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library(semTable)
pp <- read_csv("personal.projects.merged.v7.csv") #wide format
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## recruit.source = col_character(),
## pp.1 = col_character(),
## pp.2 = col_character(),
## pp.3 = col_character(),
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## )
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pp.full <- read_csv("personal.projects.merged.v5.csv") #long format
##
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## cols(
## id...1 = col_double(),
## pp.type = col_character(),
## type = col_character(),
## id...4 = col_double(),
## pp.support = col_character(),
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## competence = col_double(),
## id...10 = col_double(),
## pp.autonomy = col_character(),
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## )
pp$ethnicity <- factor(pp$ethnicity,levels = c(1,2,3),labels = c("white", "black", "asian"))
pp$student <- factor(pp$student,levels = c(1,2,3),labels = c("full-time", "part-time", "not a student"))
pp$gender <- factor(pp$gender,levels = c(1,2,3, 4),labels = c("female", "male", "non-binary", "perfer not to answer"))
pp$occupation <- factor(pp$occupation,levels = c(1,2,3),labels = c("full-time", "part-time", "not working"))
#########################################################################################################
### DESCRIPTIVE STATISTICS ###
#scales
descriptives(pp, vars = vars(bpns, age, autonomy, competence, relatedness, pp.support, pp.competence, pp.autonomy), missing = TRUE, sd = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## bpns age autonomy competence relatedness pp.support pp.competence pp.autonomy
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## N 323 303 323 323 322 303 303 303
## Missing 4 24 4 4 5 24 24 24
## Mean 5.037668 24.94719 5.391641 5.088235 4.627329 6.918592 7.613861 8.224422
## Median 5.166667 21.00000 5.500000 5.500000 5.000000 7.666667 8.000000 8.666667
## Standard deviation 1.107744 10.49490 1.278352 1.296478 1.654108 2.333012 1.932488 1.903547
## Minimum 1.000000 17.00000 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000
## Maximum 7.000000 72.00000 7.000000 7.000000 7.000000 10.00000 10.00000 10.00000
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
descriptives(pp, vars = vars(pp.importance, pp.difficulty, pp.visibility, pp.control, pp.respons, pp.outcome,
pp.othersview, pp.congruency, pp.progress, pp.challenge, pp.absorp), missing = TRUE, sd = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## pp.importance pp.difficulty pp.visibility pp.control pp.respons pp.outcome pp.othersview pp.congruency pp.progress pp.challenge pp.absorp
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## N 327 327 327 327 327 327 327 327 327 327 327
## Missing 0 0 0 0 0 0 0 0 0 0 0
## Mean 23.77676 20.39144 21.29358 19.55963 23.23547 21.13456 19.48318 22.36391 21.45872 20.64832 19.90826
## Median 27.00000 23.00000 23.00000 21.00000 26.00000 24.00000 22.00000 25.00000 24.00000 23.00000 22.00000
## Standard deviation 8.429917 7.779929 8.002265 7.876849 8.500608 8.034832 8.486304 8.314688 8.138222 7.949719 7.982966
## Minimum 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## Maximum 30.00000 30.00000 30.00000 30.00000 30.00000 30.00000 30.00000 30.00000 30.00000 30.00000 30.00000
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
descriptives(pp, vars = vars(pp.timeadeq, pp.trademark), missing = TRUE, sd = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ─────────────────────────────────────────────────────
## pp.timeadeq pp.trademark
## ─────────────────────────────────────────────────────
## N 327 327
## Missing 0 0
## Mean 17.22936 19.83486
## Median 19.00000 22.00000
## Standard deviation 7.903521 8.542403
## Minimum 0.000000 0.000000
## Maximum 31.00000 31.00000
## ─────────────────────────────────────────────────────
descriptives(pp, vars = vars(passion, vlq, zestR, engagementR), missing = TRUE, sd = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ──────────────────────────────────────────────────────────────────────────
## passion vlq zestR engagementR
## ──────────────────────────────────────────────────────────────────────────
## N 313 308 310 310
## Missing 14 19 17 17
## Mean 4.173513 52.05205 4.556896 4.051452
## Median 4.375000 50.15000 4.500000 4.166667
## Standard deviation 0.8088683 17.58257 1.216113 0.5646091
## Minimum 1.000000 1.000000 1.333333 2.500000
## Maximum 5.000000 100.0000 6.916667 5.166667
## ──────────────────────────────────────────────────────────────────────────
descriptives(pp, vars = vars(as.5f, as.6f, aps.stand, aps.discrep), missing = TRUE, sd = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ────────────────────────────────────────────────────────────────────────────
## as.5f as.6f aps.stand aps.discrep
## ────────────────────────────────────────────────────────────────────────────
## N 327 327 314 315
## Missing 0 0 13 12
## Mean 4.691957 4.403874 5.806688 4.233790
## Median 4.666667 4.375000 6.000000 4.250000
## Standard deviation 0.5494878 0.6245790 1.026802 1.434279
## Minimum 1.000000 1.000000 1.000000 1.000000
## Maximum 7.000000 7.000000 7.000000 7.000000
## ────────────────────────────────────────────────────────────────────────────
descriptives(pp, vars = vars(mhcR, mhc.e, mhc.s, mhc.pR), missing = TRUE, sd = TRUE)
##
## DESCRIPTIVES
##
## Descriptives
## ──────────────────────────────────────────────────────────────────────
## mhcR mhc.e mhc.s mhc.pR
## ──────────────────────────────────────────────────────────────────────
## N 317 317 317 317
## Missing 10 10 10 10
## Mean 3.881712 4.390116 3.381178 4.213354
## Median 4.000000 4.666667 3.500000 4.400000
## Standard deviation 1.031149 1.185472 1.231826 1.156744
## Minimum 1.000000 1.000000 1.000000 1.000000
## Maximum 6.000000 6.000000 6.000000 6.000000
## ──────────────────────────────────────────────────────────────────────
##################################################################################################################
#H1: Achievement striving leads to satisfaction of competence needs at both the psychological and project level,
# which in turn leads to well-being, even when controlling for perfectionist standards and discrepancies
##################################################################################################################
mmed1.mhcR <-
'
mhcR ~ b1 * competence + b2 * pp.competence + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.competence ~ a1 * as.5f
pp.competence ~ aa1 * aps.stand
pp.competence ~ aaa1 * aps.discrep
competence ~ d21 * pp.competence
competence ~ aa2 * aps.stand
competence ~ aaa2 * aps.discrep
competence ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
competence ~ sample
pp.competence ~ sample
mhcR ~ sample
'
#covariance section of output - sample is positively related w. A.S. (because dal students, sona = 1)
#aps.r standards subscale a little different from self-oriented perfectionism
fit1.mhcR <- sem(model = mmed1.mhcR, data = pp)
summary(fit1.mhcR, standardized = TRUE)
## lavaan 0.6-8 ended normally after 46 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## 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|) Std.lv Std.all
## mhcR ~
## comptnc (b1) 0.268 0.044 6.069 0.000 0.268 0.344
## pp.cmpt (b2) 0.069 0.027 2.541 0.011 0.069 0.132
## as.5f (c1) 0.184 0.093 1.986 0.047 0.184 0.099
## aps.stn (c2) 0.068 0.055 1.236 0.216 0.068 0.067
## aps.dsc (c3) -0.205 0.036 -5.630 0.000 -0.205 -0.288
## pp.competence ~
## as.5f (a1) 0.654 0.191 3.424 0.001 0.654 0.185
## aps.stn (aa1) 0.653 0.107 6.111 0.000 0.653 0.336
## aps.dsc (aaa1) -0.365 0.070 -5.248 0.000 -0.365 -0.270
## competence ~
## pp.cmpt (d21) 0.128 0.035 3.691 0.000 0.128 0.190
## aps.stn (aa2) 0.399 0.068 5.855 0.000 0.399 0.305
## aps.dsc (aaa2) -0.318 0.044 -7.279 0.000 -0.318 -0.349
## as.5f (a2) 0.517 0.117 4.415 0.000 0.517 0.217
## sample -0.148 0.131 -1.131 0.258 -0.148 -0.053
## pp.competence ~
## sample 0.156 0.218 0.713 0.476 0.156 0.037
## mhcR ~
## sample -0.139 0.101 -1.380 0.167 -0.139 -0.063
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.190 0.033 5.766 0.000 0.190 0.352
## sample 0.034 0.015 2.320 0.020 0.034 0.135
## aps.discrep 0.060 0.045 1.350 0.177 0.060 0.078
## aps.stand ~~
## aps.discrep 0.212 0.082 2.575 0.010 0.212 0.150
## sample 0.098 0.027 3.643 0.000 0.098 0.214
## aps.discrep ~~
## sample 0.101 0.038 2.659 0.008 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mhcR 0.605 0.049 12.288 0.000 0.605 0.593
## .pp.competence 2.846 0.232 12.288 0.000 2.846 0.766
## .competence 1.030 0.084 12.288 0.000 1.030 0.611
## as.5f 0.297 0.024 12.288 0.000 0.297 1.000
## aps.stand 0.986 0.080 12.288 0.000 0.986 1.000
## aps.discrep 2.024 0.165 12.288 0.000 2.024 1.000
## sample 0.212 0.017 12.288 0.000 0.212 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.216 0.061 3.533 0.000 0.216 0.097
## standardsndrct 0.208 0.042 4.992 0.000 0.208 0.164
## discrepindirct -0.123 0.026 -4.750 0.000 -0.123 -0.145
## total1 0.400 0.103 3.886 0.000 0.400 0.196
## total2 0.276 0.058 4.727 0.000 0.276 0.231
## total3 -0.327 0.038 -8.728 0.000 -0.327 -0.434
fit1.mhcR <- sem(
model = mmed1.mhcR,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit1.mhcR, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 46 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 454.555
## Degrees of freedom 21
## 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) -2738.949
## Loglikelihood unrestricted model (H1) -2738.949
##
## Akaike (AIC) 5533.898
## Bayesian (BIC) 5637.790
## Sample-size adjusted Bayesian (BIC) 5548.989
##
## 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
## mhcR ~
## comptnc (b1) 0.268 0.045 5.999 0.000 0.180 0.355
## pp.cmpt (b2) 0.069 0.031 2.244 0.025 0.011 0.130
## as.5f (c1) 0.184 0.102 1.795 0.073 -0.028 0.373
## aps.stn (c2) 0.068 0.058 1.177 0.239 -0.054 0.173
## aps.dsc (c3) -0.205 0.038 -5.381 0.000 -0.278 -0.130
## pp.competence ~
## as.5f (a1) 0.654 0.204 3.210 0.001 0.224 1.014
## aps.stn (aa1) 0.653 0.119 5.505 0.000 0.429 0.894
## aps.dsc (aaa1) -0.365 0.074 -4.905 0.000 -0.513 -0.221
## competence ~
## pp.cmpt (d21) 0.128 0.039 3.270 0.001 0.051 0.206
## aps.stn (aa2) 0.399 0.077 5.189 0.000 0.243 0.541
## aps.dsc (aaa2) -0.318 0.046 -6.860 0.000 -0.408 -0.227
## as.5f (a2) 0.517 0.141 3.672 0.000 0.231 0.784
## sample -0.148 0.128 -1.158 0.247 -0.400 0.099
## pp.competence ~
## sample 0.156 0.230 0.677 0.498 -0.299 0.606
## mhcR ~
## sample -0.139 0.106 -1.313 0.189 -0.340 0.076
## Std.lv Std.all
##
## 0.268 0.344
## 0.069 0.132
## 0.184 0.099
## 0.068 0.067
## -0.205 -0.288
##
## 0.654 0.185
## 0.653 0.336
## -0.365 -0.270
##
## 0.128 0.190
## 0.399 0.305
## -0.318 -0.349
## 0.517 0.217
## -0.148 -0.053
##
## 0.156 0.037
##
## -0.139 -0.063
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.190 0.068 2.787 0.005 0.079 0.341
## sample 0.034 0.017 2.029 0.042 0.002 0.067
## aps.discrep 0.060 0.060 1.014 0.311 -0.048 0.186
## aps.stand ~~
## aps.discrep 0.212 0.096 2.203 0.028 0.025 0.409
## sample 0.098 0.029 3.350 0.001 0.041 0.155
## aps.discrep ~~
## sample 0.101 0.038 2.639 0.008 0.025 0.176
## Std.lv Std.all
##
## 0.190 0.352
## 0.034 0.135
## 0.060 0.078
##
## 0.212 0.150
## 0.098 0.214
##
## 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .mhcR 0.605 0.047 12.931 0.000 0.502 0.684
## .pp.competence 2.846 0.297 9.584 0.000 2.246 3.405
## .competence 1.030 0.082 12.515 0.000 0.847 1.166
## as.5f 0.297 0.053 5.582 0.000 0.208 0.412
## aps.stand 0.986 0.122 8.082 0.000 0.768 1.246
## aps.discrep 2.024 0.132 15.306 0.000 1.756 2.286
## sample 0.212 0.010 20.176 0.000 0.188 0.230
## Std.lv Std.all
## 0.605 0.593
## 2.846 0.766
## 1.030 0.611
## 0.297 1.000
## 0.986 1.000
## 2.024 1.000
## 0.212 1.000
##
## R-Square:
## Estimate
## mhcR 0.407
## pp.competence 0.234
## competence 0.389
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.216 0.065 3.311 0.001 0.087 0.345
## standardsndrct 0.208 0.041 5.037 0.000 0.134 0.295
## discrepindirct -0.123 0.028 -4.422 0.000 -0.183 -0.072
## total1 0.400 0.116 3.443 0.001 0.146 0.607
## total2 0.276 0.059 4.697 0.000 0.154 0.383
## total3 -0.327 0.038 -8.521 0.000 -0.400 -0.253
## Std.lv Std.all
## 0.216 0.097
## 0.208 0.164
## -0.123 -0.145
## 0.400 0.196
## 0.276 0.231
## -0.327 -0.434
standardizedSolution(fit1.mhcR, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 mhcR ~ competence 0.344 0.056 6.100
## 2 mhcR ~ pp.competence 0.132 0.057 2.325
## 3 mhcR ~ as.5f 0.099 0.058 1.695
## 4 mhcR ~ aps.stand 0.067 0.057 1.165
## 5 mhcR ~ aps.discrep -0.288 0.053 -5.433
## 6 pp.competence ~ as.5f 0.185 0.061 3.016
## 7 pp.competence ~ aps.stand 0.336 0.058 5.777
## 8 pp.competence ~ aps.discrep -0.270 0.054 -4.965
## 9 competence ~ pp.competence 0.190 0.057 3.316
## 10 competence ~ aps.stand 0.305 0.063 4.808
## 11 competence ~ aps.discrep -0.349 0.049 -7.052
## 12 competence ~ as.5f 0.217 0.062 3.497
## 13 as.5f ~~ aps.stand 0.352 0.090 3.928
## 14 as.5f ~~ sample 0.135 0.062 2.187
## 15 as.5f ~~ aps.discrep 0.078 0.072 1.083
## 16 aps.stand ~~ aps.discrep 0.150 0.062 2.404
## 17 aps.stand ~~ sample 0.214 0.059 3.614
## 18 aps.discrep ~~ sample 0.155 0.057 2.696
## 19 competence ~ sample -0.053 0.045 -1.163
## 20 pp.competence ~ sample 0.037 0.055 0.678
## 21 mhcR ~ sample -0.063 0.048 -1.311
## 22 mhcR ~~ mhcR 0.593 0.046 12.955
## 23 pp.competence ~~ pp.competence 0.766 0.046 16.824
## 24 competence ~~ competence 0.611 0.051 12.061
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.097 0.030 3.231
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.164 0.032 5.118
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.145 0.034 -4.275
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.196 0.068 2.888
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.231 0.058 4.018
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.434 0.051 -8.561
## pvalue ci.lower ci.upper
## 1 0.000 0.234 0.455
## 2 0.020 0.021 0.242
## 3 0.090 -0.015 0.214
## 4 0.244 -0.046 0.180
## 5 0.000 -0.392 -0.184
## 6 0.003 0.065 0.305
## 7 0.000 0.222 0.450
## 8 0.000 -0.376 -0.163
## 9 0.001 0.078 0.302
## 10 0.000 0.181 0.429
## 11 0.000 -0.446 -0.252
## 12 0.000 0.095 0.338
## 13 0.000 0.176 0.527
## 14 0.029 0.014 0.256
## 15 0.279 -0.063 0.219
## 16 0.016 0.028 0.272
## 17 0.000 0.098 0.331
## 18 0.007 0.042 0.267
## 19 0.245 -0.141 0.036
## 20 0.498 -0.070 0.145
## 21 0.190 -0.158 0.031
## 22 0.000 0.503 0.683
## 23 0.000 0.677 0.855
## 24 0.000 0.511 0.710
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.001 0.038 0.155
## 30 0.000 0.101 0.227
## 31 0.000 -0.212 -0.079
## 32 0.004 0.063 0.329
## 33 0.000 0.118 0.344
## 34 0.000 -0.533 -0.334
parameterEstimates(fit1.mhcR, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 mhcR ~ competence b1
## 2 mhcR ~ pp.competence b2
## 3 mhcR ~ as.5f c1
## 4 mhcR ~ aps.stand c2
## 5 mhcR ~ aps.discrep c3
## 6 pp.competence ~ as.5f a1
## 7 pp.competence ~ aps.stand aa1
## 8 pp.competence ~ aps.discrep aaa1
## 9 competence ~ pp.competence d21
## 10 competence ~ aps.stand aa2
## 11 competence ~ aps.discrep aaa2
## 12 competence ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 competence ~ sample
## 20 pp.competence ~ sample
## 21 mhcR ~ sample
## 22 mhcR ~~ mhcR
## 23 pp.competence ~~ pp.competence
## 24 competence ~~ competence
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.268 0.045 5.999 0.000 0.181 0.357
## 2 0.069 0.031 2.244 0.025 0.010 0.130
## 3 0.184 0.102 1.795 0.073 -0.024 0.375
## 4 0.068 0.058 1.177 0.239 -0.047 0.178
## 5 -0.205 0.038 -5.381 0.000 -0.276 -0.127
## 6 0.654 0.204 3.210 0.001 0.262 1.039
## 7 0.653 0.119 5.505 0.000 0.428 0.894
## 8 -0.365 0.074 -4.905 0.000 -0.512 -0.220
## 9 0.128 0.039 3.270 0.001 0.052 0.206
## 10 0.399 0.077 5.189 0.000 0.242 0.540
## 11 -0.318 0.046 -6.860 0.000 -0.409 -0.228
## 12 0.517 0.141 3.672 0.000 0.236 0.790
## 13 0.190 0.068 2.787 0.005 0.089 0.366
## 14 0.034 0.017 2.029 0.042 0.003 0.068
## 15 0.060 0.060 1.014 0.311 -0.043 0.193
## 16 0.212 0.096 2.203 0.028 0.045 0.429
## 17 0.098 0.029 3.350 0.001 0.043 0.157
## 18 0.101 0.038 2.639 0.008 0.025 0.177
## 19 -0.148 0.128 -1.158 0.247 -0.408 0.093
## 20 0.156 0.230 0.677 0.498 -0.285 0.621
## 21 -0.139 0.106 -1.313 0.189 -0.348 0.063
## 22 0.605 0.047 12.931 0.000 0.531 0.726
## 23 2.846 0.297 9.584 0.000 2.363 3.567
## 24 1.030 0.082 12.515 0.000 0.897 1.228
## 25 0.297 0.053 5.582 0.000 0.216 0.431
## 26 0.986 0.122 8.082 0.000 0.786 1.271
## 27 2.024 0.132 15.306 0.000 1.782 2.313
## 28 0.212 0.010 20.176 0.000 0.189 0.231
## 29 0.216 0.065 3.311 0.001 0.102 0.361
## 30 0.208 0.041 5.037 0.000 0.139 0.304
## 31 -0.123 0.028 -4.422 0.000 -0.187 -0.075
## 32 0.400 0.116 3.443 0.001 0.157 0.616
## 33 0.276 0.059 4.697 0.000 0.161 0.389
## 34 -0.327 0.038 -8.521 0.000 -0.397 -0.248
#########################################################################################################
