In what follows, please find the results of additional analyses. These include models, results without covariates, results with all participants (hence, including those removed due to speeding).
Load packages.
# define packages
packages <- c("cowplot", "devtools", "faoutlier", "GGally", "kableExtra", "knitr", "lavaan", "magrittr", "MVN", "psych",
"pwr", "quanteda", "semTools", "tidyverse")
# load packages
lapply(packages, library, character.only = TRUE, quietly = TRUE)Load data.
# load workspace
load("data/workspace.rdata")In what follows, you can find estimations of variance inflation factors, which help gauge multicollinearity. Generally, values above 5 or even 10 are considered problematic. However, these are only rules of thumb, and multcollinearity can occour with lower values. Indeed, although the values reported below are not above 5, they are increased, suggesting that multicollinearity might be at play here, which the regular analyses also confirm.
# Self-Efficacy
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ a*SE01_02
SE01_03 ~~ a*SE01_04
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
trust_spec =~ trust_community + trust_provider
self_eff ~ pri_con + grats_gen + pri_delib + trust_spec
# Covariates
GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit <- sem(model, data = d, estimator = "MLR", missing = "ML")
r_self_eff <- inspect(fit, what = "rsquare")["self_eff"] # extract rsquare
vif_self_eff <- 1/(1 - r_self_eff) # compute vif
# Privacy Deliberation
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ a*SE01_02
SE01_03 ~~ a*SE01_04
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
trust_spec =~ trust_community + trust_provider
pri_delib ~ self_eff + pri_con + grats_gen + trust_spec
# Covariates
GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit <- sem(model, data = d, estimator = "MLR", missing = "ML")
r_pri_delib <- inspect(fit, what = "rsquare")["pri_delib"]
vif_pri_delib <- 1/(1 - r_pri_delib)
## Privacy Concerns
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ a*SE01_02
SE01_03 ~~ a*SE01_04
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
trust_spec =~ trust_community + trust_provider
pri_con ~ self_eff + pri_delib + grats_gen + trust_spec
# Covariates
GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit <- sem(model, data = d, estimator = "MLR", missing = "ML")
r_pri_con <- inspect(fit, what = "rsquare")["pri_con"]
vif_pri_con <- 1/(1 - r_pri_con)
# Gratifications
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ a*SE01_02
SE01_03 ~~ a*SE01_04
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
trust_spec =~ trust_community + trust_provider
grats_gen ~ self_eff + pri_con + pri_delib + trust_spec
# Covariates
GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit <- sem(model, data = d, estimator = "MLR", missing = "ML")
r_grats_gen <- inspect(fit, what = "rsquare")["grats_gen"]
vif_grats_gen <- 1/(1 - r_grats_gen)
# Trust
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ a*SE01_02
SE01_03 ~~ a*SE01_04
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
trust_spec =~ trust_community + trust_provider
trust_spec ~ self_eff + pri_con + grats_gen + pri_delib
# Covariates
GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit <- sem(model, data = d, estimator = "MLR", missing = "ML")
r_trust_spec <- inspect(fit, what = "rsquare")["trust_spec"]
vif_trust_spec <- 1/(1 - r_trust_spec)# Table
tibble(Gratifications = vif_grats_gen, `Trust Specific` = vif_trust_spec, `Privacy Concerns` = vif_pri_con, `Privacy Deliberation` = vif_pri_delib,
`Self-Efficacy` = vif_self_eff) %>% kable() %>% kable_styling("striped")| Gratifications | Trust Specific | Privacy Concerns | Privacy Deliberation | Self-Efficacy |
|---|---|---|---|---|
| 2.73 | 3.58 | 1.61 | 1.51 | 1.51 |
In what follows, we report the results for the not-logged, that is the regular measure of communication.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
trust_spec =~ trust_community + trust_provider
COMM ~ a1*pri_con + b1*grats_gen + c1*pri_delib + d1*self_eff + e1*trust_spec
# Covariates
COMM + GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit_prereg <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit_prereg, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 487 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 198
Number of equality constraints 1
Used Total
Number of observations 558 559
Number of missing patterns 3
Model Test User Model:
Standard Robust
Test Statistic 1234.274 876.173
Degrees of freedom 388 388
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.409
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 13267.702 9991.515
Degrees of freedom 525 525
P-value 0.000 0.000
Scaling correction factor 1.328
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.934 0.948
Tucker-Lewis Index (TLI) 0.910 0.930
Robust Comparative Fit Index (CFI) 0.945
Robust Tucker-Lewis Index (TLI) 0.926
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -26494.519 -26494.519
Scaling correction factor 1.260
for the MLR correction
Loglikelihood unrestricted model (H1) -25877.382 -25877.382
Scaling correction factor 1.361
for the MLR correction
Akaike (AIC) 53383.039 53383.039
Bayesian (BIC) 54234.937 54234.937
Sample-size adjusted Bayesian (BIC) 53609.566 53609.566
Root Mean Square Error of Approximation:
RMSEA 0.063 0.047
90 Percent confidence interval - lower 0.059 0.044
90 Percent confidence interval - upper 0.066 0.051
P-value RMSEA <= 0.05 0.000 0.878
Robust RMSEA 0.056
90 Percent confidence interval - lower 0.051
90 Percent confidence interval - upper 0.061
Standardized Root Mean Square Residual:
SRMR 0.048 0.048
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.595 0.926
PC01_02 0.990 0.027 36.230 0.000 1.579 0.894
PC01_04 0.972 0.027 35.705 0.000 1.551 0.884
PC01_05 1.002 0.024 42.447 0.000 1.599 0.907
PC01_06 0.855 0.038 22.675 0.000 1.363 0.795
PC01_07 0.994 0.023 43.788 0.000 1.586 0.920
grats_gen =~
GR02_01 1.000 1.134 0.844
GR02_02 1.118 0.033 33.699 0.000 1.267 0.894
GR02_03 1.019 0.047 21.533 0.000 1.155 0.863
GR02_04 0.983 0.048 20.455 0.000 1.115 0.848
GR02_05 1.071 0.040 27.024 0.000 1.214 0.847
pri_delib =~
PD01_01 1.000 1.478 0.856
PD01_02 0.666 0.048 13.926 0.000 0.984 0.641
PD01_03 0.703 0.055 12.878 0.000 1.039 0.667
PD01_04 0.840 0.047 17.729 0.000 1.242 0.728
PD01_05 0.714 0.050 14.338 0.000 1.055 0.637
self_eff =~
SE01_01 1.000 1.113 0.807
SE01_02 0.811 0.057 14.170 0.000 0.903 0.669
SE01_03 0.934 0.046 20.276 0.000 1.039 0.775
SE01_04 0.959 0.044 21.863 0.000 1.066 0.791
trust_community =~
TR01_02 1.000 1.024 0.807
TR01_03 0.820 0.052 15.873 0.000 0.839 0.764
TR01_04 0.918 0.046 19.760 0.000 0.939 0.814
trust_provider =~
TR01_06 1.000 1.046 0.871
TR01_07 0.855 0.039 21.944 0.000 0.894 0.773
TR01_08 0.834 0.040 21.100 0.000 0.872 0.788
TR01_10 0.788 0.038 20.821 0.000 0.824 0.700
TR01_11 0.821 0.052 15.891 0.000 0.859 0.662
TR01_12 1.098 0.038 28.600 0.000 1.148 0.854
trust_spec =~
trust_communty 1.000 0.877 0.877
trust_provider 1.111 0.077 14.375 0.000 0.953 0.953
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
COMM ~
pri_con (a1) -0.297 7.082 -0.042 0.967 -0.474 -0.002
grats_gen (b1) 24.215 18.848 1.285 0.199 27.451 0.110
pri_delib (c1) -15.936 7.721 -2.064 0.039 -23.555 -0.094
self_eff (d1) 60.877 18.774 3.243 0.001 67.732 0.271
trust_spc (e1) -37.144 25.075 -1.481 0.139 -33.332 -0.133
male -7.984 23.598 -0.338 0.735 -7.984 -0.016
age 0.343 0.551 0.623 0.533 0.343 0.021
edu 12.177 15.167 0.803 0.422 12.177 0.041
GR02_01 ~
male -0.127 0.116 -1.096 0.273 -0.127 -0.047
age 0.000 0.004 0.091 0.927 0.000 0.004
edu 0.005 0.068 0.073 0.942 0.005 0.003
GR02_02 ~
male -0.067 0.120 -0.559 0.576 -0.067 -0.024
age 0.006 0.004 1.542 0.123 0.006 0.068
edu -0.080 0.071 -1.127 0.260 -0.080 -0.047
GR02_03 ~
male -0.025 0.116 -0.220 0.826 -0.025 -0.009
age 0.001 0.004 0.310 0.756 0.001 0.014
edu -0.083 0.067 -1.237 0.216 -0.083 -0.052
GR02_04 ~
male 0.028 0.113 0.250 0.802 0.028 0.011
age 0.005 0.004 1.304 0.192 0.005 0.057
edu -0.072 0.067 -1.072 0.284 -0.072 -0.046
GR02_05 ~
male -0.140 0.124 -1.136 0.256 -0.140 -0.049
age -0.004 0.004 -0.874 0.382 -0.004 -0.039
edu 0.013 0.073 0.173 0.862 0.013 0.007
PC01_01 ~
male -0.182 0.151 -1.206 0.228 -0.182 -0.053
age -0.004 0.005 -0.820 0.412 -0.004 -0.036
edu 0.110 0.087 1.255 0.210 0.110 0.054
PC01_02 ~
male -0.302 0.154 -1.966 0.049 -0.302 -0.085
age -0.008 0.005 -1.663 0.096 -0.008 -0.072
edu 0.047 0.089 0.522 0.601 0.047 0.022
PC01_04 ~
male -0.225 0.152 -1.475 0.140 -0.225 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.269 0.204 0.113 0.054
PC01_05 ~
male -0.098 0.154 -0.636 0.525 -0.098 -0.028
age -0.006 0.005 -1.164 0.244 -0.006 -0.051
edu 0.090 0.090 0.996 0.319 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.722 0.470 -0.108 -0.032
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.491 0.623 0.043 0.021
PC01_07 ~
male -0.174 0.150 -1.160 0.246 -0.174 -0.050
age -0.006 0.005 -1.337 0.181 -0.006 -0.058
edu 0.081 0.087 0.934 0.351 0.081 0.040
TR01_02 ~
male -0.297 0.108 -2.744 0.006 -0.297 -0.117
age -0.004 0.004 -1.103 0.270 -0.004 -0.049
edu 0.005 0.062 0.086 0.931 0.005 0.004
TR01_03 ~
male -0.140 0.095 -1.480 0.139 -0.140 -0.064
age -0.002 0.003 -0.566 0.571 -0.002 -0.025
edu 0.023 0.053 0.434 0.664 0.023 0.018
TR01_04 ~
male -0.134 0.099 -1.361 0.173 -0.134 -0.058
age -0.004 0.003 -1.211 0.226 -0.004 -0.055
edu -0.003 0.060 -0.046 0.964 -0.003 -0.002
TR01_06 ~
male -0.086 0.104 -0.831 0.406 -0.086 -0.036
age 0.000 0.003 0.110 0.912 0.000 0.005
edu -0.051 0.058 -0.881 0.379 -0.051 -0.036
TR01_07 ~
male -0.045 0.099 -0.450 0.653 -0.045 -0.019
age 0.001 0.003 0.344 0.731 0.001 0.015
edu 0.018 0.058 0.309 0.757 0.018 0.013
TR01_08 ~
male 0.046 0.095 0.480 0.631 0.046 0.021
age -0.004 0.003 -1.250 0.211 -0.004 -0.053
edu 0.025 0.056 0.445 0.656 0.025 0.019
TR01_10 ~
male 0.091 0.100 0.914 0.361 0.091 0.039
age -0.004 0.003 -1.176 0.239 -0.004 -0.050
edu -0.055 0.058 -0.946 0.344 -0.055 -0.039
TR01_11 ~
male 0.027 0.112 0.245 0.806 0.027 0.011
age 0.003 0.004 0.824 0.410 0.003 0.035
edu -0.093 0.065 -1.435 0.151 -0.093 -0.061
TR01_12 ~
male -0.121 0.115 -1.044 0.296 -0.121 -0.045
age -0.002 0.004 -0.406 0.685 -0.002 -0.018
edu -0.146 0.068 -2.156 0.031 -0.146 -0.091
PD01_01 ~
male -0.177 0.148 -1.197 0.231 -0.177 -0.051
age -0.015 0.005 -3.275 0.001 -0.015 -0.137
edu -0.026 0.085 -0.310 0.756 -0.026 -0.013
PD01_02 ~
male -0.119 0.131 -0.906 0.365 -0.119 -0.039
age -0.014 0.004 -3.443 0.001 -0.014 -0.142
edu 0.031 0.077 0.405 0.686 0.031 0.017
PD01_03 ~
male -0.321 0.132 -2.425 0.015 -0.321 -0.103
age -0.004 0.004 -1.024 0.306 -0.004 -0.044
edu 0.065 0.080 0.807 0.420 0.065 0.035
PD01_04 ~
male -0.412 0.145 -2.847 0.004 -0.412 -0.121
age -0.009 0.005 -1.904 0.057 -0.009 -0.082
edu 0.103 0.085 1.207 0.227 0.103 0.051
PD01_05 ~
male -0.205 0.142 -1.439 0.150 -0.205 -0.062
age -0.012 0.004 -2.696 0.007 -0.012 -0.111
edu -0.002 0.084 -0.025 0.980 -0.002 -0.001
SE01_01 ~
male 0.120 0.118 1.016 0.310 0.120 0.043
age 0.000 0.004 0.011 0.991 0.000 0.000
edu 0.212 0.068 3.122 0.002 0.212 0.129
SE01_02 ~
male 0.059 0.112 0.531 0.595 0.059 0.022
age -0.013 0.004 -3.595 0.000 -0.013 -0.151
edu 0.198 0.066 3.006 0.003 0.198 0.124
SE01_03 ~
male 0.195 0.114 1.702 0.089 0.195 0.073
age 0.001 0.004 0.249 0.803 0.001 0.011
edu 0.143 0.067 2.141 0.032 0.143 0.090
SE01_04 ~
male 0.053 0.115 0.466 0.641 0.053 0.020
age 0.007 0.004 2.051 0.040 0.007 0.086
edu 0.127 0.066 1.915 0.055 0.127 0.079
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.106 0.044 2.439 0.015 0.106 0.140
.SE01_03 ~~
.SE01_04 (x) 0.106 0.044 2.439 0.015 0.106 0.158
pri_con ~~
grats_gen -0.283 0.096 -2.949 0.003 -0.156 -0.156
pri_delib 1.328 0.131 10.153 0.000 0.563 0.563
self_eff -0.375 0.091 -4.124 0.000 -0.211 -0.211
trust_spec -0.416 0.074 -5.587 0.000 -0.290 -0.290
grats_gen ~~
pri_delib -0.069 0.103 -0.673 0.501 -0.041 -0.041
self_eff 0.469 0.067 7.036 0.000 0.372 0.372
trust_spec 0.803 0.085 9.411 0.000 0.790 0.790
pri_delib ~~
self_eff -0.326 0.094 -3.454 0.001 -0.198 -0.198
trust_spec -0.142 0.086 -1.650 0.099 -0.107 -0.107
self_eff ~~
trust_spec 0.553 0.060 9.228 0.000 0.554 0.554
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.557 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.398 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.035
.PC01_05 3.414 0.304 11.229 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.155 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.780 0.000 3.461 2.008
.GR02_01 4.319 0.224 19.252 0.000 4.319 3.215
.GR02_02 4.492 0.244 18.372 0.000 4.492 3.170
.GR02_03 5.244 0.222 23.667 0.000 5.244 3.917
.GR02_04 4.988 0.221 22.522 0.000 4.988 3.795
.GR02_05 4.905 0.254 19.323 0.000 4.905 3.422
.PD01_01 4.493 0.290 15.507 0.000 4.493 2.602
.PD01_02 3.997 0.248 16.102 0.000 3.997 2.601
.PD01_03 4.432 0.270 16.438 0.000 4.432 2.845
.PD01_04 4.506 0.295 15.283 0.000 4.506 2.640
.PD01_05 5.000 0.276 18.089 0.000 5.000 3.018
.SE01_01 4.822 0.249 19.353 0.000 4.822 3.496
.SE01_02 5.729 0.234 24.455 0.000 5.729 4.249
.SE01_03 4.817 0.226 21.322 0.000 4.817 3.595
