00 | 7.00 |
| Median | 27.00 | 32.50 | 32.50 |
| Max | 45.00 | 208.00 | 120.00 |
| N.Valid | 335.00 | 330.00 | 28.00 |
| Pct.Valid | 99.70 | 98.21 | 8.33 |
# age
p <- plot_hist(var = "P1Aage", var_name = "age in years", data = data, bin_width = 1,
var_sex = "Asex", female_male = c("-1", "1"))
if(signif(var = "P1Aage",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# relationship length
plot_hist(var = "P1Amtotalmo", var_name = "marriage length in months", data = data,
bin_width = 1,
var_sex = "Asex", female_male = c("-1", "1"))
# sex
ggplot(data[which(data$time == 0),],
aes(factor(Asex,
levels = c(-1, 1),
labels = c("-1", "1")))) +
geom_bar(fill = c("#f1c232", "#710c0c")) +
geom_text(stat='count', aes(label=..count..), vjust = -1) +
labs(
title = "Count of Sex of the Partners",
subtitle = "All couples are heterosexual",
x = NULL
) +
ylim(0, 200) +
theme_classic()
# baby sex
ggplot(data[which(data$time == 0),],
aes(factor(Ababysex,
levels = c(-1, 1),
labels = c("-1", "1")))) +
geom_bar(fill = c("#f1c232", "#710c0c")) +
geom_text(stat='count', aes(label=..count..), vjust = -1) +
labs(
title = "Count of Sex of the Baby",
x = NULL
) +
ylim(0, 200) +
theme_classic()
# count of race
ggplot(data[which(data$time == 0 & !is.na(data$P1Arace)),],
aes(factor(P1Arace,
levels = 1:4,
labels = c("white", "2","3","4")))) +
geom_bar(fill = c("#710c0c")) +
geom_text(stat='count', aes(label=..count..), vjust = -1) +
labs(
title = "Count of race",
x = NULL
) +
ylim(0, 300) +
theme_classic()
# count of level of education
ggplot(data[which(data$time == 0 & !is.na(data$P1Aeducate)),],
aes(factor(P1Aeducate,
levels = c(-1, 2:7),
labels = as.character(c(1:7))))) +
geom_bar(fill = c("#710c0c")) +
geom_text(stat='count', aes(label=..count..), vjust = -1) +
labs(
title = "Count of education levels",
x = NULL
) +
ylim(0, 175) +
theme_classic()
# count of income
ggplot(data[which(data$time == 0 & !is.na(data$P1Aincome)),],
aes(factor(P1Aincome,
levels = 1:7,
labels = as.character(c(1:7))))) +
geom_bar(fill = c("#710c0c")) +
geom_text(stat='count', aes(label=..count..), vjust = -1) +
labs(
title = "Count of income levels",
x = NULL
) +
ylim(0, 120) +
theme_classic()
Baseline personality
data_wide %>%
filter(time == 0) %>%
select(bfi_e_1:bfi_o_2) %>%
descr(stats = "common") %>%
knitr::kable(
caption = "Baseline Big Five descriptives for all participants"
) %>%
kable_styling() %>%
scroll_box(height = "300px")| bfi_a_1 | bfi_a_2 | bfi_c_1 | bfi_c_2 | bfi_e_1 | bfi_e_2 | bfi_n_1 | bfi_n_2 | bfi_o_1 | bfi_o_2 | |
|---|---|---|---|---|---|---|---|---|---|---|
| Mean | 3.780 | 3.809 | 3.84 | 3.598 | 3.278 | 3.392 | 2.810 | 2.190 | 3.395 | 3.649 |
| Std.Dev | 0.594 | 0.554 | 0.60 | 0.585 | 0.892 | 0.764 | 0.805 | 0.652 | 0.686 | 0.621 |
| Min | 1.714 | 2.143 | 2.14 | 1.714 | 1.286 | 1.714 | 1.143 | 1.000 | 1.714 | 2.143 |
| Median | 3.857 | 3.857 | 3.79 | 3.571 | 3.286 | 3.429 | 2.857 | 2.143 | 3.310 | 3.571 |
| Max | 4.857 | 4.857 | 5.00 | 5.000 | 5.000 | 5.000 | 4.857 | 4.429 | 5.000 | 5.000 |
| N.Valid | 168.000 | 168.000 | 168.00 | 168.000 | 168.000 | 168.000 | 168.000 | 168.000 | 168.000 | 168.000 |
| Pct.Valid | 100.000 | 100.000 | 100.00 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 |
# agreeableness
p <- plot_hist(var = "P1AbfiA", var_name = "BFI Agreeableness",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,5)
if(signif(var = "P1AbfiA",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# conscientiousness
p <- plot_hist(var = "P1AbfiC", var_name = "BFI Conscientiousness",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,5)
if(signif(var = "P1AbfiC",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# extraversion
