#### ON TIME-USE AND WELL-BEING: THE ROLE OF PERSONAL AND PROJECT CHARACTERISTICS ###
#SPRING 2021
setwd("~/Google Drive/PhD/Projects/Personal projects and well-being/data")
library(car)
library(psych)
library(tidyverse)
library(QuantPsyc)
library(readr)
library(dplyr)
library(rockchalk)
library(corrplot)
library(ggplot2)
library(GGally)
library(Hmisc)
library(PerformanceAnalytics)
library(jmv)
library(stargazer)
library(multilevel)
library(bda)
library(mediation)
library(lavaan)
library(knitr)
library(MBESS)
library(semPlot)
#read data in
pp <- read.csv("personal.projects.cleaned.v2.csv")
#descriptives
descriptives(pp, vars = vars(as.5f, as.6f, bpns, autonomy, competence, relatedness, passion,
vlq, zest, engagement), missing = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## as.5f as.6f bpns autonomy competence relatedness passion vlq zest engagement
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## N 192 192 192 192 192 192 192 191 192 192
## Missing 0 0 0 0 0 0 0 1 0 0
## Mean 4.755053 4.403987 5.037326 5.450521 5.070312 4.591146 4.194010 52.51812 4.383712 3.195226
## Median 4.777778 4.375000 5.166667 5.500000 5.500000 5.000000 4.437500 50.77778 4.416667 3.166667
## Minimum 3.111111 2.625000 1.500000 2.000000 1.000000 1.000000 1.375000 12.90000 1.916667 1.000000
## Maximum 6.111111 5.875000 7.000000 7.000000 7.000000 7.000000 5.000000 98.00000 6.416667 5.000000
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
descriptives(pp, vars = vars(as.5f, as.6f, pp1.14, pp.2.14, pp.3.14, pp.competence, aps.standards), missing = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## as.5f as.6f pp1.14 pp.2.14 pp.3.14 pp.competence aps.standards
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
## N 192 192 191 185 185 192 192
## Missing 0 0 1 7 7 0 0
## Mean 4.755053 4.403987 8.513089 8.600000 3.816216 20.43229 41.71354
## Median 4.777778 4.375000 9 9 4 21.00000 42.00000
## Minimum 3.111111 2.625000 1 1 1 2 23
## Maximum 6.111111 5.875000 10 10 7 27 49
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────
# well-being variables for correlations
pp.wb<- pp[,c("as.5f", "as.6f", "autonomy", "aps.standards", "competence", "pp.competence", "relatedness", "passion", "vlq", "zest", "engagement")]
#Calculating correlations and CIs
cor1 <- cor.mtest(pp.wb, use="pairwise.complete.obs", conf.level = 0.95)
cor1
## $p
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.000000e+00 0.560470946 1.281221e-05 4.096158e-02 2.466828e-04
