#### 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 191 191 191 191 191 191 191 190 191 191
## Missing 0 0 0 0 0 0 0 1 0 0
## Mean 4.755516 4.405448 5.042757 5.450262 5.078534 4.599476 4.193063 52.68466 4.387625 3.195201
## Median 4.777778 4.375000 5.166667 5.500000 5.500000 5.000000 4.500000 51.13889 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 191 191 190 184 184 191 191
## Missing 0 0 1 7 7 0 0
## Mean 4.755516 4.405448 8.505263 8.592391 3.826087 20.42408 41.71204
## Median 4.777778 4.375000 9.000000 9.000000 4.000000 21 42
## 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 0.565858341 1.343457e-05 4.140706e-02 2.590771e-04
## [2,] 5.658583e-01 0.000000000 9.889010e-01 9.164794e-01 5.172522e-01
## [3,] 1.343457e-05 0.988901048 0.000000e+00 5.165033e-03 1.018359e-22
## [4,] 4.140706e-02 0.916479428 5.165033e-03 0.000000e+00 2.632195e-07
## [5,] 2.590771e-04 0.517252194 1.018359e-22 2.632195e-07 0.000000e+00
## [6,] 6.171758e-01 0.272885439 1.990726e-04 2.368762e-05 2.552889e-03
## [7,] 9.653685e-02 0.474134233 4.622112e-08 5.061281e-02 1.997229e-11
## [8,] 1.040594e-03 0.909560239 1.640097e-08 5.155935e-06 5.205198e-08
## [9,] 4.440177e-04 0.723248449 6.275581e-07 4.554646e-02 1.408462e-08
## [10,] 2.265509e-02 0.007866747 1.338143e-02 1.165403e-01 1.244400e-02
## [11,] 8.949248e-01 0.006364637 9.118677e-02 1.470294e-01 5.760085e-02
## [,6] [,7] [,8] [,9] [,10]
## [1,] 6.171758e-01 9.653685e-02 1.040594e-03 4.440177e-04 2.265509e-02
## [2,] 2.728854e-01 4.741342e-01 9.095602e-01 7.232484e-01 7.866747e-03
## [3,] 1.990726e-04 4.622112e-08 1.640097e-08 6.275581e-07 1.338143e-02
## [4,] 2.368762e-05 5.061281e-02 5.155935e-06 4.554646e-02 1.165403e-01
## [5,] 2.552889e-03 1.997229e-11 5.205198e-08 1.408462e-08 1.244400e-02
## [6,] 0.000000e+00 1.521318e-04 5.894576e-06 6.083929e-05 1.631258e-04
## [7,] 1.521318e-04 0.000000e+00 2.995544e-07 8.760185e-12 2.629393e-02
## [8,] 5.894576e-06 2.995544e-07 0.000000e+00 4.479783e-06 5.635796e-05
## [9,] 6.083929e-05 8.760185e-12 4.479783e-06 0.000000e+00 4.926631e-03
## [10,] 1.631258e-04 2.629393e-02 5.635796e-05 4.926631e-03 0.000000e+00
## [11,] 5.202758e-01 7.285191e-02 1.135053e-02 2.075489e-01 7.246950e-03
## [,11]
## [1,] 0.894924797
## [2,] 0.006364637
## [3,] 0.091186767
## [4,] 0.147029425
## [5,] 0.057600851
## [6,] 0.520275762
## [7,] 0.072851906
## [8,] 0.011350534
## [9,] 0.207548917
## [10,] 0.007246950
## [11,] 0.000000000
##
## $lowCI
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.000000000 -0.10077632 0.17494175 0.0058709010 0.12405975
## [2,] -0.100776324 1.00000000 -0.14098642 -0.1494553799 -0.09547725
## [3,] 0.174941751 -0.14098642 1.00000000 0.0613705576 0.53867588
## [4,] 0.005870901 -0.14945538 0.06137056 1.0000000000 0.23210928
## [5,] 0.124059751 -0.09547725 0.53867588 0.2321092829 1.00000000
## [6,] -0.106132467 -0.06295931 0.12895375 0.1658164545 0.07752818
## [7,] -0.021750005 -0.09055114 0.25466038 -0.0003295592 0.34103208
## [8,] 0.096737873 -0.15007654 0.26744748 0.1897889032 0.25316476
## [9,] 0.114092815 -0.16759339 0.22086754 0.0029603710 0.26999936
## [10,] 0.023438590 