p <- plot_hist(var = "P1AbfiE", var_name = "BFI Extraversion",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,5)
if(signif(var = "P1AbfiE",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# neuroticism
p <- plot_hist(var = "P1AbfiN", var_name = "BFI Neuroticism",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,5)
if(signif(var = "P1AbfiN",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# openness
p <- plot_hist(var = "P1AbfiO", var_name = "BFI Openness",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,5)
if(signif(var = "P1AbfiO",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
Baseline relationship
data_wide %>%
filter(time == 0) %>%
select(ecr_avoid_1:con_verba_self_2, das_satis_1:das_coh_2) %>%
descr(stats = "common") %>%
knitr::kable(
caption = "Baseline relationship CAs descriptives for all participants"
) %>%
kable_styling() %>%
scroll_box(height = "300px")| con_capit_partner_1 | con_capit_partner_2 | con_capit_self_1 | con_capit_self_2 | con_collab_partner_1 | con_collab_partner_2 | con_collab_self_1 | con_collab_self_2 | con_stale_partner_1 | con_stale_partner_2 | con_stale_self_1 | con_stale_self_2 | con_verba_partner_1 | con_verba_partner_2 | con_verba_self_1 | con_verba_self_2 | das_coh_1 | das_coh_2 | das_satis_1 | das_satis_2 | ecr_anx_1 | ecr_anx_2 | ecr_avoid_1 | ecr_avoid_2 | ks_comp_partner_1 | ks_comp_partner_2 | ks_comp_self_1 | ks_comp_self_2 | ks_coop_partner_1 | ks_coop_partner_2 | ks_coop_self_1 | ks_coop_self_2 | ks_prox_partner_1 | ks_prox_partner_2 | ks_prox_self_1 | ks_prox_self_2 | ks_sens_partner_1 | ks_sens_partner_2 | ks_sens_self_1 | ks_sens_self_2 | sp_partner_1 | sp_partner_2 | sp_self_1 | sp_self_2 | ss_partner_1 | ss_partner_2 | ss_self_1 | ss_self_2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | 2.023 | 2.082 | 1.854 | 2.28 | 4.77 | 4.47 | 5.13 | 4.76 | 1.285 | 1.626 | 1.611 | 1.44 | 1.805 | 2.019 | 2.030 | 2.035 | 18.67 | 18.21 | 43.20 | 42.62 | 3.29 | 2.710 | 2.316 | 2.432 | 5.449 | 5.019 | 4.51 | 4.789 | 5.63 | 5.474 | 5.21 | 4.96 | 6.21 | 6.269 | 6.545 | 6.071 | 5.23 | 5.39 | 5.32 | 5.11 | 0.809 | 0.816 | 0.825 | 0.819 | 6.212 | 6.027 | 6.340 | 6.23 |
| Std.Dev | 0.855 | 0.761 | 0.879 | 1.01 | 1.16 | 1.23 | 1.12 | 1.31 | 0.557 | 0.691 | 0.783 | 0.63 | 0.882 | 0.989 | 0.979 | 0.842 | 2.58 | 3.16 | 4.29 | 4.75 | 1.04 | 0.885 | 0.906 | 0.878 | 0.785 | 0.897 | 1.19 | 0.925 | 1.06 | 0.974 | 1.09 | 1.03 | 1.01 | 0.702 | 0.648 | 0.833 | 1.23 | 1.02 | 1.03 | 1.03 | 0.209 | 0.177 | 0.172 | 0.169 | 0.764 | 0.814 | 0.625 | 0.64 |
| Min | 1.000 | 1.000 | 1.000 | 1.00 | 1.25 | 1.00 | 1.25 | 1.00 | 1.000 | 1.000 | 1.000 | 1.00 | 1.000 | 1.000 | 1.000 | 1.000 | 9.00 | 5.00 | 20.00 | 18.00 | 1.00 | 1.056 | 1.000 | 1.000 | 3.125 | 3.000 | 1.25 | 2.125 | 2.38 | 2.625 | 2.25 | 2.00 | 2.25 | 4.000 | 3.750 | 2.500 | 1.12 | 2.50 | 2.00 | 2.50 | -0.357 | 0.143 | 0.214 | 0.143 | 2.286 | 2.857 | 4.143 | 3.86 |
| Median | 1.857 | 1.857 | 1.714 | 2.00 | 5.00 | 4.54 | 5.38 | 4.88 | 1.000 | 1.400 | 1.400 | 1.20 | 1.500 | 1.750 | 1.750 | 1.875 | 19.00 | 19.00 | 44.00 | 43.00 | 3.22 | 2.694 | 2.083 | 2.278 | 5.500 | 5.000 | 4.62 | 4.812 | 5.88 | 5.500 | 5.38 | 5.00 | 6.62 | 6.500 | 6.875 | 6.250 | 5.38 | 5.50 | 5.38 | 5.25 | 0.857 | 0.857 | 0.857 | 0.857 | 6.429 | 6.143 | 6.429 | 6.29 |
| Max | 4.714 | 4.000 | 5.714 | 5.43 | 6.75 | 6.62 | 7.00 | 7.00 | 5.600 | 4.000 | 5.600 | 4.40 | 4.750 | 5.375 | 5.375 | 4.875 | 24.00 | 24.00 | 50.00 | 50.00 | 5.67 | 5.056 | 5.333 | 5.611 | 7.000 | 7.000 | 7.00 | 7.000 | 7.00 | 7.000 | 7.00 | 7.00 | 7.00 | 7.000 | 7.000 | 7.000 | 7.00 | 7.00 | 7.00 | 7.00 | 1.000 | 1.000 | 1.000 | 1.000 | 7.000 | 7.000 | 7.000 | 7.00 |