## [2,] 5.604709e-01 0.000000000 9.901076e-01 9.149568e-01 4.905519e-01
## [3,] 1.281221e-05 0.990107643 0.000000e+00 5.045867e-03 1.370982e-22
## [4,] 4.096158e-02 0.914956807 5.045867e-03 0.000000e+00 2.819366e-07
## [5,] 2.466828e-04 0.490551876 1.370982e-22 2.819366e-07 0.000000e+00
## [6,] 6.200131e-01 0.277730629 1.915565e-04 2.255691e-05 2.896390e-03
## [7,] 9.375999e-02 0.453127943 4.735015e-08 5.101829e-02 1.207790e-11
## [8,] 1.023695e-03 0.903156510 1.503672e-08 4.863224e-06 6.217574e-08
## [9,] 4.367471e-04 0.773031558 7.665335e-07 4.758358e-02 6.990444e-09
## [10,] 2.182575e-02 0.006966574 1.373290e-02 1.183619e-01 9.219113e-03
## [11,] 8.947897e-01 0.006267636 9.034165e-02 1.459756e-01 5.779363e-02
## [,6] [,7] [,8] [,9] [,10]
## [1,] 6.200131e-01 9.375999e-02 1.023695e-03 4.367471e-04 0.0218257471
## [2,] 2.777306e-01 4.531279e-01 9.031565e-01 7.730316e-01 0.0069665745
## [3,] 1.915565e-04 4.735015e-08 1.503672e-08 7.665335e-07 0.0137328964
## [4,] 2.255691e-05 5.101829e-02 4.863224e-06 4.758358e-02 0.1183618563
## [5,] 2.896390e-03 1.207790e-11 6.217574e-08 6.990444e-09 0.0092191129
## [6,] 0.000000e+00 1.708009e-04 5.462004e-06 8.369439e-05 0.0001959114
## [7,] 1.708009e-04 0.000000e+00 3.312111e-07 4.765741e-12 0.0210150006
## [8,] 5.462004e-06 3.312111e-07 0.000000e+00 6.028332e-06 0.0000645129
## [9,] 8.369439e-05 4.765741e-12 6.028332e-06 0.000000e+00 0.0030789718
## [10,] 1.959114e-04 2.101500e-02 6.451290e-05 3.078972e-03 0.0000000000
## [11,] 5.195168e-01 7.269995e-02 1.115231e-02 2.097341e-01 0.0073915546
## [,11]
## [1,] 0.894789738
## [2,] 0.006267636
## [3,] 0.090341655
## [4,] 0.145975555
## [5,] 0.057793631
## [6,] 0.519516774
## [7,] 0.072699953
## [8,] 0.011152308
## [9,] 0.209734072
## [10,] 0.007391555
## [11,] 0.000000000
##
## $lowCI
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.000000000 -0.09993676 0.17522600 0.0061827559 0.12464266
## [2,] -0.099936763 1.00000000 -0.14072557 -0.1492017287 -0.09221079
## [3,] 0.175225996 -0.14072557 1.00000000 0.0617570115 0.53567574
## [4,] 0.006182756 -0.14920173 0.06175701 1.0000000000 0.23057881
## [5,] 0.124642658 -0.09221079 0.53567574 0.2305788095 1.00000000
## [6,] -0.106142412 -0.06359052 0.12931547 0.1661702223 0.07450869
## [7,] -0.020671410 -0.08782840 0.25368932 -0.0005787949 0.34512449
## [8,] 0.096815953 -0.15025890 0.26779644 0.1901674130 0.25025657
## [9,] 0.114106469 -0.16249906 0.21749929 0.0015973500 0.27764546
## [10,] 0.024412660 0.05403138 0.03690001 -0.0289909076 0.04711207
## [11,] -0.132181568 0.05659101 -0.25966554 -0.2433011042 -0.27346707
## [,6] [,7] [,8] [,9] [,10] [,11]
## [1,] -0.10614241 -0.0206714103 0.09681595 0.11410647 0.02441266 -0.13218157
## [2,] -0.06359052 -0.0878283993 -0.15025890 -0.16249906 0.05403138 0.05659101
## [3,] 0.12931547 0.2536893189 0.26779644 0.21749929 0.03690001 -0.25966554
## [4,] 0.16617022 -0.0005787949 0.19016741 0.00159735 -0.02899091 -0.24330110
## [5,] 0.07450869 0.3451244899 0.25025657 0.27764546 0.04711207 -0.27346707
## [6,] 1.00000000 0.1314099542 0.18841207 0.14450113 0.12890317 -0.18713225
## [7,] 0.13140995 1.0000000000 0.22841581 0.35504671 0.02546011 -0.26652957
## [8,] 0.18841207 0.2284158052 1.00000000 0.18741100 0.14862159 -0.31622640
## [9,] 0.14450113 0.3550467120 0.18741100 1.00000000 0.07333224 -0.23016717
## [10,] 0.12890317 0.0254601118 0.14862159 0.07333224 1.00000000 0.05258569
## [11,] -0.18713225 -0.2665295661 -0.31622640 -0.23016717 0.05258569 1.00000000
##
## $uppCI
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 1.0000000 0.1827839 0.43186416 0.28334528 0.38883520 0.1767143 0.25846092