0.05119212 0.03767948 -0.0285003001 0.03957441
## [11,] -0.132540814 0.05637193 -0.26002182 -0.2436608232 -0.27425571
## [,6] [,7] [,8] [,9] [,10]
## [1,] -0.10613247 -0.0217500047 0.09673787 0.114092815 0.02343859
## [2,] -0.06295931 -0.0905511361 -0.15007654 -0.167593391 0.05119212
## [3,] 0.12895375 0.2546603820 0.26744748 0.220867536 0.03767948
## [4,] 0.16581645 -0.0003295592 0.18978890 0.002960371 -0.02850030
## [5,] 0.07752818 0.3410320836 0.25316476 0.269999360 0.03957441
## [6,] 1.00000000 0.1338664381 0.18775337 0.150430857 0.13259963
## [7,] 0.13386644 1.0000000000 0.23037411 0.350054789 0.01923470
## [8,] 0.18775337 0.2303741052 1.00000000 0.192426183 0.15133855
## [9,] 0.15043086 0.3500547892 0.19242618 1.000000000 0.06265629
## [10,] 0.13259963 0.0192347005 0.15133855 0.062656291 1.00000000
## [11,] -0.18753500 -0.2671386957 -0.31660237 -0.231177680 0.05321163
## [,11]
## [1,] -0.13254081
## [2,] 0.05637193
## [3,] -0.26002182
## [4,] -0.24366082
## [5,] -0.27425571
## [6,] -0.18753500
## [7,] -0.26713870
## [8,] -0.31660237
## [9,] -0.23117768
## [10,] 0.05321163
## [11,] 1.00000000
##
## $uppCI
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1.0000000 0.1826962 0.43224162 0.28375492 0.388975466 0.17745766
## [2,] 0.1826962 1.0000000 0.14297199 0.13448708 0.187863093 0.21923033
## [3,] 0.4322416 0.1429720 1.00000000 0.33401256 0.710492864 0.39318838
## [4,] 0.2837549 0.1344871 0.33401256 1.00000000 0.479479243 0.42456838
## [5,] 0.3889755 0.1878631 0.71049286 0.47947924 1.000000000 0.34835840
## [6,] 0.1774577 0.2192303 0.39318838 0.42456838 0.348358396 1.00000000
## [7,] 0.2581606 0.1926523 0.49772714 0.27804366 0.565682141 0.39740624
## [8,] 0.3652506 0.1338631 0.50797948 0.44464711 0.496523543 0.44295210
## [9,] 0.3810140 0.1169268 0.47089637 0.28177941 0.510581983 0.41218017
## [10,] 0.2998302 0.3249101 0.31274711 0.25184531 0.314458233 0.39631966
## [11,] 0.1513921 0.3295487 0.01975575 0.03721061 0.004430199 0.09581421
## [,7] [,8] [,9] [,10] [,11]
## [1,] 0.25816063 0.36525057 0.3810140 0.2998302 0.151392129
## [2,] 0.19265233 0.13386310 0.1169268 0.3249101 0.329548703
## [3,] 0.49772714 0.50797948 0.4708964 0.3127471 0.019755746
## [4,] 0.27804366 0.44464711 0.2817794 0.2518453 0.037210609
## [5,] 0.56568214 0.49652354 0.5105820 0.3144582 0.004430199
## [6,] 0.39740624 0.44295210 0.4121802 0.3963197 0.095814214
## [7,] 1.00000000 0.47806625 0.5731221 0.2959977 0.012109451
## [8,] 0.47806625 1.00000000 0.4474513 0.4123175 -0.041951667
## [9,] 0.57312208 0.44745126 1.0000000 0.3358362 0.051175496
## [10,] 0.29599767 0.41231750 0.3358362 1.0000000 0.326720183
## [11,] 0.01210945 -0.04195167 0.0511755 0.3267202 1.000000000
#Correlation Matrix
corrplot(cor(pp.wb, use="pairwise.complete.obs"), method="number", type="upper",
addCoef.col = "orchid4", tl.col="hotpink3", tl.srt=45, p.mat = cor1$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 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 191