#Mediation model with zest as outcome controlling for perfectionistic standards and discrepancies
mmed1.zest <-
'
zestR ~ b1 * competence + b2 * pp.competence + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.competence ~ a1 * as.5f
pp.competence ~ aa1 * aps.stand
pp.competence ~ aaa1 * aps.discrep
competence ~ d21 * pp.competence
competence ~ aa2 * aps.stand
competence ~ aaa2 * aps.discrep
competence ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
competence ~ sample
pp.competence ~ sample
zestR ~ sample
'
fit1.zest <- sem(model = mmed1.zest, data = pp)
summary(fit1.zest, standardized = TRUE)
## lavaan 0.6-8 ended normally after 45 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## 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|) Std.lv Std.all
## zestR ~
## comptnc (b1) 0.221 0.055 4.014 0.000 0.221 0.237
## pp.cmpt (b2) 0.052 0.034 1.540 0.124 0.052 0.083
## as.5f (c1) 0.166 0.116 1.432 0.152 0.166 0.074
## aps.stn (c2) 0.038 0.069 0.553 0.580 0.038 0.031
## aps.dsc (c3) -0.358 0.045 -7.877 0.000 -0.358 -0.419
## pp.competence ~
## as.5f (a1) 0.654 0.191 3.424 0.001 0.654 0.185
## aps.stn (aa1) 0.653 0.107 6.111 0.000 0.653 0.336
## aps.dsc (aaa1) -0.365 0.070 -5.248 0.000 -0.365 -0.270
## competence ~
## pp.cmpt (d21) 0.128 0.035 3.691 0.000 0.128 0.190
## aps.stn (aa2) 0.399 0.068 5.855 0.000 0.399 0.305
## aps.dsc (aaa2) -0.318 0.044 -7.279 0.000 -0.318 -0.349
## as.5f (a2) 0.517 0.117 4.415 0.000 0.517 0.217
## sample -0.148 0.131 -1.131 0.258 -0.148 -0.053
## pp.competence ~
## sample 0.156 0.218 0.713 0.476 0.156 0.037
## zestR ~
## sample -0.011 0.126 -0.090 0.929 -0.011 -0.004
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.190 0.033 5.766 0.000 0.190 0.352
## sample 0.034 0.015 2.320 0.020 0.034 0.135
## aps.discrep 0.060 0.045 1.350 0.177 0.060 0.078
## aps.stand ~~
## aps.discrep 0.212 0.082 2.575 0.010 0.212 0.150
## sample 0.098 0.027 3.643 0.000 0.098 0.214
## aps.discrep ~~
## sample 0.101 0.038 2.659 0.008 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .zestR 0.945 0.077 12.288 0.000 0.945 0.642
## .pp.competence 2.846 0.232 12.288 0.000 2.846 0.766
## .competence 1.030 0.084 12.288 0.000 1.030 0.611
## as.5f 0.297 0.024 12.288 0.000 0.297 1.000
## aps.stand 0.986 0.080 12.288 0.000 0.986 1.000
## aps.discrep 2.024 0.165 12.288 0.000 2.024 1.000
## sample 0.212 0.017 12.288 0.000 0.212 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.176 0.058 3.046 0.002 0.176 0.065
## standardsndrct 0.170 0.044 3.869 0.000 0.170 0.110
## discrepindirct -0.100 0.027 -3.745 0.000 -0.100 -0.097
## total1 0.341 0.118 2.882 0.004 0.341 0.139
## total2 0.208 0.067 3.084 0.002 0.208 0.141
## total3 -0.458 0.043 -10.598 0.000 -0.458 -0.516
fit1.zest <- sem(
model = mmed1.zest,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit1.zest, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 45 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 430.781
## Degrees of freedom 21
## 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) -2806.271
## Loglikelihood unrestricted model (H1) -2806.271
##
## Akaike (AIC) 5668.541
## Bayesian (BIC) 5772.433
## Sample-size adjusted Bayesian (BIC) 5683.632
##
## 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
## zestR ~
## comptnc (b1) 0.221 0.058 3.795 0.000 0.107 0.337
## pp.cmpt (b2) 0.052 0.043 1.222 0.222 -0.029 0.138
## as.5f (c1) 0.166 0.115 1.445 0.149 -0.064 0.392
## aps.stn (c2) 0.038 0.070 0.542 0.588 -0.100 0.176
## aps.dsc (c3) -0.358 0.047 -7.565 0.000 -0.451 -0.265
## pp.competence ~
## as.5f (a1) 0.654 0.202 3.237 0.001 0.228 1.020
## aps.stn (aa1) 0.653 0.121 5.397 0.000 0.428 0.898
## aps.dsc (aaa1) -0.365 0.074 -4.955 0.000 -0.511 -0.224
## competence ~
## pp.cmpt (d21) 0.128 0.039 3.312 0.001 0.053 0.205
## aps.stn (aa2) 0.399 0.077 5.160 0.000 0.238 0.543
## aps.dsc (aaa2) -0.318 0.047 -6.786 0.000 -0.409 -0.226
## as.5f (a2) 0.517 0.142 3.637 0.000 0.225 0.790
## sample -0.148 0.128 -1.158 0.247 -0.394 0.114
## pp.competence ~
## sample 0.156 0.227 0.685 0.494 -0.279 0.622
## zestR ~
## sample -0.011 0.135 -0.084 0.933 -0.280 0.248
## Std.lv Std.all
##
## 0.221 0.237
## 0.052 0.083
## 0.166 0.074
## 0.038 0.031
## -0.358 -0.419
##
## 0.654 0.185
## 0.653 0.336
## -0.365 -0.270
##
## 0.128 0.190
## 0.399 0.305
## -0.318 -0.349
## 0.517 0.217
## -0.148 -0.053
##
## 0.156 0.037
##
## -0.011 -0.004
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.190 0.067 2.855 0.004 0.078 0.344
## sample 0.034 0.017 2.024 0.043 0.002 0.067
## aps.discrep 0.060 0.059 1.028 0.304 -0.047 0.185
## aps.stand ~~
## aps.discrep 0.212 0.095 2.229 0.026 0.031 0.410
## sample 0.098 0.029 3.425 0.001 0.044 0.157
## aps.discrep ~~
## sample 0.101 0.039 2.620 0.009 0.027 0.180
## Std.lv Std.all
##
## 0.190 0.352
## 0.034 0.135
## 0.060 0.078
##
## 0.212 0.150
## 0.098 0.214
##
## 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .zestR 0.945 0.077 12.266 0.000 0.771 1.072
## .pp.competence 2.846 0.297 9.596 0.000 2.231 3.398
## .competence 1.030 0.082 12.565 0.000 0.855 1.176
## as.5f 0.297 0.052 5.714 0.000 0.210 0.410
## aps.stand 0.986 0.119 8.294 0.000 0.767 1.233
## aps.discrep 2.024 0.131 15.502 0.000 1.767 2.272
## sample 0.212 0.010 20.528 0.000 0.190 0.230
## Std.lv Std.all
## 0.945 0.642
## 2.846 0.766
## 1.030 0.611
## 0.297 1.000
## 0.986 1.000
## 2.024 1.000
## 0.212 1.000
##
## R-Square:
## Estimate
## zestR 0.358
## pp.competence 0.234
## competence 0.389
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.176 0.064 2.764 0.006 0.059 0.309
## standardsndrct 0.170 0.050 3.418 0.001 0.082 0.277
## discrepindirct -0.100 0.030 -3.305 0.001 -0.165 -0.045
## total1 0.341 0.111 3.064 0.002 0.109 0.552
## total2 0.208 0.070 2.986 0.003 0.070 0.345
## total3 -0.458 0.046 -9.986 0.000 -0.547 -0.366
## Std.lv Std.all
## 0.176 0.065
## 0.170 0.110
## -0.100 -0.097
## 0.341 0.139
## 0.208 0.141
## -0.458 -0.516
standardizedSolution(fit1.zest, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 zestR ~ competence 0.237 0.062 3.841
## 2 zestR ~ pp.competence 0.083 0.067 1.239
## 3 zestR ~ as.5f 0.074 0.052 1.427
## 4 zestR ~ aps.stand 0.031 0.058 0.541
## 5 zestR ~ aps.discrep -0.419 0.052 -8.061
## 6 pp.competence ~ as.5f 0.185 0.061 3.049
## 7 pp.competence ~ aps.stand 0.336 0.059 5.683
## 8 pp.competence ~ aps.discrep -0.270 0.054 -5.032
## 9 competence ~ pp.competence 0.190 0.056 3.367
## 10 competence ~ aps.stand 0.305 0.063 4.826
## 11 competence ~ aps.discrep -0.349 0.050 -7.032
## 12 competence ~ as.5f 0.217 0.062 3.473
## 13 as.5f ~~ aps.stand 0.352 0.088 3.993
## 14 as.5f ~~ sample 0.135 0.062 2.170
## 15 as.5f ~~ aps.discrep 0.078 0.071 1.096
## 16 aps.stand ~~ aps.discrep 0.150 0.062 2.434
## 17 aps.stand ~~ sample 0.214 0.058 3.675
## 18 aps.discrep ~~ sample 0.155 0.058 2.678
## 19 competence ~ sample -0.053 0.045 -1.164
## 20 pp.competence ~ sample 0.037 0.054 0.685
## 21 zestR ~ sample -0.004 0.051 -0.084
## 22 zestR ~~ zestR 0.642 0.053 12.066
## 23 pp.competence ~~ pp.competence 0.766 0.046 16.567
## 24 competence ~~ competence 0.611 0.050 12.149
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.065 0.026 2.514
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.110 0.034 3.261
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.097 0.033 -2.907
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.139 0.052 2.678
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.141 0.055 2.591
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.516 0.049 -10.609
## pvalue ci.lower ci.upper
## 1 0.000 0.116 0.358
## 2 0.215 -0.048 0.214
## 3 0.154 -0.028 0.176
## 4 0.589 -0.082 0.144
## 5 0.000 -0.521 -0.317
## 6 0.002 0.066 0.303
## 7 0.000 0.220 0.452
## 8 0.000 -0.375 -0.165
## 9 0.001 0.079 0.300
## 10 0.000 0.181 0.429
## 11 0.000 -0.446 -0.251
## 12 0.001 0.094 0.339
## 13 0.000 0.179 0.524
## 14 0.030 0.013 0.256
## 15 0.273 -0.061 0.217
## 16 0.015 0.029 0.270
## 17 0.000 0.100 0.329
## 18 0.007 0.041 0.268
## 19 0.244 -0.141 0.036
## 20 0.493 -0.069 0.143
## 21 0.933 -0.105 0.096
## 22 0.000 0.537 0.746
## 23 0.000 0.675 0.857
## 24 0.000 0.512 0.709
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.012 0.014 0.115
## 30 0.001 0.044 0.176
## 31 0.004 -0.162 -0.032
## 32 0.007 0.037 0.241
## 33 0.010 0.034 0.248
## 34 0.000 -0.612 -0.421
parameterEstimates(fit1.zest, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 zestR ~ competence b1
## 2 zestR ~ pp.competence b2
## 3 zestR ~ as.5f c1
## 4 zestR ~ aps.stand c2
## 5 zestR ~ aps.discrep c3
## 6 pp.competence ~ as.5f a1
## 7 pp.competence ~ aps.stand aa1
## 8 pp.competence ~ aps.discrep aaa1
## 9 competence ~ pp.competence d21
## 10 competence ~ aps.stand aa2
## 11 competence ~ aps.discrep aaa2
## 12 competence ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 competence ~ sample
## 20 pp.competence ~ sample
## 21 zestR ~ sample
## 22 zestR ~~ zestR
## 23 pp.competence ~~ pp.competence
## 24 competence ~~ competence
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.221 0.058 3.795 0.000 0.110 0.342
## 2 0.052 0.043 1.222 0.222 -0.030 0.137
## 3 0.166 0.115 1.445 0.149 -0.067 0.389
## 4 0.038 0.070 0.542 0.588 -0.100 0.176
## 5 -0.358 0.047 -7.565 0.000 -0.446 -0.261
## 6 0.654 0.202 3.237 0.001 0.250 1.048
## 7 0.653 0.121 5.397 0.000 0.433 0.908
## 8 -0.365 0.074 -4.955 0.000 -0.508 -0.219
## 9 0.128 0.039 3.312 0.001 0.051 0.203
## 10 0.399 0.077 5.160 0.000 0.242 0.546
## 11 -0.318 0.047 -6.786 0.000 -0.410 -0.227
## 12 0.517 0.142 3.637 0.000 0.241 0.804
## 13 0.190 0.067 2.855 0.004 0.091 0.369
## 14 0.034 0.017 2.024 0.043 0.002 0.068
## 15 0.060 0.059 1.028 0.304 -0.041 0.193
## 16 0.212 0.095 2.229 0.026 0.041 0.421
## 17 0.098 0.029 3.425 0.001 0.044 0.157
## 18 0.101 0.039 2.620 0.009 0.029 0.181
## 19 -0.148 0.128 -1.158 0.247 -0.400 0.108
## 20 0.156 0.227 0.685 0.494 -0.285 0.616
## 21 -0.011 0.135 -0.084 0.933 -0.272 0.256
## 22 0.945 0.077 12.266 0.000 0.820 1.117
## 23 2.846 0.297 9.596 0.000 2.345 3.505
## 24 1.030 0.082 12.565 0.000 0.896 1.223
## 25 0.297 0.052 5.714 0.000 0.219 0.435
## 26 0.986 0.119 8.294 0.000 0.790 1.259
## 27 2.024 0.131 15.502 0.000 1.779 2.289
## 28 0.212 0.010 20.528 0.000 0.190 0.230
## 29 0.176 0.064 2.764 0.006 0.073 0.333
## 30 0.170 0.050 3.418 0.001 0.086 0.286
## 31 -0.100 0.030 -3.305 0.001 -0.168 -0.048
## 32 0.341 0.111 3.064 0.002 0.111 0.555
## 33 0.208 0.070 2.986 0.003 0.071 0.347
## 34 -0.458 0.046 -9.986 0.000 -0.544 -0.363
#########################################################################################################
#Mediation model with engagement as outcome controlling for perfectionistic standards and discrepancies
mmed1.eng <-
'
engagementR ~ b1 * competence + b2 * pp.competence + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.competence ~ a1 * as.5f
pp.competence ~ aa1 * aps.stand
pp.competence ~ aaa1 * aps.discrep
competence ~ d21 * pp.competence
competence ~ aa2 * aps.stand
competence ~ aaa2 * aps.discrep
competence ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
competence ~ sample
pp.competence ~ sample
engagementR ~ sample
'
fit1.eng <- sem(model = mmed1.eng, data = pp)
summary(fit1.eng, standardized = TRUE)
## lavaan 0.6-8 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## 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|) Std.lv Std.all
## engagementR ~
## comptnc (b1) 0.080 0.025 3.224 0.001 0.080 0.187
## pp.cmpt (b2) 0.050 0.015 3.243 0.001 0.050 0.172
## as.5f (c1) 0.126 0.052 2.424 0.015 0.126 0.124
## aps.stn (c2) 0.050 0.031 1.623 0.105 0.050 0.090
## aps.dsc (c3) -0.149 0.020 -7.292 0.000 -0.149 -0.382
## pp.competence ~
## as.5f (a1) 0.654 0.191 3.424 0.001 0.654 0.185
## aps.stn (aa1) 0.653 0.107 6.111 0.000 0.653 0.336
## aps.dsc (aaa1) -0.365 0.070 -5.248 0.000 -0.365 -0.270
## competence ~
## pp.cmpt (d21) 0.128 0.035 3.691 0.000 0.128 0.190
## aps.stn (aa2) 0.399 0.068 5.855 0.000 0.399 0.305
## aps.dsc (aaa2) -0.318 0.044 -7.279 0.000 -0.318 -0.349
## as.5f (a2) 0.517 0.117 4.415 0.000 0.517 0.217
## sample -0.148 0.131 -1.131 0.258 -0.148 -0.053
## pp.competence ~
## sample 0.156 0.218 0.713 0.476 0.156 0.037
## engagementR ~
## sample -0.054 0.057 -0.955 0.339 -0.054 -0.045
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.190 0.033 5.766 0.000 0.190 0.352
## sample 0.034 0.015 2.320 0.020 0.034 0.135
## aps.discrep 0.060 0.045 1.350 0.177 0.060 0.078
## aps.stand ~~
## aps.discrep 0.212 0.082 2.575 0.010 0.212 0.150
## sample 0.098 0.027 3.643 0.000 0.098 0.214
## aps.discrep ~~
## sample 0.101 0.038 2.659 0.008 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .engagementR 0.192 0.016 12.288 0.000 0.192 0.620
## .pp.competence 2.846 0.232 12.288 0.000 2.846 0.766
## .competence 1.030 0.084 12.288 0.000 1.030 0.611
## as.5f 0.297 0.024 12.288 0.000 0.297 1.000
## aps.stand 0.986 0.080 12.288 0.000 0.986 1.000
## aps.discrep 2.024 0.165 12.288 0.000 2.024 1.000
## sample 0.212 0.017 12.288 0.000 0.212 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.082 0.024 3.378 0.001 0.082 0.078
## standardsndrct 0.076 0.019 3.998 0.000 0.076 0.126
## discrepindirct -0.047 0.012 -4.106 0.000 -0.047 -0.119
## total1 0.209 0.053 3.965 0.000 0.209 0.201
## total2 0.127 0.030 4.232 0.000 0.127 0.216
## total3 -0.197 0.019 -10.261 0.000 -0.197 -0.501
fit1.eng <- sem(
model = mmed1.eng,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit1.eng, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 441.014
## Degrees of freedom 21
## 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) -2565.805
## Loglikelihood unrestricted model (H1) -2565.805
##
## Akaike (AIC) 5187.610
## Bayesian (BIC) 5291.502
## Sample-size adjusted Bayesian (BIC) 5202.702
##
## 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
## engagementR ~
## comptnc (b1) 0.080 0.025 3.204 0.001 0.032 0.130
## pp.cmpt (b2) 0.050 0.018 2.832 0.005 0.016 0.086
## as.5f (c1) 0.126 0.053 2.389 0.017 0.020 0.228
## aps.stn (c2) 0.050 0.033 1.526 0.127 -0.012 0.117
## aps.dsc (c3) -0.149 0.023 -6.599 0.000 -0.192 -0.104
## pp.competence ~
## as.5f (a1) 0.654 0.207 3.164 0.002 0.213 1.026
## aps.stn (aa1) 0.653 0.116 5.611 0.000 0.433 0.888
## aps.dsc (aaa1) -0.365 0.074 -4.963 0.000 -0.508 -0.221
## competence ~
## pp.cmpt (d21) 0.128 0.039 3.267 0.001 0.051 0.206
## aps.stn (aa2) 0.399 0.076 5.250 0.000 0.239 0.538
## aps.dsc (aaa2) -0.318 0.047 -6.843 0.000 -0.412 -0.228
## as.5f (a2) 0.517 0.140 3.688 0.000 0.233 0.782
## sample -0.148 0.131 -1.133 0.257 -0.401 0.119
## pp.competence ~
## sample 0.156 0.229 0.678 0.498 -0.298 0.598
## engagementR ~
## sample -0.054 0.060 -0.900 0.368 -0.179 0.063
## Std.lv Std.all
##
## 0.080 0.187
## 0.050 0.172
## 0.126 0.124
## 0.050 0.090
## -0.149 -0.382
##
## 0.654 0.185
## 0.653 0.336
## -0.365 -0.270
##
## 0.128 0.190
## 0.399 0.305
## -0.318 -0.349
## 0.517 0.217
## -0.148 -0.053
##
## 0.156 0.037
##
## -0.054 -0.045
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.190 0.069 2.777 0.005 0.079 0.344
## sample 0.034 0.017 2.032 0.042 0.002 0.068
## aps.discrep 0.060 0.060 1.010 0.312 -0.048 0.186
## aps.stand ~~
## aps.discrep 0.212 0.095 2.219 0.026 0.034 0.410
## sample 0.098 0.030 3.320 0.001 0.041 0.156
## aps.discrep ~~
## sample 0.101 0.038 2.655 0.008 0.028 0.177
## Std.lv Std.all
##
## 0.190 0.352
## 0.034 0.135
## 0.060 0.078
##
## 0.212 0.150
## 0.098 0.214
##
## 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .engagementR 0.192 0.015 12.762 0.000 0.159 0.217
## .pp.competence 2.846 0.293 9.721 0.000 2.233 3.383
## .competence 1.030 0.082 12.585 0.000 0.851 1.172
## as.5f 0.297 0.054 5.509 0.000 0.209 0.419
## aps.stand 0.986 0.121 8.128 0.000 0.767 1.244
## aps.discrep 2.024 0.134 15.158 0.000 1.761 2.285
## sample 0.212 0.011 20.132 0.000 0.188 0.230
## Std.lv Std.all
## 0.192 0.620
## 2.846 0.766
## 1.030 0.611
## 0.297 1.000
## 0.986 1.000
## 2.024 1.000
## 0.212 1.000
##
## R-Square:
## Estimate
## engagementR 0.380
## pp.competence 0.234
## competence 0.389
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.082 0.028 2.925 0.003 0.031 0.141
## standardsndrct 0.076 0.021 3.671 0.000 0.039 0.121
## discrepindirct -0.047 0.013 -3.727 0.000 -0.074 -0.025
## total1 0.209 0.052 4.016 0.000 0.099 0.304
## total2 0.127 0.032 3.896 0.000 0.067 0.194
## total3 -0.197 0.021 -9.272 0.000 -0.237 -0.154
## Std.lv Std.all
## 0.082 0.078
## 0.076 0.126
## -0.047 -0.119
## 0.209 0.201
## 0.127 0.216
## -0.197 -0.501
standardizedSolution(fit1.eng, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 engagementR ~ competence 0.187 0.058 3.244