## [2,] 0.1827839 1.0000000 0.14249079 0.13399816 0.19031050 0.2179057 0.19456508
## [3,] 0.4318642 0.1424908 1.00000000 0.33368432 0.70802087 0.3928591 0.49637527
## [4,] 0.2833453 0.1339982 0.33368432 1.00000000 0.47764868 0.4242457 0.27711466
## [5,] 0.3888352 0.1903105 0.70802087 0.47764868 1.00000000 0.3450201 0.56831547
## [6,] 0.1767143 0.2179057 0.39285907 0.42424573 0.34502009 1.0000000 0.39465940
## [7,] 0.2584609 0.1945651 0.49637527 0.27711466 0.56831547 0.3946594 1.00000000
## [8,] 0.3646624 0.1329360 0.50769641 0.44435451 0.49360790 0.4428922 0.47588442
## [9,] 0.3803731 0.1213369 0.46754182 0.27982066 0.51610738 0.4064988 0.57642809
## [10,] 0.3000279 0.3267781 0.31135898 0.25067578 0.32056777 0.3925044 0.30098140
## [11,] 0.1510094 0.3290695 0.01938064 0.03683619 0.00452546 0.0954774 0.01200796
## [,8] [,9] [,10] [,11]
## [1,] 0.36466244 0.3803731 0.3000279 0.15100941
## [2,] 0.13293601 0.1213369 0.3267781 0.32906945
## [3,] 0.50769641 0.4675418 0.3113590 0.01938064
## [4,] 0.44435451 0.2798207 0.2506758 0.03683619
## [5,] 0.49360790 0.5161074 0.3205678 0.00452546
## [6,] 0.44289223 0.4064988 0.3925044 0.09547740
## [7,] 0.47588442 0.5764281 0.3009814 0.01200796
## [8,] 1.00000000 0.4426668 0.4093777 -0.04229059
## [9,] 0.44266682 1.0000000 0.3446451 0.05147860
## [10,] 0.40937769 0.3446451 1.0000000 0.32548245
## [11,] -0.04229059 0.0514786 0.3254825 1.00000000
#Correlation Matrix
corrplot(cor(pp.wb, use="pairwise.complete.obs"), method="number", type="upper",
addCoef.col = "orchid4", tl.col="hotpink3", tl.srt=45, p.mat = cor1$p,
sig.level = 0.05, insig = "blank", diag=FALSE)
ggpairs(pp.wb) + theme_classic()
chart.Correlation(pp.wb, histogram=TRUE)
#multiple mediators
# effect of standards on well-being is mediated by satisfying psych need for competence and personal project dimension of competence
pp$X <- pp$aps.standards
pp$M1 <- pp$competence
pp$M2 <- pp$pp.competence
pp$Y <- pp$mhc
mmed <- '
Y ~ b1 * M1 + b2 * M2 + c * X
M1 ~ a1 * X
M2 ~ a2 * X
indirect1 := a1 * b1
indirect2 := a2 * b2
total := c + (a1 * b1) + (a2 * b2)
M1 ~~ M2
'
fit <- sem(model = mmed, data = pp)
summary(fit)
## lavaan 0.6-8 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 192
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Y ~
## M1 (b1) 0.405 0.048 8.472 0.000
## M2 (b2) 0.050 0.014 3.593 0.000
## X (c) -0.007 0.011 -0.697 0.486
## M1 ~
## X (a1) 0.078 0.015 5.354 0.000
## M2 ~
## X (a2) 0.220 0.050 4.369 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .M1 ~~
## .M2 0.584 0.358 1.632 0.103
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Y 0.621 0.063 9.798 0.000