##
## 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) 0.399 0.048 8.318 0.000
## M2 (b2) 0.051 0.014 3.642 0.000
## X (c) -0.007 0.011 -0.664 0.507
## M1 ~
## X (a1) 0.079 0.015 5.370 0.000
## M2 ~
## X (a2) 0.220 0.050 4.358 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .M1 ~~
## .M2 0.599 0.359 1.670 0.095
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Y 0.619 0.063 9.772 0.000
## .M1 1.428 0.146 9.772 0.000
## .M2 16.975 1.737 9.772 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## indirect1 0.031 0.007 4.511 0.000
## indirect2 0.011 0.004 2.795 0.005
## total 0.035 0.012 3.026 0.002
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 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 191
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 131.298
## 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) -1070.307
## Loglikelihood unrestricted model (H1) -1070.307
##
## Akaike (AIC) 2158.613
## Bayesian (BIC) 2187.884
## Sample-size adjusted Bayesian (BIC) 2159.375
##
## 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) 0.399 0.045 8.954 0.000 0.313 0.486
## M2 (b2) 0.051 0.012 4.206 0.000 0.028 0.075
## X (c) -0.007 0.011 -0.652 0.514 -0.028 0.014
## M1 ~
## X (a1) 0.079 0.013 5.855 0.000 0.053 0.105
## M2 ~
## X (a2) 0.220 0.056 3.925 0.000 0.113 0.332
## Std.lv Std.all
##
## 0.399 0.523
## 0.051 0.224
## -0.007 -0.043
##
## 0.079 0.362
##
## 0.220 0.301
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .M1 ~~
## .M2 0.599 0.471 1.272 0.203 -0.257 1.594
## Std.lv Std.all
##
## 0.599 0.122
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .Y 0.619 0.066 9.382 0.000 0.489 0.744
## .M1 1.428 0.163 8.735 0.000 1.108 1.752
## .M2 16.975 2.618 6.483 0.000 11.933 22.153
## Std.lv Std.all
## 0.619 0.646
## 1.428 0.869
## 16.975 0.910
##
## R-Square:
## Estimate
## Y 0.354
## M1 0.131
## M2 0.090
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## indirect1 0.031 0.007 4.794 0.000 0.020 0.045
## indirect2 0.011 0.004 2.710 0.007 0.004 0.021
## total 0.035 0.011 3.194 0.001 0.014 0.057
## Std.lv Std.all
## 0.031 0.189
## 0.011 0.067
## 0.035 0.214
parameterEstimates(fit, boot.ci.type="bca.simple")
## lhs op rhs label est se z pvalue ci.lower
## 1 Y ~ M1 b1 0.399 0.045 8.954 0.000 0.311
## 2 Y ~ M2 b2 0.051 0.012 4.206 0.000 0.027
## 3 Y ~ X c -0.007 0.011 -0.652 0.514 -0.027
## 4 M1 ~ X a1 0.079 0.013 5.855 0.000 0.053
## 5 M2 ~ X a2 0.220 0.056 3.925 0.000 0.117
## 6 M1 ~~ M2 0.599 0.471 1.272 0.203 -0.182
## 7 Y ~~ Y 0.619 0.066 9.382 0.000 0.508
## 8 M1 ~~ M1 1.428 0.163 8.735 0.000 1.138
## 9 M2 ~~ M2 16.975 2.618 6.483 0.000 12.576
## 10 X ~~ X 34.969 0.000 NA NA 34.969
## 11 indirect1 := a1*b1 indirect1 0.031 0.007 4.794 0.000 0.020
## 12 indirect2 := a2*b2 indirect2 0.011 0.004 2.710 0.007 0.005
## 13 total := c+(a1*b1)+(a2*b2) total 0.035 0.011 3.194 0.001 0.015
## ci.upper
## 1 0.485
## 2 0.075
## 3 0.015
## 4 0.105
## 5 0.336
## 6 1.706
## 7 0.772
## 8 1.796
## 9 23.158
## 10 34.969
## 11 0.046
## 12 0.021
## 13 0.058
with(pp, mediation.effect.plot(x = aps.standards, mediator = competence, dv = mhc, ylab = "Well-Being", xlab = "Perfectionistic standards"))
semPaths(fit, "model", "est", layout ="tree2", node.label.cex=4, title = TRUE, edge.label.cex=.7, curve=TRUE)