| N.Valid | 168.000 | 168.000 | 168.000 | 168.00 | 168.00 | 168.00 | 168.00 | 168.00 | 168.000 | 168.000 | 168.000 | 168.00 | 168.000 | 168.000 | 168.000 | 168.000 | 168.00 | 168.00 | 168.00 | 168.00 | 168.00 | 168.000 | 168.000 | 168.000 | 168.000 | 168.000 | 168.00 | 168.000 | 168.00 | 168.000 | 168.00 | 168.00 | 168.00 | 168.000 | 168.000 | 167.000 | 168.00 | 168.00 | 168.00 | 167.00 | 168.000 | 166.000 | 168.000 | 168.000 | 168.000 | 167.000 | 168.000 | 168.00 |
| Pct.Valid | 100.000 | 100.000 | 100.000 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.000 | 100.000 | 100.000 | 100.00 | 100.000 | 100.000 | 100.000 | 100.000 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.000 | 100.000 | 100.000 | 100.000 | 100.000 | 100.00 | 100.000 | 100.00 | 100.000 | 100.00 | 100.00 | 100.00 | 100.000 | 100.000 | 99.405 | 100.00 | 100.00 | 100.00 | 99.41 | 100.000 | 98.810 | 100.000 | 100.000 | 100.000 | 99.405 | 100.000 | 100.00 |
Experiences in Close Relationships
# global avoidance
p <- plot_hist(var = "ABAvoid", var_name = "ECR Global Avoidance",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "ABAvoid",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# global anxiety
p <- plot_hist(var = "ABAnx", var_name = "ECR Global Anxiety",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "ABAnx",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
Sarason’s Social Support
# perceived from partner
p <- plot_hist(var = "Assp", var_name = "Social Support - Perceived from Partner",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "Assp",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# self-reported given to partner
p <- plot_hist(var = "Assind", var_name = "Social Support - Social Support - Self to Partner",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "Assind",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
Cutrona’s Procial Provisions
# perceived from partner
p <- plot_hist(var = "Aspsp", var_name = "Social Provisions - Perceived from Partner",
data = data, bin_width = 0.1,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(-1,1)
if(signif(var = "Aspsp",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# self-reported given to partner
p <- plot_hist(var = "Aisps", var_name = "Social Provisions - Self to Partner",
data = data, bin_width = 0.1,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(-1,1)
if(signif(var = "Aisps",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
Kunce and Shaver
# self-reported Proximity v. Distance
p <- plot_hist(var = "AiProx", var_name = "Self-Reported Proximity v. Distance",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)+
ylim(0,60)
if(signif(var = "AiProx",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# perceived-partner Proximity v. Distance
p <- plot_hist(var = "ApProx", var_name = "Perceived-Partner's Proximity v. Distance",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)+
ylim(0,60)
if(signif(var = "ApProx",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# self-reported Sensitivity v. Insensitivity
p <- plot_hist(var = "AiSens", var_name = "Self-Reported Sensitivity v. Insensitivity",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "AiSens",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# perceived-partner Sensitivity v. Insensitivity
p <- plot_hist(var = "Appsens", var_name = "Perceived-Partner's Sensitivity v. Insensitivity",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "Appsens",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# self-reported Cooperation v. Control