## 2 engagementR ~ pp.competence 0.172 0.060 2.851
## 3 engagementR ~ as.5f 0.124 0.054 2.300
## 4 engagementR ~ aps.stand 0.090 0.058 1.539
## 5 engagementR ~ aps.discrep -0.382 0.055 -6.994
## 6 pp.competence ~ as.5f 0.185 0.062 2.972
## 7 pp.competence ~ aps.stand 0.336 0.058 5.843
## 8 pp.competence ~ aps.discrep -0.270 0.053 -5.049
## 9 competence ~ pp.competence 0.190 0.057 3.326
## 10 competence ~ aps.stand 0.305 0.062 4.879
## 11 competence ~ aps.discrep -0.349 0.050 -7.013
## 12 competence ~ as.5f 0.217 0.062 3.501
## 13 as.5f ~~ aps.stand 0.352 0.090 3.927
## 14 as.5f ~~ sample 0.135 0.062 2.189
## 15 as.5f ~~ aps.discrep 0.078 0.072 1.081
## 16 aps.stand ~~ aps.discrep 0.150 0.062 2.424
## 17 aps.stand ~~ sample 0.214 0.060 3.568
## 18 aps.discrep ~~ sample 0.155 0.057 2.717
## 19 competence ~ sample -0.053 0.046 -1.139
## 20 pp.competence ~ sample 0.037 0.055 0.679
## 21 engagementR ~ sample -0.045 0.050 -0.897
## 22 engagementR ~~ engagementR 0.620 0.048 12.792
## 23 pp.competence ~~ pp.competence 0.766 0.046 16.606
## 24 competence ~~ competence 0.611 0.050 12.096
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.078 0.027 2.850
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.126 0.032 3.902
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.119 0.031 -3.800
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.201 0.056 3.601
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.216 0.054 4.018
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.501 0.048 -10.393
## pvalue ci.lower ci.upper
## 1 0.001 0.074 0.300
## 2 0.004 0.054 0.290
## 3 0.021 0.018 0.229
## 4 0.124 -0.025 0.204
## 5 0.000 -0.489 -0.275
## 6 0.003 0.063 0.306
## 7 0.000 0.223 0.449
## 8 0.000 -0.374 -0.165
## 9 0.001 0.078 0.301
## 10 0.000 0.182 0.427
## 11 0.000 -0.446 -0.251
## 12 0.000 0.095 0.338
## 13 0.000 0.176 0.527
## 14 0.029 0.014 0.255
## 15 0.280 -0.063 0.219
## 16 0.015 0.029 0.271
## 17 0.000 0.097 0.332
## 18 0.007 0.043 0.267
## 19 0.255 -0.143 0.038
## 20 0.497 -0.070 0.144
## 21 0.370 -0.143 0.053
## 22 0.000 0.525 0.715
## 23 0.000 0.676 0.856
## 24 0.000 0.512 0.710
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.004 0.024 0.131
## 30 0.000 0.063 0.189
## 31 0.000 -0.180 -0.058
## 32 0.000 0.092 0.311
## 33 0.000 0.111 0.321
## 34 0.000 -0.595 -0.406
parameterEstimates(fit1.eng, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 engagementR ~ competence b1
## 2 engagementR ~ pp.competence b2
## 3 engagementR ~ as.5f c1
## 4 engagementR ~ aps.stand c2
## 5 engagementR ~ aps.discrep c3
## 6 pp.competence ~ as.5f a1
## 7 pp.competence ~ aps.stand aa1
## 8 pp.competence ~ aps.discrep aaa1
## 9 competence ~ pp.competence d21
## 10 competence ~ aps.stand aa2
## 11 competence ~ aps.discrep aaa2
## 12 competence ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 competence ~ sample
## 20 pp.competence ~ sample
## 21 engagementR ~ sample
## 22 engagementR ~~ engagementR
## 23 pp.competence ~~ pp.competence
## 24 competence ~~ competence
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.080 0.025 3.204 0.001 0.031 0.129
## 2 0.050 0.018 2.832 0.005 0.015 0.083
## 3 0.126 0.053 2.389 0.017 0.025 0.234
## 4 0.050 0.033 1.526 0.127 -0.012 0.117
## 5 -0.149 0.023 -6.599 0.000 -0.195 -0.107
## 6 0.654 0.207 3.164 0.002 0.255 1.061
## 7 0.653 0.116 5.611 0.000 0.431 0.886
## 8 -0.365 0.074 -4.963 0.000 -0.507 -0.221
## 9 0.128 0.039 3.267 0.001 0.051 0.207
## 10 0.399 0.076 5.250 0.000 0.241 0.539
## 11 -0.318 0.047 -6.843 0.000 -0.411 -0.226
## 12 0.517 0.140 3.688 0.000 0.238 0.791
## 13 0.190 0.069 2.777 0.005 0.088 0.366
## 14 0.034 0.017 2.032 0.042 0.004 0.070
## 15 0.060 0.060 1.010 0.312 -0.041 0.196
## 16 0.212 0.095 2.219 0.026 0.046 0.423
## 17 0.098 0.030 3.320 0.001 0.041 0.156
## 18 0.101 0.038 2.655 0.008 0.029 0.178
## 19 -0.148 0.131 -1.133 0.257 -0.410 0.108
## 20 0.156 0.229 0.678 0.498 -0.283 0.605
## 21 -0.054 0.060 -0.900 0.368 -0.173 0.068
## 22 0.192 0.015 12.762 0.000 0.168 0.226
## 23 2.846 0.293 9.721 0.000 2.350 3.507
## 24 1.030 0.082 12.585 0.000 0.897 1.220
## 25 0.297 0.054 5.509 0.000 0.217 0.443
## 26 0.986 0.121 8.128 0.000 0.784 1.276
## 27 2.024 0.134 15.158 0.000 1.778 2.305
## 28 0.212 0.011 20.132 0.000 0.190 0.231
## 29 0.082 0.028 2.925 0.003 0.035 0.148
## 30 0.076 0.021 3.671 0.000 0.039 0.121
## 31 -0.047 0.013 -3.727 0.000 -0.075 -0.025
## 32 0.209 0.052 4.016 0.000 0.107 0.309
## 33 0.127 0.032 3.896 0.000 0.065 0.192
## 34 -0.197 0.021 -9.272 0.000 -0.238 -0.156
#########################################################################################################
#Mediation model with passion as outcome controlling for perfectionistic standards and discrepancies
mmed1.p <-
'
passion ~ b1 * competence + b2 * pp.competence + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.competence ~ a1 * as.5f
pp.competence ~ aa1 * aps.stand
pp.competence ~ aaa1 * aps.discrep
competence ~ d21 * pp.competence
competence ~ aa2 * aps.stand
competence ~ aaa2 * aps.discrep
competence ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
competence ~ sample
pp.competence ~ sample
passion ~ sample
'
fit1.p <- sem(model = mmed1.p, data = pp)
summary(fit1.p, standardized = TRUE)
## lavaan 0.6-8 ended normally after 46 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## 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|) Std.lv Std.all
## passion ~
## comptnc (b1) 0.086 0.039 2.238 0.025 0.086 0.139
## pp.cmpt (b2) 0.073 0.024 3.091 0.002 0.073 0.175
## as.5f (c1) 0.202 0.081 2.495 0.013 0.202 0.136
## aps.stn (c2) 0.178 0.048 3.696 0.000 0.178 0.219
## aps.dsc (c3) -0.107 0.032 -3.375 0.001 -0.107 -0.189
## pp.competence ~
## as.5f (a1) 0.654 0.191 3.424 0.001 0.654 0.185
## aps.stn (aa1) 0.653 0.107 6.111 0.000 0.653 0.336
## aps.dsc (aaa1) -0.365 0.070 -5.248 0.000 -0.365 -0.270
## competence ~
## pp.cmpt (d21) 0.128 0.035 3.691 0.000 0.128 0.190
## aps.stn (aa2) 0.399 0.068 5.855 0.000 0.399 0.305
## aps.dsc (aaa2) -0.318 0.044 -7.279 0.000 -0.318 -0.349
## as.5f (a2) 0.517 0.117 4.415 0.000 0.517 0.217
## sample -0.148 0.131 -1.131 0.258 -0.148 -0.053
## pp.competence ~
## sample 0.156 0.218 0.713 0.476 0.156 0.037
## passion ~
## sample 0.024 0.088 0.273 0.784 0.024 0.014
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.190 0.033 5.766 0.000 0.190 0.352
## sample 0.034 0.015 2.320 0.020 0.034 0.135
## aps.discrep 0.060 0.045 1.350 0.177 0.060 0.078
## aps.stand ~~
## aps.discrep 0.212 0.082 2.575 0.010 0.212 0.150
## sample 0.098 0.027 3.643 0.000 0.098 0.214
## aps.discrep ~~
## sample 0.101 0.038 2.659 0.008 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .passion 0.464 0.038 12.288 0.000 0.464 0.711
## .pp.competence 2.846 0.232 12.288 0.000 2.846 0.766
## .competence 1.030 0.084 12.288 0.000 1.030 0.611
## as.5f 0.297 0.024 12.288 0.000 0.297 1.000
## aps.stand 0.986 0.080 12.288 0.000 0.986 1.000
## aps.discrep 2.024 0.165 12.288 0.000 2.024 1.000
## sample 0.212 0.017 12.288 0.000 0.212 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.101 0.033 3.037 0.002 0.101 0.070
## standardsndrct 0.092 0.028 3.301 0.001 0.092 0.111
## discrepindirct -0.058 0.017 -3.505 0.000 -0.058 -0.108
## total1 0.303 0.080 3.799 0.000 0.303 0.206
## total2 0.270 0.045 5.955 0.000 0.270 0.330
## total3 -0.166 0.029 -5.707 0.000 -0.166 -0.297
fit1.p <- sem(
model = mmed1.p,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit1.p, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 46 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 399.685
## Degrees of freedom 21
## 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) -2698.814
## Loglikelihood unrestricted model (H1) -2698.814
##
## Akaike (AIC) 5453.628
## Bayesian (BIC) 5557.520
## Sample-size adjusted Bayesian (BIC) 5468.719
##
## 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
## passion ~
## comptnc (b1) 0.086 0.040 2.174 0.030 0.012 0.170
## pp.cmpt (b2) 0.073 0.026 2.846 0.004 0.022 0.122
## as.5f (c1) 0.202 0.095 2.132 0.033 0.008 0.382
## aps.stn (c2) 0.178 0.054 3.284 0.001 0.076 0.287
## aps.dsc (c3) -0.107 0.032 -3.401 0.001 -0.170 -0.046
## pp.competence ~
## as.5f (a1) 0.654 0.205 3.196 0.001 0.226 1.031
## aps.stn (aa1) 0.653 0.118 5.528 0.000 0.428 0.891
## aps.dsc (aaa1) -0.365 0.073 -5.021 0.000 -0.509 -0.226
## competence ~
## pp.cmpt (d21) 0.128 0.039 3.301 0.001 0.053 0.205
## aps.stn (aa2) 0.399 0.077 5.202 0.000 0.242 0.542
## aps.dsc (aaa2) -0.318 0.046 -6.909 0.000 -0.403 -0.224
## as.5f (a2) 0.517 0.143 3.619 0.000 0.227 0.788
## sample -0.148 0.129 -1.149 0.250 -0.393 0.111
## pp.competence ~
## sample 0.156 0.232 0.670 0.503 -0.290 0.624
## passion ~
## sample 0.024 0.086 0.279 0.780 -0.145 0.192
## Std.lv Std.all
##
## 0.086 0.139
## 0.073 0.175
## 0.202 0.136
## 0.178 0.219
## -0.107 -0.189
##
## 0.654 0.185
## 0.653 0.336
## -0.365 -0.270
##
## 0.128 0.190
## 0.399 0.305
## -0.318 -0.349
## 0.517 0.217
## -0.148 -0.053
##
## 0.156 0.037
##
## 0.024 0.014
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.190 0.068 2.815 0.005 0.079 0.341
## sample 0.034 0.017 2.010 0.044 0.002 0.067
## aps.discrep 0.060 0.059 1.015 0.310 -0.046 0.189
## aps.stand ~~
## aps.discrep 0.212 0.098 2.159 0.031 0.030 0.414
## sample 0.098 0.029 3.408 0.001 0.044 0.155
## aps.discrep ~~
## sample 0.101 0.040 2.556 0.011 0.023 0.179
## Std.lv Std.all
##
## 0.190 0.352
## 0.034 0.135
## 0.060 0.078
##
## 0.212 0.150
## 0.098 0.214
##
## 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .passion 0.464 0.047 9.909 0.000 0.361 0.547
## .pp.competence 2.846 0.297 9.585 0.000 2.250 3.406
## .competence 1.030 0.082 12.612 0.000 0.844 1.168
## as.5f 0.297 0.053 5.639 0.000 0.210 0.414
## aps.stand 0.986 0.121 8.149 0.000 0.769 1.239
## aps.discrep 2.024 0.131 15.409 0.000 1.767 2.290
## sample 0.212 0.010 20.524 0.000 0.190 0.231
## Std.lv Std.all
## 0.464 0.711
## 2.846 0.766
## 1.030 0.611
## 0.297 1.000
## 0.986 1.000
## 2.024 1.000
## 0.212 1.000
##
## R-Square:
## Estimate
## passion 0.289
## pp.competence 0.234
## competence 0.389
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.101 0.034 2.932 0.003 0.039 0.173
## standardsndrct 0.092 0.029 3.213 0.001 0.041 0.155
## discrepindirct -0.058 0.017 -3.415 0.001 -0.094 -0.027
## total1 0.303 0.099 3.068 0.002 0.097 0.483
## total2 0.270 0.055 4.905 0.000 0.171 0.384
## total3 -0.166 0.029 -5.699 0.000 -0.223 -0.110
## Std.lv Std.all
## 0.101 0.070
## 0.092 0.111
## -0.058 -0.108
## 0.303 0.206
## 0.270 0.330
## -0.166 -0.297
standardizedSolution(fit1.p, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 passion ~ competence 0.139 0.062 2.243
## 2 passion ~ pp.competence 0.175 0.063 2.790
## 3 passion ~ as.5f 0.136 0.066 2.072
## 4 passion ~ aps.stand 0.219 0.063 3.478
## 5 passion ~ aps.discrep -0.189 0.054 -3.506
## 6 pp.competence ~ as.5f 0.185 0.061 3.016
## 7 pp.competence ~ aps.stand 0.336 0.058 5.750
## 8 pp.competence ~ aps.discrep -0.270 0.053 -5.108
## 9 competence ~ pp.competence 0.190 0.057 3.348
## 10 competence ~ aps.stand 0.305 0.064 4.800
## 11 competence ~ aps.discrep -0.349 0.049 -7.088
## 12 competence ~ as.5f 0.217 0.063 3.462
## 13 as.5f ~~ aps.stand 0.352 0.089 3.953
## 14 as.5f ~~ sample 0.135 0.062 2.156
## 15 as.5f ~~ aps.discrep 0.078 0.072 1.084
## 16 aps.stand ~~ aps.discrep 0.150 0.064 2.354
## 17 aps.stand ~~ sample 0.214 0.059 3.658
## 18 aps.discrep ~~ sample 0.155 0.059 2.615
## 19 competence ~ sample -0.053 0.046 -1.156
## 20 pp.competence ~ sample 0.037 0.055 0.670
## 21 passion ~ sample 0.014 0.049 0.279
## 22 passion ~~ passion 0.711 0.053 13.517
## 23 pp.competence ~~ pp.competence 0.766 0.045 16.875
## 24 competence ~~ competence 0.611 0.050 12.136
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.070 0.024 2.946
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.111 0.030 3.716
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.108 0.029 -3.697
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.206 0.070 2.950
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.330 0.058 5.725
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.297 0.048 -6.147
## pvalue ci.lower ci.upper
## 1 0.025 0.018 0.260
## 2 0.005 0.052 0.298
## 3 0.038 0.007 0.265
## 4 0.001 0.096 0.343
## 5 0.000 -0.295 -0.083
## 6 0.003 0.065 0.305
## 7 0.000 0.222 0.451
## 8 0.000 -0.373 -0.166
## 9 0.001 0.079 0.301
## 10 0.000 0.180 0.429
## 11 0.000 -0.445 -0.252
## 12 0.001 0.094 0.339
## 13 0.000 0.177 0.526
## 14 0.031 0.012 0.257
## 15 0.278 -0.063 0.219
## 16 0.019 0.025 0.275
## 17 0.000 0.100 0.329
## 18 0.009 0.039 0.271
## 19 0.248 -0.142 0.037
## 20 0.503 -0.071 0.146
## 21 0.781 -0.083 0.111
## 22 0.000 0.608 0.814
## 23 0.000 0.677 0.855
## 24 0.000 0.512 0.709
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.003 0.023 0.116
## 30 0.000 0.053 0.170
## 31 0.000 -0.165 -0.051
## 32 0.003 0.069 0.343
## 33 0.000 0.217 0.444
## 34 0.000 -0.391 -0.202
parameterEstimates(fit1.p, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 passion ~ competence b1
## 2 passion ~ pp.competence b2
## 3 passion ~ as.5f c1
## 4 passion ~ aps.stand c2
## 5 passion ~ aps.discrep c3
## 6 pp.competence ~ as.5f a1
## 7 pp.competence ~ aps.stand aa1
## 8 pp.competence ~ aps.discrep aaa1
## 9 competence ~ pp.competence d21
## 10 competence ~ aps.stand aa2
## 11 competence ~ aps.discrep aaa2
## 12 competence ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 competence ~ sample
## 20 pp.competence ~ sample
## 21 passion ~ sample
## 22 passion ~~ passion
## 23 pp.competence ~~ pp.competence
## 24 competence ~~ competence
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.086 0.040 2.174 0.030 0.010 0.167
## 2 0.073 0.026 2.846 0.004 0.024 0.124
## 3 0.202 0.095 2.132 0.033 0.018 0.390
## 4 0.178 0.054 3.284 0.001 0.075 0.286
## 5 -0.107 0.032 -3.401 0.001 -0.169 -0.045
## 6 0.654 0.205 3.196 0.001 0.237 1.045
## 7 0.653 0.118 5.528 0.000 0.431 0.895
## 8 -0.365 0.073 -5.021 0.000 -0.506 -0.223
## 9 0.128 0.039 3.301 0.001 0.053 0.205
## 10 0.399 0.077 5.202 0.000 0.240 0.541
## 11 -0.318 0.046 -6.909 0.000 -0.405 -0.226
## 12 0.517 0.143 3.619 0.000 0.240 0.800
## 13 0.190 0.068 2.815 0.005 0.084 0.349
## 14 0.034 0.017 2.010 0.044 0.001 0.067
## 15 0.060 0.059 1.015 0.310 -0.044 0.191
## 16 0.212 0.098 2.159 0.031 0.032 0.415
## 17 0.098 0.029 3.408 0.001 0.045 0.156
## 18 0.101 0.040 2.556 0.011 0.022 0.176
## 19 -0.148 0.129 -1.149 0.250 -0.395 0.110
## 20 0.156 0.232 0.670 0.503 -0.276 0.645
## 21 0.024 0.086 0.279 0.780 -0.142 0.194
## 22 0.464 0.047 9.909 0.000 0.388 0.574
## 23 2.846 0.297 9.585 0.000 2.344 3.523
## 24 1.030 0.082 12.612 0.000 0.900 1.223
## 25 0.297 0.053 5.639 0.000 0.214 0.419
## 26 0.986 0.121 8.149 0.000 0.778 1.248
## 27 2.024 0.131 15.409 0.000 1.783 2.304
## 28 0.212 0.010 20.524 0.000 0.187 0.229
## 29 0.101 0.034 2.932 0.003 0.043 0.182
## 30 0.092 0.029 3.213 0.001 0.043 0.156
## 31 -0.058 0.017 -3.415 0.001 -0.095 -0.028
## 32 0.303 0.099 3.068 0.002 0.103 0.488
## 33 0.270 0.055 4.905 0.000 0.171 0.386
## 34 -0.166 0.029 -5.699 0.000 -0.222 -0.109
##################################################################################################################
#H2: Achievement striving leads to satisfaction of relatedness needs at both the psychological and project level,
# which in turn leads to well-being, even when controlling for perfectionistic standards and discrepancies
##################################################################################################################
#Mediation model with positive mental health as outcome controlling for perfectionistic standards and discrepancies
mmed2.mhcR <-
'
mhcR ~ b1 * relatedness + b2 * pp.support + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.support ~ a1 * as.5f
pp.support ~ aa1 * aps.stand
pp.support ~ aaa1 * aps.discrep
relatedness ~ d21 * pp.support
relatedness ~ aa2 * aps.stand
relatedness ~ aaa2 * aps.discrep
relatedness ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
relatedness ~ sample
pp.support ~ sample
mhcR ~ sample
'
fit2.mhcR <- sem(model = mmed2.mhcR, data = pp)
summary(fit2.mhcR, standardized = TRUE)
## lavaan 0.6-8 ended normally after 57 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 301 327
##
## 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|) Std.lv Std.all
## mhcR ~
## rltdnss (b1) 0.253 0.029 8.764 0.000 0.253 0.405
## pp.sppr (b2) 0.058 0.019 3.008 0.003 0.058 0.134
## as.5f (c1) 0.271 0.084 3.243 0.001 0.271 0.146
## aps.stn (c2) 0.155 0.047 3.284 0.001 0.155 0.153
## aps.dsc (c3) -0.216 0.033 -6.654 0.000 -0.216 -0.305
## pp.support ~
## as.5f (a1) 0.568 0.252 2.258 0.024 0.568 0.133
## aps.stn (aa1) 0.452 0.141 3.209 0.001 0.452 0.192
## aps.dsc (aaa1) -0.118 0.092 -1.282 0.200 -0.118 -0.072
## relatedness ~
## pp.sppr (d21) 0.117 0.038 3.088 0.002 0.117 0.168
## aps.stn (aa2) 0.198 0.094 2.108 0.035 0.198 0.121
## aps.dsc (aaa2) -0.413 0.060 -6.827 0.000 -0.413 -0.362
## as.5f (a2) 0.258 0.166 1.550 0.121 0.258 0.087
## sample -0.148 0.190 -0.777 0.437 -0.148 -0.042
## pp.support ~
## sample 0.537 0.289 1.861 0.063 0.537 0.106
## mhcR ~
## sample -0.158 0.095 -1.652 0.098 -0.158 -0.072
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.191 0.033 5.764 0.000 0.191 0.352