.SE01_04 4.531 0.234 19.373 0.000 4.531 3.359
.TR01_02 5.083 0.219 23.224 0.000 5.083 4.010
.TR01_03 4.951 0.189 26.178 0.000 4.951 4.505
.TR01_04 4.871 0.200 24.361 0.000 4.871 4.223
.TR01_06 5.521 0.205 26.991 0.000 5.521 4.600
.TR01_07 5.135 0.197 26.024 0.000 5.135 4.440
.TR01_08 5.232 0.188 27.764 0.000 5.232 4.728
.TR01_10 5.955 0.193 30.911 0.000 5.955 5.060
.TR01_11 4.855 0.218 22.270 0.000 4.855 3.745
.TR01_12 5.581 0.231 24.175 0.000 5.581 4.149
.COMM 42.157 29.818 1.414 0.157 42.157 0.168
pri_con 0.000 0.000 0.000
grats_gen 0.000 0.000 0.000
pri_delib 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
.trust_communty 0.000 0.000 0.000
.trust_provider 0.000 0.000 0.000
trust_spec 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.403 0.050 8.081 0.000 0.403 0.136
.PC01_02 0.580 0.102 5.662 0.000 0.580 0.186
.PC01_04 0.627 0.077 8.140 0.000 0.627 0.204
.PC01_05 0.533 0.064 8.359 0.000 0.533 0.172
.PC01_06 1.069 0.116 9.252 0.000 1.069 0.364
.PC01_07 0.431 0.065 6.592 0.000 0.431 0.145
.GR02_01 0.515 0.053 9.675 0.000 0.515 0.286
.GR02_02 0.387 0.039 9.902 0.000 0.387 0.193
.GR02_03 0.453 0.073 6.190 0.000 0.453 0.253
.GR02_04 0.475 0.048 9.908 0.000 0.475 0.275
.GR02_05 0.571 0.062 9.199 0.000 0.571 0.278
.PD01_01 0.729 0.111 6.593 0.000 0.729 0.244
.PD01_02 1.337 0.127 10.515 0.000 1.337 0.566
.PD01_03 1.313 0.129 10.211 0.000 1.313 0.541
.PD01_04 1.298 0.147 8.847 0.000 1.298 0.446
.PD01_05 1.584 0.128 12.396 0.000 1.584 0.577
.SE01_01 0.626 0.089 7.071 0.000 0.626 0.329
.SE01_02 0.928 0.120 7.725 0.000 0.928 0.510
.SE01_03 0.689 0.097 7.112 0.000 0.689 0.384
.SE01_04 0.657 0.077 8.536 0.000 0.657 0.361
.TR01_02 0.532 0.067 7.884 0.000 0.532 0.331
.TR01_03 0.498 0.055 9.056 0.000 0.498 0.412
.TR01_04 0.439 0.045 9.669 0.000 0.439 0.330
.TR01_06 0.343 0.035 9.884 0.000 0.343 0.238
.TR01_07 0.537 0.052 10.304 0.000 0.537 0.402
.TR01_08 0.459 0.041 11.146 0.000 0.459 0.375
.TR01_10 0.699 0.056 12.549 0.000 0.699 0.505
.TR01_11 0.935 0.079 11.776 0.000 0.935 0.556
.TR01_12 0.469 0.052 8.955 0.000 0.469 0.259
.COMM 57563.169 0.027 2117441.807 0.000 57563.169 0.918
pri_con 2.545 0.144 17.627 0.000 1.000 1.000
grats_gen 1.285 0.114 11.236 0.000 1.000 1.000
pri_delib 2.185 0.157 13.927 0.000 1.000 1.000
self_eff 1.238 0.114 10.879 0.000 1.000 1.000
.trust_communty 0.242 0.044 5.474 0.000 0.231 0.231
.trust_provider 0.101 0.043 2.363 0.018 0.092 0.092
trust_spec 0.805 0.099 8.148 0.000 1.000 1.000rsquare_fit_prereg <- inspect(fit_prereg, what = "rsquare")["comm"]Building on the preregistered model, instead of general gratifications and specific trust, we now use specific gratifications and general trust.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_gen =~ TR01_01 + TR01_05 + TR01_09
COMM ~ a1*pri_con + b1*grats_spec + c1*pri_delib + d1*self_eff + e1*trust_gen
# Covariates
COMM + GR01_01 + GR01_02 + GR01_03 + GR01_04 + GR01_05 + GR01_06 + GR01_07 + GR01_08 + GR01_09 + GR01_10 + GR01_11 + GR01_12 + GR01_13 + GR01_14 + GR01_15 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_01 + TR01_05 + TR01_09 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit_adapted <- sem(model, data = d, estimator = "MLR", missing = "ML", missing = "ML")
summary(fit_adapted, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 530 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 225
Number of equality constraints 1
Used Total
Number of observations 558 559
Number of missing patterns 2
Model Test User Model:
Standard Robust
Test Statistic 1503.066 1076.148
Degrees of freedom 507 507
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.397
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 14290.539 10703.369
Degrees of freedom 663 663
P-value 0.000 0.000
Scaling correction factor 1.335
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.927 0.943
Tucker-Lewis Index (TLI) 0.904 0.926
Robust Comparative Fit Index (CFI) 0.941
Robust Tucker-Lewis Index (TLI) 0.922
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -30640.163 -30640.163
Scaling correction factor 1.267
for the MLR correction
Loglikelihood unrestricted model (H1) -29888.630 -29888.630
Scaling correction factor 1.359
for the MLR correction
Akaike (AIC) 61728.326 61728.326
Bayesian (BIC) 62696.983 62696.983
Sample-size adjusted Bayesian (BIC) 61985.900 61985.900
Root Mean Square Error of Approximation:
RMSEA 0.059 0.045
90 Percent confidence interval - lower 0.056 0.042
90 Percent confidence interval - upper 0.063 0.048
P-value RMSEA <= 0.05 0.000 0.997
Robust RMSEA 0.053
90 Percent confidence interval - lower 0.049
90 Percent confidence interval - upper 0.057
Standardized Root Mean Square Residual:
SRMR 0.058 0.058
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.595 0.926
PC01_02 0.990 0.027 36.429 0.000 1.579 0.894
PC01_04 0.972 0.027 35.955 0.000 1.550 0.884
PC01_05 1.003 0.024 42.521 0.000 1.599 0.907
PC01_06 0.855 0.038 22.771 0.000 1.364 0.796
PC01_07 0.995 0.023 43.981 0.000 1.587 0.920
grats_inf =~
GR01_01 1.000 0.953 0.677
GR01_02 1.041 0.076 13.678 0.000 0.992 0.813
GR01_03 1.142 0.078 14.576 0.000 1.088 0.846
grats_rel =~
GR01_04 1.000 1.178 0.892
GR01_05 0.939 0.038 24.895 0.000 1.106 0.855
GR01_06 0.876 0.046 18.886 0.000 1.032 0.698
grats_par =~
GR01_07 1.000 1.187 0.815
GR01_08 0.940 0.039 23.868 0.000 1.116 0.814
GR01_09 0.963 0.038 25.225 0.000 1.142 0.816
grats_ide =~
GR01_10 1.000 1.142 0.786
GR01_11 1.008 0.042 24.020 0.000 1.151 0.887
GR01_12 0.928 0.041 22.520 0.000 1.059 0.759
grats_ext =~
GR01_13 1.000 0.832 0.507
GR01_14 1.019 0.103 9.916 0.000 0.848 0.506
GR01_15 1.557 0.186 8.363 0.000 1.296 0.848
grats_spec =~
grats_inf 1.000 0.844 0.844
grats_rel 1.352 0.108 12.519 0.000 0.923 0.923
grats_par 1.428 0.119 11.964 0.000 0.968 0.968
grats_ide 1.316 0.110 11.995 0.000 0.927 0.927
grats_ext 0.801 0.107 7.522 0.000 0.775 0.775
pri_delib =~
PD01_01 1.000 1.488 0.862
PD01_02 0.663 0.048 13.905 0.000 0.986 0.642
PD01_03 0.692 0.054 12.765 0.000 1.030 0.661
PD01_04 0.835 0.047 17.695 0.000 1.242 0.728
PD01_05 0.703 0.050 14.167 0.000 1.045 0.631
self_eff =~
SE01_01 1.000 1.116 0.808
SE01_02 0.808 0.058 14.005 0.000 0.902 0.669
SE01_03 0.928 0.045 20.498 0.000 1.035 0.772
SE01_04 0.959 0.044 21.832 0.000 1.070 0.793
trust_gen =~
TR01_01 1.000 0.769 0.667
TR01_05 1.326 0.070 18.940 0.000 1.019 0.887
TR01_09 1.453 0.081 18.040 0.000 1.117 0.926
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
COMM ~
pri_con (a1) -5.841 6.084 -0.960 0.337 -9.315 -0.037
grats_spc (b1) 51.215 22.046 2.323 0.020 41.194 0.165
pri_delib (c1) -20.690 8.949 -2.312 0.021 -30.776 -0.123
self_eff (d1) 52.391 15.990 3.276 0.001 58.450 0.233
trust_gen (e1) -61.623 27.583 -2.234 0.025 -47.379 -0.189
male -8.869 23.596 -0.376 0.707 -8.869 -0.018
age 0.347 0.551 0.629 0.529 0.347 0.022
edu 12.318 15.167 0.812 0.417 12.318 0.041
GR01_01 ~
male -0.343 0.121 -2.837 0.005 -0.343 -0.122
age -0.005 0.004 -1.333 0.183 -0.005 -0.059
edu 0.001 0.072 0.007 0.994 0.001 0.000
GR01_02 ~
male -0.142 0.103 -1.383 0.167 -0.142 -0.058
age -0.007 0.003 -2.106 0.035 -0.007 -0.088
edu -0.037 0.060 -0.616 0.538 -0.037 -0.026
GR01_03 ~
male -0.187 0.109 -1.718 0.086 -0.187 -0.073
age -0.006 0.004 -1.703 0.089 -0.006 -0.075
edu -0.076 0.063 -1.203 0.229 -0.076 -0.050
GR01_04 ~
male -0.005 0.113 -0.043 0.966 -0.005 -0.002
age 0.001 0.004 0.263 0.793 0.001 0.011
edu -0.021 0.066 -0.318 0.751 -0.021 -0.013
GR01_05 ~
male -0.069 0.112 -0.619 0.536 -0.069 -0.027
age -0.001 0.004 -0.197 0.843 -0.001 -0.009
edu 0.025 0.064 0.389 0.697 0.025 0.016
GR01_06 ~
male 0.029 0.125 0.237 0.813 0.029 0.010
age -0.011 0.004 -2.567 0.010 -0.011 -0.111
edu -0.087 0.074 -1.175 0.240 -0.087 -0.050
GR01_07 ~
male 0.074 0.125 0.597 0.551 0.074 0.026
age -0.006 0.004 -1.391 0.164 -0.006 -0.060
edu 0.004 0.072 0.058 0.954 0.004 0.002
GR01_08 ~
male 0.005 0.116 0.046 0.963 0.005 0.002
age -0.004 0.004 -1.121 0.262 -0.004 -0.051
edu 0.113 0.068 1.676 0.094 0.113 0.070
GR01_09 ~
male 0.089 0.119 0.752 0.452 0.089 0.032
age -0.004 0.004 -0.951 0.342 -0.004 -0.041
edu 0.115 0.069 1.668 0.095 0.115 0.069
GR01_10 ~
male -0.020 0.125 -0.161 0.872 -0.020 -0.007
age -0.008 0.004 -1.992 0.046 -0.008 -0.089
edu -0.020 0.074 -0.276 0.783 -0.020 -0.012
GR01_11 ~
male -0.084 0.111 -0.754 0.451 -0.084 -0.032
age -0.001 0.004 -0.312 0.755 -0.001 -0.014
edu -0.053 0.065 -0.820 0.412 -0.053 -0.034
GR01_12 ~
male -0.219 0.120 -1.825 0.068 -0.219 -0.078
age -0.004 0.004 -1.009 0.313 -0.004 -0.043
edu -0.049 0.071 -0.684 0.494 -0.049 -0.029
GR01_13 ~
male -0.181 0.138 -1.313 0.189 -0.181 -0.055
age -0.023 0.004 -5.515 0.000 -0.023 -0.219
edu 0.078 0.081 0.960 0.337 0.078 0.040
GR01_14 ~
male -0.304 0.145 -2.094 0.036 -0.304 -0.091
age -0.007 0.005 -1.540 0.124 -0.007 -0.065
edu 0.030 0.084 0.362 0.718 0.030 0.015
GR01_15 ~
male 0.047 0.132 0.355 0.722 0.047 0.015
age -0.005 0.004 -1.213 0.225 -0.005 -0.053
edu 0.024 0.078 0.302 0.763 0.024 0.013
PC01_01 ~
male -0.181 0.151 -1.202 0.229 -0.181 -0.053
age -0.004 0.005 -0.820 0.412 -0.004 -0.036
edu 0.110 0.087 1.254 0.210 0.110 0.054
PC01_02 ~
male -0.301 0.154 -1.963 0.050 -0.301 -0.085
age -0.008 0.005 -1.664 0.096 -0.008 -0.072
edu 0.047 0.089 0.521 0.602 0.047 0.022
PC01_04 ~
male -0.224 0.152 -1.472 0.141 -0.224 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.268 0.205 0.113 0.054
PC01_05 ~
male -0.097 0.154 -0.633 0.527 -0.097 -0.028
age -0.006 0.005 -1.165 0.244 -0.006 -0.051
edu 0.090 0.090 0.995 0.320 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.719 0.472 -0.108 -0.031
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.490 0.624 0.043 0.021
PC01_07 ~
male -0.173 0.150 -1.157 0.247 -0.173 -0.050
age -0.006 0.005 -1.338 0.181 -0.006 -0.058
edu 0.081 0.087 0.933 0.351 0.081 0.040
TR01_01 ~
male -0.156 0.099 -1.573 0.116 -0.156 -0.068
age -0.003 0.003 -0.812 0.417 -0.003 -0.038
edu 0.026 0.060 0.434 0.664 0.026 0.019
TR01_05 ~
male 0.076 0.100 0.758 0.448 0.076 0.033
age -0.004 0.003 -1.215 0.224 -0.004 -0.053
edu 0.088 0.059 1.491 0.136 0.088 0.064
TR01_09 ~
male 0.064 0.104 0.615 0.538 0.064 0.026
age -0.007 0.004 -1.938 0.053 -0.007 -0.088
edu -0.018 0.060 -0.294 0.769 -0.018 -0.012
PD01_01 ~
male -0.176 0.148 -1.192 0.233 -0.176 -0.051
age -0.015 0.005 -3.276 0.001 -0.015 -0.137
edu -0.026 0.085 -0.312 0.755 -0.026 -0.013
PD01_02 ~
male -0.119 0.131 -0.902 0.367 -0.119 -0.039
age -0.014 0.004 -3.443 0.001 -0.014 -0.142
edu 0.031 0.077 0.404 0.686 0.031 0.017
PD01_03 ~
male -0.321 0.132 -2.421 0.015 -0.321 -0.103
age -0.004 0.004 -1.024 0.306 -0.004 -0.044
edu 0.065 0.080 0.806 0.420 0.065 0.035
PD01_04 ~
male -0.411 0.145 -2.842 0.004 -0.411 -0.120
age -0.009 0.005 -1.905 0.057 -0.009 -0.082
edu 0.103 0.085 1.206 0.228 0.103 0.051
PD01_05 ~
male -0.204 0.142 -1.435 0.151 -0.204 -0.062
age -0.012 0.004 -2.697 0.007 -0.012 -0.111
edu -0.002 0.084 -0.026 0.979 -0.002 -0.001
SE01_01 ~
male 0.117 0.118 0.994 0.320 0.117 0.043
age 0.000 0.004 0.000 1.000 0.000 0.000
edu 0.210 0.068 3.099 0.002 0.210 0.128
SE01_02 ~
male 0.057 0.112 0.513 0.608 0.057 0.021
age -0.013 0.004 -3.604 0.000 -0.013 -0.152
edu 0.197 0.066 2.989 0.003 0.197 0.123
SE01_03 ~
male 0.192 0.114 1.681 0.093 0.192 0.072
age 0.001 0.004 0.239 0.811 0.001 0.010
edu 0.142 0.067 2.121 0.034 0.142 0.089
SE01_04 ~
male 0.051 0.115 0.445 0.656 0.051 0.019
age 0.007 0.004 2.039 0.041 0.007 0.085
edu 0.126 0.066 1.894 0.058 0.126 0.079
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.108 0.044 2.463 0.014 0.108 0.142
.SE01_03 ~~
.SE01_04 (x) 0.108 0.044 2.463 0.014 0.108 0.160
pri_con ~~
grats_spec -0.118 0.068 -1.721 0.085 -0.092 -0.092
pri_delib 1.339 0.130 10.283 0.000 0.564 0.564
self_eff -0.379 0.091 -4.161 0.000 -0.213 -0.213
trust_gen -0.524 0.066 -7.956 0.000 -0.427 -0.427
grats_spec ~~
pri_delib -0.007 0.074 -0.089 0.929 -0.005 -0.005
self_eff 0.489 0.060 8.116 0.000 0.545 0.545
trust_gen 0.403 0.058 6.889 0.000 0.652 0.652
pri_delib ~~
self_eff -0.330 0.095 -3.472 0.001 -0.199 -0.199
trust_gen -0.317 0.073 -4.362 0.000 -0.278 -0.278
self_eff ~~
trust_gen 0.449 0.056 8.051 0.000 0.523 0.523
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.557 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.398 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.035
.PC01_05 3.414 0.304 11.229 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.155 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.780 0.000 3.461 2.008
.GR01_01 5.296 0.247 21.424 0.000 5.296 3.763
.GR01_02 5.895 0.205 28.693 0.000 5.895 4.835
.GR01_03 5.800 0.221 26.271 0.000 5.800 4.511
.GR01_04 4.923 0.211 23.377 0.000 4.923 3.730
.GR01_05 5.109 0.217 23.539 0.000 5.109 3.952
.GR01_06 5.297 0.252 21.013 0.000 5.297 3.581
.GR01_07 4.896 0.236 20.768 0.000 4.896 3.363
.GR01_08 5.062 0.238 21.232 0.000 5.062 3.691
.GR01_09 4.755 0.237 20.070 0.000 4.755 3.395
.GR01_10 4.977 0.255 19.492 0.000 4.977 3.425
.GR01_11 5.158 0.227 22.705 0.000 5.158 3.973
.GR01_12 5.136 0.233 22.001 0.000 5.136 3.679
.GR01_13 5.091 0.259 19.644 0.000 5.091 3.101
.GR01_14 3.454 0.281 12.310 0.000 3.454 2.061
.GR01_15 4.582 0.266 17.224 0.000 4.582 2.998
.PD01_01 4.493 0.290 15.507 0.000 4.493 2.602