p <- plot_hist(var = "AiCoop", var_name = "Self-Reported Cooperation v. Control",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "AiCoop",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# perceived-partner Cooperation v. Control
p <- plot_hist(var = "ApCoop", var_name = "Perceived-Partner's Cooperation v. Control",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "ApCoop",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# self-reported Compulsive Caregiving
p <- plot_hist(var = "AiComp", var_name = "Self-Reported Compulsive Caregiving",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "AiComp",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# perceived-partner Compulsive Caregiving
p <- plot_hist(var = "ApComp", var_name = "Perceived-Partner's Compulsive Caregiving",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "ApComp",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
Kerig Conflict Strategies
# self-reported Collaboration
p <- plot_hist(var = "Aikrcll", var_name = "Self-Reported Collaboration",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)+
ylim(0,50)
if(signif(var = "Aikrcll",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# perceived-partner Collaboration
p <- plot_hist(var = "Apkrcll", var_name = "Perceived-Partner's Collaboration",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)+
ylim(0,50)
if(signif(var = "Apkrcll",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# self-reported Avoidance - Capitulation
p <- plot_hist(var = "Aikrac", var_name = "Self-Reported Avoidance - Capitulation",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "Aikrac",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# perceived-partner Avoidance - Capitulation
p <- plot_hist(var = "Apkrac", var_name = "Perceived-Partner's Avoidance - Capitulation",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "Apkrac",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# self-reported Stalemate
p <- plot_hist(var = "Aikrs", var_name = "Self-Reported Stalemate",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)+
ylim(0,50)
if(signif(var = "Aikrs",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# perceived-partner Stalemate
p <- plot_hist(var = "Apkrs", var_name = "Perceived-Partner's Stalemate",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)+
ylim(0,50)
if(signif(var = "Apkrs",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# self-reported Verbal Aggression
p <- plot_hist(var = "Aikrva", var_name = "Self-Reported Verbal Aggression",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "Aikrva",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# perceived-partner Verbal Aggression
p <- plot_hist(var = "Apkrva", var_name = "Perceived-Partner's Verbal Aggression",
data = data, bin_width = 0.2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,7)
if(signif(var = "Apkrva",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
Dyadic Satisfaction and Cohesion
# dyadic satisfaction
p <- plot_hist(var = "Asatis", var_name = "Dyadic Satisfaction",
data = data, bin_width = 2,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,50)
if(signif(var = "Asatis",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
# dyadic cohesion
p <- plot_hist(var = "Acohes", var_name = "Dyadic Cohesion",
data = data, bin_width = 1,
var_sex = "Asex", female_male = c("-1", "1")) +
xlim(1,25)
if(signif(var = "Acohes",
var_sex = "Asex",
data = data,
female_male = c("-1", "1"))) {
p <- p + labs(subtitle = "Significant sex difference (p < 0.05)")
}
p
III. Longitudinal Descriptives
For ease of presentation, all longitudinal plots are shown with a random sample of 100 participants.