## sample 0.034 0.015 2.333 0.020 0.034 0.136
## aps.discrep 0.060 0.045 1.345 0.179 0.060 0.078
## aps.stand ~~
## aps.discrep 0.215 0.082 2.608 0.009 0.215 0.152
## sample 0.097 0.027 3.597 0.000 0.097 0.212
## aps.discrep ~~
## sample 0.104 0.038 2.738 0.006 0.104 0.160
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mhcR 0.532 0.043 12.268 0.000 0.532 0.521
## .pp.support 4.944 0.403 12.268 0.000 4.944 0.907
## .relatedness 2.127 0.173 12.268 0.000 2.127 0.809
## as.5f 0.298 0.024 12.268 0.000 0.298 1.000
## aps.stand 0.988 0.081 12.268 0.000 0.988 1.000
## aps.discrep 2.026 0.165 12.268 0.000 2.026 1.000
## sample 0.211 0.017 12.268 0.000 0.211 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.162 0.068 2.387 0.017 0.162 0.068
## standardsndrct 0.129 0.039 3.285 0.001 0.129 0.099
## discrepindirct -0.054 0.025 -2.134 0.033 -0.054 -0.079
## total1 0.433 0.106 4.086 0.000 0.433 0.215
## total2 0.284 0.060 4.766 0.000 0.284 0.251
## total3 -0.271 0.040 -6.718 0.000 -0.271 -0.384
fit2.mhcR <- sem(
model = mmed2.mhcR,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit2.mhcR, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 57 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 301 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 356.606
## Degrees of freedom 21
## 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) -2902.987
## Loglikelihood unrestricted model (H1) -2902.987
##
## Akaike (AIC) 5861.973
## Bayesian (BIC) 5965.772
## Sample-size adjusted Bayesian (BIC) 5876.972
##
## 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
## mhcR ~
## rltdnss (b1) 0.253 0.029 8.825 0.000 0.195 0.307
## pp.sppr (b2) 0.058 0.020 2.903 0.004 0.020 0.097
## as.5f (c1) 0.271 0.083 3.258 0.001 0.098 0.426
## aps.stn (c2) 0.155 0.051 3.043 0.002 0.048 0.250
## aps.dsc (c3) -0.216 0.033 -6.602 0.000 -0.281 -0.153
## pp.support ~
## as.5f (a1) 0.568 0.239 2.382 0.017 0.073 0.999
## aps.stn (aa1) 0.452 0.141 3.212 0.001 0.159 0.719
## aps.dsc (aaa1) -0.118 0.092 -1.279 0.201 -0.299 0.058
## relatedness ~
## pp.sppr (d21) 0.117 0.038 3.104 0.002 0.044 0.192
## aps.stn (aa2) 0.198 0.095 2.094 0.036 0.004 0.373
## aps.dsc (aaa2) -0.413 0.067 -6.142 0.000 -0.543 -0.281
## as.5f (a2) 0.258 0.199 1.299 0.194 -0.174 0.598
## sample -0.148 0.195 -0.759 0.448 -0.530 0.241
## pp.support ~
## sample 0.537 0.292 1.839 0.066 -0.063 1.112
## mhcR ~
## sample -0.158 0.096 -1.647 0.099 -0.349 0.028
## Std.lv Std.all
##
## 0.253 0.405
## 0.058 0.134
## 0.271 0.146
## 0.155 0.153
## -0.216 -0.305
##
## 0.568 0.133
## 0.452 0.192
## -0.118 -0.072
##
## 0.117 0.168
## 0.198 0.121
## -0.413 -0.362
## 0.258 0.087
## -0.148 -0.042
##
## 0.537 0.106
##
## -0.158 -0.072
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.191 0.068 2.804 0.005 0.081 0.342
## sample 0.034 0.017 2.053 0.040 0.003 0.068
## aps.discrep 0.060 0.060 1.010 0.313 -0.052 0.180
## aps.stand ~~
## aps.discrep 0.215 0.096 2.248 0.025 0.031 0.405
## sample 0.097 0.029 3.362 0.001 0.043 0.156
## aps.discrep ~~
## sample 0.104 0.038 2.726 0.006 0.029 0.180
## Std.lv Std.all
##
## 0.191 0.352
## 0.034 0.136
## 0.060 0.078
##
## 0.215 0.152
## 0.097 0.212
##
## 0.104 0.160
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .mhcR 0.532 0.042 12.579 0.000 0.438 0.602
## .pp.support 4.944 0.381 12.976 0.000 4.139 5.642
## .relatedness 2.127 0.160 13.259 0.000 1.781 2.398
## as.5f 0.298 0.053 5.580 0.000 0.211 0.417
## aps.stand 0.988 0.123 8.036 0.000 0.762 1.244
## aps.discrep 2.026 0.131 15.492 0.000 1.762 2.280
## sample 0.211 0.010 20.182 0.000 0.189 0.228
## Std.lv Std.all
## 0.532 0.521
## 4.944 0.907
## 2.127 0.809
## 0.298 1.000
## 0.988 1.000
## 2.026 1.000
## 0.211 1.000
##
## R-Square:
## Estimate
## mhcR 0.479
## pp.support 0.093
## relatedness 0.191
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.162 0.067 2.425 0.015 0.026 0.287
## standardsndrct 0.129 0.038 3.402 0.001 0.052 0.202
## discrepindirct -0.054 0.026 -2.065 0.039 -0.107 -0.004
## total1 0.433 0.114 3.791 0.000 0.190 0.642
## total2 0.284 0.063 4.490 0.000 0.150 0.398
## total3 -0.271 0.041 -6.634 0.000 -0.351 -0.190
## Std.lv Std.all
## 0.162 0.068
## 0.129 0.099
## -0.054 -0.079
## 0.433 0.215
## 0.284 0.251
## -0.271 -0.384
standardizedSolution(fit2.mhcR, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 mhcR ~ relatedness 0.405 0.043 9.326
## 2 mhcR ~ pp.support 0.134 0.044 3.002
## 3 mhcR ~ as.5f 0.146 0.050 2.931
## 4 mhcR ~ aps.stand 0.153 0.052 2.937
## 5 mhcR ~ aps.discrep -0.305 0.046 -6.611
## 6 pp.support ~ as.5f 0.133 0.059 2.265
## 7 pp.support ~ aps.stand 0.192 0.061 3.144
## 8 pp.support ~ aps.discrep -0.072 0.056 -1.277
## 9 relatedness ~ pp.support 0.168 0.053 3.151
## 10 relatedness ~ aps.stand 0.121 0.059 2.052
## 11 relatedness ~ aps.discrep -0.362 0.057 -6.386
## 12 relatedness ~ as.5f 0.087 0.070 1.236
## 13 as.5f ~~ aps.stand 0.352 0.089 3.945
## 14 as.5f ~~ sample 0.136 0.062 2.199
## 15 as.5f ~~ aps.discrep 0.078 0.072 1.077
## 16 aps.stand ~~ aps.discrep 0.152 0.062 2.455
## 17 aps.stand ~~ sample 0.212 0.059 3.614
## 18 aps.discrep ~~ sample 0.160 0.057 2.791
## 19 relatedness ~ sample -0.042 0.055 -0.758
## 20 pp.support ~ sample 0.106 0.057 1.839
## 21 mhcR ~ sample -0.072 0.044 -1.642
## 22 mhcR ~~ mhcR 0.521 0.044 11.845
## 23 pp.support ~~ pp.support 0.907 0.040 22.584
## 24 relatedness ~~ relatedness 0.809 0.044 18.314
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.068 0.030 2.294
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.099 0.027 3.585
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.079 0.031 -2.564
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.215 0.065 3.287
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.251 0.061 4.129
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.384 0.053 -7.211
## pvalue ci.lower ci.upper
## 1 0.000 0.320 0.491
## 2 0.003 0.046 0.221
## 3 0.003 0.048 0.244
## 4 0.003 0.051 0.255
## 5 0.000 -0.395 -0.214
## 6 0.023 0.018 0.248
## 7 0.002 0.072 0.312
## 8 0.201 -0.182 0.038
## 9 0.002 0.064 0.273
## 10 0.040 0.005 0.237
## 11 0.000 -0.474 -0.251
## 12 0.217 -0.051 0.224
## 13 0.000 0.177 0.527
## 14 0.028 0.015 0.257
## 15 0.282 -0.064 0.219
## 16 0.014 0.031 0.273
## 17 0.000 0.097 0.327
## 18 0.005 0.048 0.272
## 19 0.448 -0.150 0.066
## 20 0.066 -0.007 0.218
## 21 0.101 -0.157 0.014
## 22 0.000 0.435 0.608
## 23 0.000 0.828 0.985
## 24 0.000 0.723 0.896
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.022 0.010 0.127
## 30 0.000 0.045 0.152
## 31 0.010 -0.140 -0.019
## 32 0.001 0.087 0.343
## 33 0.000 0.132 0.371
## 34 0.000 -0.488 -0.279
parameterEstimates(fit2.mhcR, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 mhcR ~ relatedness b1
## 2 mhcR ~ pp.support b2
## 3 mhcR ~ as.5f c1
## 4 mhcR ~ aps.stand c2
## 5 mhcR ~ aps.discrep c3
## 6 pp.support ~ as.5f a1
## 7 pp.support ~ aps.stand aa1
## 8 pp.support ~ aps.discrep aaa1
## 9 relatedness ~ pp.support d21
## 10 relatedness ~ aps.stand aa2
## 11 relatedness ~ aps.discrep aaa2
## 12 relatedness ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 relatedness ~ sample
## 20 pp.support ~ sample
## 21 mhcR ~ sample
## 22 mhcR ~~ mhcR
## 23 pp.support ~~ pp.support
## 24 relatedness ~~ relatedness
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.253 0.029 8.825 0.000 0.196 0.309
## 2 0.058 0.020 2.903 0.004 0.019 0.096
## 3 0.271 0.083 3.258 0.001 0.101 0.428
## 4 0.155 0.051 3.043 0.002 0.056 0.254
## 5 -0.216 0.033 -6.602 0.000 -0.279 -0.151
## 6 0.568 0.239 2.382 0.017 0.077 1.003
## 7 0.452 0.141 3.212 0.001 0.174 0.728
## 8 -0.118 0.092 -1.279 0.201 -0.293 0.065
## 9 0.117 0.038 3.104 0.002 0.045 0.194
## 10 0.198 0.095 2.094 0.036 -0.001 0.370
## 11 -0.413 0.067 -6.142 0.000 -0.541 -0.278
## 12 0.258 0.199 1.299 0.194 -0.149 0.620
## 13 0.191 0.068 2.804 0.005 0.093 0.370
## 14 0.034 0.017 2.053 0.040 0.003 0.069
## 15 0.060 0.060 1.010 0.313 -0.043 0.190
## 16 0.215 0.096 2.248 0.025 0.042 0.417
## 17 0.097 0.029 3.362 0.001 0.045 0.158
## 18 0.104 0.038 2.726 0.006 0.030 0.180
## 19 -0.148 0.195 -0.759 0.448 -0.536 0.234
## 20 0.537 0.292 1.839 0.066 -0.047 1.123
## 21 -0.158 0.096 -1.647 0.099 -0.356 0.020
## 22 0.532 0.042 12.579 0.000 0.463 0.637
## 23 4.944 0.381 12.976 0.000 4.291 5.799
## 24 2.127 0.160 13.259 0.000 1.867 2.525
## 25 0.298 0.053 5.580 0.000 0.219 0.440
## 26 0.988 0.123 8.036 0.000 0.777 1.268
## 27 2.026 0.131 15.492 0.000 1.775 2.300
## 28 0.211 0.010 20.182 0.000 0.189 0.229
## 29 0.162 0.067 2.425 0.015 0.034 0.295
## 30 0.129 0.038 3.402 0.001 0.060 0.208
## 31 -0.054 0.026 -2.065 0.039 -0.106 -0.003
## 32 0.433 0.114 3.791 0.000 0.198 0.649
## 33 0.284 0.063 4.490 0.000 0.158 0.404
## 34 -0.271 0.041 -6.634 0.000 -0.348 -0.187
#########################################################################################################
#Mediation model with zest as outcome controlling for perfectionistic standards and discrepancies
mmed2.zest <-
'
zestR ~ b1 * relatedness + b2 * pp.support + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.support ~ a1 * as.5f
pp.support ~ aa1 * aps.stand
pp.support ~ aaa1 * aps.discrep
relatedness ~ d21 * pp.support
relatedness ~ aa2 * aps.stand
relatedness ~ aaa2 * aps.discrep
relatedness ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
relatedness ~ sample
pp.support~ sample
zestR ~ sample
'
fit2.zest <- sem(model = mmed2.zest, data = pp)
summary(fit2.zest, standardized = TRUE)
## lavaan 0.6-8 ended normally after 56 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 301 327
##
## 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|) Std.lv Std.all
## zestR ~
## rltdnss (b1) 0.206 0.038 5.487 0.000 0.206 0.275
## pp.sppr (b2) 0.045 0.025 1.812 0.070 0.045 0.087
## as.5f (c1) 0.240 0.109 2.202 0.028 0.240 0.108
## aps.stn (c2) 0.107 0.062 1.731 0.083 0.107 0.087
## aps.dsc (c3) -0.364 0.042 -8.611 0.000 -0.364 -0.427
## pp.support ~
## as.5f (a1) 0.568 0.252 2.258 0.024 0.568 0.133
## aps.stn (aa1) 0.452 0.141 3.209 0.001 0.452 0.192
## aps.dsc (aaa1) -0.118 0.092 -1.282 0.200 -0.118 -0.072
## relatedness ~
## pp.sppr (d21) 0.117 0.038 3.088 0.002 0.117 0.168
## aps.stn (aa2) 0.198 0.094 2.108 0.035 0.198 0.121
## aps.dsc (aaa2) -0.413 0.060 -6.827 0.000 -0.413 -0.362
## as.5f (a2) 0.258 0.166 1.550 0.121 0.258 0.087
## sample -0.148 0.190 -0.777 0.437 -0.148 -0.042
## pp.support ~
## sample 0.537 0.289 1.861 0.063 0.537 0.106
## zestR ~
## sample -0.038 0.124 -0.304 0.761 -0.038 -0.014
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.191 0.033 5.764 0.000 0.191 0.352
## sample 0.034 0.015 2.333 0.020 0.034 0.136
## aps.discrep 0.060 0.045 1.345 0.179 0.060 0.078
## aps.stand ~~
## aps.discrep 0.215 0.082 2.608 0.009 0.215 0.152
## sample 0.097 0.027 3.597 0.000 0.097 0.212
## aps.discrep ~~
## sample 0.104 0.038 2.738 0.006 0.104 0.160
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .zestR 0.903 0.074 12.268 0.000 0.903 0.612
## .pp.support 4.944 0.403 12.268 0.000 4.944 0.907
## .relatedness 2.127 0.173 12.268 0.000 2.127 0.809
## as.5f 0.298 0.024 12.268 0.000 0.298 1.000
## aps.stand 0.988 0.081 12.268 0.000 0.988 1.000
## aps.discrep 2.026 0.165 12.268 0.000 2.026 1.000
## sample 0.211 0.017 12.268 0.000 0.211 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.132 0.058 2.279 0.023 0.132 0.046
## standardsndrct 0.104 0.035 3.025 0.002 0.104 0.066
## discrepindirct -0.044 0.022 -1.947 0.052 -0.044 -0.052
## total1 0.371 0.121 3.076 0.002 0.371 0.154
## total2 0.211 0.068 3.108 0.002 0.211 0.154
## total3 -0.408 0.046 -8.794 0.000 -0.408 -0.479
fit2.zest <- sem(
model = mmed2.zest,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit2.zest, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 56 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 301 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 308.591
## Degrees of freedom 21
## 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) -2982.484
## Loglikelihood unrestricted model (H1) -2982.484
##
## Akaike (AIC) 6020.969
## Bayesian (BIC) 6124.768
## Sample-size adjusted Bayesian (BIC) 6035.968
##
## 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
## zestR ~
## rltdnss (b1) 0.206 0.041 4.975 0.000 0.124 0.289
## pp.sppr (b2) 0.045 0.028 1.605 0.108 -0.010 0.101
## as.5f (c1) 0.240 0.100 2.398 0.016 0.045 0.437
## aps.stn (c2) 0.107 0.065 1.638 0.101 -0.024 0.235
## aps.dsc (c3) -0.364 0.045 -8.061 0.000 -0.452 -0.274
## pp.support ~
## as.5f (a1) 0.568 0.236 2.405 0.016 0.064 1.004
## aps.stn (aa1) 0.452 0.140 3.235 0.001 0.178 0.730
## aps.dsc (aaa1) -0.118 0.092 -1.281 0.200 -0.300 0.063
## relatedness ~
## pp.sppr (d21) 0.117 0.038 3.084 0.002 0.044 0.192
## aps.stn (aa2) 0.198 0.095 2.092 0.036 0.001 0.377
## aps.dsc (aaa2) -0.413 0.066 -6.237 0.000 -0.543 -0.284
## as.5f (a2) 0.258 0.206 1.253 0.210 -0.186 0.618
## sample -0.148 0.194 -0.761 0.447 -0.525 0.233
## pp.support ~
## sample 0.537 0.296 1.812 0.070 -0.050 1.126
## zestR ~
## sample -0.038 0.131 -0.289 0.772 -0.293 0.214
## Std.lv Std.all
##
## 0.206 0.275
## 0.045 0.087
## 0.240 0.108
## 0.107 0.087
## -0.364 -0.427
##
## 0.568 0.133
## 0.452 0.192
## -0.118 -0.072
##
## 0.117 0.168
## 0.198 0.121
## -0.413 -0.362
## 0.258 0.087
## -0.148 -0.042
##
## 0.537 0.106
##
## -0.038 -0.014
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.191 0.070 2.733 0.006 0.078 0.349
## sample 0.034 0.017 2.033 0.042 0.003 0.068
## aps.discrep 0.060 0.061 0.993 0.321 -0.047 0.191
## aps.stand ~~
## aps.discrep 0.215 0.098 2.193 0.028 0.035 0.416
## sample 0.097 0.029 3.342 0.001 0.042 0.157
## aps.discrep ~~
## sample 0.104 0.038 2.721 0.007 0.029 0.180
## Std.lv Std.all
##
## 0.191 0.352
## 0.034 0.136
## 0.060 0.078
##
## 0.215 0.152
## 0.097 0.212
##
## 0.104 0.160
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .zestR 0.903 0.077 11.795 0.000 0.734 1.031
## .pp.support 4.944 0.376 13.157 0.000 4.127 5.613
## .relatedness 2.127 0.161 13.232 0.000 1.771 2.403
## as.5f 0.298 0.054 5.517 0.000 0.212 0.420
## aps.stand 0.988 0.123 8.050 0.000 0.766 1.244
## aps.discrep 2.026 0.134 15.164 0.000 1.767 2.291
## sample 0.211 0.010 20.286 0.000 0.189 0.229
## Std.lv Std.all
## 0.903 0.612
## 4.944 0.907
## 2.127 0.809
## 0.298 1.000
## 0.988 1.000
## 2.026 1.000
## 0.211 1.000
##
## R-Square:
## Estimate
## zestR 0.388
## pp.support 0.093
## relatedness 0.191
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.132 0.058 2.259 0.024 0.018 0.250
## standardsndrct 0.104 0.035 2.978 0.003 0.043 0.181
## discrepindirct -0.044 0.024 -1.836 0.066 -0.090 0.004
## total1 0.371 0.112 3.308 0.001 0.147 0.588
## total2 0.211 0.072 2.944 0.003 0.068 0.349
## total3 -0.408 0.050 -8.144 0.000 -0.502 -0.307
## Std.lv Std.all
## 0.132 0.046
## 0.104 0.066
## -0.044 -0.052
## 0.371 0.154
## 0.211 0.154
## -0.408 -0.479
standardizedSolution(fit2.zest, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 zestR ~ relatedness 0.275 0.055 5.018
## 2 zestR ~ pp.support 0.087 0.054 1.619
## 3 zestR ~ as.5f 0.108 0.045 2.385
## 4 zestR ~ aps.stand 0.087 0.054 1.620
## 5 zestR ~ aps.discrep -0.427 0.049 -8.663
## 6 pp.support ~ as.5f 0.133 0.058 2.281
## 7 pp.support ~ aps.stand 0.192 0.061 3.172
## 8 pp.support ~ aps.discrep -0.072 0.056 -1.277
## 9 relatedness ~ pp.support 0.168 0.054 3.119
## 10 relatedness ~ aps.stand 0.121 0.059 2.045
## 11 relatedness ~ aps.discrep -0.362 0.056 -6.480
## 12 relatedness ~ as.5f 0.087 0.073 1.194
## 13 as.5f ~~ aps.stand 0.352 0.091 3.850
## 14 as.5f ~~ sample 0.136 0.062 2.188
## 15 as.5f ~~ aps.discrep 0.078 0.073 1.063
## 16 aps.stand ~~ aps.discrep 0.152 0.063 2.399
## 17 aps.stand ~~ sample 0.212 0.059 3.594
## 18 aps.discrep ~~ sample 0.160 0.057 2.786
## 19 relatedness ~ sample -0.042 0.055 -0.759
## 20 pp.support ~ sample 0.106 0.058 1.811
## 21 zestR ~ sample -0.014 0.049 -0.289
## 22 zestR ~~ zestR 0.612 0.051 12.097
## 23 pp.support ~~ pp.support 0.907 0.041 22.353
## 24 relatedness ~~ relatedness 0.809 0.044 18.306
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.046 0.021 2.161
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.066 0.021 3.109
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.052 0.026 -1.991
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.154 0.050 3.079
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.154 0.057 2.699
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.479 0.054 -8.855