.PD01_02 3.997 0.248 16.102 0.000 3.997 2.601
.PD01_03 4.432 0.270 16.437 0.000 4.432 2.845
.PD01_04 4.506 0.295 15.283 0.000 4.506 2.640
.PD01_05 5.000 0.276 18.089 0.000 5.000 3.019
.SE01_01 4.827 0.249 19.358 0.000 4.827 3.498
.SE01_02 5.733 0.234 24.465 0.000 5.733 4.250
.SE01_03 4.822 0.226 21.324 0.000 4.822 3.597
.SE01_04 4.536 0.234 19.379 0.000 4.536 3.362
.TR01_01 5.001 0.210 23.817 0.000 5.001 4.339
.TR01_05 5.361 0.200 26.772 0.000 5.361 4.664
.TR01_09 5.704 0.213 26.794 0.000 5.704 4.727
.COMM 42.176 29.819 1.414 0.157 42.176 0.168
pri_con 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
grats_spec 0.000 0.000 0.000
pri_delib 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
trust_gen 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.405 0.050 8.128 0.000 0.405 0.136
.PC01_02 0.582 0.102 5.692 0.000 0.582 0.187
.PC01_04 0.628 0.077 8.151 0.000 0.628 0.204
.PC01_05 0.531 0.064 8.332 0.000 0.531 0.171
.PC01_06 1.067 0.115 9.254 0.000 1.067 0.363
.PC01_07 0.431 0.066 6.560 0.000 0.431 0.145
.GR01_01 1.034 0.107 9.647 0.000 1.034 0.522
.GR01_02 0.484 0.066 7.345 0.000 0.484 0.325
.GR01_03 0.445 0.067 6.648 0.000 0.445 0.269
.GR01_04 0.354 0.043 8.159 0.000 0.354 0.203
.GR01_05 0.447 0.056 7.952 0.000 0.447 0.267
.GR01_06 1.093 0.108 10.074 0.000 1.093 0.499
.GR01_07 0.703 0.072 9.827 0.000 0.703 0.332
.GR01_08 0.620 0.068 9.060 0.000 0.620 0.330
.GR01_09 0.640 0.071 9.050 0.000 0.640 0.326
.GR01_10 0.791 0.071 11.059 0.000 0.791 0.374
.GR01_11 0.356 0.054 6.536 0.000 0.356 0.211
.GR01_12 0.807 0.076 10.555 0.000 0.807 0.414
.GR01_13 1.853 0.149 12.432 0.000 1.853 0.687
.GR01_14 2.049 0.124 16.494 0.000 2.049 0.730
.GR01_15 0.649 0.115 5.620 0.000 0.649 0.278
.PD01_01 0.701 0.108 6.506 0.000 0.701 0.235
.PD01_02 1.334 0.127 10.480 0.000 1.334 0.565
.PD01_03 1.334 0.130 10.283 0.000 1.334 0.549
.PD01_04 1.299 0.146 8.875 0.000 1.299 0.446
.PD01_05 1.604 0.129 12.396 0.000 1.604 0.584
.SE01_01 0.622 0.088 7.083 0.000 0.622 0.327
.SE01_02 0.930 0.120 7.772 0.000 0.930 0.511
.SE01_03 0.699 0.095 7.336 0.000 0.699 0.389
.SE01_04 0.652 0.077 8.470 0.000 0.652 0.358
.TR01_01 0.729 0.060 12.110 0.000 0.729 0.549
.TR01_05 0.271 0.032 8.577 0.000 0.271 0.205
.TR01_09 0.196 0.037 5.345 0.000 0.196 0.134
.COMM 56796.242 0.019 2985141.230 0.000 56796.242 0.906
pri_con 2.544 0.144 17.644 0.000 1.000 1.000
.grats_inf 0.260 0.041 6.287 0.000 0.287 0.287
.grats_rel 0.204 0.048 4.288 0.000 0.147 0.147
.grats_par 0.090 0.050 1.814 0.070 0.064 0.064
.grats_ide 0.184 0.046 3.976 0.000 0.141 0.141
.grats_ext 0.277 0.070 3.966 0.000 0.400 0.400
grats_spec 0.647 0.117 5.518 0.000 1.000 1.000
pri_delib 2.213 0.156 14.184 0.000 1.000 1.000
self_eff 1.245 0.113 10.973 0.000 1.000 1.000
trust_gen 0.591 0.072 8.259 0.000 1.000 1.000rsquare_fit_adapted <- inspect(fit_adapted, what = "rsquare")["comm"]We now use only variables, that is specific gratifications and privacy concerns.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
COMM ~ a1*pri_con + b1*grats_spec
# Covariates
COMM + GR01_01 + GR01_02 + GR01_03 + GR01_04 + GR01_05 + GR01_06 + GR01_07 + GR01_08 + GR01_09 + GR01_10 + GR01_11 + GR01_12 + GR01_13 + GR01_14 + GR01_15 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 ~ male + age + edu
"
fit_simple <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit_simple, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 365 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 139
Used Total
Number of observations 558 559
Number of missing patterns 1
Model Test User Model:
Standard Robust
Test Statistic 708.157 486.954
Degrees of freedom 202 202
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.454
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 9611.689 6684.867
Degrees of freedom 297 297
P-value 0.000 0.000
Scaling correction factor 1.438
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.946 0.955
Tucker-Lewis Index (TLI) 0.920 0.934
Robust Comparative Fit Index (CFI) 0.955
Robust Tucker-Lewis Index (TLI) 0.934
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -20777.548 -20777.548
Scaling correction factor 1.570
for the MLR correction
Loglikelihood unrestricted model (H1) -20423.470 -20423.470
Scaling correction factor 1.501
for the MLR correction
Akaike (AIC) 41833.097 41833.097
Bayesian (BIC) 42434.183 42434.183
Sample-size adjusted Bayesian (BIC) 41992.931 41992.931
Root Mean Square Error of Approximation:
RMSEA 0.067 0.050
90 Percent confidence interval - lower 0.062 0.046
90 Percent confidence interval - upper 0.072 0.055
P-value RMSEA <= 0.05 0.000 0.454
Robust RMSEA 0.061
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.068
Standardized Root Mean Square Residual:
SRMR 0.051 0.051
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.598 0.927
PC01_02 0.988 0.027 36.084 0.000 1.579 0.894
PC01_04 0.969 0.027 35.588 0.000 1.548 0.882
PC01_05 0.999 0.024 42.481 0.000 1.597 0.906
PC01_06 0.851 0.038 22.585 0.000 1.360 0.793
PC01_07 0.994 0.023 43.512 0.000 1.589 0.922
grats_inf =~
GR01_01 1.000 0.951 0.676
GR01_02 1.041 0.078 13.366 0.000 0.990 0.812
GR01_03 1.146 0.081 14.150 0.000 1.090 0.848
grats_rel =~
GR01_04 1.000 1.180 0.894
GR01_05 0.933 0.038 24.854 0.000 1.101 0.852
GR01_06 0.878 0.047 18.811 0.000 1.036 0.701
grats_par =~
GR01_07 1.000 1.197 0.822
GR01_08 0.926 0.040 23.180 0.000 1.108 0.808
GR01_09 0.952 0.039 24.499 0.000 1.139 0.814
grats_ide =~
GR01_10 1.000 1.150 0.791
GR01_11 0.998 0.043 23.449 0.000 1.148 0.884
GR01_12 0.919 0.042 22.057 0.000 1.056 0.757
grats_ext =~
GR01_13 1.000 0.823 0.501
GR01_14 1.030 0.104 9.865 0.000 0.847 0.506
GR01_15 1.583 0.187 8.476 0.000 1.302 0.852
grats_spec =~
grats_inf 1.000 0.827 0.827
grats_rel 1.384 0.116 11.969 0.000 0.923 0.923
grats_par 1.479 0.133 11.147 0.000 0.971 0.971
grats_ide 1.358 0.121 11.205 0.000 0.929 0.929
grats_ext 0.825 0.112 7.370 0.000 0.789 0.789
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
COMM ~
pri_con (a1) -11.794 5.895 -2.001 0.045 -18.845 -0.075
grats_spc (b1) 52.406 16.759 3.127 0.002 41.218 0.165
male -7.983 23.598 -0.338 0.735 -7.983 -0.016
age 0.343 0.551 0.623 0.533 0.343 0.021
edu 12.176 15.167 0.803 0.422 12.176 0.041
GR01_01 ~
male -0.342 0.121 -2.833 0.005 -0.342 -0.122
age -0.005 0.004 -1.333 0.183 -0.005 -0.059
edu 0.000 0.072 0.006 0.995 0.000 0.000
GR01_02 ~
male -0.142 0.103 -1.378 0.168 -0.142 -0.058
age -0.007 0.003 -2.106 0.035 -0.007 -0.088
edu -0.037 0.060 -0.617 0.537 -0.037 -0.026
GR01_03 ~
male -0.186 0.109 -1.713 0.087 -0.186 -0.073
age -0.006 0.004 -1.704 0.088 -0.006 -0.075
edu -0.076 0.063 -1.204 0.228 -0.076 -0.050
GR01_04 ~
male -0.004 0.113 -0.037 0.971 -0.004 -0.002
age 0.001 0.004 0.262 0.793 0.001 0.011
edu -0.021 0.066 -0.319 0.749 -0.021 -0.013
GR01_05 ~
male -0.069 0.112 -0.614 0.540 -0.069 -0.027
age -0.001 0.004 -0.198 0.843 -0.001 -0.009
edu 0.025 0.064 0.387 0.699 0.025 0.016
GR01_06 ~
male 0.030 0.125 0.241 0.809 0.030 0.010
age -0.011 0.004 -2.567 0.010 -0.011 -0.111
edu -0.087 0.074 -1.176 0.240 -0.087 -0.050
GR01_07 ~
male 0.075 0.125 0.602 0.547 0.075 0.026
age -0.006 0.004 -1.391 0.164 -0.006 -0.060
edu 0.004 0.072 0.057 0.955 0.004 0.002
GR01_08 ~
male 0.006 0.116 0.052 0.959 0.006 0.002
age -0.004 0.004 -1.121 0.262 -0.004 -0.051
edu 0.113 0.068 1.675 0.094 0.113 0.070
GR01_09 ~
male 0.090 0.119 0.757 0.449 0.090 0.032
age -0.004 0.004 -0.952 0.341 -0.004 -0.041
edu 0.115 0.069 1.667 0.096 0.115 0.069
GR01_10 ~
male -0.019 0.125 -0.156 0.876 -0.019 -0.007
age -0.008 0.004 -1.993 0.046 -0.008 -0.089
edu -0.021 0.074 -0.277 0.781 -0.021 -0.012
GR01_11 ~
male -0.083 0.111 -0.749 0.454 -0.083 -0.032
age -0.001 0.004 -0.313 0.755 -0.001 -0.014
edu -0.053 0.065 -0.822 0.411 -0.053 -0.034
GR01_12 ~
male -0.218 0.120 -1.820 0.069 -0.218 -0.078
age -0.004 0.004 -1.009 0.313 -0.004 -0.043
edu -0.049 0.071 -0.685 0.493 -0.049 -0.029
GR01_13 ~
male -0.181 0.138 -1.311 0.190 -0.181 -0.055
age -0.023 0.004 -5.515 0.000 -0.023 -0.219
edu 0.078 0.081 0.959 0.338 0.078 0.040
GR01_14 ~
male -0.303 0.145 -2.091 0.036 -0.303 -0.090
age -0.007 0.005 -1.540 0.124 -0.007 -0.065
edu 0.030 0.084 0.361 0.718 0.030 0.015
GR01_15 ~
male 0.048 0.132 0.360 0.719 0.048 0.016
age -0.005 0.004 -1.213 0.225 -0.005 -0.053
edu 0.024 0.078 0.300 0.764 0.024 0.013
PC01_01 ~
male -0.182 0.151 -1.206 0.228 -0.182 -0.053
age -0.004 0.005 -0.820 0.412 -0.004 -0.036
edu 0.110 0.087 1.255 0.210 0.110 0.054
PC01_02 ~
male -0.302 0.154 -1.966 0.049 -0.302 -0.085
age -0.008 0.005 -1.663 0.096 -0.008 -0.072
edu 0.047 0.089 0.522 0.601 0.047 0.022
PC01_04 ~
male -0.225 0.152 -1.475 0.140 -0.225 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.269 0.204 0.113 0.054
PC01_05 ~
male -0.098 0.154 -0.636 0.525 -0.098 -0.028
age -0.006 0.005 -1.164 0.244 -0.006 -0.051
edu 0.090 0.090 0.996 0.319 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.722 0.470 -0.108 -0.032
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.491 0.623 0.043 0.021
PC01_07 ~
male -0.174 0.150 -1.160 0.246 -0.174 -0.050
age -0.006 0.005 -1.337 0.181 -0.006 -0.058
edu 0.081 0.087 0.934 0.351 0.081 0.040
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con ~~
grats_spec -0.115 0.067 -1.712 0.087 -0.091 -0.091
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.557 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.398 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.035
.PC01_05 3.414 0.304 11.229 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.155 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.780 0.000 3.461 2.008
.GR01_01 5.296 0.247 21.424 0.000 5.296 3.763
.GR01_02 5.895 0.205 28.694 0.000 5.895 4.835
.GR01_03 5.800 0.221 26.272 0.000 5.800 4.511
.GR01_04 4.923 0.211 23.378 0.000 4.923 3.730
.GR01_05 5.109 0.217 23.539 0.000 5.109 3.952
.GR01_06 5.297 0.252 21.013 0.000 5.297 3.581
.GR01_07 4.896 0.236 20.768 0.000 4.896 3.363
.GR01_08 5.062 0.238 21.233 0.000 5.062 3.691
.GR01_09 4.754 0.237 20.070 0.000 4.754 3.395
.GR01_10 4.977 0.255 19.492 0.000 4.977 3.425
.GR01_11 5.158 0.227 22.705 0.000 5.158 3.974
.GR01_12 5.136 0.233 22.001 0.000 5.136 3.679
.GR01_13 5.091 0.259 19.644 0.000 5.091 3.101
.GR01_14 3.454 0.281 12.310 0.000 3.454 2.061
.GR01_15 4.582 0.266 17.224 0.000 4.582 2.998
.COMM 42.157 29.818 1.414 0.157 42.157 0.168
pri_con 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
grats_spec 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.395 0.050 7.926 0.000 0.395 0.133
.PC01_02 0.580 0.104 5.598 0.000 0.580 0.186
.PC01_04 0.634 0.078 8.082 0.000 0.634 0.206
.PC01_05 0.540 0.065 8.276 0.000 0.540 0.174
.PC01_06 1.079 0.117 9.221 0.000 1.079 0.367
.PC01_07 0.425 0.064 6.633 0.000 0.425 0.143
.GR01_01 1.036 0.109 9.524 0.000 1.036 0.523
.GR01_02 0.487 0.066 7.410 0.000 0.487 0.327
.GR01_03 0.440 0.068 6.489 0.000 0.440 0.266
.GR01_04 0.349 0.043 8.132 0.000 0.349 0.201
.GR01_05 0.458 0.057 8.078 0.000 0.458 0.274
.GR01_06 1.084 0.108 10.005 0.000 1.084 0.495
.GR01_07 0.677 0.069 9.775 0.000 0.677 0.320
.GR01_08 0.637 0.069 9.282 0.000 0.637 0.339
.GR01_09 0.646 0.072 9.008 0.000 0.646 0.330
.GR01_10 0.773 0.070 11.079 0.000 0.773 0.366
.GR01_11 0.364 0.055 6.562 0.000 0.364 0.216
.GR01_12 0.814 0.077 10.579 0.000 0.814 0.418
.GR01_13 1.869 0.147 12.731 0.000 1.869 0.693
.GR01_14 2.051 0.123 16.728 0.000 2.051 0.731
.GR01_15 0.632 0.112 5.622 0.000 0.632 0.271
.COMM 60348.228 23641.021 2.553 0.011 60348.228 0.963
pri_con 2.553 0.144 17.705 0.000 1.000 1.000
.grats_inf 0.286 0.045 6.402 0.000 0.316 0.316
.grats_rel 0.207 0.049 4.184 0.000 0.149 0.149
.grats_par 0.081 0.053 1.541 0.123 0.056 0.056
.grats_ide 0.180 0.047 3.811 0.000 0.137 0.137
.grats_ext 0.255 0.065 3.910 0.000 0.377 0.377
grats_spec 0.619 0.119 5.219 0.000 1.000 1.000rsquare_fit_simple <- inspect(fit_simple, what = "rsquare")["comm"]We first analyze the same models as before, but analyzing self-disclosure instead of communication. This was how we initially preregistered the study.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
trust_spec =~ trust_community + trust_provider
self_dis_log ~ a1*pri_con + b1*grats_gen + c1*pri_delib + d1*self_eff + e1*trust_spec
# Covariates
self_dis_log + GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit_prereg <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit_prereg, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 324 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 198
Number of equality constraints 1
Used Total
Number of observations 558 559
Number of missing patterns 3
Model Test User Model:
Standard Robust
Test Statistic 1244.969 953.447
Degrees of freedom 388 388
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.306
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 13322.891 9995.405
Degrees of freedom 525 525
P-value 0.000 0.000
Scaling correction factor 1.333
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.933 0.940
Tucker-Lewis Index (TLI) 0.909 0.919
Robust Comparative Fit Index (CFI) 0.942
Robust Tucker-Lewis Index (TLI) 0.921
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -23851.439 -23851.439
Scaling correction factor 1.260
for the MLR correction
Loglikelihood unrestricted model (H1) -23228.954 -23228.954