set.seed(202211)
sample <- sample(unique(data$IDg), size = 100)
sample <- data %>% filter(IDg %in% c(sample))Experiences in Close Relationships
# global avoidance
p <- ggplot(data = sample,
aes(x = time, y = ABAvoid, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$ABAvoid, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Global Avoidance") +
theme_classic()
if(signifc("ABAvoid", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
#global anxiety
p <- ggplot(data = sample,
aes(x = time, y = ABAnx, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$ABAnx, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Global Anxiety") +
theme_classic()
if(signifc("ABAnx", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
Sarason’s Social Support
# perceived from partner
p <- ggplot(data = sample,
aes(x = time, y = Assp, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Assp, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Perceived social support from partner") +
theme_classic()
if(signifc("Assp", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# self-reported given to partner
p <- ggplot(data = sample,
aes(x = time, y = Assind, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Assind, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported social support given to partner") +
theme_classic()
if(signifc("Assind", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
Cutrona’s Procial Provisions
# perceived from partner
p <- ggplot(data = sample,
aes(x = time, y = Aspsp, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Aspsp, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Perceived social provisions from partner") +
theme_classic()
if(signifc("Aspsp", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# self-reported given to partner
p <- ggplot(data = sample,
aes(x = time, y = Aisps, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Aisps, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported social provisions given to partner") +
theme_classic()
if(signifc("Aisps", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
Kunce and Shaver
# self-reported Proximity v. Distance
p <- ggplot(data = sample,
aes(x = time, y = AiProx, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$AiProx, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported Proximity v. Distance") +
theme_classic()
if(signifc("AiProx", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# perceived-partner Proximity v. Distance
p <- ggplot(data = sample,
aes(x = time, y = ApProx, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$ApProx, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Perceived-partner Proximity v. Distance") +
theme_classic()
if(signifc("ApProx", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# self-reported Sensitivity v. Insensitivity
p <- ggplot(data = sample,
aes(x = time, y = AiSens, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$AiSens, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported Sensitivity v. Insensitivity") +
theme_classic()
if(signifc("AiSens", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# perceived-partner Sensitivity v. Insensitivity
p <- ggplot(data = sample,
aes(x = time, y = Appsens, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Appsens, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Perceived-partner Sensitivity v. Insensitivity") +
theme_classic()
if(signifc("Appsens", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# self-reported Cooperation v. Control
p <- ggplot(data = sample,
aes(x = time, y = AiCoop, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$AiCoop, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported Cooperation v. Control") +
theme_classic()