## pvalue ci.lower ci.upper
## 1 0.000 0.168 0.382
## 2 0.106 -0.018 0.193
## 3 0.017 0.019 0.196
## 4 0.105 -0.018 0.193
## 5 0.000 -0.524 -0.330
## 6 0.023 0.019 0.247
## 7 0.002 0.074 0.311
## 8 0.202 -0.182 0.038
## 9 0.002 0.062 0.274
## 10 0.041 0.005 0.238
## 11 0.000 -0.472 -0.253
## 12 0.232 -0.056 0.229
## 13 0.000 0.173 0.532
## 14 0.029 0.014 0.257
## 15 0.288 -0.066 0.221
## 16 0.016 0.028 0.276
## 17 0.000 0.096 0.327
## 18 0.005 0.047 0.272
## 19 0.448 -0.150 0.066
## 20 0.070 -0.009 0.220
## 21 0.773 -0.111 0.083
## 22 0.000 0.512 0.711
## 23 0.000 0.827 0.986
## 24 0.000 0.723 0.896
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.031 0.004 0.088
## 30 0.002 0.024 0.108
## 31 0.047 -0.104 -0.001
## 32 0.002 0.056 0.251
## 33 0.007 0.042 0.265
## 34 0.000 -0.585 -0.373
parameterEstimates(fit2.zest, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 zestR ~ relatedness b1
## 2 zestR ~ pp.support b2
## 3 zestR ~ as.5f c1
## 4 zestR ~ aps.stand c2
## 5 zestR ~ aps.discrep c3
## 6 pp.support ~ as.5f a1
## 7 pp.support ~ aps.stand aa1
## 8 pp.support ~ aps.discrep aaa1
## 9 relatedness ~ pp.support d21
## 10 relatedness ~ aps.stand aa2
## 11 relatedness ~ aps.discrep aaa2
## 12 relatedness ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 relatedness ~ sample
## 20 pp.support ~ sample
## 21 zestR ~ sample
## 22 zestR ~~ zestR
## 23 pp.support ~~ pp.support
## 24 relatedness ~~ relatedness
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.206 0.041 4.975 0.000 0.124 0.289
## 2 0.045 0.028 1.605 0.108 -0.009 0.102
## 3 0.240 0.100 2.398 0.016 0.045 0.437
## 4 0.107 0.065 1.638 0.101 -0.025 0.233
## 5 -0.364 0.045 -8.061 0.000 -0.451 -0.274
## 6 0.568 0.236 2.405 0.016 0.047 0.991
## 7 0.452 0.140 3.235 0.001 0.187 0.734
## 8 -0.118 0.092 -1.281 0.200 -0.296 0.068
## 9 0.117 0.038 3.084 0.002 0.040 0.189
## 10 0.198 0.095 2.092 0.036 0.004 0.381
## 11 -0.413 0.066 -6.237 0.000 -0.544 -0.285
## 12 0.258 0.206 1.253 0.210 -0.176 0.632
## 13 0.191 0.070 2.733 0.006 0.087 0.375
## 14 0.034 0.017 2.033 0.042 0.003 0.068
## 15 0.060 0.061 0.993 0.321 -0.042 0.197
## 16 0.215 0.098 2.193 0.028 0.038 0.421
## 17 0.097 0.029 3.342 0.001 0.043 0.157
## 18 0.104 0.038 2.721 0.007 0.030 0.181
## 19 -0.148 0.194 -0.761 0.447 -0.525 0.234
## 20 0.537 0.296 1.812 0.070 -0.048 1.128
## 21 -0.038 0.131 -0.289 0.772 -0.282 0.232
## 22 0.903 0.077 11.795 0.000 0.778 1.093
## 23 4.944 0.376 13.157 0.000 4.309 5.785
## 24 2.127 0.161 13.232 0.000 1.864 2.509
## 25 0.298 0.054 5.517 0.000 0.218 0.436
## 26 0.988 0.123 8.050 0.000 0.779 1.263
## 27 2.026 0.134 15.164 0.000 1.780 2.309
## 28 0.211 0.010 20.286 0.000 0.189 0.228
## 29 0.132 0.058 2.259 0.024 0.025 0.259
## 30 0.104 0.035 2.978 0.003 0.048 0.190
## 31 -0.044 0.024 -1.836 0.066 -0.092 0.003
## 32 0.371 0.112 3.308 0.001 0.141 0.583
## 33 0.211 0.072 2.944 0.003 0.068 0.349
## 34 -0.408 0.050 -8.144 0.000 -0.502 -0.306
#########################################################################################################
#Mediation model with engagement as outcome controlling for perfectionistic standards and discrepancies
mmed2.eng <-
'
engagementR ~ b1 * relatedness + b2 * pp.support + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.support ~ a1 * as.5f
pp.support ~ aa1 * aps.stand
pp.support ~ aaa1 * aps.discrep
relatedness ~ d21 * pp.support
relatedness ~ aa2 * aps.stand
relatedness ~ aaa2 * aps.discrep
relatedness ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
relatedness ~ sample
pp.support ~ sample
engagementR ~ sample
'
fit2.eng <- sem(model = mmed2.eng, data = pp)
summary(fit2.eng, standardized = TRUE)
## lavaan 0.6-8 ended normally after 60 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 301 327
##
## 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|) Std.lv Std.all
## engagementR ~
## rltdnss (b1) 0.087 0.017 5.085 0.000 0.087 0.254
## pp.sppr (b2) 0.022 0.011 1.884 0.060 0.022 0.090
## as.5f (c1) 0.166 0.050 3.335 0.001 0.166 0.162
## aps.stn (c2) 0.090 0.028 3.204 0.001 0.090 0.161
## aps.dsc (c3) -0.158 0.019 -8.148 0.000 -0.158 -0.402
## pp.support ~
## as.5f (a1) 0.568 0.252 2.258 0.024 0.568 0.133
## aps.stn (aa1) 0.452 0.141 3.209 0.001 0.452 0.192
## aps.dsc (aaa1) -0.118 0.092 -1.282 0.200 -0.118 -0.072
## relatedness ~
## pp.sppr (d21) 0.117 0.038 3.088 0.002 0.117 0.168
## aps.stn (aa2) 0.198 0.094 2.108 0.035 0.198 0.121
## aps.dsc (aaa2) -0.413 0.060 -6.827 0.000 -0.413 -0.362
## as.5f (a2) 0.258 0.166 1.550 0.121 0.258 0.087
## sample -0.148 0.190 -0.777 0.437 -0.148 -0.042
## pp.support ~
## sample 0.537 0.289 1.861 0.063 0.537 0.106
## engagementR ~
## sample -0.059 0.057 -1.033 0.301 -0.059 -0.048
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.191 0.033 5.764 0.000 0.191 0.352
## sample 0.034 0.015 2.333 0.020 0.034 0.136
## aps.discrep 0.060 0.045 1.345 0.179 0.060 0.078
## aps.stand ~~
## aps.discrep 0.215 0.082 2.608 0.009 0.215 0.152
## sample 0.097 0.027 3.597 0.000 0.097 0.212
## aps.discrep ~~
## sample 0.104 0.038 2.738 0.006 0.104 0.160
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .engagementR 0.188 0.015 12.268 0.000 0.188 0.606
## .pp.support 4.944 0.403 12.268 0.000 4.944 0.907
## .relatedness 2.127 0.173 12.268 0.000 2.127 0.809
## as.5f 0.298 0.024 12.268 0.000 0.298 1.000
## aps.stand 0.988 0.081 12.268 0.000 0.988 1.000
## aps.discrep 2.026 0.165 12.268 0.000 2.026 1.000
## sample 0.211 0.017 12.268 0.000 0.211 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.057 0.025 2.274 0.023 0.057 0.044
## standardsndrct 0.045 0.015 2.993 0.003 0.045 0.063
## discrepindirct -0.019 0.010 -2.006 0.045 -0.019 -0.052
## total1 0.222 0.054 4.084 0.000 0.222 0.206
## total2 0.135 0.031 4.406 0.000 0.135 0.224
## total3 -0.177 0.021 -8.451 0.000 -0.177 -0.454
fit2.eng <- sem(
model = mmed2.eng,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit2.eng, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 60 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 301 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 311.373
## Degrees of freedom 21
## 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) -2746.629
## Loglikelihood unrestricted model (H1) -2746.629
##
## Akaike (AIC) 5549.257
## Bayesian (BIC) 5653.056
## Sample-size adjusted Bayesian (BIC) 5564.256
##
## 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
## engagementR ~
## rltdnss (b1) 0.087 0.019 4.684 0.000 0.051 0.123
## pp.sppr (b2) 0.022 0.012 1.734 0.083 -0.003 0.046
## as.5f (c1) 0.166 0.049 3.383 0.001 0.068 0.263
## aps.stn (c2) 0.090 0.031 2.874 0.004 0.030 0.152
## aps.dsc (c3) -0.158 0.021 -7.681 0.000 -0.195 -0.115
## pp.support ~
## as.5f (a1) 0.568 0.238 2.389 0.017 0.053 0.998
## aps.stn (aa1) 0.452 0.142 3.191 0.001 0.170 0.731
## aps.dsc (aaa1) -0.118 0.092 -1.279 0.201 -0.296 0.060
## relatedness ~
## pp.sppr (d21) 0.117 0.037 3.147 0.002 0.047 0.191
## aps.stn (aa2) 0.198 0.094 2.100 0.036 0.008 0.381
## aps.dsc (aaa2) -0.413 0.067 -6.201 0.000 -0.543 -0.282
## as.5f (a2) 0.258 0.201 1.285 0.199 -0.187 0.603
## sample -0.148 0.190 -0.778 0.437 -0.510 0.232
## pp.support ~
## sample 0.537 0.294 1.826 0.068 -0.048 1.131
## engagementR ~
## sample -0.059 0.062 -0.942 0.346 -0.185 0.057
## Std.lv Std.all
##
## 0.087 0.254
## 0.022 0.090
## 0.166 0.162
## 0.090 0.161
## -0.158 -0.402
##
## 0.568 0.133
## 0.452 0.192
## -0.118 -0.072
##
## 0.117 0.168
## 0.198 0.121
## -0.413 -0.362
## 0.258 0.087
## -0.148 -0.042
##
## 0.537 0.106
##
## -0.059 -0.048
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.191 0.067 2.838 0.005 0.082 0.341
## sample 0.034 0.017 2.019 0.044 0.001 0.067
## aps.discrep 0.060 0.060 0.999 0.318 -0.052 0.186
## aps.stand ~~
## aps.discrep 0.215 0.097 2.212 0.027 0.026 0.411
## sample 0.097 0.029 3.387 0.001 0.040 0.153
## aps.discrep ~~
## sample 0.104 0.039 2.705 0.007 0.030 0.180
## Std.lv Std.all
##
## 0.191 0.352
## 0.034 0.136
## 0.060 0.078
##
## 0.215 0.152
## 0.097 0.212
##
## 0.104 0.160
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .engagementR 0.188 0.016 12.128 0.000 0.155 0.215
## .pp.support 4.944 0.378 13.095 0.000 4.124 5.601
## .relatedness 2.127 0.158 13.481 0.000 1.782 2.394
## as.5f 0.298 0.053 5.652 0.000 0.213 0.413
## aps.stand 0.988 0.122 8.128 0.000 0.767 1.248
## aps.discrep 2.026 0.130 15.639 0.000 1.764 2.280
## sample 0.211 0.010 20.346 0.000 0.189 0.230
## Std.lv Std.all
## 0.188 0.606
## 4.944 0.907
## 2.127 0.809
## 0.298 1.000
## 0.988 1.000
## 2.026 1.000
## 0.211 1.000
##
## R-Square:
## Estimate
## engagementR 0.394
## pp.support 0.093
## relatedness 0.191
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.057 0.025 2.255 0.024 0.008 0.107
## standardsndrct 0.045 0.015 2.900 0.004 0.017 0.078
## discrepindirct -0.019 0.010 -1.942 0.052 -0.040 0.000
## total1 0.222 0.056 3.996 0.000 0.107 0.329
## total2 0.135 0.034 3.929 0.000 0.071 0.206
## total3 -0.177 0.022 -7.875 0.000 -0.219 -0.130
## Std.lv Std.all
## 0.057 0.044
## 0.045 0.063
## -0.019 -0.052
## 0.222 0.206
## 0.135 0.224
## -0.177 -0.454
standardizedSolution(fit2.eng, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 engagementR ~ relatedness 0.254 0.054 4.705
## 2 engagementR ~ pp.support 0.090 0.052 1.732
## 3 engagementR ~ as.5f 0.162 0.050 3.232
## 4 engagementR ~ aps.stand 0.161 0.055 2.941
## 5 engagementR ~ aps.discrep -0.402 0.050 -8.123
## 6 pp.support ~ as.5f 0.133 0.059 2.261
## 7 pp.support ~ aps.stand 0.192 0.061 3.133
## 8 pp.support ~ aps.discrep -0.072 0.056 -1.277
## 9 relatedness ~ pp.support 0.168 0.053 3.191
## 10 relatedness ~ aps.stand 0.121 0.059 2.054
## 11 relatedness ~ aps.discrep -0.362 0.056 -6.478
## 12 relatedness ~ as.5f 0.087 0.071 1.225
## 13 as.5f ~~ aps.stand 0.352 0.088 3.989
## 14 as.5f ~~ sample 0.136 0.063 2.163
## 15 as.5f ~~ aps.discrep 0.078 0.073 1.066
## 16 aps.stand ~~ aps.discrep 0.152 0.063 2.406
## 17 aps.stand ~~ sample 0.212 0.059 3.621
## 18 aps.discrep ~~ sample 0.160 0.058 2.765
## 19 relatedness ~ sample -0.042 0.054 -0.777
## 20 pp.support ~ sample 0.106 0.058 1.826
## 21 engagementR ~ sample -0.048 0.051 -0.940
## 22 engagementR ~~ engagementR 0.606 0.049 12.282
## 23 pp.support ~~ pp.support 0.907 0.039 23.051
## 24 relatedness ~~ relatedness 0.809 0.044 18.557
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.044 0.020 2.147
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.063 0.020 3.084
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.052 0.024 -2.130
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.206 0.056 3.648
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.224 0.057 3.955
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.454 0.054 -8.426
## pvalue ci.lower ci.upper
## 1 0.000 0.148 0.359
## 2 0.083 -0.012 0.192
## 3 0.001 0.064 0.261
## 4 0.003 0.054 0.268
## 5 0.000 -0.499 -0.305
## 6 0.024 0.018 0.248
## 7 0.002 0.072 0.313
## 8 0.202 -0.182 0.038
## 9 0.001 0.065 0.271
## 10 0.040 0.006 0.237
## 11 0.000 -0.472 -0.253
## 12 0.220 -0.052 0.226
## 13 0.000 0.179 0.525
## 14 0.031 0.013 0.259
## 15 0.287 -0.065 0.221
## 16 0.016 0.028 0.276
## 17 0.000 0.097 0.327
## 18 0.006 0.047 0.273
## 19 0.437 -0.148 0.064
## 20 0.068 -0.008 0.219
## 21 0.347 -0.149 0.052
## 22 0.000 0.509 0.703
## 23 0.000 0.830 0.984
## 24 0.000 0.724 0.895
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.032 0.004 0.083
## 30 0.002 0.023 0.102
## 31 0.033 -0.100 -0.004
## 32 0.000 0.095 0.316
## 33 0.000 0.113 0.334
## 34 0.000 -0.560 -0.348
parameterEstimates(fit2.eng, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 engagementR ~ relatedness b1
## 2 engagementR ~ pp.support b2
## 3 engagementR ~ as.5f c1
## 4 engagementR ~ aps.stand c2
## 5 engagementR ~ aps.discrep c3
## 6 pp.support ~ as.5f a1
## 7 pp.support ~ aps.stand aa1
## 8 pp.support ~ aps.discrep aaa1
## 9 relatedness ~ pp.support d21
## 10 relatedness ~ aps.stand aa2
## 11 relatedness ~ aps.discrep aaa2
## 12 relatedness ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 relatedness ~ sample
## 20 pp.support ~ sample
## 21 engagementR ~ sample
## 22 engagementR ~~ engagementR
## 23 pp.support ~~ pp.support
## 24 relatedness ~~ relatedness
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.087 0.019 4.684 0.000 0.050 0.122
## 2 0.022 0.012 1.734 0.083 -0.002 0.047
## 3 0.166 0.049 3.383 0.001 0.072 0.266
## 4 0.090 0.031 2.874 0.004 0.030 0.153
## 5 -0.158 0.021 -7.681 0.000 -0.196 -0.116
## 6 0.568 0.238 2.389 0.017 0.053 0.997
## 7 0.452 0.142 3.191 0.001 0.164 0.724
## 8 -0.118 0.092 -1.279 0.201 -0.292 0.065
## 9 0.117 0.037 3.147 0.002 0.046 0.190
## 10 0.198 0.094 2.100 0.036 0.008 0.380
## 11 -0.413 0.067 -6.201 0.000 -0.538 -0.277
## 12 0.258 0.201 1.285 0.199 -0.164 0.617
## 13 0.191 0.067 2.838 0.005 0.092 0.363
## 14 0.034 0.017 2.019 0.044 0.002 0.068
## 15 0.060 0.060 0.999 0.318 -0.046 0.195
## 16 0.215 0.097 2.212 0.027 0.036 0.418
## 17 0.097 0.029 3.387 0.001 0.042 0.155
## 18 0.104 0.039 2.705 0.007 0.031 0.180
## 19 -0.148 0.190 -0.778 0.437 -0.514 0.229
## 20 0.537 0.294 1.826 0.068 -0.049 1.130
## 21 -0.059 0.062 -0.942 0.346 -0.182 0.059
## 22 0.188 0.016 12.128 0.000 0.164 0.226
## 23 4.944 0.378 13.095 0.000 4.295 5.761
## 24 2.127 0.158 13.481 0.000 1.873 2.513
## 25 0.298 0.053 5.652 0.000 0.220 0.435
## 26 0.988 0.122 8.128 0.000 0.782 1.266
## 27 2.026 0.130 15.639 0.000 1.782 2.296
## 28 0.211 0.010 20.346 0.000 0.189 0.229
## 29 0.057 0.025 2.255 0.024 0.012 0.111
## 30 0.045 0.015 2.900 0.004 0.019 0.081
## 31 -0.019 0.010 -1.942 0.052 -0.040 0.000
## 32 0.222 0.056 3.996 0.000 0.111 0.334
## 33 0.135 0.034 3.929 0.000 0.072 0.207
## 34 -0.177 0.022 -7.875 0.000 -0.219 -0.130
#########################################################################################################
#Mediation model with passion as outcome controlling for perfectionistic standards and discrepancies
mmed2.p <-
'
passion ~ b1 * relatedness + b2 * pp.support + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.support ~ a1 * as.5f
pp.support ~ aa1 * aps.stand
pp.support ~ aaa1 * aps.discrep
relatedness ~ d21 * pp.support
relatedness ~ aa2 * aps.stand
relatedness ~ aaa2 * aps.discrep
relatedness ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
relatedness ~ sample
pp.support ~ sample
passion ~ sample
'
fit2.p <- sem(model = mmed2.p, data = pp)
summary(fit2.p, standardized = TRUE)
## lavaan 0.6-8 ended normally after 57 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 301 327
##
## 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|) Std.lv Std.all
## passion ~
## rltdnss (b1) 0.081 0.027 3.005 0.003 0.081 0.163
## pp.sppr (b2) 0.031 0.018 1.743 0.081 0.031 0.091
## as.5f (c1) 0.256 0.079 3.260 0.001 0.256 0.173
## aps.stn (c2) 0.234 0.044 5.272 0.000 0.234 0.288
## aps.dsc (c3) -0.129 0.031 -4.221 0.000 -0.129 -0.227
## pp.support ~
## as.5f (a1) 0.568 0.252 2.258 0.024 0.568 0.133
## aps.stn (aa1) 0.452 0.141 3.209 0.001 0.452 0.192
## aps.dsc (aaa1) -0.118 0.092 -1.282 0.200 -0.118 -0.072
## relatedness ~
## pp.sppr (d21) 0.117 0.038 3.088 0.002 0.117 0.168
## aps.stn (aa2) 0.198 0.094 2.108 0.035 0.198 0.121
## aps.dsc (aaa2) -0.413 0.060 -6.827 0.000 -0.413 -0.362
## as.5f (a2) 0.258 0.166 1.550 0.121 0.258 0.087
## sample -0.148 0.190 -0.777 0.437 -0.148 -0.042
## pp.support ~
## sample 0.537 0.289 1.861 0.063 0.537 0.106
## passion ~
## sample 0.021 0.090 0.239 0.811 0.021 0.012
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.191 0.033 5.764 0.000 0.191 0.352
## sample 0.034 0.015 2.333 0.020 0.034 0.136
## aps.discrep 0.060 0.045 1.345 0.179 0.060 0.078
## aps.stand ~~
## aps.discrep 0.215 0.082 2.608 0.009 0.215 0.152
## sample 0.097 0.027 3.597 0.000 0.097 0.212
## aps.discrep ~~
## sample 0.104 0.038 2.738 0.006 0.104 0.160
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .passion 0.470 0.038 12.268 0.000 0.470 0.718
## .pp.support 4.944 0.403 12.268 0.000 4.944 0.907
## .relatedness 2.127 0.173 12.268 0.000 2.127 0.809
## as.5f 0.298 0.024 12.268 0.000 0.298 1.000
## aps.stand 0.988 0.081 12.268 0.000 0.988 1.000
## aps.discrep 2.026 0.165 12.268 0.000 2.026 1.000
## sample 0.211 0.017 12.268 0.000 0.211 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.056 0.027 2.097 0.036 0.056 0.032
## standardsndrct 0.045 0.017 2.567 0.010 0.045 0.045
## discrepindirct -0.023 0.011 -2.053 0.040 -0.023 -0.046
## total1 0.312 0.081 3.854 0.000 0.312 0.204
## total2 0.279 0.046 6.112 0.000 0.279 0.334
## total3 -0.152 0.031 -4.840 0.000 -0.152 -0.273
fit2.p <- sem(
model = mmed2.p,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit2.p, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 57 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 301 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 260.197