Scaling correction factor 1.293
for the MLR correction
Akaike (AIC) 48096.877 48096.877
Bayesian (BIC) 48948.776 48948.776
Sample-size adjusted Bayesian (BIC) 48323.404 48323.404
Root Mean Square Error of Approximation:
RMSEA 0.063 0.051
90 Percent confidence interval - lower 0.059 0.048
90 Percent confidence interval - upper 0.067 0.055
P-value RMSEA <= 0.05 0.000 0.301
Robust RMSEA 0.058
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.063
Standardized Root Mean Square Residual:
SRMR 0.049 0.049
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.595 0.926
PC01_02 0.990 0.027 36.223 0.000 1.579 0.894
PC01_04 0.972 0.027 35.677 0.000 1.550 0.884
PC01_05 1.002 0.024 42.437 0.000 1.599 0.907
PC01_06 0.854 0.038 22.681 0.000 1.363 0.795
PC01_07 0.994 0.023 43.799 0.000 1.586 0.920
grats_gen =~
GR02_01 1.000 1.133 0.844
GR02_02 1.118 0.033 33.646 0.000 1.267 0.894
GR02_03 1.019 0.047 21.479 0.000 1.155 0.863
GR02_04 0.983 0.048 20.415 0.000 1.115 0.848
GR02_05 1.072 0.040 27.031 0.000 1.215 0.847
pri_delib =~
PD01_01 1.000 1.473 0.853
PD01_02 0.670 0.048 13.874 0.000 0.987 0.643
PD01_03 0.709 0.055 12.918 0.000 1.044 0.670
PD01_04 0.842 0.047 17.852 0.000 1.241 0.727
PD01_05 0.717 0.050 14.327 0.000 1.056 0.638
self_eff =~
SE01_01 1.000 1.115 0.808
SE01_02 0.811 0.057 14.207 0.000 0.904 0.670
SE01_03 0.933 0.046 20.149 0.000 1.039 0.776
SE01_04 0.954 0.043 22.155 0.000 1.063 0.789
trust_community =~
TR01_02 1.000 1.024 0.808
TR01_03 0.820 0.052 15.878 0.000 0.839 0.763
TR01_04 0.917 0.046 19.760 0.000 0.939 0.814
trust_provider =~
TR01_06 1.000 1.046 0.871
TR01_07 0.855 0.039 21.941 0.000 0.894 0.773
TR01_08 0.834 0.040 21.090 0.000 0.872 0.788
TR01_10 0.788 0.038 20.840 0.000 0.824 0.700
TR01_11 0.821 0.052 15.891 0.000 0.859 0.662
TR01_12 1.098 0.038 28.624 0.000 1.149 0.854
trust_spec =~
trust_communty 1.000 0.877 0.877
trust_provider 1.109 0.077 14.318 0.000 0.952 0.952
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
self_dis_log ~
pri_con (a1) -0.062 0.080 -0.781 0.435 -0.100 -0.044
grats_gen (b1) 0.140 0.166 0.844 0.399 0.159 0.070
pri_delib (c1) -0.160 0.093 -1.724 0.085 -0.235 -0.103
self_eff (d1) 0.783 0.148 5.289 0.000 0.872 0.382
trust_spc (e1) -0.304 0.270 -1.129 0.259 -0.273 -0.120
male -0.021 0.197 -0.109 0.913 -0.021 -0.005
age 0.003 0.006 0.570 0.569 0.003 0.024
edu 0.212 0.116 1.836 0.066 0.212 0.078
GR02_01 ~
male -0.127 0.116 -1.096 0.273 -0.127 -0.047
age 0.000 0.004 0.091 0.927 0.000 0.004
edu 0.005 0.068 0.074 0.941 0.005 0.003
GR02_02 ~
male -0.067 0.120 -0.559 0.576 -0.067 -0.024
age 0.006 0.004 1.542 0.123 0.006 0.068
edu -0.080 0.071 -1.127 0.260 -0.080 -0.047
GR02_03 ~
male -0.025 0.116 -0.220 0.826 -0.025 -0.009
age 0.001 0.004 0.310 0.756 0.001 0.014
edu -0.083 0.067 -1.237 0.216 -0.083 -0.052
GR02_04 ~
male 0.028 0.113 0.250 0.803 0.028 0.011
age 0.005 0.004 1.304 0.192 0.005 0.057
edu -0.072 0.067 -1.072 0.284 -0.072 -0.046
GR02_05 ~
male -0.140 0.124 -1.136 0.256 -0.140 -0.049
age -0.004 0.004 -0.874 0.382 -0.004 -0.039
edu 0.013 0.073 0.173 0.862 0.013 0.007
PC01_01 ~
male -0.182 0.151 -1.206 0.228 -0.182 -0.053
age -0.004 0.005 -0.820 0.412 -0.004 -0.036
edu 0.110 0.087 1.255 0.209 0.110 0.054
PC01_02 ~
male -0.302 0.154 -1.967 0.049 -0.302 -0.085
age -0.008 0.005 -1.663 0.096 -0.008 -0.072
edu 0.047 0.089 0.522 0.601 0.047 0.022
PC01_04 ~
male -0.225 0.152 -1.476 0.140 -0.225 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.269 0.204 0.113 0.054
PC01_05 ~
male -0.098 0.154 -0.636 0.524 -0.098 -0.028
age -0.006 0.005 -1.164 0.244 -0.006 -0.051
edu 0.090 0.090 0.996 0.319 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.722 0.470 -0.108 -0.032
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.491 0.623 0.043 0.021
PC01_07 ~
male -0.174 0.150 -1.160 0.246 -0.174 -0.050
age -0.006 0.005 -1.337 0.181 -0.006 -0.058
edu 0.081 0.087 0.934 0.350 0.081 0.040
TR01_02 ~
male -0.297 0.108 -2.744 0.006 -0.297 -0.117
age -0.004 0.004 -1.102 0.270 -0.004 -0.049
edu 0.005 0.062 0.086 0.931 0.005 0.004
TR01_03 ~
male -0.140 0.095 -1.480 0.139 -0.140 -0.064
age -0.002 0.003 -0.566 0.571 -0.002 -0.025
edu 0.023 0.053 0.434 0.664 0.023 0.018
TR01_04 ~
male -0.134 0.099 -1.362 0.173 -0.134 -0.058
age -0.004 0.003 -1.211 0.226 -0.004 -0.055
edu -0.003 0.060 -0.045 0.964 -0.003 -0.002
TR01_06 ~
male -0.086 0.104 -0.831 0.406 -0.086 -0.036
age 0.000 0.003 0.110 0.912 0.000 0.005
edu -0.051 0.058 -0.880 0.379 -0.051 -0.036
TR01_07 ~
male -0.045 0.099 -0.450 0.653 -0.045 -0.019
age 0.001 0.003 0.344 0.731 0.001 0.015
edu 0.018 0.058 0.309 0.757 0.018 0.013
TR01_08 ~
male 0.046 0.095 0.480 0.631 0.046 0.021
age -0.004 0.003 -1.250 0.211 -0.004 -0.053
edu 0.025 0.056 0.445 0.656 0.025 0.019
TR01_10 ~
male 0.091 0.100 0.913 0.361 0.091 0.039
age -0.004 0.003 -1.176 0.239 -0.004 -0.050
edu -0.055 0.058 -0.946 0.344 -0.055 -0.039
TR01_11 ~
male 0.027 0.112 0.245 0.806 0.027 0.011
age 0.003 0.004 0.824 0.410 0.003 0.035
edu -0.093 0.065 -1.435 0.151 -0.093 -0.061
TR01_12 ~
male -0.121 0.115 -1.045 0.296 -0.121 -0.045
age -0.002 0.004 -0.405 0.685 -0.002 -0.018
edu -0.146 0.068 -2.156 0.031 -0.146 -0.091
PD01_01 ~
male -0.177 0.148 -1.197 0.231 -0.177 -0.051
age -0.015 0.005 -3.275 0.001 -0.015 -0.137
edu -0.026 0.085 -0.310 0.756 -0.026 -0.013
PD01_02 ~
male -0.119 0.131 -0.906 0.365 -0.119 -0.039
age -0.014 0.004 -3.443 0.001 -0.014 -0.142
edu 0.031 0.077 0.405 0.686 0.031 0.017
PD01_03 ~
male -0.321 0.132 -2.425 0.015 -0.321 -0.103
age -0.004 0.004 -1.024 0.306 -0.004 -0.044
edu 0.065 0.080 0.807 0.419 0.065 0.035
PD01_04 ~
male -0.412 0.145 -2.847 0.004 -0.412 -0.121
age -0.009 0.005 -1.904 0.057 -0.009 -0.082
edu 0.103 0.085 1.207 0.227 0.103 0.051
PD01_05 ~
male -0.205 0.142 -1.439 0.150 -0.205 -0.062
age -0.012 0.004 -2.696 0.007 -0.012 -0.111
edu -0.002 0.084 -0.025 0.980 -0.002 -0.001
SE01_01 ~
male 0.119 0.118 1.012 0.312 0.119 0.043
age 0.000 0.004 0.008 0.994 0.000 0.000
edu 0.211 0.068 3.115 0.002 0.211 0.129
SE01_02 ~
male 0.059 0.112 0.527 0.598 0.059 0.022
age -0.013 0.004 -3.598 0.000 -0.013 -0.151
edu 0.198 0.066 3.000 0.003 0.198 0.124
SE01_03 ~
male 0.194 0.114 1.698 0.090 0.194 0.072
age 0.001 0.004 0.246 0.806 0.001 0.011
edu 0.143 0.067 2.134 0.033 0.143 0.090
SE01_04 ~
male 0.053 0.115 0.462 0.644 0.053 0.020
age 0.007 0.004 2.047 0.041 0.007 0.086
edu 0.127 0.066 1.908 0.056 0.127 0.079
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.107 0.044 2.426 0.015 0.107 0.141
.SE01_03 ~~
.SE01_04 (x) 0.107 0.044 2.426 0.015 0.107 0.158
pri_con ~~
grats_gen -0.283 0.096 -2.949 0.003 -0.156 -0.156
pri_delib 1.323 0.131 10.120 0.000 0.563 0.563
self_eff -0.376 0.091 -4.131 0.000 -0.212 -0.212
trust_spec -0.416 0.074 -5.584 0.000 -0.290 -0.290
grats_gen ~~
pri_delib -0.068 0.103 -0.659 0.510 -0.041 -0.041
self_eff 0.470 0.067 7.042 0.000 0.372 0.372
trust_spec 0.804 0.086 9.396 0.000 0.790 0.790
pri_delib ~~
self_eff -0.325 0.094 -3.447 0.001 -0.198 -0.198
trust_spec -0.139 0.086 -1.624 0.104 -0.105 -0.105
self_eff ~~
trust_spec 0.555 0.060 9.235 0.000 0.554 0.554
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.557 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.398 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.035
.PC01_05 3.414 0.304 11.229 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.155 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.780 0.000 3.461 2.008
.GR02_01 4.319 0.224 19.252 0.000 4.319 3.215
.GR02_02 4.492 0.244 18.372 0.000 4.492 3.170
.GR02_03 5.244 0.222 23.667 0.000 5.244 3.917
.GR02_04 4.988 0.221 22.522 0.000 4.988 3.795
.GR02_05 4.905 0.254 19.323 0.000 4.905 3.422
.PD01_01 4.493 0.290 15.507 0.000 4.493 2.602
.PD01_02 3.997 0.248 16.102 0.000 3.997 2.601
.PD01_03 4.432 0.270 16.437 0.000 4.432 2.845
.PD01_04 4.506 0.295 15.283 0.000 4.506 2.640
.PD01_05 5.000 0.276 18.089 0.000 5.000 3.018
.SE01_01 4.824 0.249 19.357 0.000 4.824 3.496
.SE01_02 5.730 0.234 24.461 0.000 5.730 4.248
.SE01_03 4.819 0.226 21.326 0.000 4.819 3.597
.SE01_04 4.533 0.234 19.377 0.000 4.533 3.361
.TR01_02 5.083 0.219 23.224 0.000 5.083 4.010
.TR01_03 4.951 0.189 26.178 0.000 4.951 4.505
.TR01_04 4.871 0.200 24.361 0.000 4.871 4.223
.TR01_06 5.521 0.205 26.991 0.000 5.521 4.600
.TR01_07 5.134 0.197 26.024 0.000 5.134 4.440
.TR01_08 5.232 0.188 27.764 0.000 5.232 4.728
.TR01_10 5.955 0.193 30.911 0.000 5.955 5.060
.TR01_11 4.855 0.218 22.270 0.000 4.855 3.745
.TR01_12 5.581 0.231 24.175 0.000 5.581 4.149
.self_dis_log 1.376 0.375 3.673 0.000 1.376 0.602
pri_con 0.000 0.000 0.000
grats_gen 0.000 0.000 0.000
pri_delib 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
.trust_communty 0.000 0.000 0.000
.trust_provider 0.000 0.000 0.000
trust_spec 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.403 0.050 8.073 0.000 0.403 0.136
.PC01_02 0.580 0.103 5.654 0.000 0.580 0.186
.PC01_04 0.628 0.077 8.138 0.000 0.628 0.204
.PC01_05 0.534 0.064 8.354 0.000 0.534 0.172
.PC01_06 1.069 0.116 9.252 0.000 1.069 0.364
.PC01_07 0.431 0.065 6.589 0.000 0.431 0.145
.GR02_01 0.516 0.053 9.674 0.000 0.516 0.286
.GR02_02 0.386 0.039 9.891 0.000 0.386 0.192
.GR02_03 0.452 0.073 6.174 0.000 0.452 0.252
.GR02_04 0.476 0.048 9.905 0.000 0.476 0.275
.GR02_05 0.571 0.062 9.185 0.000 0.571 0.278
.PD01_01 0.743 0.111 6.688 0.000 0.743 0.249
.PD01_02 1.331 0.128 10.434 0.000 1.331 0.564
.PD01_03 1.304 0.128 10.195 0.000 1.304 0.537
.PD01_04 1.300 0.147 8.860 0.000 1.300 0.446
.PD01_05 1.580 0.128 12.374 0.000 1.580 0.576
.SE01_01 0.623 0.087 7.131 0.000 0.623 0.327
.SE01_02 0.926 0.119 7.787 0.000 0.926 0.509
.SE01_03 0.688 0.096 7.169 0.000 0.688 0.383
.SE01_04 0.663 0.077 8.579 0.000 0.663 0.365
.TR01_02 0.531 0.067 7.881 0.000 0.531 0.331
.TR01_03 0.498 0.055 9.064 0.000 0.498 0.412
.TR01_04 0.439 0.045 9.677 0.000 0.439 0.330
.TR01_06 0.343 0.035 9.880 0.000 0.343 0.238
.TR01_07 0.537 0.052 10.304 0.000 0.537 0.402
.TR01_08 0.459 0.041 11.152 0.000 0.459 0.375
.TR01_10 0.699 0.056 12.551 0.000 0.699 0.505
.TR01_11 0.935 0.079 11.779 0.000 0.935 0.556
.TR01_12 0.468 0.052 8.951 0.000 0.468 0.259
.self_dis_log 4.365 0.210 20.740 0.000 4.365 0.837
pri_con 2.545 0.144 17.629 0.000 1.000 1.000
grats_gen 1.285 0.114 11.231 0.000 1.000 1.000
pri_delib 2.171 0.158 13.773 0.000 1.000 1.000
self_eff 1.242 0.113 10.962 0.000 1.000 1.000
.trust_communty 0.242 0.044 5.485 0.000 0.231 0.231
.trust_provider 0.101 0.043 2.374 0.018 0.093 0.093
trust_spec 0.806 0.099 8.135 0.000 1.000 1.000rsquare_fit_prereg <- inspect(fit_prereg, what = "rsquare")["self_dis_log"]Building on the preregistered model, instead of general gratifications and specific trust, we now use specific gratifications and general trust.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_gen =~ TR01_01 + TR01_05 + TR01_09
self_dis_log ~ a1*pri_con + b1*grats_spec + c1*pri_delib + d1*self_eff + e1*trust_gen
# Covariates
self_dis_log + GR01_01 + GR01_02 + GR01_03 + GR01_04 + GR01_05 + GR01_06 + GR01_07 + GR01_08 + GR01_09 + GR01_10 + GR01_11 + GR01_12 + GR01_13 + GR01_14 + GR01_15 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_01 + TR01_05 + TR01_09 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit_adapted <- sem(model, data = d, estimator = "MLR", missing = "ML", missing = "ML")
summary(fit_adapted, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 352 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 225
Number of equality constraints 1
Used Total
Number of observations 558 559
Number of missing patterns 2
Model Test User Model:
Standard Robust
Test Statistic 1501.143 1138.746
Degrees of freedom 507 507
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.318
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 14334.705 10703.287
Degrees of freedom 663 663
P-value 0.000 0.000
Scaling correction factor 1.339
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.927 0.937
Tucker-Lewis Index (TLI) 0.905 0.918
Robust Comparative Fit Index (CFI) 0.938
Robust Tucker-Lewis Index (TLI) 0.919
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -27996.285 -27996.285
Scaling correction factor 1.267
for the MLR correction
Loglikelihood unrestricted model (H1) -27245.714 -27245.714
Scaling correction factor 1.304
for the MLR correction
Akaike (AIC) 56440.570 56440.570
Bayesian (BIC) 57409.226 57409.226
Sample-size adjusted Bayesian (BIC) 56698.144 56698.144
Root Mean Square Error of Approximation:
RMSEA 0.059 0.047
90 Percent confidence interval - lower 0.056 0.044
90 Percent confidence interval - upper 0.063 0.050
P-value RMSEA <= 0.05 0.000 0.921
Robust RMSEA 0.054
90 Percent confidence interval - lower 0.050
90 Percent confidence interval - upper 0.058
Standardized Root Mean Square Residual:
SRMR 0.059 0.059
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.595 0.926
PC01_02 0.990 0.027 36.425 0.000 1.579 0.894
PC01_04 0.972 0.027 35.939 0.000 1.550 0.883
PC01_05 1.003 0.024 42.507 0.000 1.599 0.907
PC01_06 0.855 0.038 22.775 0.000 1.364 0.796
PC01_07 0.995 0.023 44.000 0.000 1.587 0.920
grats_inf =~
GR01_01 1.000 0.952 0.677
GR01_02 1.042 0.076 13.676 0.000 0.992 0.814
GR01_03 1.142 0.078 14.572 0.000 1.088 0.846
grats_rel =~
GR01_04 1.000 1.178 0.893