## .M1 1.433 0.146 9.798 0.000
## .M2 16.899 1.725 9.798 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## indirect1 0.032 0.007 4.526 0.000
## indirect2 0.011 0.004 2.775 0.006
## total 0.035 0.012 3.009 0.003
fit <- sem(
model = mmed,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 192
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 132.465
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1076.194
## Loglikelihood unrestricted model (H1) -1076.194
##
## Akaike (AIC) 2170.388
## Bayesian (BIC) 2199.705
## Sample-size adjusted Bayesian (BIC) 2171.196
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 5000
## Number of successful bootstrap draws 5000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## Y ~
## M1 (b1) 0.405 0.044 9.154 0.000 0.320 0.493
## M2 (b2) 0.050 0.012 4.179 0.000 0.027 0.075
## X (c) -0.007 0.011 -0.686 0.493 -0.029 0.014
## M1 ~
## X (a1) 0.078 0.013 5.815 0.000 0.052 0.105
## M2 ~
## X (a2) 0.220 0.057 3.856 0.000 0.110 0.333
## Std.lv Std.all
##
## 0.405 0.529
## 0.050 0.220
## -0.007 -0.045
##
## 0.078 0.360
##
## 0.220 0.301
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .M1 ~~
## .M2 0.584 0.469 1.245 0.213 -0.265 1.582
## Std.lv Std.all
##
## 0.584 0.119
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .Y 0.621 0.065 9.511 0.000 0.490 0.744
## .M1 1.433 0.160 8.967 0.000 1.123 1.743
## .M2 16.899 2.600 6.499 0.000 12.048 22.118
## Std.lv Std.all
## 0.621 0.643
## 1.433 0.870
## 16.899 0.910
##
## R-Square:
## Estimate
## Y 0.357
## M1 0.130
## M2 0.090
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## indirect1 0.032 0.007 4.811 0.000 0.020 0.046
## indirect2 0.011 0.004 2.645 0.008 0.004 0.020
## total 0.035 0.011 3.244 0.001 0.014 0.057
## Std.lv Std.all
## 0.032 0.191
## 0.011 0.066
## 0.035 0.212
parameterEstimates(fit, boot.ci.type="bca.simple")
## lhs op rhs label est se z pvalue ci.lower
## 1 Y ~ M1 b1 0.405 0.044 9.154 0.000 0.317
## 2 Y ~ M2 b2 0.050 0.012 4.179 0.000 0.027
## 3 Y ~ X c -0.007 0.011 -0.686 0.493 -0.027
## 4 M1 ~ X a1 0.078 0.013 5.815 0.000 0.052
## 5 M2 ~ X a2 0.220 0.057 3.856 0.000 0.111
## 6 M1 ~~ M2 0.584 0.469 1.245 0.213 -0.211
## 7 Y ~~ Y 0.621 0.065 9.511 0.000 0.512
## 8 M1 ~~ M1 1.433 0.160 8.967 0.000 1.153
## 9 M2 ~~ M2 16.899 2.600 6.499 0.000 12.615
## 10 X ~~ X 34.788 0.000 NA NA 34.788
## 11 indirect1 := a1*b1 indirect1 0.032 0.007 4.811 0.000 0.020
## 12 indirect2 := a2*b2 indirect2 0.011 0.004 2.645 0.008 0.004
## 13 total := c+(a1*b1)+(a2*b2) total 0.035 0.011 3.244 0.001 0.015
## ci.upper
## 1 0.490
## 2 0.074
## 3 0.015
## 4 0.105
## 5 0.335
## 6 1.696
## 7 0.774
## 8 1.783
## 9 23.034
## 10 34.788
## 11 0.047
## 12 0.021
## 13 0.057
with(pp, mediation.effect.plot(x = aps.standards, mediator = competence, dv = mhc, ylab = "Well-Being", xlab = "Perfectionistic standards"))