if(signifc("AiCoop", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# perceived-partner Cooperation v. Control
p <- ggplot(data = sample,
aes(x = time, y = ApCoop, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$ApCoop, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Perceived-partner Cooperation v. Control") +
theme_classic()
if(signifc("ApCoop", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# self-reported Compulsive Caregiving
p <- ggplot(data = sample,
aes(x = time, y = AiComp, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$AiComp, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported Compulsive Caregiving") +
theme_classic()
if(signifc("AiComp", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# perceived-partner Compulsive Caregiving
p <- ggplot(data = sample,
aes(x = time, y = ApComp, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$ApComp, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Perceived-partner Compulsive Caregiving") +
theme_classic()
if(signifc("ApComp", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
Kerig Conflict Strategies
# self-reported Collaboration
p <- ggplot(data = sample,
aes(x = time, y = Aikrcll, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Aikrcll, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported Collaboration") +
theme_classic()
if(signifc("Aikrcll", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# perceived-partner Collaboration
p <- ggplot(data = sample,
aes(x = time, y = Apkrcll, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Apkrcll, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Perceived-partner Collaboration") +
theme_classic()
if(signifc("Apkrcll", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# self-reported Avoidance - Capitulation
p <- ggplot(data = sample,
aes(x = time, y = Aikrac, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Aikrac, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported Avoidance - Capitulation") +
theme_classic()
if(signifc("Aikrac", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# perceived-partner Avoidance - Capitulation
p <- ggplot(data = sample,
aes(x = time, y = Apkrac, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Apkrac, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Perceived-partner Avoidance - Capitulation") +
theme_classic()
if(signifc("Apkrac", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# self-reported Stalemate
p <- ggplot(data = sample,
aes(x = time, y = Aikrs, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Aikrs, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported Stalemate") +
theme_classic()
if(signifc("Aikrs", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# perceived-partner Stalemate
p <- ggplot(data = sample,
aes(x = time, y = Apkrs, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Apkrs, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported Stalemate") +
theme_classic()
if(signifc("Apkrs", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# self-reported Verbal Aggression
p <- ggplot(data = sample,
aes(x = time, y = Aikrva, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Aikrva, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported Verbal Aggression") +
theme_classic()
if(signifc("Aikrva", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# perceived-partner Verbal Aggression
p <- ggplot(data = sample,
aes(x = time, y = Apkrva, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Apkrva, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Self-reported Verbal Aggression") +
theme_classic()
if(signifc("Apkrva", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
Dyadic Satisfaction and Cohesion
# dyadic satisfaction
p <- ggplot(data = sample,