GR01_05 0.939 0.038 24.902 0.000 1.106 0.855
GR01_06 0.876 0.046 18.875 0.000 1.032 0.697
grats_par =~
GR01_07 1.000 1.187 0.815
GR01_08 0.941 0.039 23.835 0.000 1.117 0.814
GR01_09 0.963 0.038 25.234 0.000 1.142 0.816
grats_ide =~
GR01_10 1.000 1.142 0.786
GR01_11 1.007 0.042 24.159 0.000 1.150 0.886
GR01_12 0.928 0.041 22.498 0.000 1.060 0.759
grats_ext =~
GR01_13 1.000 0.831 0.506
GR01_14 1.020 0.103 9.924 0.000 0.848 0.506
GR01_15 1.559 0.187 8.349 0.000 1.296 0.848
grats_spec =~
grats_inf 1.000 0.844 0.844
grats_rel 1.354 0.108 12.492 0.000 0.924 0.924
grats_par 1.428 0.119 11.966 0.000 0.967 0.967
grats_ide 1.317 0.109 12.034 0.000 0.927 0.927
grats_ext 0.800 0.106 7.516 0.000 0.774 0.774
pri_delib =~
PD01_01 1.000 1.482 0.858
PD01_02 0.668 0.048 13.846 0.000 0.990 0.644
PD01_03 0.699 0.054 12.839 0.000 1.035 0.664
PD01_04 0.838 0.047 17.842 0.000 1.241 0.727
PD01_05 0.707 0.050 14.189 0.000 1.047 0.632
self_eff =~
SE01_01 1.000 1.118 0.810
SE01_02 0.808 0.058 14.040 0.000 0.904 0.670
SE01_03 0.926 0.045 20.413 0.000 1.035 0.773
SE01_04 0.954 0.043 22.096 0.000 1.066 0.791
trust_gen =~
TR01_01 1.000 0.769 0.667
TR01_05 1.326 0.070 18.936 0.000 1.020 0.887
TR01_09 1.453 0.081 18.032 0.000 1.117 0.926
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
self_dis_log ~
pri_con (a1) -0.106 0.080 -1.320 0.187 -0.169 -0.074
grats_spc (b1) 0.462 0.205 2.255 0.024 0.371 0.162
pri_delib (c1) -0.204 0.094 -2.174 0.030 -0.303 -0.133
self_eff (d1) 0.683 0.143 4.776 0.000 0.764 0.334
trust_gen (e1) -0.531 0.218 -2.439 0.015 -0.409 -0.179
male -0.022 0.197 -0.109 0.913 -0.022 -0.005
age 0.003 0.006 0.569 0.569 0.003 0.024
edu 0.212 0.116 1.836 0.066 0.212 0.078
GR01_01 ~
male -0.342 0.121 -2.833 0.005 -0.342 -0.122
age -0.005 0.004 -1.333 0.182 -0.005 -0.059
edu 0.000 0.072 0.006 0.995 0.000 0.000
GR01_02 ~
male -0.142 0.103 -1.378 0.168 -0.142 -0.058
age -0.007 0.003 -2.107 0.035 -0.007 -0.088
edu -0.037 0.060 -0.617 0.537 -0.037 -0.026
GR01_03 ~
male -0.186 0.109 -1.713 0.087 -0.186 -0.073
age -0.006 0.004 -1.704 0.088 -0.006 -0.075
edu -0.076 0.063 -1.204 0.228 -0.076 -0.050
GR01_04 ~
male -0.004 0.113 -0.037 0.971 -0.004 -0.002
age 0.001 0.004 0.262 0.793 0.001 0.011
edu -0.021 0.066 -0.320 0.749 -0.021 -0.013
GR01_05 ~
male -0.069 0.112 -0.614 0.539 -0.069 -0.027
age -0.001 0.004 -0.198 0.843 -0.001 -0.009
edu 0.025 0.064 0.387 0.699 0.025 0.016
GR01_06 ~
male 0.030 0.125 0.241 0.809 0.030 0.010
age -0.011 0.004 -2.567 0.010 -0.011 -0.111
edu -0.087 0.074 -1.176 0.240 -0.087 -0.050
GR01_07 ~
male 0.075 0.125 0.602 0.547 0.075 0.026
age -0.006 0.004 -1.391 0.164 -0.006 -0.060
edu 0.004 0.072 0.057 0.955 0.004 0.002
GR01_08 ~
male 0.006 0.116 0.052 0.959 0.006 0.002
age -0.004 0.004 -1.121 0.262 -0.004 -0.051
edu 0.113 0.068 1.675 0.094 0.113 0.070
GR01_09 ~
male 0.090 0.119 0.757 0.449 0.090 0.032
age -0.004 0.004 -0.952 0.341 -0.004 -0.041
edu 0.115 0.069 1.666 0.096 0.115 0.069
GR01_10 ~
male -0.019 0.125 -0.156 0.876 -0.019 -0.007
age -0.008 0.004 -1.993 0.046 -0.008 -0.089
edu -0.021 0.074 -0.278 0.781 -0.021 -0.012
GR01_11 ~
male -0.083 0.111 -0.749 0.454 -0.083 -0.032
age -0.001 0.004 -0.313 0.755 -0.001 -0.014
edu -0.053 0.065 -0.822 0.411 -0.053 -0.034
GR01_12 ~
male -0.218 0.120 -1.820 0.069 -0.218 -0.078
age -0.004 0.004 -1.010 0.313 -0.004 -0.043
edu -0.049 0.071 -0.686 0.493 -0.049 -0.029
GR01_13 ~
male -0.181 0.138 -1.311 0.190 -0.181 -0.055
age -0.023 0.004 -5.515 0.000 -0.023 -0.219
edu 0.078 0.081 0.959 0.338 0.078 0.040
GR01_14 ~
male -0.303 0.145 -2.092 0.036 -0.303 -0.090
age -0.007 0.005 -1.540 0.124 -0.007 -0.065
edu 0.030 0.084 0.361 0.718 0.030 0.015
GR01_15 ~
male 0.048 0.132 0.360 0.719 0.048 0.016
age -0.005 0.004 -1.214 0.225 -0.005 -0.053
edu 0.024 0.078 0.300 0.764 0.024 0.013
PC01_01 ~
male -0.182 0.151 -1.206 0.228 -0.182 -0.053
age -0.004 0.005 -0.819 0.413 -0.004 -0.036
edu 0.110 0.087 1.255 0.209 0.110 0.054
PC01_02 ~
male -0.302 0.154 -1.966 0.049 -0.302 -0.085
age -0.008 0.005 -1.663 0.096 -0.008 -0.072
edu 0.047 0.089 0.523 0.601 0.047 0.022
PC01_04 ~
male -0.225 0.152 -1.475 0.140 -0.225 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.269 0.204 0.113 0.054
PC01_05 ~
male -0.098 0.154 -0.636 0.525 -0.098 -0.028
age -0.006 0.005 -1.164 0.244 -0.006 -0.051
edu 0.090 0.090 0.997 0.319 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.722 0.470 -0.108 -0.032
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.491 0.623 0.043 0.021
PC01_07 ~
male -0.174 0.150 -1.160 0.246 -0.174 -0.050
age -0.006 0.005 -1.337 0.181 -0.006 -0.058
edu 0.081 0.087 0.934 0.350 0.081 0.040
TR01_01 ~
male -0.156 0.099 -1.570 0.116 -0.156 -0.068
age -0.003 0.003 -0.813 0.416 -0.003 -0.038
edu 0.026 0.060 0.433 0.665 0.026 0.019
TR01_05 ~
male 0.076 0.100 0.762 0.446 0.076 0.033
age -0.004 0.003 -1.216 0.224 -0.004 -0.053
edu 0.088 0.059 1.490 0.136 0.088 0.064
TR01_09 ~
male 0.064 0.104 0.619 0.536 0.064 0.027
age -0.007 0.004 -1.939 0.053 -0.007 -0.088
edu -0.018 0.060 -0.295 0.768 -0.018 -0.012
PD01_01 ~
male -0.177 0.148 -1.197 0.231 -0.177 -0.051
age -0.015 0.005 -3.275 0.001 -0.015 -0.137
edu -0.026 0.085 -0.310 0.757 -0.026 -0.013
PD01_02 ~
male -0.119 0.131 -0.906 0.365 -0.119 -0.039
age -0.014 0.004 -3.442 0.001 -0.014 -0.142
edu 0.031 0.077 0.405 0.685 0.031 0.017
PD01_03 ~
male -0.321 0.132 -2.425 0.015 -0.321 -0.103
age -0.004 0.004 -1.023 0.306 -0.004 -0.044
edu 0.065 0.080 0.808 0.419 0.065 0.035
PD01_04 ~
male -0.412 0.145 -2.847 0.004 -0.412 -0.121
age -0.009 0.005 -1.904 0.057 -0.009 -0.082
edu 0.103 0.085 1.208 0.227 0.103 0.051
PD01_05 ~
male -0.205 0.142 -1.439 0.150 -0.205 -0.062
age -0.012 0.004 -2.696 0.007 -0.012 -0.111
edu -0.002 0.084 -0.025 0.980 -0.002 -0.001
SE01_01 ~
male 0.118 0.118 1.000 0.317 0.118 0.043
age -0.000 0.004 -0.003 0.998 -0.000 -0.000
edu 0.210 0.068 3.092 0.002 0.210 0.128
SE01_02 ~
male 0.058 0.112 0.518 0.605 0.058 0.021
age -0.013 0.004 -3.607 0.000 -0.013 -0.152
edu 0.197 0.066 2.983 0.003 0.197 0.123
SE01_03 ~
male 0.193 0.114 1.687 0.092 0.193 0.072
age 0.001 0.004 0.236 0.813 0.001 0.010
edu 0.142 0.067 2.114 0.035 0.142 0.089
SE01_04 ~
male 0.052 0.115 0.451 0.652 0.052 0.019
age 0.007 0.004 2.036 0.042 0.007 0.085
edu 0.125 0.066 1.887 0.059 0.125 0.078
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.108 0.044 2.451 0.014 0.108 0.143
.SE01_03 ~~
.SE01_04 (x) 0.108 0.044 2.451 0.014 0.108 0.160
pri_con ~~
grats_spec -0.118 0.068 -1.722 0.085 -0.092 -0.092
pri_delib 1.333 0.130 10.241 0.000 0.564 0.564
self_eff -0.380 0.091 -4.167 0.000 -0.213 -0.213
trust_gen -0.524 0.066 -7.966 0.000 -0.427 -0.427
grats_spec ~~
pri_delib -0.005 0.073 -0.072 0.942 -0.004 -0.004
self_eff 0.489 0.060 8.116 0.000 0.545 0.545
trust_gen 0.403 0.058 6.891 0.000 0.652 0.652
pri_delib ~~
self_eff -0.329 0.095 -3.462 0.001 -0.199 -0.199
trust_gen -0.315 0.073 -4.331 0.000 -0.276 -0.276
self_eff ~~
trust_gen 0.450 0.056 8.052 0.000 0.523 0.523
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.556 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.398 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.035
.PC01_05 3.414 0.304 11.228 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.154 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.779 0.000 3.461 2.008
.GR01_01 5.296 0.247 21.425 0.000 5.296 3.763
.GR01_02 5.895 0.205 28.694 0.000 5.895 4.835
.GR01_03 5.800 0.221 26.272 0.000 5.800 4.511
.GR01_04 4.923 0.211 23.378 0.000 4.923 3.730
.GR01_05 5.109 0.217 23.539 0.000 5.109 3.952
.GR01_06 5.297 0.252 21.013 0.000 5.297 3.581
.GR01_07 4.896 0.236 20.768 0.000 4.896 3.363
.GR01_08 5.062 0.238 21.233 0.000 5.062 3.691
.GR01_09 4.755 0.237 20.071 0.000 4.755 3.395
.GR01_10 4.977 0.255 19.492 0.000 4.977 3.425
.GR01_11 5.158 0.227 22.706 0.000 5.158 3.974
.GR01_12 5.136 0.233 22.001 0.000 5.136 3.679
.GR01_13 5.091 0.259 19.644 0.000 5.091 3.101
.GR01_14 3.454 0.281 12.310 0.000 3.454 2.061
.GR01_15 4.582 0.266 17.224 0.000 4.582 2.998
.PD01_01 4.493 0.290 15.507 0.000 4.493 2.602
.PD01_02 3.997 0.248 16.101 0.000 3.997 2.601
.PD01_03 4.432 0.270 16.437 0.000 4.432 2.845
.PD01_04 4.506 0.295 15.282 0.000 4.506 2.640
.PD01_05 5.000 0.276 18.089 0.000 5.000 3.018
.SE01_01 4.828 0.249 19.360 0.000 4.828 3.497
.SE01_02 5.734 0.234 24.468 0.000 5.734 4.249
.SE01_03 4.822 0.226 21.326 0.000 4.822 3.598
.SE01_04 4.537 0.234 19.380 0.000 4.537 3.363
.TR01_01 5.001 0.210 23.817 0.000 5.001 4.339
.TR01_05 5.361 0.200 26.772 0.000 5.361 4.664
.TR01_09 5.704 0.213 26.795 0.000 5.704 4.727
.self_dis_log 1.376 0.375 3.673 0.000 1.376 0.602
pri_con 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
grats_spec 0.000 0.000 0.000
pri_delib 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
trust_gen 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.404 0.050 8.120 0.000 0.404 0.136
.PC01_02 0.582 0.102 5.685 0.000 0.582 0.187
.PC01_04 0.629 0.077 8.154 0.000 0.629 0.204
.PC01_05 0.532 0.064 8.325 0.000 0.532 0.171
.PC01_06 1.067 0.115 9.253 0.000 1.067 0.363
.PC01_07 0.430 0.066 6.558 0.000 0.430 0.145
.GR01_01 1.034 0.107 9.647 0.000 1.034 0.522
.GR01_02 0.484 0.066 7.348 0.000 0.484 0.325
.GR01_03 0.445 0.067 6.650 0.000 0.445 0.269
.GR01_04 0.353 0.043 8.165 0.000 0.353 0.203
.GR01_05 0.447 0.056 7.965 0.000 0.447 0.268
.GR01_06 1.094 0.109 10.078 0.000 1.094 0.500
.GR01_07 0.703 0.072 9.829 0.000 0.703 0.332
.GR01_08 0.619 0.068 9.053 0.000 0.619 0.329
.GR01_09 0.640 0.071 9.069 0.000 0.640 0.327
.GR01_10 0.790 0.071 11.102 0.000 0.790 0.374
.GR01_11 0.357 0.054 6.566 0.000 0.357 0.212
.GR01_12 0.806 0.076 10.574 0.000 0.806 0.414
.GR01_13 1.854 0.149 12.435 0.000 1.854 0.688
.GR01_14 2.050 0.124 16.509 0.000 2.050 0.730
.GR01_15 0.647 0.116 5.604 0.000 0.647 0.277
.PD01_01 0.718 0.108 6.633 0.000 0.718 0.241
.PD01_02 1.327 0.128 10.374 0.000 1.327 0.562
.PD01_03 1.322 0.129 10.277 0.000 1.322 0.544
.PD01_04 1.300 0.146 8.891 0.000 1.300 0.446
.PD01_05 1.600 0.129 12.378 0.000 1.600 0.583
.SE01_01 0.619 0.087 7.133 0.000 0.619 0.325
.SE01_02 0.929 0.119 7.821 0.000 0.929 0.510
.SE01_03 0.697 0.094 7.381 0.000 0.697 0.388
.SE01_04 0.658 0.077 8.513 0.000 0.658 0.362
.TR01_01 0.729 0.060 12.107 0.000 0.729 0.548
.TR01_05 0.271 0.032 8.574 0.000 0.271 0.205
.TR01_09 0.196 0.037 5.342 0.000 0.196 0.135
.self_dis_log 4.301 0.202 21.310 0.000 4.301 0.824
pri_con 2.544 0.144 17.653 0.000 1.000 1.000
.grats_inf 0.261 0.041 6.304 0.000 0.287 0.287
.grats_rel 0.203 0.048 4.266 0.000 0.146 0.146
.grats_par 0.090 0.049 1.829 0.067 0.064 0.064
.grats_ide 0.184 0.046 3.979 0.000 0.141 0.141
.grats_ext 0.277 0.070 3.967 0.000 0.401 0.401
grats_spec 0.646 0.117 5.515 0.000 1.000 1.000
pri_delib 2.195 0.157 14.006 0.000 1.000 1.000
self_eff 1.249 0.113 11.070 0.000 1.000 1.000
trust_gen 0.591 0.072 8.264 0.000 1.000 1.000rsquare_fit_adapted <- inspect(fit_adapted, what = "rsquare")["self_dis_log"]We now use only variables, that is specific gratifications and privacy concerns.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
self_dis_log ~ a1*pri_con + b1*grats_spec
# Covariates
self_dis_log + GR01_01 + GR01_02 + GR01_03 + GR01_04 + GR01_05 + GR01_06 + GR01_07 + GR01_08 + GR01_09 + GR01_10 + GR01_11 + GR01_12 + GR01_13 + GR01_14 + GR01_15 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 ~ male + age + edu
"
fit_simple <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit_simple, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 259 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 139
Used Total
Number of observations 558 559
Number of missing patterns 1
Model Test User Model:
Standard Robust
Test Statistic 712.530 491.293
Degrees of freedom 202 202
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.450
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 9640.314 6718.543
Degrees of freedom 297 297
P-value 0.000 0.000
Scaling correction factor 1.435
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.945 0.955
Tucker-Lewis Index (TLI) 0.920 0.934
Robust Comparative Fit Index (CFI) 0.954
Robust Tucker-Lewis Index (TLI) 0.933
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -18144.589 -18144.589
Scaling correction factor 1.263
for the MLR correction
Loglikelihood unrestricted model (H1) -17788.324 -17788.324
Scaling correction factor 1.374
for the MLR correction
Akaike (AIC) 36567.177 36567.177
Bayesian (BIC) 37168.263 37168.263
Sample-size adjusted Bayesian (BIC) 36727.011 36727.011
Root Mean Square Error of Approximation:
RMSEA 0.067 0.051
90 Percent confidence interval - lower 0.062 0.046
90 Percent confidence interval - upper 0.073 0.055
P-value RMSEA <= 0.05 0.000 0.402
Robust RMSEA 0.061
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.068
Standardized Root Mean Square Residual:
SRMR 0.052 0.052
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.598 0.928
PC01_02 0.988 0.027 36.091 0.000 1.579 0.894
PC01_04 0.968 0.027 35.589 0.000 1.548 0.882
PC01_05 0.999 0.024 42.444 0.000 1.597 0.906
PC01_06 0.851 0.038 22.598 0.000 1.360 0.793
PC01_07 0.994 0.023 43.552 0.000 1.589 0.922
grats_inf =~
GR01_01 1.000 0.951 0.675
GR01_02 1.042 0.078 13.380 0.000 0.991 0.813
GR01_03 1.147 0.081 14.163 0.000 1.090 0.848
grats_rel =~