## Degrees of freedom 21
## 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) -2884.162
## Loglikelihood unrestricted model (H1) -2884.162
##
## Akaike (AIC) 5824.324
## Bayesian (BIC) 5928.124
## Sample-size adjusted Bayesian (BIC) 5839.323
##
## 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
## passion ~
## rltdnss (b1) 0.081 0.030 2.751 0.006 0.024 0.141
## pp.sppr (b2) 0.031 0.021 1.479 0.139 -0.010 0.073
## as.5f (c1) 0.256 0.093 2.756 0.006 0.065 0.431
## aps.stn (c2) 0.234 0.052 4.519 0.000 0.134 0.338
## aps.dsc (c3) -0.129 0.029 -4.416 0.000 -0.186 -0.072
## pp.support ~
## as.5f (a1) 0.568 0.240 2.369 0.018 0.057 1.003
## aps.stn (aa1) 0.452 0.142 3.184 0.001 0.160 0.715
## aps.dsc (aaa1) -0.118 0.093 -1.272 0.203 -0.306 0.059
## relatedness ~
## pp.sppr (d21) 0.117 0.038 3.103 0.002 0.042 0.191
## aps.stn (aa2) 0.198 0.095 2.095 0.036 0.005 0.374
## aps.dsc (aaa2) -0.413 0.067 -6.182 0.000 -0.543 -0.281
## as.5f (a2) 0.258 0.201 1.283 0.200 -0.184 0.605
## sample -0.148 0.190 -0.779 0.436 -0.512 0.229
## pp.support ~
## sample 0.537 0.285 1.886 0.059 -0.025 1.103
## passion ~
## sample 0.021 0.090 0.239 0.811 -0.153 0.196
## Std.lv Std.all
##
## 0.081 0.163
## 0.031 0.091
## 0.256 0.173
## 0.234 0.288
## -0.129 -0.227
##
## 0.568 0.133
## 0.452 0.192
## -0.118 -0.072
##
## 0.117 0.168
## 0.198 0.121
## -0.413 -0.362
## 0.258 0.087
## -0.148 -0.042
##
## 0.537 0.106
##
## 0.021 0.012
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.191 0.067 2.870 0.004 0.081 0.337
## sample 0.034 0.016 2.071 0.038 0.002 0.067
## aps.discrep 0.060 0.059 1.030 0.303 -0.045 0.183
## aps.stand ~~
## aps.discrep 0.215 0.095 2.253 0.024 0.033 0.412
## sample 0.097 0.028 3.398 0.001 0.041 0.152
## aps.discrep ~~
## sample 0.104 0.039 2.712 0.007 0.027 0.180
## Std.lv Std.all
##
## 0.191 0.352
## 0.034 0.136
## 0.060 0.078
##
## 0.215 0.152
## 0.097 0.212
##
## 0.104 0.160
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .passion 0.470 0.046 10.263 0.000 0.370 0.550
## .pp.support 4.944 0.377 13.103 0.000 4.153 5.607
## .relatedness 2.127 0.159 13.383 0.000 1.774 2.397
## as.5f 0.298 0.052 5.745 0.000 0.211 0.408
## aps.stand 0.988 0.119 8.295 0.000 0.770 1.236
## aps.discrep 2.026 0.128 15.771 0.000 1.773 2.270
## sample 0.211 0.010 20.356 0.000 0.189 0.229
## Std.lv Std.all
## 0.470 0.718
## 4.944 0.907
## 2.127 0.809
## 0.298 1.000
## 0.988 1.000
## 2.026 1.000
## 0.211 1.000
##
## R-Square:
## Estimate
## passion 0.282
## pp.support 0.093
## relatedness 0.191
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.056 0.029 1.975 0.048 0.007 0.116
## standardsndrct 0.045 0.019 2.364 0.018 0.013 0.086
## discrepindirct -0.023 0.013 -1.833 0.067 -0.049 0.002
## total1 0.312 0.097 3.214 0.001 0.114 0.489
## total2 0.279 0.054 5.187 0.000 0.177 0.387
## total3 -0.152 0.031 -4.959 0.000 -0.210 -0.090
## Std.lv Std.all
## 0.056 0.032
## 0.045 0.045
## -0.023 -0.046
## 0.312 0.204
## 0.279 0.334
## -0.152 -0.273
standardizedSolution(fit2.p, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 passion ~ relatedness 0.163 0.058 2.801
## 2 passion ~ pp.support 0.091 0.061 1.479
## 3 passion ~ as.5f 0.173 0.065 2.667
## 4 passion ~ aps.stand 0.288 0.059 4.877
## 5 passion ~ aps.discrep -0.227 0.049 -4.612
## 6 pp.support ~ as.5f 0.133 0.059 2.256
## 7 pp.support ~ aps.stand 0.192 0.062 3.120
## 8 pp.support ~ aps.discrep -0.072 0.057 -1.270
## 9 relatedness ~ pp.support 0.168 0.054 3.138
## 10 relatedness ~ aps.stand 0.121 0.059 2.048
## 11 relatedness ~ aps.discrep -0.362 0.056 -6.457
## 12 relatedness ~ as.5f 0.087 0.071 1.227
## 13 as.5f ~~ aps.stand 0.352 0.088 4.000
## 14 as.5f ~~ sample 0.136 0.061 2.220
## 15 as.5f ~~ aps.discrep 0.078 0.071 1.097
## 16 aps.stand ~~ aps.discrep 0.152 0.062 2.451
## 17 aps.stand ~~ sample 0.212 0.058 3.627
## 18 aps.discrep ~~ sample 0.160 0.058 2.775
## 19 relatedness ~ sample -0.042 0.054 -0.778
## 20 pp.support ~ sample 0.106 0.056 1.883
## 21 passion ~ sample 0.012 0.051 0.238
## 22 passion ~~ passion 0.718 0.052 13.729
## 23 pp.support ~~ pp.support 0.907 0.040 22.540
## 24 relatedness ~~ relatedness 0.809 0.044 18.334
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.032 0.016 1.941
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.045 0.018 2.535
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.046 0.025 -1.820
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.204 0.068 3.002
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.334 0.059 5.636
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.273 0.053 -5.132
## pvalue ci.lower ci.upper
## 1 0.005 0.049 0.277
## 2 0.139 -0.030 0.211
## 3 0.008 0.046 0.300
## 4 0.000 0.172 0.404
## 5 0.000 -0.323 -0.130
## 6 0.024 0.017 0.248
## 7 0.002 0.072 0.313
## 8 0.204 -0.183 0.039
## 9 0.002 0.063 0.273
## 10 0.041 0.005 0.238
## 11 0.000 -0.472 -0.252
## 12 0.220 -0.052 0.225
## 13 0.000 0.180 0.525
## 14 0.026 0.016 0.255
## 15 0.273 -0.061 0.217
## 16 0.014 0.030 0.274
## 17 0.000 0.097 0.326
## 18 0.006 0.047 0.273
## 19 0.437 -0.148 0.064
## 20 0.060 -0.004 0.216
## 21 0.812 -0.088 0.112
## 22 0.000 0.616 0.821
## 23 0.000 0.828 0.986
## 24 0.000 0.723 0.896
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.052 0.000 0.063
## 30 0.011 0.010 0.080
## 31 0.069 -0.095 0.004
## 32 0.003 0.071 0.338
## 33 0.000 0.218 0.450
## 34 0.000 -0.377 -0.168
parameterEstimates(fit2.p, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 passion ~ relatedness b1
## 2 passion ~ pp.support b2
## 3 passion ~ as.5f c1
## 4 passion ~ aps.stand c2
## 5 passion ~ aps.discrep c3
## 6 pp.support ~ as.5f a1
## 7 pp.support ~ aps.stand aa1
## 8 pp.support ~ aps.discrep aaa1
## 9 relatedness ~ pp.support d21
## 10 relatedness ~ aps.stand aa2
## 11 relatedness ~ aps.discrep aaa2
## 12 relatedness ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 relatedness ~ sample
## 20 pp.support ~ sample
## 21 passion ~ sample
## 22 passion ~~ passion
## 23 pp.support ~~ pp.support
## 24 relatedness ~~ relatedness
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.081 0.030 2.751 0.006 0.026 0.142
## 2 0.031 0.021 1.479 0.139 -0.009 0.074
## 3 0.256 0.093 2.756 0.006 0.077 0.442
## 4 0.234 0.052 4.519 0.000 0.136 0.339
## 5 -0.129 0.029 -4.416 0.000 -0.187 -0.073
## 6 0.568 0.240 2.369 0.018 0.057 1.003
## 7 0.452 0.142 3.184 0.001 0.157 0.713
## 8 -0.118 0.093 -1.272 0.203 -0.297 0.066
## 9 0.117 0.038 3.103 0.002 0.042 0.190
## 10 0.198 0.095 2.095 0.036 0.008 0.378
## 11 -0.413 0.067 -6.182 0.000 -0.540 -0.278
## 12 0.258 0.201 1.283 0.200 -0.157 0.628
## 13 0.191 0.067 2.870 0.004 0.092 0.363
## 14 0.034 0.016 2.071 0.038 0.004 0.068
## 15 0.060 0.059 1.030 0.303 -0.039 0.191
## 16 0.215 0.095 2.253 0.024 0.041 0.423
## 17 0.097 0.028 3.398 0.001 0.040 0.152
## 18 0.104 0.039 2.712 0.007 0.026 0.179
## 19 -0.148 0.190 -0.779 0.436 -0.527 0.215
## 20 0.537 0.285 1.886 0.059 -0.026 1.099
## 21 0.021 0.090 0.239 0.811 -0.151 0.198
## 22 0.470 0.046 10.263 0.000 0.397 0.586
## 23 4.944 0.377 13.103 0.000 4.283 5.734
## 24 2.127 0.159 13.383 0.000 1.867 2.516
## 25 0.298 0.052 5.745 0.000 0.220 0.431
## 26 0.988 0.119 8.295 0.000 0.787 1.260
## 27 2.026 0.128 15.771 0.000 1.789 2.290
## 28 0.211 0.010 20.356 0.000 0.189 0.229
## 29 0.056 0.029 1.975 0.048 0.011 0.126
## 30 0.045 0.019 2.364 0.018 0.015 0.091
## 31 -0.023 0.013 -1.833 0.067 -0.049 0.001
## 32 0.312 0.097 3.214 0.001 0.128 0.507
## 33 0.279 0.054 5.187 0.000 0.180 0.390
## 34 -0.152 0.031 -4.959 0.000 -0.211 -0.091
###############################################################################################################
#H3: Achievement striving leads to satisfaction of autonomy needs at both the psychological and project level,
# which in turn leads to well-being, even when controlling for perfectionistic standards and discrepancies
###############################################################################################################
#Mediation model with positive mental health as outcome controlling for perfectionistic standards and discrepancies
mmed3.mhcR <-
'
mhcR ~ b1 * autonomy + b2 * pp.autonomy + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.autonomy ~ a1 * as.5f
pp.autonomy ~ aa1 * aps.stand
pp.autonomy ~ aaa1 * aps.discrep
autonomy ~ d21 * pp.autonomy
autonomy ~ aa2 * aps.stand
autonomy ~ aaa2 * aps.discrep
autonomy ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
autonomy ~ sample
pp.autonomy ~ sample
mhcR ~ sample
'
fit3.mhcR <- sem(model = mmed3.mhcR, data = pp)
summary(fit3.mhcR, standardized = TRUE)
## lavaan 0.6-8 ended normally after 51 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## 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|) Std.lv Std.all
## mhcR ~
## autonmy (b1) 0.257 0.040 6.485 0.000 0.257 0.321
## pp.tnmy (b2) 0.012 0.026 0.462 0.644 0.012 0.023
## as.5f (c1) 0.279 0.091 3.070 0.002 0.279 0.150
## aps.stn (c2) 0.160 0.054 2.959 0.003 0.160 0.157
## aps.dsc (c3) -0.261 0.034 -7.667 0.000 -0.261 -0.367
## pp.autonomy ~
## as.5f (a1) 0.358 0.197 1.820 0.069 0.358 0.103
## aps.stn (aa1) 0.704 0.110 6.400 0.000 0.704 0.368
## aps.dsc (aaa1) -0.042 0.072 -0.584 0.559 -0.042 -0.031
## autonomy ~
## pp.tnmy (d21) 0.067 0.038 1.777 0.076 0.067 0.101
## aps.stn (aa2) 0.240 0.077 3.112 0.002 0.240 0.189
## aps.dsc (aaa2) -0.254 0.047 -5.386 0.000 -0.254 -0.287
## as.5f (a2) 0.390 0.130 2.991 0.003 0.390 0.168
## sample 0.051 0.148 0.346 0.729 0.051 0.019
## pp.autonomy ~
## sample -0.221 0.225 -0.981 0.327 -0.221 -0.053
## mhcR ~
## sample -0.170 0.102 -1.663 0.096 -0.170 -0.077
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.190 0.033 5.766 0.000 0.190 0.352
## sample 0.034 0.015 2.320 0.020 0.034 0.135
## aps.discrep 0.060 0.045 1.350 0.177 0.060 0.078
## aps.stand ~~
## aps.discrep 0.212 0.082 2.575 0.010 0.212 0.150
## sample 0.098 0.027 3.643 0.000 0.098 0.214
## aps.discrep ~~
## sample 0.101 0.038 2.659 0.008 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mhcR 0.620 0.050 12.288 0.000 0.620 0.608
## .pp.autonomy 3.024 0.246 12.288 0.000 3.024 0.837
## .autonomy 1.310 0.107 12.288 0.000 1.310 0.823
## as.5f 0.297 0.024 12.288 0.000 0.297 1.000
## aps.stand 0.986 0.080 12.288 0.000 0.986 1.000
## aps.discrep 2.024 0.165 12.288 0.000 2.024 1.000
## sample 0.212 0.017 12.288 0.000 0.212 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.097 0.053 1.814 0.070 0.097 0.037
## standardsndrct 0.184 0.040 4.614 0.000 0.184 0.123
## discrepindirct -0.014 0.020 -0.705 0.481 -0.014 -0.017
## total1 0.376 0.103 3.666 0.000 0.376 0.187
## total2 0.344 0.061 5.643 0.000 0.344 0.280
## total3 -0.275 0.039 -7.068 0.000 -0.275 -0.384
fit3.mhcR <- sem(
model = mmed3.mhcR,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit3.mhcR, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 51 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 330.336
## Degrees of freedom 21
## 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) -2788.208
## Loglikelihood unrestricted model (H1) -2788.208
##
## Akaike (AIC) 5632.416
## Bayesian (BIC) 5736.308
## Sample-size adjusted Bayesian (BIC) 5647.507
##
## 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
## mhcR ~
## autonmy (b1) 0.257 0.040 6.398 0.000 0.180 0.336
## pp.tnmy (b2) 0.012 0.028 0.430 0.667 -0.042 0.068
## as.5f (c1) 0.279 0.094 2.973 0.003 0.079 0.448
## aps.stn (c2) 0.160 0.055 2.920 0.004 0.049 0.260
## aps.dsc (c3) -0.261 0.037 -7.095 0.000 -0.333 -0.188
## pp.autonomy ~
## as.5f (a1) 0.358 0.207 1.734 0.083 -0.076 0.734
## aps.stn (aa1) 0.704 0.134 5.238 0.000 0.459 0.986
## aps.dsc (aaa1) -0.042 0.070 -0.595 0.552 -0.184 0.091
## autonomy ~
## pp.tnmy (d21) 0.067 0.042 1.591 0.112 -0.016 0.150
## aps.stn (aa2) 0.240 0.112 2.151 0.031 0.019 0.456
## aps.dsc (aaa2) -0.254 0.052 -4.924 0.000 -0.356 -0.153
## as.5f (a2) 0.390 0.160 2.434 0.015 0.057 0.682
## sample 0.051 0.166 0.310 0.757 -0.253 0.393
## pp.autonomy ~
## sample -0.221 0.223 -0.990 0.322 -0.649 0.226
## mhcR ~
## sample -0.170 0.107 -1.583 0.113 -0.374 0.044
## Std.lv Std.all
##
## 0.257 0.321
## 0.012 0.023
## 0.279 0.150
## 0.160 0.157
## -0.261 -0.367
##
## 0.358 0.103
## 0.704 0.368
## -0.042 -0.031
##
## 0.067 0.101
## 0.240 0.189
## -0.254 -0.287
## 0.390 0.168
## 0.051 0.019
##
## -0.221 -0.053
##
## -0.170 -0.077
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.190 0.069 2.748 0.006 0.082 0.349
## sample 0.034 0.017 2.033 0.042 0.002 0.068
## aps.discrep 0.060 0.060 1.004 0.315 -0.048 0.188
## aps.stand ~~
## aps.discrep 0.212 0.096 2.215 0.027 0.033 0.408
## sample 0.098 0.029 3.376 0.001 0.041 0.157
## aps.discrep ~~
## sample 0.101 0.039 2.607 0.009 0.025 0.177
## Std.lv Std.all
##
## 0.190 0.352
## 0.034 0.135
## 0.060 0.078
##
## 0.212 0.150
## 0.098 0.214
##
## 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .mhcR 0.620 0.049 12.554 0.000 0.512 0.705
## .pp.autonomy 3.024 0.346 8.729 0.000 2.321 3.662
## .autonomy 1.310 0.143 9.192 0.000 1.009 1.563
## as.5f 0.297 0.053 5.561 0.000 0.209 0.419
## aps.stand 0.986 0.122 8.109 0.000 0.764 1.233
## aps.discrep 2.024 0.131 15.488 0.000 1.763 2.268
## sample 0.212 0.010 20.195 0.000 0.188 0.230
## Std.lv Std.all
## 0.620 0.608
## 3.024 0.837
## 1.310 0.823
## 0.297 1.000
## 0.986 1.000
## 2.024 1.000
## 0.212 1.000
##
## R-Square:
## Estimate
## mhcR 0.392
## pp.autonomy 0.163
## autonomy 0.177
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.097 0.058 1.677 0.094 -0.013 0.217
## standardsndrct 0.184 0.045 4.137 0.000 0.108 0.284
## discrepindirct -0.014 0.020 -0.695 0.487 -0.055 0.024
## total1 0.376 0.111 3.386 0.001 0.138 0.569
## total2 0.344 0.061 5.611 0.000 0.220 0.465
## total3 -0.275 0.040 -6.846 0.000 -0.354 -0.195
## Std.lv Std.all
## 0.097 0.037
## 0.184 0.123
## -0.014 -0.017
## 0.376 0.187
## 0.344 0.280
## -0.275 -0.384
standardizedSolution(fit3.mhcR, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 mhcR ~ autonomy 0.321 0.050 6.431
## 2 mhcR ~ pp.autonomy 0.023 0.053 0.432
## 3 mhcR ~ as.5f 0.150 0.056 2.686
## 4 mhcR ~ aps.stand 0.157 0.055 2.879
## 5 mhcR ~ aps.discrep -0.367 0.050 -7.308
## 6 pp.autonomy ~ as.5f 0.103 0.061 1.679
## 7 pp.autonomy ~ aps.stand 0.368 0.061 6.081
## 8 pp.autonomy ~ aps.discrep -0.031 0.053 -0.595
## 9 autonomy ~ pp.autonomy 0.101 0.064 1.574
## 10 autonomy ~ aps.stand 0.189 0.090 2.105
## 11 autonomy ~ aps.discrep -0.287 0.058 -4.911
## 12 autonomy ~ as.5f 0.168 0.075 2.234
## 13 as.5f ~~ aps.stand 0.352 0.091 3.874
## 14 as.5f ~~ sample 0.135 0.062 2.179
## 15 as.5f ~~ aps.discrep 0.078 0.073 1.072
## 16 aps.stand ~~ aps.discrep 0.150 0.062 2.420
## 17 aps.stand ~~ sample 0.214 0.059 3.632
## 18 aps.discrep ~~ sample 0.155 0.058 2.674
## 19 autonomy ~ sample 0.019 0.060 0.311
## 20 pp.autonomy ~ sample -0.053 0.054 -0.994
## 21 mhcR ~ sample -0.077 0.049 -1.571
## 22 mhcR ~~ mhcR 0.608 0.046 13.071
## 23 pp.autonomy ~~ pp.autonomy 0.837 0.053 15.902
## 24 autonomy ~~ autonomy 0.823 0.049 16.871
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.037 0.023 1.605
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.123 0.029 4.304
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.017 0.023 -0.721
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.187 0.064 2.937
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.280 0.054 5.192
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.384 0.054 -7.126
## pvalue ci.lower ci.upper
## 1 0.000 0.223 0.419
## 2 0.666 -0.081 0.126
## 3 0.007 0.041 0.260
## 4 0.004 0.050 0.264
## 5 0.000 -0.466 -0.269
## 6 0.093 -0.017 0.222
## 7 0.000 0.249 0.487
## 8 0.552 -0.135 0.072
## 9 0.116 -0.025 0.228
## 10 0.035 0.013 0.365
## 11 0.000 -0.401 -0.172
## 12 0.025 0.021 0.315
## 13 0.000 0.174 0.530
## 14 0.029 0.014 0.256
## 15 0.284 -0.065 0.220
## 16 0.016 0.028 0.271
## 17 0.000 0.099 0.330
## 18 0.008 0.041 0.268
## 19 0.756 -0.099 0.137
## 20 0.320 -0.159 0.052
## 21 0.116 -0.174 0.019
## 22 0.000 0.517 0.699
## 23 0.000 0.734 0.940
## 24 0.000 0.727 0.918
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.108 -0.008 0.082
## 30 0.000 0.067 0.179
## 31 0.471 -0.062 0.029
## 32 0.003 0.062 0.312
## 33 0.000 0.174 0.386
## 34 0.000 -0.490 -0.278
parameterEstimates(fit3.mhcR, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 mhcR ~ autonomy b1
## 2 mhcR ~ pp.autonomy b2
## 3 mhcR ~ as.5f c1
## 4 mhcR ~ aps.stand c2
## 5 mhcR ~ aps.discrep c3
## 6 pp.autonomy ~ as.5f a1
## 7 pp.autonomy ~ aps.stand aa1
## 8 pp.autonomy ~ aps.discrep aaa1
## 9 autonomy ~ pp.autonomy d21
## 10 autonomy ~ aps.stand aa2
## 11 autonomy ~ aps.discrep aaa2
## 12 autonomy ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 autonomy ~ sample
## 20 pp.autonomy ~ sample
## 21 mhcR ~ sample
## 22 mhcR ~~ mhcR
## 23 pp.autonomy ~~ pp.autonomy
## 24 autonomy ~~ autonomy
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.257 0.040 6.398 0.000 0.179 0.335