aes(x = time, y = Asatis, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Asatis, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Dyadic Satisfaction") +
theme_classic()
if(signifc("Asatis", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
# dyadic cohesion
p <- ggplot(data = sample,
aes(x = time, y = Acohes, group = IDg)) +
geom_line(linetype = "dashed") +
stat_smooth(aes(data = sample$Acohes, group = 1),
method = "lm",
formula = y ~ poly(x, 2),
lwd = 1.5, color = "red") +
labs(x = "Time in months",
y = "Dyadic Cohesion") +
theme_classic()
if(signifc("Acohes", data = data)) {
p <- p +
labs(subtitle = "Significant linear slope (p < .05)")
}
p
Test-retest reliabilities
# create list of all variables
base_vars <- c(
# CA
"ecr_avoid", "ecr_anx",
"ss_self", "sp_self",
"ks_prox_self", "ks_sens_self",
"ks_coop_self", "ks_comp_self",
"con_collab_self", "con_capit_self",
"con_stale_self", "con_verba_self",
# quality
"das_satis", "das_coh"
)
var_list <- rep(
paste0(rep(base_vars, each = 2), c("_1", "_2")),
each = 1
)
# calculate ICC for all longitudinal variables
ICC_df <- data.frame(matrix(ncol=9,nrow=0))
names(ICC_df) <- c("variable", "type", "ICC", "F", "df1", "df2",
"p", "lower bound", "upper bound")
for(var in var_list){
# find ICC
result <- icc_function(ID = "couple", var = var, time_var = "time",
df = data_wide) %>% as.data.frame()
result <- cbind(variable = var, result)
# append to df
ICC_df <- rbind(ICC_df, result)
}
rownames(ICC_df) <- NULL
# print
ICC_df %>%
knitr::kable(caption = "Test-retest reliabilities") %>%
kableExtra::kable_styling() %>%
scroll_box(height = "300px")| variable | type | ICC | F | df1 | df2 | p | lower bound | upper bound |
|---|---|---|---|---|---|---|---|---|
| ecr_avoid_1 | ICC1k | 0.918 | 12.18 | 167 | 672 | 0 | 0.896 | 0.936 |
| ecr_avoid_2 | ICC1k | 0.905 | 10.57 | 167 | 672 | 0 | 0.881 | 0.926 |
| ecr_anx_1 | ICC1k | 0.932 | 14.61 | 167 | 672 | 0 | 0.914 | 0.947 |
| ecr_anx_2 | ICC1k | 0.898 | 9.82 | 167 | 672 | 0 | 0.872 | 0.921 |
| ss_self_1 | ICC1k | 0.896 | 9.62 | 167 | 672 | 0 | 0.869 | 0.919 |
| ss_self_2 | ICC1k | 0.813 | 5.35 | 167 | 672 | 0 | 0.764 | 0.854 |
| sp_self_1 | ICC1k | 0.850 | 6.68 | 167 | 672 | 0 | 0.811 | 0.883 |
| sp_self_2 | ICC1k | 0.829 | 5.84 | 167 | 672 | 0 | 0.784 | 0.866 |
| ks_prox_self_1 | ICC1k | 0.893 | 9.31 | 167 | 672 | 0 | 0.865 | 0.916 |
| ks_prox_self_2 | ICC1k | 0.875 | 7.98 | 167 | 672 | 0 | 0.842 | 0.902 |
| ks_sens_self_1 | ICC1k | 0.898 | 9.80 | 167 | 672 | 0 | 0.871 | 0.920 |
| ks_sens_self_2 | ICC1k | 0.872 | 7.79 | 167 | 672 | 0 | 0.838 | 0.900 |
| ks_coop_self_1 | ICC1k | 0.925 | 13.31 | 167 | 672 | 0 | 0.905 | 0.941 |
| ks_coop_self_2 | ICC1k | 0.907 | 10.75 | 167 | 672 | 0 | 0.883 | 0.927 |
| ks_comp_self_1 | ICC1k | 0.886 | 8.80 | 167 | 672 | 0 | 0.857 | 0.911 |
| ks_comp_self_2 | ICC1k | 0.876 | 8.08 | 167 | 672 | 0 | 0.844 | 0.904 |
| con_collab_self_1 | ICC1k | 0.830 | 5.87 | 167 | 672 | 0 | 0.785 | 0.867 |
| con_collab_self_2 | ICC1k | 0.880 | 8.36 | 167 | 672 | 0 | 0.849 | 0.907 |
| con_capit_self_1 | ICC1k | 0.881 | 8.38 | 167 | 672 | 0 | 0.850 | 0.907 |
| con_capit_self_2 | ICC1k | 0.864 | 7.36 | 167 | 672 | 0 | 0.829 | 0.894 |
| con_stale_self_1 | ICC1k | 0.877 | 8.16 | 167 | 672 | 0 | 0.845 | 0.904 |
| con_stale_self_2 | ICC1k | 0.845 | 6.46 | 167 | 672 | 0 | 0.805 | 0.879 |
| con_verba_self_1 | ICC1k | 0.883 | 8.56 | 167 | 672 | 0 | 0.853 | 0.909 |
| con_verba_self_2 | ICC1k | 0.891 | 9.14 | 167 | 672 | 0 | 0.862 | 0.915 |
| das_satis_1 | ICC1k | 0.869 | 7.66 | 167 | 672 | 0 | 0.835 | 0.898 |
| das_satis_2 | ICC1k | 0.862 | 7.26 | 167 | 672 | 0 | 0.826 | 0.893 |
| das_coh_1 | ICC1k | 0.857 | 6.99 | 167 | 672 | 0 | 0.820 | 0.888 |
| das_coh_2 | ICC1k | 0.859 | 7.07 | 167 | 672 | 0 | 0.822 | 0.890 |