GR01_04 1.000 1.180 0.894
GR01_05 0.933 0.038 24.850 0.000 1.101 0.852
GR01_06 0.878 0.047 18.792 0.000 1.036 0.700
grats_par =~
GR01_07 1.000 1.197 0.822
GR01_08 0.927 0.040 23.137 0.000 1.110 0.809
GR01_09 0.952 0.039 24.510 0.000 1.139 0.814
grats_ide =~
GR01_10 1.000 1.150 0.791
GR01_11 0.998 0.042 23.513 0.000 1.147 0.884
GR01_12 0.919 0.042 22.025 0.000 1.057 0.757
grats_ext =~
GR01_13 1.000 0.822 0.500
GR01_14 1.030 0.105 9.859 0.000 0.847 0.505
GR01_15 1.586 0.188 8.443 0.000 1.303 0.853
grats_spec =~
grats_inf 1.000 0.827 0.827
grats_rel 1.387 0.116 11.953 0.000 0.923 0.923
grats_par 1.480 0.132 11.178 0.000 0.972 0.972
grats_ide 1.359 0.121 11.252 0.000 0.929 0.929
grats_ext 0.823 0.112 7.363 0.000 0.787 0.787
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
self_dis_log ~
pri_con (a1) -0.198 0.061 -3.258 0.001 -0.316 -0.138
grats_spc (b1) 0.645 0.145 4.453 0.000 0.507 0.222
male -0.021 0.197 -0.109 0.913 -0.021 -0.005
age 0.003 0.006 0.570 0.569 0.003 0.024
edu 0.212 0.116 1.836 0.066 0.212 0.078
GR01_01 ~
male -0.342 0.121 -2.833 0.005 -0.342 -0.122
age -0.005 0.004 -1.333 0.182 -0.005 -0.059
edu 0.000 0.072 0.006 0.995 0.000 0.000
GR01_02 ~
male -0.142 0.103 -1.378 0.168 -0.142 -0.058
age -0.007 0.003 -2.106 0.035 -0.007 -0.088
edu -0.037 0.060 -0.617 0.537 -0.037 -0.026
GR01_03 ~
male -0.186 0.109 -1.713 0.087 -0.186 -0.073
age -0.006 0.004 -1.704 0.088 -0.006 -0.075
edu -0.076 0.063 -1.204 0.228 -0.076 -0.050
GR01_04 ~
male -0.004 0.113 -0.037 0.971 -0.004 -0.002
age 0.001 0.004 0.262 0.793 0.001 0.011
edu -0.021 0.066 -0.319 0.749 -0.021 -0.013
GR01_05 ~
male -0.069 0.112 -0.613 0.540 -0.069 -0.027
age -0.001 0.004 -0.198 0.843 -0.001 -0.009
edu 0.025 0.064 0.387 0.699 0.025 0.016
GR01_06 ~
male 0.030 0.125 0.242 0.809 0.030 0.010
age -0.011 0.004 -2.567 0.010 -0.011 -0.111
edu -0.087 0.074 -1.176 0.240 -0.087 -0.050
GR01_07 ~
male 0.075 0.125 0.603 0.547 0.075 0.026
age -0.006 0.004 -1.391 0.164 -0.006 -0.060
edu 0.004 0.072 0.057 0.955 0.004 0.002
GR01_08 ~
male 0.006 0.116 0.052 0.959 0.006 0.002
age -0.004 0.004 -1.121 0.262 -0.004 -0.051
edu 0.113 0.068 1.675 0.094 0.113 0.070
GR01_09 ~
male 0.090 0.119 0.758 0.449 0.090 0.032
age -0.004 0.004 -0.952 0.341 -0.004 -0.041
edu 0.115 0.069 1.667 0.096 0.115 0.069
GR01_10 ~
male -0.019 0.125 -0.155 0.877 -0.019 -0.007
age -0.008 0.004 -1.993 0.046 -0.008 -0.089
edu -0.021 0.074 -0.278 0.781 -0.021 -0.012
GR01_11 ~
male -0.083 0.111 -0.748 0.454 -0.083 -0.032
age -0.001 0.004 -0.313 0.755 -0.001 -0.014
edu -0.053 0.065 -0.822 0.411 -0.053 -0.034
GR01_12 ~
male -0.218 0.120 -1.820 0.069 -0.218 -0.078
age -0.004 0.004 -1.009 0.313 -0.004 -0.043
edu -0.049 0.071 -0.685 0.493 -0.049 -0.029
GR01_13 ~
male -0.181 0.138 -1.311 0.190 -0.181 -0.055
age -0.023 0.004 -5.515 0.000 -0.023 -0.219
edu 0.078 0.081 0.959 0.338 0.078 0.040
GR01_14 ~
male -0.303 0.145 -2.091 0.036 -0.303 -0.090
age -0.007 0.005 -1.540 0.124 -0.007 -0.065
edu 0.030 0.084 0.361 0.718 0.030 0.015
GR01_15 ~
male 0.048 0.132 0.360 0.719 0.048 0.016
age -0.005 0.004 -1.214 0.225 -0.005 -0.053
edu 0.024 0.078 0.300 0.764 0.024 0.013
PC01_01 ~
male -0.182 0.151 -1.206 0.228 -0.182 -0.053
age -0.004 0.005 -0.820 0.412 -0.004 -0.036
edu 0.110 0.087 1.255 0.210 0.110 0.054
PC01_02 ~
male -0.302 0.154 -1.967 0.049 -0.302 -0.085
age -0.008 0.005 -1.663 0.096 -0.008 -0.072
edu 0.047 0.089 0.522 0.601 0.047 0.022
PC01_04 ~
male -0.225 0.152 -1.476 0.140 -0.225 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.269 0.204 0.113 0.054
PC01_05 ~
male -0.098 0.154 -0.637 0.524 -0.098 -0.028
age -0.006 0.005 -1.164 0.244 -0.006 -0.051
edu 0.090 0.090 0.996 0.319 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.722 0.470 -0.108 -0.032
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.491 0.623 0.043 0.021
PC01_07 ~
male -0.174 0.150 -1.160 0.246 -0.174 -0.050
age -0.006 0.005 -1.337 0.181 -0.006 -0.058
edu 0.081 0.087 0.934 0.351 0.081 0.040
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con ~~
grats_spec -0.115 0.067 -1.715 0.086 -0.092 -0.092
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.557 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.398 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.035
.PC01_05 3.414 0.304 11.229 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.155 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.780 0.000 3.461 2.008
.GR01_01 5.296 0.247 21.424 0.000 5.296 3.763
.GR01_02 5.895 0.205 28.694 0.000 5.895 4.835
.GR01_03 5.800 0.221 26.272 0.000 5.800 4.511
.GR01_04 4.923 0.211 23.378 0.000 4.923 3.730
.GR01_05 5.109 0.217 23.539 0.000 5.109 3.952
.GR01_06 5.297 0.252 21.013 0.000 5.297 3.581
.GR01_07 4.896 0.236 20.768 0.000 4.896 3.363
.GR01_08 5.062 0.238 21.233 0.000 5.062 3.691
.GR01_09 4.754 0.237 20.070 0.000 4.754 3.395
.GR01_10 4.977 0.255 19.492 0.000 4.977 3.425
.GR01_11 5.158 0.227 22.705 0.000 5.158 3.974
.GR01_12 5.136 0.233 22.001 0.000 5.136 3.679
.GR01_13 5.091 0.259 19.644 0.000 5.091 3.101
.GR01_14 3.454 0.281 12.310 0.000 3.454 2.061
.GR01_15 4.582 0.266 17.224 0.000 4.582 2.998
.self_dis_log 1.376 0.375 3.673 0.000 1.376 0.602
pri_con 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
grats_spec 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.394 0.050 7.919 0.000 0.394 0.133
.PC01_02 0.580 0.104 5.592 0.000 0.580 0.186
.PC01_04 0.636 0.079 8.086 0.000 0.636 0.207
.PC01_05 0.540 0.065 8.266 0.000 0.540 0.174
.PC01_06 1.079 0.117 9.221 0.000 1.079 0.367
.PC01_07 0.424 0.064 6.635 0.000 0.424 0.143
.GR01_01 1.038 0.109 9.534 0.000 1.038 0.524
.GR01_02 0.486 0.066 7.412 0.000 0.486 0.327
.GR01_03 0.440 0.068 6.498 0.000 0.440 0.266
.GR01_04 0.349 0.043 8.145 0.000 0.349 0.200
.GR01_05 0.458 0.057 8.096 0.000 0.458 0.274
.GR01_06 1.085 0.108 10.006 0.000 1.085 0.496
.GR01_07 0.679 0.070 9.765 0.000 0.679 0.321
.GR01_08 0.635 0.068 9.275 0.000 0.635 0.337
.GR01_09 0.647 0.072 9.034 0.000 0.647 0.330
.GR01_10 0.773 0.070 11.111 0.000 0.773 0.366
.GR01_11 0.364 0.055 6.572 0.000 0.364 0.216
.GR01_12 0.813 0.077 10.575 0.000 0.813 0.417
.GR01_13 1.871 0.147 12.728 0.000 1.871 0.694
.GR01_14 2.052 0.123 16.716 0.000 2.052 0.731
.GR01_15 0.629 0.113 5.576 0.000 0.629 0.270
.self_dis_log 4.798 0.202 23.739 0.000 4.798 0.920
pri_con 2.554 0.144 17.722 0.000 1.000 1.000
.grats_inf 0.286 0.045 6.423 0.000 0.317 0.317
.grats_rel 0.205 0.049 4.144 0.000 0.147 0.147
.grats_par 0.080 0.052 1.540 0.123 0.056 0.056
.grats_ide 0.182 0.047 3.836 0.000 0.137 0.137
.grats_ext 0.257 0.066 3.914 0.000 0.381 0.381
grats_spec 0.617 0.118 5.223 0.000 1.000 1.000rsquare_fit_simple <- inspect(fit_simple, what = "rsquare")["self_dis_log"]Building on the Model “Adapted”, we now remove the variable Trust General.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
self_dis_log ~ a1*pri_con + b1*grats_spec + c1*pri_delib + d1*self_eff
# Covariates
self_dis_log + GR01_01 + GR01_02 + GR01_03 + GR01_04 + GR01_05 + GR01_06 + GR01_07 + GR01_08 + GR01_09 + GR01_10 + GR01_11 + GR01_12 + GR01_13 + GR01_14 + GR01_15 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 346 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 202
Number of equality constraints 1
Used Total
Number of observations 558 559
Number of missing patterns 2
Model Test User Model:
Standard Robust
Test Statistic 1253.386 932.347
Degrees of freedom 419 419
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.344
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 12739.213 9344.473
Degrees of freedom 558 558
P-value 0.000 0.000
Scaling correction factor 1.363
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.932 0.942
Tucker-Lewis Index (TLI) 0.909 0.922
Robust Comparative Fit Index (CFI) 0.942
Robust Tucker-Lewis Index (TLI) 0.923
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -26032.954 -26032.954
Scaling correction factor 1.271
for the MLR correction
Loglikelihood unrestricted model (H1) -25406.261 -25406.261
Scaling correction factor 1.323
for the MLR correction
Akaike (AIC) 52467.908 52467.908
Bayesian (BIC) 53337.104 53337.104
Sample-size adjusted Bayesian (BIC) 52699.034 52699.034
Root Mean Square Error of Approximation:
RMSEA 0.060 0.047
90 Percent confidence interval - lower 0.056 0.043
90 Percent confidence interval - upper 0.064 0.050
P-value RMSEA <= 0.05 0.000 0.931
Robust RMSEA 0.054
90 Percent confidence interval - lower 0.050
90 Percent confidence interval - upper 0.059
Standardized Root Mean Square Residual:
SRMR 0.058 0.058
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.596 0.926
PC01_02 0.990 0.027 36.230 0.000 1.579 0.894
PC01_04 0.971 0.027 35.512 0.000 1.549 0.883
PC01_05 1.002 0.024 42.333 0.000 1.598 0.907
PC01_06 0.855 0.038 22.653 0.000 1.364 0.795
PC01_07 0.995 0.023 43.722 0.000 1.587 0.921
grats_inf =~
GR01_01 1.000 0.947 0.673
GR01_02 1.048 0.078 13.486 0.000 0.992 0.814
GR01_03 1.153 0.081 14.304 0.000 1.092 0.849
grats_rel =~
GR01_04 1.000 1.179 0.893
GR01_05 0.935 0.038 24.875 0.000 1.102 0.853
GR01_06 0.878 0.047 18.776 0.000 1.036 0.700
grats_par =~
GR01_07 1.000 1.193 0.819
GR01_08 0.933 0.039 23.636 0.000 1.113 0.812
GR01_09 0.955 0.039 24.573 0.000 1.139 0.813
grats_ide =~
GR01_10 1.000 1.149 0.790
GR01_11 1.000 0.042 23.710 0.000 1.148 0.885
GR01_12 0.920 0.041 22.193 0.000 1.056 0.757
grats_ext =~
GR01_13 1.000 0.824 0.502
GR01_14 1.028 0.104 9.885 0.000 0.847 0.506
GR01_15 1.578 0.189 8.355 0.000 1.301 0.851
grats_spec =~
grats_inf 1.000 0.831 0.831
grats_rel 1.387 0.114 12.164 0.000 0.925 0.925
grats_par 1.479 0.128 11.522 0.000 0.975 0.975
grats_ide 1.350 0.116 11.646 0.000 0.924 0.924
grats_ext 0.816 0.109 7.451 0.000 0.778 0.778
pri_delib =~
PD01_01 1.000 1.476 0.855
PD01_02 0.669 0.048 13.896 0.000 0.988 0.643
PD01_03 0.705 0.054 13.047 0.000 1.041 0.668
PD01_04 0.841 0.047 17.974 0.000 1.242 0.728
PD01_05 0.713 0.049 14.456 0.000 1.052 0.635
self_eff =~
SE01_01 1.000 1.118 0.810
SE01_02 0.807 0.059 13.767 0.000 0.902 0.669
SE01_03 0.927 0.047 19.527 0.000 1.036 0.773
SE01_04 0.955 0.044 21.876 0.000 1.068 0.791
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
self_dis_log ~
pri_con (a1) -0.027 0.076 -0.354 0.723 -0.043 -0.019
grats_spc (b1) 0.193 0.171 1.126 0.260 0.152 0.066
pri_delib (c1) -0.187 0.094 -1.995 0.046 -0.277 -0.121
self_eff (d1) 0.628 0.134 4.693 0.000 0.702 0.307
male -0.022 0.197 -0.109 0.913 -0.022 -0.005
age 0.003 0.006 0.570 0.569 0.003 0.024
edu 0.213 0.116 1.836 0.066 0.213 0.078
GR01_01 ~
male -0.342 0.121 -2.833 0.005 -0.342 -0.122
age -0.005 0.004 -1.333 0.182 -0.005 -0.059
edu 0.000 0.072 0.006 0.995 0.000 0.000
GR01_02 ~
male -0.142 0.103 -1.378 0.168 -0.142 -0.058
age -0.007 0.003 -2.107 0.035 -0.007 -0.088
edu -0.037 0.060 -0.617 0.537 -0.037 -0.026
GR01_03 ~
male -0.186 0.109 -1.713 0.087 -0.186 -0.073
age -0.006 0.004 -1.704 0.088 -0.006 -0.075
edu -0.076 0.063 -1.204 0.228 -0.076 -0.050
GR01_04 ~
male -0.004 0.113 -0.037 0.971 -0.004 -0.002
age 0.001 0.004 0.262 0.793 0.001 0.011
edu -0.021 0.066 -0.319 0.749 -0.021 -0.013
GR01_05 ~
male -0.069 0.112 -0.614 0.539 -0.069 -0.027
age -0.001 0.004 -0.198 0.843 -0.001 -0.009
edu 0.025 0.064 0.387 0.699 0.025 0.016
GR01_06 ~
male 0.030 0.125 0.241 0.809 0.030 0.010
age -0.011 0.004 -2.567 0.010 -0.011 -0.111
edu -0.087 0.074 -1.176 0.240 -0.087 -0.050
GR01_07 ~
male 0.075 0.125 0.602 0.547 0.075 0.026
age -0.006 0.004 -1.391 0.164 -0.006 -0.060
edu 0.004 0.072 0.057 0.955 0.004 0.002
GR01_08 ~
male 0.006 0.116 0.052 0.959 0.006 0.002
age -0.004 0.004 -1.121 0.262 -0.004 -0.051
edu 0.113 0.068 1.675 0.094 0.113 0.070
GR01_09 ~
male 0.090 0.119 0.757 0.449 0.090 0.032
age -0.004 0.004 -0.952 0.341 -0.004 -0.041
edu 0.115 0.069 1.667 0.096 0.115 0.069
GR01_10 ~
male -0.019 0.125 -0.156 0.876 -0.019 -0.007
age -0.008 0.004 -1.993 0.046 -0.008 -0.089
edu -0.021 0.074 -0.277 0.781 -0.021 -0.012
GR01_11 ~
male -0.083 0.111 -0.749 0.454 -0.083 -0.032
age -0.001 0.004 -0.313 0.755 -0.001 -0.014
edu -0.053 0.065 -0.822 0.411 -0.053 -0.034
GR01_12 ~
male -0.218 0.120 -1.820 0.069 -0.218 -0.078
age -0.004 0.004 -1.010 0.313 -0.004 -0.043
edu -0.049 0.071 -0.685 0.493 -0.049 -0.029
GR01_13 ~
male -0.181 0.138 -1.311 0.190 -0.181 -0.055
age -0.023 0.004 -5.515 0.000 -0.023 -0.219
edu 0.078 0.081 0.959 0.338 0.078 0.040
GR01_14 ~
male -0.303 0.145 -2.092 0.036 -0.303 -0.090
age -0.007 0.005 -1.540 0.124 -0.007 -0.065
edu 0.030 0.084 0.361 0.718 0.030 0.015
GR01_15 ~
male 0.048 0.132 0.360 0.719 0.048 0.016
age -0.005 0.004 -1.214 0.225 -0.005 -0.053
edu 0.024 0.078 0.300 0.764 0.024 0.013
PC01_01 ~
male -0.182 0.151 -1.206 0.228 -0.182 -0.053
age -0.004 0.005 -0.819 0.413 -0.004 -0.036
edu 0.110 0.087 1.255 0.210 0.110 0.054
PC01_02 ~
male -0.302 0.154 -1.966 0.049 -0.302 -0.085
age -0.008 0.005 -1.663 0.096 -0.008 -0.072
edu 0.047 0.089 0.522 0.601 0.047 0.022
PC01_04 ~
male -0.225 0.152 -1.475 0.140 -0.225 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.269 0.204 0.113 0.054
PC01_05 ~
male -0.098 0.154 -0.636 0.525 -0.098 -0.028
age -0.006 0.005 -1.164 0.244 -0.006 -0.051
edu 0.090 0.090 0.996 0.319 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.722 0.470 -0.108 -0.032