## 2 0.012 0.028 0.430 0.667 -0.041 0.068
## 3 0.279 0.094 2.973 0.003 0.088 0.454
## 4 0.160 0.055 2.920 0.004 0.054 0.267
## 5 -0.261 0.037 -7.095 0.000 -0.330 -0.185
## 6 0.358 0.207 1.734 0.083 -0.050 0.749
## 7 0.704 0.134 5.238 0.000 0.459 0.986
## 8 -0.042 0.070 -0.595 0.552 -0.176 0.097
## 9 0.067 0.042 1.591 0.112 -0.015 0.150
## 10 0.240 0.112 2.151 0.031 0.034 0.471
## 11 -0.254 0.052 -4.924 0.000 -0.355 -0.151
## 12 0.390 0.160 2.434 0.015 0.056 0.679
## 13 0.190 0.069 2.748 0.006 0.089 0.367
## 14 0.034 0.017 2.033 0.042 0.003 0.069
## 15 0.060 0.060 1.004 0.315 -0.043 0.197
## 16 0.212 0.096 2.215 0.027 0.041 0.420
## 17 0.098 0.029 3.376 0.001 0.044 0.160
## 18 0.101 0.039 2.607 0.009 0.026 0.178
## 19 0.051 0.166 0.310 0.757 -0.257 0.391
## 20 -0.221 0.223 -0.990 0.322 -0.656 0.222
## 21 -0.170 0.107 -1.583 0.113 -0.383 0.034
## 22 0.620 0.049 12.554 0.000 0.539 0.743
## 23 3.024 0.346 8.729 0.000 2.429 3.803
## 24 1.310 0.143 9.192 0.000 1.082 1.659
## 25 0.297 0.053 5.561 0.000 0.216 0.434
## 26 0.986 0.122 8.109 0.000 0.775 1.268
## 27 2.024 0.131 15.488 0.000 1.777 2.289
## 28 0.212 0.010 20.195 0.000 0.188 0.230
## 29 0.097 0.058 1.677 0.094 -0.005 0.227
## 30 0.184 0.045 4.137 0.000 0.110 0.288
## 31 -0.014 0.020 -0.695 0.487 -0.053 0.027
## 32 0.376 0.111 3.386 0.001 0.144 0.576
## 33 0.344 0.061 5.611 0.000 0.225 0.469
## 34 -0.275 0.040 -6.846 0.000 -0.348 -0.189
#########################################################################################################
#Mediation model with zest as outcome controlling for perfectionistic standards and discrepancies
mmed3.zest <-
'
zestR ~ b1 * autonomy + b2 * pp.autonomy + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.autonomy ~ a1 * as.5f
pp.autonomy ~ aa1 * aps.stand
pp.autonomy ~ aaa1 * aps.discrep
autonomy ~ d21 * pp.autonomy
autonomy ~ aa2 * aps.stand
autonomy ~ aaa2 * aps.discrep
autonomy ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
autonomy ~ sample
pp.autonomy ~ sample
zestR ~ sample
'
fit3.zest <- sem(model = mmed3.zest, data = pp)
summary(fit3.zest, standardized = TRUE)
## lavaan 0.6-8 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## 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|) Std.lv Std.all
## zestR ~
## autonmy (b1) 0.260 0.048 5.398 0.000 0.260 0.270
## pp.tnmy (b2) 0.037 0.032 1.170 0.242 0.037 0.058
## as.5f (c1) 0.212 0.110 1.916 0.055 0.212 0.095
## aps.stn (c2) 0.078 0.066 1.187 0.235 0.078 0.064
## aps.dsc (c3) -0.389 0.041 -9.408 0.000 -0.389 -0.456
## pp.autonomy ~
## as.5f (a1) 0.358 0.197 1.820 0.069 0.358 0.103
## aps.stn (aa1) 0.704 0.110 6.400 0.000 0.704 0.368
## aps.dsc (aaa1) -0.042 0.072 -0.584 0.559 -0.042 -0.031
## autonomy ~
## pp.tnmy (d21) 0.067 0.038 1.777 0.076 0.067 0.101
## aps.stn (aa2) 0.240 0.077 3.112 0.002 0.240 0.189
## aps.dsc (aaa2) -0.254 0.047 -5.386 0.000 -0.254 -0.287
## as.5f (a2) 0.390 0.130 2.991 0.003 0.390 0.168
## sample 0.051 0.148 0.346 0.729 0.051 0.019
## pp.autonomy ~
## sample -0.221 0.225 -0.981 0.327 -0.221 -0.053
## zestR ~
## sample -0.033 0.124 -0.265 0.791 -0.033 -0.012
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.190 0.033 5.766 0.000 0.190 0.352
## sample 0.034 0.015 2.320 0.020 0.034 0.135
## aps.discrep 0.060 0.045 1.350 0.177 0.060 0.078
## aps.stand ~~
## aps.discrep 0.212 0.082 2.575 0.010 0.212 0.150
## sample 0.098 0.027 3.643 0.000 0.098 0.214
## aps.discrep ~~
## sample 0.101 0.038 2.659 0.008 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .zestR 0.916 0.075 12.288 0.000 0.916 0.622
## .pp.autonomy 3.024 0.246 12.288 0.000 3.024 0.837
## .autonomy 1.310 0.107 12.288 0.000 1.310 0.823
## as.5f 0.297 0.024 12.288 0.000 0.297 1.000
## aps.stand 0.986 0.080 12.288 0.000 0.986 1.000
## aps.discrep 2.024 0.165 12.288 0.000 2.024 1.000
## sample 0.212 0.017 12.288 0.000 0.212 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.108 0.056 1.945 0.052 0.108 0.038
## standardsndrct 0.194 0.045 4.320 0.000 0.194 0.113
## discrepindirct -0.020 0.021 -0.993 0.321 -0.020 -0.025
## total1 0.320 0.120 2.665 0.008 0.320 0.133
## total2 0.271 0.072 3.786 0.000 0.271 0.176
## total3 -0.410 0.046 -8.970 0.000 -0.410 -0.482
fit3.zest <- sem(
model = mmed3.zest,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit3.zest, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 323.288
## Degrees of freedom 21
## 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) -2847.166
## Loglikelihood unrestricted model (H1) -2847.166
##
## Akaike (AIC) 5750.332
## Bayesian (BIC) 5854.224
## Sample-size adjusted Bayesian (BIC) 5765.423
##
## 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
## zestR ~
## autonmy (b1) 0.260 0.053 4.859 0.000 0.154 0.365
## pp.tnmy (b2) 0.037 0.040 0.942 0.346 -0.037 0.120
## as.5f (c1) 0.212 0.103 2.064 0.039 0.010 0.416
## aps.stn (c2) 0.078 0.064 1.208 0.227 -0.052 0.203
## aps.dsc (c3) -0.389 0.044 -8.755 0.000 -0.475 -0.300
## pp.autonomy ~
## as.5f (a1) 0.358 0.207 1.733 0.083 -0.078 0.740
## aps.stn (aa1) 0.704 0.133 5.303 0.000 0.457 0.975
## aps.dsc (aaa1) -0.042 0.070 -0.598 0.550 -0.185 0.092
## autonomy ~
## pp.tnmy (d21) 0.067 0.043 1.577 0.115 -0.018 0.151
## aps.stn (aa2) 0.240 0.110 2.177 0.030 0.025 0.452
## aps.dsc (aaa2) -0.254 0.051 -4.988 0.000 -0.356 -0.156
## as.5f (a2) 0.390 0.164 2.373 0.018 0.056 0.693
## sample 0.051 0.166 0.308 0.758 -0.270 0.384
## pp.autonomy ~
## sample -0.221 0.224 -0.985 0.325 -0.664 0.218
## zestR ~
## sample -0.033 0.129 -0.255 0.799 -0.289 0.211
## Std.lv Std.all
##
## 0.260 0.270
## 0.037 0.058
## 0.212 0.095
## 0.078 0.064
## -0.389 -0.456
##
## 0.358 0.103
## 0.704 0.368
## -0.042 -0.031
##
## 0.067 0.101
## 0.240 0.189
## -0.254 -0.287
## 0.390 0.168
## 0.051 0.019
##
## -0.221 -0.053
##
## -0.033 -0.012
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.190 0.068 2.779 0.005 0.082 0.345
## sample 0.034 0.017 2.037 0.042 0.002 0.068
## aps.discrep 0.060 0.060 1.004 0.315 -0.047 0.189
## aps.stand ~~
## aps.discrep 0.212 0.096 2.215 0.027 0.035 0.406
## sample 0.098 0.029 3.412 0.001 0.044 0.155
## aps.discrep ~~
## sample 0.101 0.039 2.607 0.009 0.026 0.179
## Std.lv Std.all
##
## 0.190 0.352
## 0.034 0.135
## 0.060 0.078
##
## 0.212 0.150
## 0.098 0.214
##
## 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .zestR 0.916 0.077 11.913 0.000 0.747 1.050
## .pp.autonomy 3.024 0.345 8.773 0.000 2.316 3.686
## .autonomy 1.310 0.141 9.311 0.000 1.013 1.559
## as.5f 0.297 0.053 5.562 0.000 0.211 0.419
## aps.stand 0.986 0.122 8.061 0.000 0.768 1.249
## aps.discrep 2.024 0.132 15.340 0.000 1.763 2.279
## sample 0.212 0.010 20.307 0.000 0.190 0.230
## Std.lv Std.all
## 0.916 0.622
## 3.024 0.837
## 1.310 0.823
## 0.297 1.000
## 0.986 1.000
## 2.024 1.000
## 0.212 1.000
##
## R-Square:
## Estimate
## zestR 0.378
## pp.autonomy 0.163
## autonomy 0.177
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.108 0.062 1.763 0.078 -0.008 0.235
## standardsndrct 0.194 0.052 3.707 0.000 0.104 0.308
## discrepindirct -0.020 0.023 -0.879 0.380 -0.070 0.021
## total1 0.320 0.108 2.968 0.003 0.101 0.517
## total2 0.271 0.074 3.678 0.000 0.129 0.429
## total3 -0.410 0.049 -8.366 0.000 -0.508 -0.312
## Std.lv Std.all
## 0.108 0.038
## 0.194 0.113
## -0.020 -0.025
## 0.320 0.133
## 0.271 0.176
## -0.410 -0.482
standardizedSolution(fit3.zest, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 zestR ~ autonomy 0.270 0.056 4.797
## 2 zestR ~ pp.autonomy 0.058 0.061 0.961
## 3 zestR ~ as.5f 0.095 0.046 2.046
## 4 zestR ~ aps.stand 0.064 0.053 1.201
## 5 zestR ~ aps.discrep -0.456 0.048 -9.500
## 6 pp.autonomy ~ as.5f 0.103 0.061 1.675
## 7 pp.autonomy ~ aps.stand 0.368 0.060 6.093
## 8 pp.autonomy ~ aps.discrep -0.031 0.052 -0.598
## 9 autonomy ~ pp.autonomy 0.101 0.065 1.566
## 10 autonomy ~ aps.stand 0.189 0.089 2.129
## 11 autonomy ~ aps.discrep -0.287 0.057 -4.990
## 12 autonomy ~ as.5f 0.168 0.077 2.182
## 13 as.5f ~~ aps.stand 0.352 0.090 3.916
## 14 as.5f ~~ sample 0.135 0.062 2.184
## 15 as.5f ~~ aps.discrep 0.078 0.073 1.071
## 16 aps.stand ~~ aps.discrep 0.150 0.062 2.409
## 17 aps.stand ~~ sample 0.214 0.058 3.674
## 18 aps.discrep ~~ sample 0.155 0.058 2.659
## 19 autonomy ~ sample 0.019 0.061 0.309
## 20 pp.autonomy ~ sample -0.053 0.054 -0.989
## 21 zestR ~ sample -0.012 0.049 -0.255
## 22 zestR ~~ zestR 0.622 0.051 12.244
## 23 pp.autonomy ~~ pp.autonomy 0.837 0.053 15.763
## 24 autonomy ~~ autonomy 0.823 0.049 16.959
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.038 0.022 1.729
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.113 0.029 3.949
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.025 0.025 -1.015
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.133 0.049 2.717
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.176 0.052 3.373
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.482 0.052 -9.232
## pvalue ci.lower ci.upper
## 1 0.000 0.160 0.381
## 2 0.337 -0.061 0.177
## 3 0.041 0.004 0.186
## 4 0.230 -0.040 0.168
## 5 0.000 -0.550 -0.362
## 6 0.094 -0.017 0.223
## 7 0.000 0.250 0.486
## 8 0.550 -0.134 0.071
## 9 0.117 -0.025 0.228
## 10 0.033 0.015 0.363
## 11 0.000 -0.400 -0.174
## 12 0.029 0.017 0.319
## 13 0.000 0.176 0.528
## 14 0.029 0.014 0.256
## 15 0.284 -0.065 0.221
## 16 0.016 0.028 0.272
## 17 0.000 0.100 0.329
## 18 0.008 0.041 0.269
## 19 0.757 -0.100 0.137
## 20 0.323 -0.159 0.052
## 21 0.799 -0.108 0.084
## 22 0.000 0.523 0.722
## 23 0.000 0.733 0.941
## 24 0.000 0.728 0.918
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.084 -0.005 0.081
## 30 0.000 0.057 0.169
## 31 0.310 -0.074 0.024
## 32 0.007 0.037 0.229
## 33 0.001 0.074 0.279
## 34 0.000 -0.584 -0.379
parameterEstimates(fit3.zest, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 zestR ~ autonomy b1
## 2 zestR ~ pp.autonomy b2
## 3 zestR ~ as.5f c1
## 4 zestR ~ aps.stand c2
## 5 zestR ~ aps.discrep c3
## 6 pp.autonomy ~ as.5f a1
## 7 pp.autonomy ~ aps.stand aa1
## 8 pp.autonomy ~ aps.discrep aaa1
## 9 autonomy ~ pp.autonomy d21
## 10 autonomy ~ aps.stand aa2
## 11 autonomy ~ aps.discrep aaa2
## 12 autonomy ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 autonomy ~ sample
## 20 pp.autonomy ~ sample
## 21 zestR ~ sample
## 22 zestR ~~ zestR
## 23 pp.autonomy ~~ pp.autonomy
## 24 autonomy ~~ autonomy
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.260 0.053 4.859 0.000 0.155 0.365
## 2 0.037 0.040 0.942 0.346 -0.042 0.116
## 3 0.212 0.103 2.064 0.039 0.014 0.422
## 4 0.078 0.064 1.208 0.227 -0.052 0.203
## 5 -0.389 0.044 -8.755 0.000 -0.474 -0.298
## 6 0.358 0.207 1.733 0.083 -0.049 0.766
## 7 0.704 0.133 5.303 0.000 0.456 0.975
## 8 -0.042 0.070 -0.598 0.550 -0.179 0.098
## 9 0.067 0.043 1.577 0.115 -0.019 0.150
## 10 0.240 0.110 2.177 0.030 0.041 0.471
## 11 -0.254 0.051 -4.988 0.000 -0.352 -0.152
## 12 0.390 0.164 2.373 0.018 0.048 0.677
## 13 0.190 0.068 2.779 0.005 0.089 0.364
## 14 0.034 0.017 2.037 0.042 0.002 0.068
## 15 0.060 0.060 1.004 0.315 -0.044 0.194
## 16 0.212 0.096 2.215 0.027 0.041 0.412
## 17 0.098 0.029 3.412 0.001 0.045 0.157
## 18 0.101 0.039 2.607 0.009 0.028 0.182
## 19 0.051 0.166 0.308 0.758 -0.280 0.373
## 20 -0.221 0.224 -0.985 0.325 -0.660 0.222
## 21 -0.033 0.129 -0.255 0.799 -0.284 0.215
## 22 0.916 0.077 11.913 0.000 0.796 1.111
## 23 3.024 0.345 8.773 0.000 2.434 3.839
## 24 1.310 0.141 9.311 0.000 1.082 1.665
## 25 0.297 0.053 5.562 0.000 0.217 0.436
## 26 0.986 0.122 8.061 0.000 0.778 1.268
## 27 2.024 0.132 15.340 0.000 1.783 2.298
## 28 0.212 0.010 20.307 0.000 0.192 0.231
## 29 0.108 0.062 1.763 0.078 0.005 0.251
## 30 0.194 0.052 3.707 0.000 0.106 0.313
## 31 -0.020 0.023 -0.879 0.380 -0.067 0.023
## 32 0.320 0.108 2.968 0.003 0.103 0.520
## 33 0.271 0.074 3.678 0.000 0.132 0.432
## 34 -0.410 0.049 -8.366 0.000 -0.503 -0.304
#########################################################################################################
#Mediation model with engagement as outcome controlling for perfectionistic standards and discrepancies
mmed3.eng <-
'
engagementR ~ b1 * autonomy + b2 * pp.autonomy + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.autonomy ~ a1 * as.5f
pp.autonomy ~ aa1 * aps.stand
pp.autonomy ~ aaa1 * aps.discrep
autonomy ~ d21 * pp.autonomy
autonomy ~ aa2 * aps.stand
autonomy ~ aaa2 * aps.discrep
autonomy ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
autonomy ~ sample
pp.autonomy ~ sample
engagementR ~ sample
'
fit3.eng <- sem(model = mmed3.eng, data = pp)
summary(fit3.eng, standardized = TRUE)
## lavaan 0.6-8 ended normally after 52 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## 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|) Std.lv Std.all
## engagementR ~
## autonmy (b1) 0.075 0.022 3.361 0.001 0.075 0.170
## pp.tnmy (b2) 0.035 0.015 2.355 0.019 0.035 0.119
## as.5f (c1) 0.163 0.051 3.193 0.001 0.163 0.160
## aps.stn (c2) 0.075 0.030 2.477 0.013 0.075 0.134
## aps.dsc (c3) -0.176 0.019 -9.179 0.000 -0.176 -0.450
## pp.autonomy ~
## as.5f (a1) 0.358 0.197 1.820 0.069 0.358 0.103
## aps.stn (aa1) 0.704 0.110 6.400 0.000 0.704 0.368
## aps.dsc (aaa1) -0.042 0.072 -0.584 0.559 -0.042 -0.031
## autonomy ~
## pp.tnmy (d21) 0.067 0.038 1.777 0.076 0.067 0.101
## aps.stn (aa2) 0.240 0.077 3.112 0.002 0.240 0.189
## aps.dsc (aaa2) -0.254 0.047 -5.386 0.000 -0.254 -0.287
## as.5f (a2) 0.390 0.130 2.991 0.003 0.390 0.168
## sample 0.051 0.148 0.346 0.729 0.051 0.019
## pp.autonomy ~
## sample -0.221 0.225 -0.981 0.327 -0.221 -0.053
## engagementR ~
## sample -0.052 0.057 -0.905 0.366 -0.052 -0.043
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.190 0.033 5.766 0.000 0.190 0.352
## sample 0.034 0.015 2.320 0.020 0.034 0.135
## aps.discrep 0.060 0.045 1.350 0.177 0.060 0.078
## aps.stand ~~
## aps.discrep 0.212 0.082 2.575 0.010 0.212 0.150
## sample 0.098 0.027 3.643 0.000 0.098 0.214
## aps.discrep ~~
## sample 0.101 0.038 2.659 0.008 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .engagementR 0.197 0.016 12.288 0.000 0.197 0.635
## .pp.autonomy 3.024 0.246 12.288 0.000 3.024 0.837
## .autonomy 1.310 0.107 12.288 0.000 1.310 0.823
## as.5f 0.297 0.024 12.288 0.000 0.297 1.000
## aps.stand 0.986 0.080 12.288 0.000 0.986 1.000
## aps.discrep 2.024 0.165 12.288 0.000 2.024 1.000
## sample 0.212 0.017 12.288 0.000 0.212 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.041 0.019 2.227 0.026 0.041 0.039
## standardsndrct 0.063 0.018 3.457 0.001 0.063 0.089
## discrepindirct -0.012 0.007 -1.744 0.081 -0.012 -0.040
## total1 0.205 0.053 3.887 0.000 0.205 0.199
## total2 0.138 0.032 4.361 0.000 0.138 0.224
## total3 -0.188 0.020 -9.348 0.000 -0.188 -0.489
fit3.eng <- sem(
model = mmed3.eng,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit3.eng, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 52 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 317.022
## Degrees of freedom 21
## 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) -2614.950
## Loglikelihood unrestricted model (H1) -2614.950
##
## Akaike (AIC) 5285.900
## Bayesian (BIC) 5389.792
## Sample-size adjusted Bayesian (BIC) 5300.992
##
## 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
## engagementR ~
## autonmy (b1) 0.075 0.026 2.884 0.004 0.024 0.125
## pp.tnmy (b2) 0.035 0.016 2.142 0.032 0.005 0.067
## as.5f (c1) 0.163 0.053 3.102 0.002 0.057 0.262
## aps.stn (c2) 0.075 0.034 2.204 0.028 0.010 0.143
## aps.dsc (c3) -0.176 0.021 -8.348 0.000 -0.217 -0.134
## pp.autonomy ~
## as.5f (a1) 0.358 0.211 1.696 0.090 -0.088 0.738
## aps.stn (aa1) 0.704 0.135 5.217 0.000 0.458 0.982
## aps.dsc (aaa1) -0.042 0.072 -0.582 0.561 -0.189 0.095
## autonomy ~
## pp.tnmy (d21) 0.067 0.042 1.596 0.111 -0.016 0.149
## aps.stn (aa2) 0.240 0.111 2.160 0.031 0.023 0.455
## aps.dsc (aaa2) -0.254 0.051 -4.946 0.000 -0.357 -0.157
## as.5f (a2) 0.390 0.163 2.383 0.017 0.040 0.693
## sample 0.051 0.163 0.314 0.753 -0.261 0.380
## pp.autonomy ~
## sample -0.221 0.221 -0.997 0.319 -0.645 0.223
## engagementR ~
## sample -0.052 0.060 -0.864 0.387 -0.172 0.065
## Std.lv Std.all
##
## 0.075 0.170
## 0.035 0.119
## 0.163 0.160
## 0.075 0.134
## -0.176 -0.450
##
## 0.358 0.103
## 0.704 0.368
## -0.042 -0.031
##
## 0.067 0.101
## 0.240 0.189
## -0.254 -0.287
## 0.390 0.168
## 0.051 0.019
##
## -0.221 -0.053
##
## -0.052 -0.043
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.190 0.067 2.840 0.005 0.083 0.342
## sample 0.034 0.017 2.009 0.044 0.001 0.068
## aps.discrep 0.060 0.059 1.030 0.303 -0.046 0.185
## aps.stand ~~
## aps.discrep 0.212 0.096 2.199 0.028 0.033 0.410
## sample 0.098 0.029 3.418 0.001 0.042 0.154
## aps.discrep ~~
## sample 0.101 0.039 2.624 0.009 0.024 0.176
## Std.lv Std.all
##
## 0.190 0.352
## 0.034 0.135
## 0.060 0.078
##
## 0.212 0.150
## 0.098 0.214
##
## 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .engagementR 0.197 0.015 12.731 0.000 0.163 0.224
## .pp.autonomy 3.024 0.348 8.685 0.000 2.335 3.698
## .autonomy 1.310 0.142 9.255 0.000 1.011 1.562
## as.5f 0.297 0.053 5.635 0.000 0.210 0.416
## aps.stand 0.986 0.120 8.194 0.000 0.774 1.243
## aps.discrep 2.024 0.134 15.086 0.000 1.758 2.288
## sample 0.212 0.010 20.401 0.000 0.190 0.230
## Std.lv Std.all
## 0.197 0.635
## 3.024 0.837
## 1.310 0.823
## 0.297 1.000
## 0.986 1.000
## 2.024 1.000
## 0.212 1.000
##
## R-Square:
## Estimate
## engagementR 0.365
## pp.autonomy 0.163
## autonomy 0.177
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.041 0.022 1.833 0.067 0.002 0.088
## standardsndrct 0.063 0.022 2.818 0.005 0.025 0.113
## discrepindirct -0.012 0.007 -1.612 0.107 -0.028 0.002
## total1 0.205 0.054 3.777 0.000 0.090 0.302
## total2 0.138 0.035 3.906 0.000 0.075 0.213
## total3 -0.188 0.022 -8.604 0.000 -0.231 -0.145
## Std.lv Std.all
## 0.041 0.039
## 0.063 0.089
## -0.012 -0.040
## 0.205 0.199
## 0.138 0.224
## -0.188 -0.489
standardizedSolution(fit3.eng, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 engagementR ~ autonomy 0.170 0.059 2.889
## 2 engagementR ~ pp.autonomy 0.119 0.054 2.195
## 3 engagementR ~ as.5f 0.160 0.054 2.958
## 4 engagementR ~ aps.stand 0.134 0.060 2.233
## 5 engagementR ~ aps.discrep -0.450 0.050 -8.918
## 6 pp.autonomy ~ as.5f 0.103 0.062 1.644
## 7 pp.autonomy ~ aps.stand 0.368 0.061 6.075
## 8 pp.autonomy ~ aps.discrep -0.031 0.054 -0.582
## 9 autonomy ~ pp.autonomy 0.101 0.064 1.580
## 10 autonomy ~ aps.stand 0.189 0.089 2.114
## 11 autonomy ~ aps.discrep -0.287 0.058 -4.931
## 12 autonomy ~ as.5f 0.168 0.077 2.193
## 13 as.5f ~~ aps.stand 0.352 0.088 3.987
## 14 as.5f ~~ sample 0.135 0.063 2.153
## 15 as.5f ~~ aps.discrep 0.078 0.071 1.100
## 16 aps.stand ~~ aps.discrep 0.150 0.062 2.398
## 17 aps.stand ~~ sample 0.214 0.059 3.654
## 18 aps.discrep ~~ sample 0.155 0.058 2.683
## 19 autonomy ~ sample 0.019 0.059 0.315
## 20 pp.autonomy ~ sample -0.053 0.053 -1.001
## 21 engagementR ~ sample -0.043 0.050 -0.861
## 22 engagementR ~~ engagementR 0.635 0.047 13.641
## 23 pp.autonomy ~~ pp.autonomy 0.837 0.052 16.123
## 24 autonomy ~~ autonomy 0.823 0.049 16.904
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.039 0.020 1.890
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.089 0.028 3.170
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.040 0.020 -2.029
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.199 0.057 3.464
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.224 0.055 4.081
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.489 0.052 -9.421
## pvalue ci.lower ci.upper
## 1 0.004 0.055 0.285
## 2 0.028 0.013 0.225
## 3 0.003 0.054 0.266
## 4 0.026 0.016 0.252
## 5 0.000 -0.549 -0.351
## 6 0.100 -0.020 0.225
## 7 0.000 0.249 0.487
## 8 0.561 -0.137 0.074
## 9 0.114 -0.024 0.227
## 10 0.034 0.014 0.364
## 11 0.000 -0.401 -0.173
## 12 0.028 0.018 0.318
## 13 0.000 0.179 0.525
## 14 0.031 0.012 0.257
## 15 0.271 -0.061 0.217
## 16 0.016 0.027 0.272
## 17 0.000 0.099 0.329
## 18 0.007 0.042 0.268
## 19 0.753 -0.098 0.135
## 20 0.317 -0.158 0.051
## 21 0.389 -0.141 0.055
## 22 0.000 0.544 0.726
## 23 0.000 0.735 0.939
## 24 0.000 0.727 0.918
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.059 -0.001 0.079
## 30 0.002 0.034 0.145
## 31 0.042 -0.078 -0.001
## 32 0.001 0.086 0.311
## 33 0.000 0.116 0.331
## 34 0.000 -0.591 -0.388
parameterEstimates(fit3.eng, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 engagementR ~ autonomy b1
## 2 engagementR ~ pp.autonomy b2
## 3 engagementR ~ as.5f c1
## 4 engagementR ~ aps.stand c2
## 5 engagementR ~ aps.discrep c3
## 6 pp.autonomy ~ as.5f a1
## 7 pp.autonomy ~ aps.stand aa1
## 8 pp.autonomy ~ aps.discrep aaa1
## 9 autonomy ~ pp.autonomy d21
## 10 autonomy ~ aps.stand aa2
## 11 autonomy ~ aps.discrep aaa2
## 12 autonomy ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 autonomy ~ sample
## 20 pp.autonomy ~ sample
## 21 engagementR ~ sample
## 22 engagementR ~~ engagementR
## 23 pp.autonomy ~~ pp.autonomy
## 24 autonomy ~~ autonomy
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.075 0.026 2.884 0.004 0.025 0.126
## 2 0.035 0.016 2.142 0.032 0.005 0.067
## 3 0.163 0.053 3.102 0.002 0.060 0.265
## 4 0.075 0.034 2.204 0.028 0.009 0.143
## 5 -0.176 0.021 -8.348 0.000 -0.218 -0.135
## 6 0.358 0.211 1.696 0.090 -0.054 0.775
## 7 0.704 0.135 5.217 0.000 0.462 0.988
## 8 -0.042 0.072 -0.582 0.561 -0.183 0.104
## 9 0.067 0.042 1.596 0.111 -0.017 0.148
## 10 0.240 0.111 2.160 0.031 0.037 0.466
## 11 -0.254 0.051 -4.946 0.000 -0.354 -0.153
## 12 0.390 0.163 2.383 0.017 0.051 0.698
## 13 0.190 0.067 2.840 0.005 0.090 0.364
## 14 0.034 0.017 2.009 0.044 0.001 0.068
## 15 0.060 0.059 1.030 0.303 -0.041 0.193
## 16 0.212 0.096 2.199 0.028 0.038 0.416
## 17 0.098 0.029 3.418 0.001 0.043 0.154
## 18 0.101 0.039 2.624 0.009 0.024 0.175
## 19 0.051 0.163 0.314 0.753 -0.267 0.372
## 20 -0.221 0.221 -0.997 0.319 -0.653 0.220
## 21 -0.052 0.060 -0.864 0.387 -0.170 0.067
## 22 0.197 0.015 12.731 0.000 0.172 0.236
## 23 3.024 0.348 8.685 0.000 2.449 3.858
## 24 1.310 0.142 9.255 0.000 1.082 1.666
## 25 0.297 0.053 5.635 0.000 0.218 0.430
## 26 0.986 0.120 8.194 0.000 0.789 1.269
## 27 2.024 0.134 15.086 0.000 1.770 2.305
## 28 0.212 0.010 20.401 0.000 0.188 0.229
## 29 0.041 0.022 1.833 0.067 0.006 0.097
## 30 0.063 0.022 2.818 0.005 0.027 0.116
## 31 -0.012 0.007 -1.612 0.107 -0.028 0.002
## 32 0.205 0.054 3.777 0.000 0.095 0.305
## 33 0.138 0.035 3.906 0.000 0.075 0.213
## 34 -0.188 0.022 -8.604 0.000 -0.231 -0.144
#########################################################################################################
#Mediation model with passion as outcome controlling for perfectionistic standards and discrepancies
mmed3.p <-
'
passion ~ b1 * autonomy + b2 * pp.autonomy + c1 * as.5f + c2 * aps.stand + c3 * aps.discrep
pp.autonomy ~ a1 * as.5f
pp.autonomy ~ aa1 * aps.stand
pp.autonomy ~ aaa1 * aps.discrep
autonomy ~ d21 * pp.autonomy
autonomy ~ aa2 * aps.stand
autonomy ~ aaa2 * aps.discrep
autonomy ~ a2 * as.5f
ach striv indirect := a1*b1 + a2*b2 + a1*d21*b2
standards indirect := aa1*b1 + aa2*b2 + aa1*d21*b2
discrep indirect := aaa1*b1 + aaa2*b2 + aaa1*d21*b2
total1 := c1 + (a1*b1 + a2*b2 + a1*d21*b2)
total2 := c2 + (aa1*b1 + aa2*b2 + aa1*d21*b2)
total3 := c3 + (aaa1*b1 + aaa2*b2 + aaa1*d21*b2)
as.5f ~~ aps.stand + sample
as.5f ~~ aps.discrep
aps.stand ~~ aps.discrep + sample
aps.discrep ~~ sample
autonomy ~ sample
pp.autonomy ~ sample
passion ~ sample
'
fit3.p <- sem(model = mmed3.p, data = pp)
summary(fit3.p, standardized = TRUE)
## lavaan 0.6-8 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## 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|) Std.lv Std.all
## passion ~
## autonmy (b1) 0.128 0.034 3.787 0.000 0.128 0.200
## pp.tnmy (b2) 0.069 0.022 3.064 0.002 0.069 0.161
## as.5f (c1) 0.224 0.078 2.892 0.004 0.224 0.151
## aps.stn (c2) 0.183 0.046 3.963 0.000 0.183 0.225
## aps.dsc (c3) -0.130 0.029 -4.470 0.000 -0.130 -0.229
## pp.autonomy ~
## as.5f (a1) 0.358 0.197 1.820 0.069 0.358 0.103
## aps.stn (aa1) 0.704 0.110 6.400 0.000 0.704 0.368
## aps.dsc (aaa1) -0.042 0.072 -0.584 0.559 -0.042 -0.031
## autonomy ~
## pp.tnmy (d21) 0.067 0.038 1.777 0.076 0.067 0.101
## aps.stn (aa2) 0.240 0.077 3.112 0.002 0.240 0.189
## aps.dsc (aaa2) -0.254 0.047 -5.386 0.000 -0.254 -0.287
## as.5f (a2) 0.390 0.130 2.991 0.003 0.390 0.168
## sample 0.051 0.148 0.346 0.729 0.051 0.019
## pp.autonomy ~
## sample -0.221 0.225 -0.981 0.327 -0.221 -0.053
## passion ~
## sample 0.035 0.087 0.401 0.689 0.035 0.020
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## as.5f ~~
## aps.stand 0.190 0.033 5.766 0.000 0.190 0.352
## sample 0.034 0.015 2.320 0.020 0.034 0.135
## aps.discrep 0.060 0.045 1.350 0.177 0.060 0.078
## aps.stand ~~
## aps.discrep 0.212 0.082 2.575 0.010 0.212 0.150
## sample 0.098 0.027 3.643 0.000 0.098 0.214
## aps.discrep ~~
## sample 0.101 0.038 2.659 0.008 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .passion 0.452 0.037 12.288 0.000 0.452 0.694
## .pp.autonomy 3.024 0.246 12.288 0.000 3.024 0.837
## .autonomy 1.310 0.107 12.288 0.000 1.310 0.823
## as.5f 0.297 0.024 12.288 0.000 0.297 1.000
## aps.stand 0.986 0.080 12.288 0.000 0.986 1.000
## aps.discrep 2.024 0.165 12.288 0.000 2.024 1.000
## sample 0.212 0.017 12.288 0.000 0.212 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## achstrivindrct 0.074 0.031 2.383 0.017 0.074 0.049
## standardsndrct 0.110 0.029 3.850 0.000 0.110 0.110
## discrepindirct -0.023 0.012 -1.983 0.047 -0.023 -0.053
## total1 0.299 0.081 3.689 0.000 0.299 0.201
## total2 0.293 0.049 6.020 0.000 0.293 0.335
## total3 -0.153 0.031 -4.950 0.000 -0.153 -0.282
fit3.p <- sem(
model = mmed3.p,
data = pp,
se = "bootstrap",
bootstrap = 5000)
summary(fit3.p, fit.measures=TRUE, standardize=TRUE, rsquare=TRUE,
estimates = TRUE, ci = TRUE)
## lavaan 0.6-8 ended normally after 49 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Used Total
## Number of observations 302 327
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 290.487
## Degrees of freedom 21
## 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) -2740.561
## Loglikelihood unrestricted model (H1) -2740.561
##
## Akaike (AIC) 5537.123
## Bayesian (BIC) 5641.015
## Sample-size adjusted Bayesian (BIC) 5552.214
##
## 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
## passion ~
## autonmy (b1) 0.128 0.038 3.336 0.001 0.058 0.210
## pp.tnmy (b2) 0.069 0.023 2.963 0.003 0.022 0.115
## as.5f (c1) 0.224 0.093 2.403 0.016 0.033 0.399
## aps.stn (c2) 0.183 0.054 3.354 0.001 0.079 0.293
## aps.dsc (c3) -0.130 0.031 -4.183 0.000 -0.192 -0.069
## pp.autonomy ~
## as.5f (a1) 0.358 0.206 1.739 0.082 -0.082 0.728
## aps.stn (aa1) 0.704 0.135 5.206 0.000 0.454 0.986
## aps.dsc (aaa1) -0.042 0.070 -0.598 0.550 -0.186 0.089
## autonomy ~
## pp.tnmy (d21) 0.067 0.042 1.588 0.112 -0.015 0.151
## aps.stn (aa2) 0.240 0.109 2.200 0.028 0.028 0.450
## aps.dsc (aaa2) -0.254 0.052 -4.885 0.000 -0.364 -0.155
## as.5f (a2) 0.390 0.165 2.362 0.018 0.049 0.688
## sample 0.051 0.165 0.311 0.756 -0.264 0.380
## pp.autonomy ~
## sample -0.221 0.222 -0.992 0.321 -0.660 0.210
## passion ~
## sample 0.035 0.088 0.395 0.693 -0.142 0.205
## Std.lv Std.all
##
## 0.128 0.200
## 0.069 0.161
## 0.224 0.151
## 0.183 0.225
## -0.130 -0.229
##
## 0.358 0.103
## 0.704 0.368
## -0.042 -0.031
##
## 0.067 0.101
## 0.240 0.189
## -0.254 -0.287
## 0.390 0.168
## 0.051 0.019
##
## -0.221 -0.053
##
## 0.035 0.020
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## as.5f ~~
## aps.stand 0.190 0.067 2.829 0.005 0.079 0.340
## sample 0.034 0.017 2.039 0.041 0.002 0.067
## aps.discrep 0.060 0.059 1.017 0.309 -0.047 0.186
## aps.stand ~~
## aps.discrep 0.212 0.096 2.213 0.027 0.028 0.407
## sample 0.098 0.029 3.382 0.001 0.041 0.155
## aps.discrep ~~
## sample 0.101 0.038 2.635 0.008 0.027 0.177
## Std.lv Std.all
##
## 0.190 0.352
## 0.034 0.135
## 0.060 0.078
##
## 0.212 0.150
## 0.098 0.214
##
## 0.101 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .passion 0.452 0.044 10.236 0.000 0.356 0.531
## .pp.autonomy 3.024 0.344 8.780 0.000 2.332 3.684
## .autonomy 1.310 0.142 9.241 0.000 1.004 1.563
## as.5f 0.297 0.053 5.626 0.000 0.211 0.415
## aps.stand 0.986 0.117 8.421 0.000 0.774 1.228
## aps.discrep 2.024 0.132 15.379 0.000 1.764 2.277
## sample 0.212 0.010 20.434 0.000 0.190 0.230
## Std.lv Std.all
## 0.452 0.694
## 3.024 0.837
## 1.310 0.823
## 0.297 1.000
## 0.986 1.000
## 2.024 1.000
## 0.212 1.000
##
## R-Square:
## Estimate
## passion 0.306
## pp.autonomy 0.163
## autonomy 0.177
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## achstrivindrct 0.074 0.036 2.071 0.038 0.005 0.145
## standardsndrct 0.110 0.035 3.166 0.002 0.054 0.189
## discrepindirct -0.023 0.012 -1.878 0.060 -0.049 -0.000
## total1 0.299 0.100 2.995 0.003 0.086 0.478
## total2 0.293 0.063 4.611 0.000 0.183 0.430
## total3 -0.153 0.033 -4.691 0.000 -0.216 -0.090
## Std.lv Std.all
## 0.074 0.049
## 0.110 0.110
## -0.023 -0.053
## 0.299 0.201
## 0.293 0.335
## -0.153 -0.282
standardizedSolution(fit3.p, type = "std.all", se = TRUE, pvalue = TRUE, ci = TRUE)
## lhs op rhs est.std se z
## 1 passion ~ autonomy 0.200 0.056 3.579
## 2 passion ~ pp.autonomy 0.161 0.054 2.996
## 3 passion ~ as.5f 0.151 0.065 2.336
## 4 passion ~ aps.stand 0.225 0.064 3.526
## 5 passion ~ aps.discrep -0.229 0.052 -4.368
## 6 pp.autonomy ~ as.5f 0.103 0.061 1.680
## 7 pp.autonomy ~ aps.stand 0.368 0.061 6.078
## 8 pp.autonomy ~ aps.discrep -0.031 0.052 -0.598
## 9 autonomy ~ pp.autonomy 0.101 0.064 1.572
## 10 autonomy ~ aps.stand 0.189 0.088 2.155
## 11 autonomy ~ aps.discrep -0.287 0.059 -4.874
## 12 autonomy ~ as.5f 0.168 0.077 2.177
## 13 as.5f ~~ aps.stand 0.352 0.089 3.971
## 14 as.5f ~~ sample 0.135 0.062 2.181
## 15 as.5f ~~ aps.discrep 0.078 0.072 1.087
## 16 aps.stand ~~ aps.discrep 0.150 0.062 2.406
## 17 aps.stand ~~ sample 0.214 0.059 3.624
## 18 aps.discrep ~~ sample 0.155 0.057 2.695
## 19 autonomy ~ sample 0.019 0.060 0.311
## 20 pp.autonomy ~ sample -0.053 0.054 -0.998
## 21 passion ~ sample 0.020 0.050 0.394
## 22 passion ~~ passion 0.694 0.053 13.137
## 23 pp.autonomy ~~ pp.autonomy 0.837 0.052 16.061
## 24 autonomy ~~ autonomy 0.823 0.047 17.344
## 25 as.5f ~~ as.5f 1.000 0.000 NA
## 26 aps.stand ~~ aps.stand 1.000 0.000 NA
## 27 aps.discrep ~~ aps.discrep 1.000 0.000 NA
## 28 sample ~~ sample 1.000 0.000 NA
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 0.049 0.023 2.162
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 0.110 0.029 3.817
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 -0.053 0.021 -2.473
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) 0.201 0.070 2.851
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) 0.335 0.061 5.479
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) -0.282 0.055 -5.125
## pvalue ci.lower ci.upper
## 1 0.000 0.091 0.310
## 2 0.003 0.056 0.267
## 3 0.019 0.024 0.278
## 4 0.000 0.100 0.349
## 5 0.000 -0.332 -0.126
## 6 0.093 -0.017 0.222
## 7 0.000 0.249 0.487
## 8 0.550 -0.134 0.071
## 9 0.116 -0.025 0.228
## 10 0.031 0.017 0.361
## 11 0.000 -0.402 -0.172
## 12 0.029 0.017 0.319
## 13 0.000 0.178 0.525
## 14 0.029 0.014 0.256
## 15 0.277 -0.063 0.218
## 16 0.016 0.028 0.272
## 17 0.000 0.098 0.330
## 18 0.007 0.042 0.267
## 19 0.755 -0.099 0.137
## 20 0.318 -0.158 0.052
## 21 0.694 -0.079 0.119
## 22 0.000 0.590 0.797
## 23 0.000 0.735 0.939
## 24 0.000 0.730 0.916
## 25 NA 1.000 1.000
## 26 NA 1.000 1.000
## 27 NA 1.000 1.000
## 28 NA 1.000 1.000
## 29 0.031 0.005 0.094
## 30 0.000 0.054 0.167
## 31 0.013 -0.095 -0.011
## 32 0.004 0.063 0.339
## 33 0.000 0.215 0.455
## 34 0.000 -0.390 -0.174
parameterEstimates(fit3.p, boot.ci.type="bca.simple")
## lhs op rhs label
## 1 passion ~ autonomy b1
## 2 passion ~ pp.autonomy b2
## 3 passion ~ as.5f c1
## 4 passion ~ aps.stand c2
## 5 passion ~ aps.discrep c3
## 6 pp.autonomy ~ as.5f a1
## 7 pp.autonomy ~ aps.stand aa1
## 8 pp.autonomy ~ aps.discrep aaa1
## 9 autonomy ~ pp.autonomy d21
## 10 autonomy ~ aps.stand aa2
## 11 autonomy ~ aps.discrep aaa2
## 12 autonomy ~ as.5f a2
## 13 as.5f ~~ aps.stand
## 14 as.5f ~~ sample
## 15 as.5f ~~ aps.discrep
## 16 aps.stand ~~ aps.discrep
## 17 aps.stand ~~ sample
## 18 aps.discrep ~~ sample
## 19 autonomy ~ sample
## 20 pp.autonomy ~ sample
## 21 passion ~ sample
## 22 passion ~~ passion
## 23 pp.autonomy ~~ pp.autonomy
## 24 autonomy ~~ autonomy
## 25 as.5f ~~ as.5f
## 26 aps.stand ~~ aps.stand
## 27 aps.discrep ~~ aps.discrep
## 28 sample ~~ sample
## 29 achstrivindirect := a1*b1+a2*b2+a1*d21*b2 achstrivindirect
## 30 standardsindirect := aa1*b1+aa2*b2+aa1*d21*b2 standardsindirect
## 31 discrepindirect := aaa1*b1+aaa2*b2+aaa1*d21*b2 discrepindirect
## 32 total1 := c1+(a1*b1+a2*b2+a1*d21*b2) total1
## 33 total2 := c2+(aa1*b1+aa2*b2+aa1*d21*b2) total2
## 34 total3 := c3+(aaa1*b1+aaa2*b2+aaa1*d21*b2) total3
## est se z pvalue ci.lower ci.upper
## 1 0.128 0.038 3.336 0.001 0.058 0.210
## 2 0.069 0.023 2.963 0.003 0.024 0.116
## 3 0.224 0.093 2.403 0.016 0.051 0.414
## 4 0.183 0.054 3.354 0.001 0.074 0.288
## 5 -0.130 0.031 -4.183 0.000 -0.192 -0.070
## 6 0.358 0.206 1.739 0.082 -0.042 0.764
## 7 0.704 0.135 5.206 0.000 0.454 0.986
## 8 -0.042 0.070 -0.598 0.550 -0.183 0.091
## 9 0.067 0.042 1.588 0.112 -0.016 0.150
## 10 0.240 0.109 2.200 0.028 0.038 0.464
## 11 -0.254 0.052 -4.885 0.000 -0.359 -0.151
## 12 0.390 0.165 2.362 0.018 0.048 0.688
## 13 0.190 0.067 2.829 0.005 0.092 0.369
## 14 0.034 0.017 2.039 0.041 0.003 0.069
## 15 0.060 0.059 1.017 0.309 -0.044 0.192
## 16 0.212 0.096 2.213 0.027 0.034 0.416
## 17 0.098 0.029 3.382 0.001 0.042 0.157
## 18 0.101 0.038 2.635 0.008 0.028 0.179
## 19 0.051 0.165 0.311 0.756 -0.269 0.372
## 20 -0.221 0.222 -0.992 0.321 -0.657 0.213
## 21 0.035 0.088 0.395 0.693 -0.137 0.210
## 22 0.452 0.044 10.236 0.000 0.379 0.553
## 23 3.024 0.344 8.780 0.000 2.434 3.804
## 24 1.310 0.142 9.241 0.000 1.078 1.636
## 25 0.297 0.053 5.626 0.000 0.220 0.435
## 26 0.986 0.117 8.421 0.000 0.791 1.256
## 27 2.024 0.132 15.379 0.000 1.785 2.295
## 28 0.212 0.010 20.434 0.000 0.190 0.231
## 29 0.074 0.036 2.071 0.038 0.016 0.161
## 30 0.110 0.035 3.166 0.002 0.054 0.189
## 31 -0.023 0.012 -1.878 0.060 -0.050 -0.001
## 32 0.299 0.100 2.995 0.003 0.108 0.492
## 33 0.293 0.063 4.611 0.000 0.181 0.425
## 34 -0.153 0.033 -4.691 0.000 -0.214 -0.088