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.491 0.623 0.043 0.021
PC01_07 ~
male -0.174 0.150 -1.160 0.246 -0.174 -0.050
age -0.006 0.005 -1.337 0.181 -0.006 -0.058
edu 0.081 0.087 0.934 0.350 0.081 0.040
PD01_01 ~
male -0.177 0.148 -1.197 0.231 -0.177 -0.051
age -0.015 0.005 -3.275 0.001 -0.015 -0.137
edu -0.026 0.085 -0.310 0.756 -0.026 -0.013
PD01_02 ~
male -0.119 0.131 -0.906 0.365 -0.119 -0.039
age -0.014 0.004 -3.443 0.001 -0.014 -0.142
edu 0.031 0.077 0.405 0.686 0.031 0.017
PD01_03 ~
male -0.321 0.132 -2.425 0.015 -0.321 -0.103
age -0.004 0.004 -1.024 0.306 -0.004 -0.044
edu 0.065 0.080 0.807 0.419 0.065 0.035
PD01_04 ~
male -0.412 0.145 -2.847 0.004 -0.412 -0.121
age -0.009 0.005 -1.904 0.057 -0.009 -0.082
edu 0.103 0.085 1.207 0.227 0.103 0.051
PD01_05 ~
male -0.205 0.142 -1.439 0.150 -0.205 -0.062
age -0.012 0.004 -2.696 0.007 -0.012 -0.111
edu -0.002 0.084 -0.025 0.980 -0.002 -0.001
SE01_01 ~
male 0.118 0.118 0.996 0.319 0.118 0.043
age -0.000 0.004 -0.007 0.995 -0.000 -0.000
edu 0.209 0.068 3.082 0.002 0.209 0.128
SE01_02 ~
male 0.057 0.112 0.514 0.607 0.057 0.021
age -0.013 0.004 -3.609 0.000 -0.013 -0.152
edu 0.197 0.066 2.975 0.003 0.197 0.123
SE01_03 ~
male 0.192 0.114 1.682 0.093 0.192 0.072
age 0.001 0.004 0.232 0.816 0.001 0.010
edu 0.141 0.067 2.105 0.035 0.141 0.089
SE01_04 ~
male 0.051 0.115 0.447 0.655 0.051 0.019
age 0.007 0.004 2.032 0.042 0.007 0.085
edu 0.125 0.066 1.878 0.060 0.125 0.078
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.108 0.045 2.421 0.015 0.108 0.143
.SE01_03 ~~
.SE01_04 (x) 0.108 0.045 2.421 0.015 0.108 0.160
pri_con ~~
grats_spec -0.115 0.067 -1.721 0.085 -0.092 -0.092
pri_delib 1.327 0.130 10.211 0.000 0.563 0.563
self_eff -0.381 0.091 -4.169 0.000 -0.213 -0.213
grats_spec ~~
pri_delib -0.004 0.071 -0.058 0.954 -0.004 -0.004
self_eff 0.479 0.061 7.916 0.000 0.545 0.545
pri_delib ~~
self_eff -0.327 0.095 -3.455 0.001 -0.198 -0.198
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.557 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.398 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.035
.PC01_05 3.414 0.304 11.229 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.155 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.780 0.000 3.461 2.008
.GR01_01 5.296 0.247 21.424 0.000 5.296 3.763
.GR01_02 5.895 0.205 28.694 0.000 5.895 4.835
.GR01_03 5.800 0.221 26.272 0.000 5.800 4.511
.GR01_04 4.923 0.211 23.378 0.000 4.923 3.730
.GR01_05 5.109 0.217 23.539 0.000 5.109 3.952
.GR01_06 5.297 0.252 21.013 0.000 5.297 3.581
.GR01_07 4.896 0.236 20.768 0.000 4.896 3.363
.GR01_08 5.062 0.238 21.233 0.000 5.062 3.691
.GR01_09 4.755 0.237 20.070 0.000 4.755 3.395
.GR01_10 4.977 0.255 19.492 0.000 4.977 3.425
.GR01_11 5.158 0.227 22.706 0.000 5.158 3.974
.GR01_12 5.136 0.233 22.001 0.000 5.136 3.679
.GR01_13 5.091 0.259 19.644 0.000 5.091 3.101
.GR01_14 3.454 0.281 12.310 0.000 3.454 2.061
.GR01_15 4.582 0.266 17.224 0.000 4.582 2.998
.PD01_01 4.493 0.290 15.507 0.000 4.493 2.602
.PD01_02 3.997 0.248 16.102 0.000 3.997 2.601
.PD01_03 4.432 0.270 16.437 0.000 4.432 2.845
.PD01_04 4.506 0.295 15.283 0.000 4.506 2.640
.PD01_05 5.000 0.276 18.089 0.000 5.000 3.018
.SE01_01 4.829 0.250 19.354 0.000 4.829 3.498
.SE01_02 5.735 0.234 24.460 0.000 5.735 4.250
.SE01_03 4.824 0.226 21.319 0.000 4.824 3.598
.SE01_04 4.538 0.234 19.372 0.000 4.538 3.363
.self_dis_log 1.376 0.375 3.673 0.000 1.376 0.602
pri_con 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
grats_spec 0.000 0.000 0.000
pri_delib 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.402 0.050 8.021 0.000 0.402 0.136
.PC01_02 0.580 0.102 5.676 0.000 0.580 0.186
.PC01_04 0.631 0.078 8.114 0.000 0.631 0.205
.PC01_05 0.534 0.064 8.333 0.000 0.534 0.172
.PC01_06 1.068 0.116 9.243 0.000 1.068 0.363
.PC01_07 0.430 0.065 6.631 0.000 0.430 0.145
.GR01_01 1.045 0.109 9.618 0.000 1.045 0.528
.GR01_02 0.483 0.065 7.420 0.000 0.483 0.325
.GR01_03 0.436 0.066 6.574 0.000 0.436 0.264
.GR01_04 0.351 0.043 8.198 0.000 0.351 0.202
.GR01_05 0.455 0.057 8.050 0.000 0.455 0.272
.GR01_06 1.086 0.108 10.011 0.000 1.086 0.496
.GR01_07 0.688 0.070 9.873 0.000 0.688 0.325
.GR01_08 0.626 0.067 9.300 0.000 0.626 0.333
.GR01_09 0.647 0.072 9.000 0.000 0.647 0.330
.GR01_10 0.775 0.070 11.048 0.000 0.775 0.367
.GR01_11 0.362 0.055 6.527 0.000 0.362 0.215
.GR01_12 0.814 0.077 10.595 0.000 0.814 0.418
.GR01_13 1.866 0.148 12.621 0.000 1.866 0.692
.GR01_14 2.050 0.124 16.543 0.000 2.050 0.731
.GR01_15 0.635 0.115 5.519 0.000 0.635 0.272
.PD01_01 0.734 0.108 6.807 0.000 0.734 0.246
.PD01_02 1.330 0.127 10.433 0.000 1.330 0.563
.PD01_03 1.310 0.128 10.274 0.000 1.310 0.540
.PD01_04 1.298 0.146 8.867 0.000 1.298 0.446
.PD01_05 1.589 0.128 12.436 0.000 1.589 0.579
.SE01_01 0.619 0.089 6.983 0.000 0.619 0.325
.SE01_02 0.931 0.118 7.890 0.000 0.931 0.511
.SE01_03 0.697 0.095 7.325 0.000 0.697 0.388
.SE01_04 0.656 0.078 8.409 0.000 0.656 0.360
.self_dis_log 4.369 0.200 21.840 0.000 4.369 0.837
pri_con 2.546 0.145 17.616 0.000 1.000 1.000
.grats_inf 0.278 0.043 6.395 0.000 0.310 0.310
.grats_rel 0.200 0.050 4.033 0.000 0.144 0.144
.grats_par 0.071 0.050 1.422 0.155 0.050 0.050
.grats_ide 0.193 0.048 4.051 0.000 0.146 0.146
.grats_ext 0.268 0.069 3.914 0.000 0.395 0.395
grats_spec 0.618 0.116 5.335 0.000 1.000 1.000
pri_delib 2.180 0.156 13.989 0.000 1.000 1.000
self_eff 1.250 0.114 10.972 0.000 1.000 1.000Interestingly, now almost all effects disappear.
As stated in our preregistration, we also provide the results of all analyses without controlling for covariates. The results remain virtually the same.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
trust_spec =~ trust_community + trust_provider
COMM_log ~ a1*pri_con + b1*grats_gen + c1*pri_delib + d1*self_eff + e1*trust_spec
"
fit_prereg <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit_prereg, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 122 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 108
Number of equality constraints 1
Number of observations 559
Number of missing patterns 3
Model Test User Model:
Standard Robust
Test Statistic 1242.924 948.812
Degrees of freedom 388 388
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.310
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 13164.929 9384.152
Degrees of freedom 435 435
P-value 0.000 0.000
Scaling correction factor 1.403
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.933 0.937
Tucker-Lewis Index (TLI) 0.925 0.930
Robust Comparative Fit Index (CFI) 0.941
Robust Tucker-Lewis Index (TLI) 0.934
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -23989.445 -23989.445
Scaling correction factor 1.466
for the MLR correction
Loglikelihood unrestricted model (H1) -23367.982 -23367.982
Scaling correction factor 1.347
for the MLR correction
Akaike (AIC) 48192.889 48192.889
Bayesian (BIC) 48655.787 48655.787
Sample-size adjusted Bayesian (BIC) 48316.117 48316.117
Root Mean Square Error of Approximation:
RMSEA 0.063 0.051
90 Percent confidence interval - lower 0.059 0.047
90 Percent confidence interval - upper 0.067 0.054
P-value RMSEA <= 0.05 0.000 0.343
Robust RMSEA 0.058
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.063
Standardized Root Mean Square Residual:
SRMR 0.054 0.054
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.602 0.929
PC01_02 0.994 0.027 36.156 0.000 1.592 0.901
PC01_04 0.977 0.027 35.592 0.000 1.566 0.892
PC01_05 1.001 0.024 41.957 0.000 1.604 0.910
PC01_06 0.854 0.038 22.423 0.000 1.368 0.798
PC01_07 0.996 0.022 44.917 0.000 1.595 0.925
grats_gen =~
GR02_01 1.000 1.132 0.844
GR02_02 1.121 0.034 33.370 0.000 1.269 0.896
GR02_03 1.022 0.048 21.321 0.000 1.157 0.865
GR02_04 0.986 0.049 20.298 0.000 1.116 0.849
GR02_05 1.070 0.040 26.535 0.000 1.211 0.845
pri_delib =~
PD01_01 1.000 1.491 0.864
PD01_02 0.677 0.047 14.364 0.000 1.009 0.657
PD01_03 0.708 0.053 13.246 0.000 1.055 0.678
PD01_04 0.848 0.047 18.192 0.000 1.264 0.741
PD01_05 0.723 0.048 14.944 0.000 1.078 0.652
self_eff =~
SE01_01 1.000 1.135 0.822
SE01_02 0.805 0.059 13.703 0.000 0.913 0.677
SE01_03 0.926 0.045 20.681 0.000 1.051 0.784
SE01_04 0.938 0.042 22.274 0.000 1.065 0.789
trust_community =~
TR01_02 1.000 1.031 0.814
TR01_03 0.819 0.051 16.027 0.000 0.844 0.768
TR01_04 0.918 0.046 19.904 0.000 0.946 0.820
trust_provider =~
TR01_06 1.000 1.047 0.873
TR01_07 0.854 0.039 21.694 0.000 0.895 0.773
TR01_08 0.832 0.041 20.230 0.000 0.871 0.786
TR01_10 0.788 0.038 20.521 0.000 0.826 0.701
TR01_11 0.821 0.052 15.878 0.000 0.860 0.663
TR01_12 1.100 0.039 28.278 0.000 1.152 0.857
trust_spec =~
trust_communty 1.000 0.872 0.872
trust_provider 1.111 0.077 14.363 0.000 0.954 0.954
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
COMM_log ~
pri_con (a1) -0.048 0.080 -0.597 0.551 -0.077 -0.033
grats_gen (b1) 0.079 0.167 0.474 0.636 0.090 0.039
pri_delib (c1) -0.154 0.092 -1.676 0.094 -0.230 -0.100
self_eff (d1) 0.817 0.147 5.546 0.000 0.927 0.401
trust_spc (e1) -0.259 0.271 -0.956 0.339 -0.233 -0.101
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.116 0.046 2.521 0.012 0.116 0.149
.SE01_03 ~~
.SE01_04 (x) 0.116 0.046 2.521 0.012 0.116 0.168
pri_con ~~
grats_gen -0.283 0.096 -2.939 0.003 -0.156 -0.156
pri_delib 1.353 0.132 10.238 0.000 0.566 0.566
self_eff -0.382 0.092 -4.159 0.000 -0.210 -0.210
trust_spec -0.415 0.075 -5.538 0.000 -0.288 -0.288
grats_gen ~~
pri_delib -0.071 0.102 -0.699 0.484 -0.042 -0.042
self_eff 0.463 0.067 6.858 0.000 0.360 0.360
trust_spec 0.800 0.086 9.309 0.000 0.786 0.786
pri_delib ~~
self_eff -0.335 0.093 -3.579 0.000 -0.198 -0.198
trust_spec -0.127 0.085 -1.487 0.137 -0.095 -0.095
self_eff ~~
trust_spec 0.553 0.060 9.298 0.000 0.542 0.542
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.293 0.073 45.160 0.000 3.293 1.910
.PC01_02 3.327 0.075 44.525 0.000 3.327 1.883
.PC01_04 3.222 0.074 43.395 0.000 3.222 1.835
.PC01_05 3.263 0.075 43.748 0.000 3.263 1.850
.PC01_06 3.004 0.073 41.410 0.000 3.004 1.751
.PC01_07 3.224 0.073 44.188 0.000 3.224 1.869
.GR02_01 4.281 0.057 75.413 0.000 4.281 3.190
.GR02_02 4.596 0.060 76.742 0.000 4.596 3.246
.GR02_03 5.131 0.057 90.628 0.000 5.131 3.833
.GR02_04 5.089 0.056 91.559 0.000 5.089 3.873
.GR02_05 4.692 0.061 77.447 0.000 4.692 3.276
.PD01_01 3.658 0.073 50.136 0.000 3.658 2.121
.PD01_02 3.352 0.065 51.628 0.000 3.352 2.184
.PD01_03 4.191 0.066 63.662 0.000 4.191 2.693
.PD01_04 4.080 0.072 56.578 0.000 4.080 2.393
.PD01_05 4.351 0.070 62.149 0.000 4.351 2.629
.SE01_01 5.278 0.058 90.221 0.000 5.278 3.823
.SE01_02 5.524 0.057 96.595 0.000 5.524 4.094
.SE01_03 5.224 0.057 92.105 0.000 5.224 3.896
.SE01_04 5.138 0.057 89.971 0.000 5.138 3.806
.TR01_02 4.764 0.054 88.923 0.000 4.764 3.761
.TR01_03 4.844 0.046 104.195 0.000 4.844 4.407
.TR01_04 4.615 0.049 94.568 0.000 4.615 4.000
.TR01_06 5.403 0.051 106.478 0.000 5.403 4.504
.TR01_07 5.200 0.049 106.180 0.000 5.200 4.491
.TR01_08 5.129 0.047 109.405 0.000 5.129 4.627
.TR01_10 5.725 0.050 114.993 0.000 5.725 4.864
.TR01_11 4.834 0.055 88.152 0.000 4.834 3.728
.TR01_12 5.179 0.057 91.089 0.000 5.179 3.853
.COMM_log 1.834 0.098 18.765 0.000 1.834 0.794
pri_con 0.000 0.000 0.000
grats_gen 0.000 0.000 0.000
pri_delib 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
.trust_communty 0.000 0.000 0.000
.trust_provider 0.000 0.000 0.000
trust_spec 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.405 0.050 8.078 0.000 0.405 0.136
.PC01_02 0.586 0.104 5.657 0.000 0.586 0.188
.PC01_04 0.631 0.077 8.206 0.000 0.631 0.205
.PC01_05 0.536 0.065 8.262 0.000 0.536 0.172
.PC01_06 1.069 0.116 9.189 0.000 1.069 0.363
.PC01_07 0.430 0.065 6.586 0.000 0.430 0.144
.GR02_01 0.519 0.055 9.502 0.000 0.519 0.288
.GR02_02 0.394 0.041 9.680 0.000 0.394 0.196
.GR02_03 0.452 0.072 6.240 0.000 0.452 0.252
.GR02_04 0.481 0.049 9.909 0.000 0.481 0.279
.GR02_05 0.585 0.066 8.887 0.000 0.585 0.285
.PD01_01 0.754 0.111 6.803 0.000 0.754 0.253
.PD01_02 1.339 0.130 10.305 0.000 1.339 0.568
.PD01_03 1.309 0.129 10.161 0.000 1.309 0.540
.PD01_04 1.311 0.147 8.905 0.000 1.311 0.451
.PD01_05 1.576 0.128 12.271 0.000 1.576 0.575
.SE01_01 0.618 0.088 6.992 0.000 0.618 0.324
.SE01_02 0.986 0.129 7.668 0.000 0.986 0.542
.SE01_03 0.694 0.096 7.224 0.000 0.694 0.386
.SE01_04 0.689 0.081 8.534 0.000 0.689 0.378
.TR01_02 0.542 0.070 7.753 0.000 0.542 0.338
.TR01_03 0.496 0.055 9.039 0.000 0.496 0.410
.TR01_04 0.436 0.045 9.767 0.000 0.436 0.328
.TR01_06 0.343 0.035 9.820 0.000 0.343 0.238
.TR01_07 0.541 0.053 10.253 0.000 0.541 0.403
.TR01_08 0.469 0.043 10.871 0.000 0.469 0.382
.TR01_10 0.704 0.057 12.267 0.000 0.704 0.508
.TR01_11 0.942 0.079 11.950 0.000 0.942 0.560
.TR01_12 0.480 0.053 9.021 0.000 0.480 0.266
.COMM_log 4.450 0.219 20.335 0.000 4.450 0.833
pri_con 2.568 0.147 17.514 0.000 1.000 1.000
grats_gen 1.282 0.115 11.166 0.000 1.000 1.000
pri_delib 2.223 0.158 14.076 0.000 1.000 1.000
self_eff 1.288 0.115 11.249 0.000 1.000 1.000
.trust_communty 0.254 0.045 5.620 0.000 0.239 0.239
.trust_provider 0.099 0.044 2.257 0.024 0.090 0.090
trust_spec 0.808 0.099 8.153 0.000 1.000 1.000rsquare_fit_prereg <- inspect(fit_prereg, what = "rsquare")["comm"]Building on the preregistered model, instead of general gratifications and specific trust, we now use specific gratifications and general trust.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_gen =~ TR01_01 + TR01_05 + TR01_09
COMM_log ~ a1*pri_con + b1*grats_spec + c1*pri_delib + d1*self_eff + e1*trust_gen
"
fit_adapted <- sem(model, data = d, estimator = "MLR", missing = "ML", missing = "ML")
summary(fit_adapted, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 132 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 123
Number of equality constraints 1
Number of observations 559
Number of missing patterns 2
Model Test User Model:
Standard Robust
Test Statistic 1513.741 1142.051
Degrees of freedom 507 507
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.325
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 14130.591 10081.550
Degrees of freedom 561 561
P-value 0.000 0.000
Scaling correction factor 1.402
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.926 0.933
Tucker-Lewis Index (TLI) 0.918 0.926
Robust Comparative Fit Index (CFI) 0.937
Robust Tucker-Lewis Index (TLI) 0.930
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -28170.779 -28170.779
Scaling correction factor 1.461
for the MLR correction
Loglikelihood unrestricted model (H1) -27413.909 -27413.909
Scaling correction factor 1.354
for the MLR correction
Akaike (AIC) 56585.559 56585.559
Bayesian (BIC) 57113.349 57113.349
Sample-size adjusted Bayesian (BIC) 56726.062 56726.062
Root Mean Square Error of Approximation:
RMSEA 0.060 0.047
90 Percent confidence interval - lower 0.056 0.044
90 Percent confidence interval - upper 0.063 0.051
P-value RMSEA <= 0.05 0.000 0.915
Robust RMSEA 0.054
90 Percent confidence interval - lower 0.050
90 Percent confidence interval - upper 0.059
Standardized Root Mean Square Residual:
SRMR 0.065 0.065
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.602 0.929
PC01_02 0.993 0.027 36.391 0.000 1.592 0.901
PC01_04 0.977 0.027 35.851 0.000 1.565 0.892
PC01_05 1.002 0.024 42.031 0.000 1.605 0.910
PC01_06 0.855 0.038 22.512 0.000 1.369 0.798
PC01_07 0.996 0.022 45.085 0.000 1.596 0.925
grats_inf =~
GR01_01 1.000 0.967 0.688
GR01_02 1.035 0.075 13.862 0.000 1.001 0.820
GR01_03 1.130 0.076 14.912 0.000 1.093 0.850
grats_rel =~
GR01_04 1.000 1.174 0.890
GR01_05 0.942 0.037 25.157 0.000 1.106 0.856
GR01_06 0.882 0.047 18.900 0.000 1.036 0.701
grats_par =~
GR01_07 1.000 1.190 0.818
GR01_08 0.942 0.040 23.593 0.000 1.121 0.818
GR01_09 0.962 0.039 24.791 0.000 1.145 0.818
grats_ide =~
GR01_10 1.000 1.147 0.790
GR01_11 1.001 0.041 24.235 0.000 1.149 0.885
GR01_12 0.929 0.041 22.543 0.000 1.065 0.764
grats_ext =~
GR01_13 1.000 0.861 0.525
GR01_14 0.996 0.098 10.114 0.000 0.857 0.512
GR01_15 1.500 0.176 8.514 0.000 1.291 0.845
grats_spec =~
grats_inf 1.000 0.841 0.841
grats_rel 1.332 0.103 12.903 0.000 0.923 0.923
grats_par 1.410 0.116 12.168 0.000 0.964 0.964
grats_ide 1.308 0.105 12.425 0.000 0.927 0.927
grats_ext 0.824 0.107 7.714 0.000 0.779 0.779
pri_delib =~
PD01_01 1.000 1.499 0.869
PD01_02 0.675 0.047 14.347 0.000 1.011 0.659
PD01_03 0.699 0.053 13.182 0.000 1.048 0.673
PD01_04 0.843 0.046 18.185 0.000 1.264 0.741
PD01_05 0.714 0.048 14.804 0.000 1.069 0.646
self_eff =~
SE01_01 1.000 1.138 0.824
SE01_02 0.804 0.059 13.589 0.000 0.915 0.678
SE01_03 0.920 0.044 21.013 0.000 1.047 0.781
SE01_04 0.936 0.042 22.277 0.000 1.066 0.789
trust_gen =~
TR01_01 1.000 0.770 0.668
TR01_05 1.330 0.071 18.836 0.000 1.024 0.890
TR01_09 1.456 0.080 18.096 0.000 1.121 0.929
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
COMM_log ~
pri_con (a1) -0.086 0.081 -1.057 0.291 -0.138 -0.060
grats_spc (b1) 0.380 0.201 1.895 0.058 0.310 0.134
pri_delib (c1) -0.194 0.093 -2.099 0.036 -0.291 -0.126
self_eff (d1) 0.737 0.143 5.138 0.000 0.839 0.363
trust_gen (e1) -0.485 0.222 -2.188 0.029 -0.374 -0.162
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.117 0.046 2.535 0.011 0.117 0.150
.SE01_03 ~~
.SE01_04 (x) 0.117 0.046 2.535 0.011 0.117 0.168
pri_con ~~
grats_spec -0.108 0.069 -1.563 0.118 -0.083 -0.083
pri_delib 1.362 0.131 10.361 0.000 0.567 0.567
self_eff -0.386 0.092 -4.191 0.000 -0.211 -0.211
trust_gen -0.519 0.066 -7.864 0.000 -0.421 -0.421
grats_spec ~~
pri_delib 0.010 0.073 0.136 0.892 0.008 0.008
self_eff 0.494 0.060 8.252 0.000 0.533 0.533
trust_gen 0.407 0.058 6.983 0.000 0.650 0.650
pri_delib ~~
self_eff -0.338 0.094 -3.592 0.000 -0.198 -0.198
trust_gen -0.299 0.071 -4.197 0.000 -0.259 -0.259
self_eff ~~
trust_gen 0.458 0.056 8.250 0.000 0.523 0.523
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.293 0.073 45.160 0.000 3.293 1.910
.PC01_02 3.327 0.075 44.525 0.000 3.327 1.883
.PC01_04 3.222 0.074 43.395 0.000 3.222 1.835
.PC01_05 3.263 0.075 43.748 0.000 3.263 1.850
.PC01_06 3.004 0.073 41.410 0.000 3.004 1.751
.PC01_07 3.224 0.073 44.188 0.000 3.224 1.869
.GR01_01 4.878 0.059 82.009 0.000 4.878 3.469
.GR01_02 5.436 0.052 105.390 0.000 5.436 4.458
.GR01_03 5.283 0.054 97.076 0.000 5.283 4.106
.GR01_04 4.925 0.056 88.264 0.000 4.925 3.733
.GR01_05 5.086 0.055 93.032 0.000 5.086 3.935
.GR01_06 4.660 0.063 74.538 0.000 4.660 3.153
.GR01_07 4.682 0.062 76.077 0.000 4.682 3.218
.GR01_08 5.066 0.058 87.374 0.000 5.066 3.696
.GR01_09 4.841 0.059 81.781 0.000 4.841 3.459
.GR01_10 4.547 0.061 74.048 0.000 4.547 3.132
.GR01_11 4.964 0.055 90.449 0.000 4.964 3.826
.GR01_12 4.760 0.059 80.678 0.000 4.760 3.412
.GR01_13 4.079 0.069 58.781 0.000 4.079 2.486
.GR01_14 3.039 0.071 42.918 0.000 3.039 1.815
.GR01_15 4.410 0.065 68.283 0.000 4.410 2.888
.PD01_01 3.658 0.073 50.136 0.000 3.658 2.121
.PD01_02 3.352 0.065 51.628 0.000 3.352 2.184
.PD01_03 4.191 0.066 63.662 0.000 4.191 2.693
.PD01_04 4.080 0.072 56.578 0.000 4.080 2.393
.PD01_05 4.351 0.070 62.149 0.000 4.351 2.629
.SE01_01 5.277 0.059 90.171 0.000 5.277 3.819
.SE01_02 5.523 0.057 96.541 0.000 5.523 4.091
.SE01_03 5.223 0.057 92.072 0.000 5.223 3.895
.SE01_04 5.137 0.057 89.932 0.000 5.137 3.805
.TR01_01 4.846 0.049 99.399 0.000 4.846 4.204
.TR01_05 5.383 0.049 110.607 0.000 5.383 4.678
.TR01_09 5.390 0.051 105.542 0.000 5.390 4.464
.COMM_log 1.834 0.098 18.765 0.000 1.834 0.794
pri_con 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
grats_spec 0.000 0.000 0.000
pri_delib 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
trust_gen 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.406 0.050 8.109 0.000 0.406 0.137
.PC01_02 0.588 0.103 5.694 0.000 0.588 0.188
.PC01_04 0.632 0.077 8.227 0.000 0.632 0.205
.PC01_05 0.534 0.065 8.238 0.000 0.534 0.172
.PC01_06 1.066 0.116 9.192 0.000 1.066 0.363
.PC01_07 0.429 0.065 6.560 0.000 0.429 0.144
.GR01_01 1.043 0.109 9.553 0.000 1.043 0.527
.GR01_02 0.486 0.065 7.429 0.000 0.486 0.327
.GR01_03 0.460 0.068 6.742 0.000 0.460 0.278
.GR01_04 0.361 0.043 8.389 0.000 0.361 0.208
.GR01_05 0.447 0.057 7.858 0.000 0.447 0.268
.GR01_06 1.112 0.110 10.084 0.000 1.112 0.509
.GR01_07 0.700 0.071 9.873 0.000 0.700 0.331
.GR01_08 0.623 0.069 9.073 0.000 0.623 0.331
.GR01_09 0.647 0.072 8.921 0.000 0.647 0.330
.GR01_10 0.792 0.072 11.058 0.000 0.792 0.375
.GR01_11 0.364 0.056 6.554 0.000 0.364 0.216
.GR01_12 0.811 0.077 10.597 0.000 0.811 0.417
.GR01_13 1.951 0.161 12.149 0.000 1.951 0.725
.GR01_14 2.069 0.124 16.673 0.000 2.069 0.738
.GR01_15 0.665 0.115 5.793 0.000 0.665 0.285
.PD01_01 0.730 0.108 6.755 0.000 0.730 0.245
.PD01_02 1.334 0.130 10.249 0.000 1.334 0.566
.PD01_03 1.326 0.130 10.218 0.000 1.326 0.547
.PD01_04 1.311 0.147 8.934 0.000 1.311 0.451
.PD01_05 1.596 0.130 12.271 0.000 1.596 0.582
.SE01_01 0.614 0.088 6.985 0.000 0.614 0.321
.SE01_02 0.985 0.128 7.686 0.000 0.985 0.541
.SE01_03 0.702 0.094 7.470 0.000 0.702 0.390
.SE01_04 0.687 0.081 8.478 0.000 0.687 0.377
.TR01_01 0.736 0.062 11.837 0.000 0.736 0.554
.TR01_05 0.275 0.032 8.504 0.000 0.275 0.208
.TR01_09 0.200 0.037 5.449 0.000 0.200 0.137
.COMM_log 4.402 0.210 20.958 0.000 4.402 0.824
pri_con 2.567 0.146 17.535 0.000 1.000 1.000
.grats_inf 0.273 0.042 6.512 0.000 0.292 0.292
.grats_rel 0.205 0.048 4.276 0.000 0.148 0.148
.grats_par 0.100 0.052 1.924 0.054 0.071 0.071
.grats_ide 0.185 0.048 3.884 0.000 0.140 0.140
.grats_ext 0.291 0.074 3.949 0.000 0.393 0.393
grats_spec 0.662 0.117 5.660 0.000 1.000 1.000
pri_delib 2.246 0.157 14.310 0.000 1.000 1.000
self_eff 1.296 0.114 11.371 0.000 1.000 1.000
trust_gen 0.593 0.071 8.310 0.000 1.000 1.000rsquare_fit_adapted <- inspect(fit_adapted, what = "rsquare")["comm"]We now use only variables, that is specific gratifications and privacy concerns.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
COMM_log ~ a1*pri_con + b1*grats_spec
"
fit_simple <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit_simple, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 51 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 73
Number of observations 559
Number of missing patterns 1
Model Test User Model:
Standard Robust
Test Statistic 725.576 498.303
Degrees of freedom 202 202
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.456
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 9529.484 6135.147
Degrees of freedom 231 231
P-value 0.000 0.000
Scaling correction factor 1.553
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.944 0.950
Tucker-Lewis Index (TLI) 0.936 0.943
Robust Comparative Fit Index (CFI) 0.953
Robust Tucker-Lewis Index (TLI) 0.946
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -18251.766 -18251.766
Scaling correction factor 1.466
for the MLR correction
Loglikelihood unrestricted model (H1) -17888.978 -17888.978
Scaling correction factor 1.459
for the MLR correction
Akaike (AIC) 36649.532 36649.532
Bayesian (BIC) 36965.341 36965.341
Sample-size adjusted Bayesian (BIC) 36733.604 36733.604
Root Mean Square Error of Approximation:
RMSEA 0.068 0.051
90 Percent confidence interval - lower 0.063 0.047
90 Percent confidence interval - upper 0.073 0.056
P-value RMSEA <= 0.05 0.000 0.327
Robust RMSEA 0.062
90 Percent confidence interval - lower 0.055
90 Percent confidence interval - upper 0.069
Standardized Root Mean Square Residual:
SRMR 0.060 0.060
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.605 0.931
PC01_02 0.992 0.028 36.060 0.000 1.592 0.901
PC01_04 0.973 0.027 35.517 0.000 1.563 0.890
PC01_05 0.998 0.024 41.964 0.000 1.602 0.909
PC01_06 0.850 0.038 22.348 0.000 1.365 0.796
PC01_07 0.995 0.022 44.601 0.000 1.598 0.926
grats_inf =~
GR01_01 1.000 0.966 0.687
GR01_02 1.035 0.076 13.555 0.000 1.000 0.820
GR01_03 1.134 0.078 14.489 0.000 1.095 0.851
grats_rel =~
GR01_04 1.000 1.176 0.892
GR01_05 0.936 0.037 25.102 0.000 1.101 0.852
GR01_06 0.884 0.047 18.823 0.000 1.040 0.704
grats_par =~
GR01_07 1.000 1.200 0.825
GR01_08 0.928 0.040 22.954 0.000 1.114 0.813
GR01_09 0.952 0.040 24.051 0.000 1.142 0.816
grats_ide =~
GR01_10 1.000 1.154 0.795
GR01_11 0.993 0.042 23.561 0.000 1.146 0.883
GR01_12 0.920 0.042 22.071 0.000 1.062 0.761
grats_ext =~
GR01_13 1.000 0.849 0.517
GR01_14 1.007 0.100 10.031 0.000 0.855 0.510
GR01_15 1.531 0.178 8.591 0.000 1.299 0.851
grats_spec =~
grats_inf 1.000 0.824 0.824
grats_rel 1.365 0.111 12.310 0.000 0.923 0.923
grats_par 1.458 0.128 11.389 0.000 0.967 0.967
grats_ide 1.348 0.116 11.635 0.000 0.929 0.929
grats_ext 0.843 0.112 7.533 0.000 0.791 0.791
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
COMM_log ~
pri_con (a1) -0.192 0.061 -3.154 0.002 -0.308 -0.133
grats_spc (b1) 0.623 0.142 4.379 0.000 0.496 0.214
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con ~~
grats_spec -0.106 0.068 -1.558 0.119 -0.083 -0.083
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.293 0.073 45.160 0.000 3.293 1.910
.PC01_02 3.327 0.075 44.525 0.000 3.327 1.883
.PC01_04 3.222 0.074 43.395 0.000 3.222 1.835
.PC01_05 3.263 0.075 43.748 0.000 3.263 1.850
.PC01_06 3.004 0.073 41.410 0.000 3.004 1.751
.PC01_07 3.224 0.073 44.188 0.000 3.224 1.869
.GR01_01 4.878 0.059 82.009 0.000 4.878 3.469
.GR01_02 5.436 0.052 105.390 0.000 5.436 4.458
.GR01_03 5.283 0.054 97.076 0.000 5.283 4.106
.GR01_04 4.925 0.056 88.264 0.000 4.925 3.733
.GR01_05 5.086 0.055 93.032 0.000 5.086 3.935
.GR01_06 4.660 0.063 74.538 0.000 4.660 3.153
.GR01_07 4.682 0.062 76.077 0.000 4.682 3.218
.GR01_08 5.066 0.058 87.374 0.000 5.066 3.696
.GR01_09 4.841 0.059 81.781 0.000 4.841 3.459
.GR01_10 4.547 0.061 74.048 0.000 4.547 3.132
.GR01_11 4.964 0.055 90.449 0.000 4.964 3.826
.GR01_12 4.760 0.059 80.678 0.000 4.760 3.412
.GR01_13 4.079 0.069 58.781 0.000 4.079 2.486
.GR01_14 3.039 0.071 42.918 0.000 3.039 1.815
.GR01_15 4.410 0.065 68.283 0.000 4.410 2.888
.COMM_log 1.834 0.098 18.765 0.000 1.834 0.794
pri_con 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
grats_spec 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.396 0.050 7.919 0.000 0.396 0.133
.PC01_02 0.587 0.105 5.607 0.000 0.587 0.188
.PC01_04 0.639 0.078 8.162 0.000 0.639 0.207
.PC01_05 0.542 0.066 8.176 0.000 0.542 0.174
.PC01_06 1.078 0.118 9.165 0.000 1.078 0.367
.PC01_07 0.423 0.064 6.630 0.000 0.423 0.142
.GR01_01 1.045 0.111 9.441 0.000 1.045 0.528
.GR01_02 0.488 0.065 7.492 0.000 0.488 0.328
.GR01_03 0.456 0.069 6.592 0.000 0.456 0.275
.GR01_04 0.356 0.043 8.343 0.000 0.356 0.205
.GR01_05 0.457 0.057 7.978 0.000 0.457 0.274
.GR01_06 1.103 0.110 10.037 0.000 1.103 0.505
.GR01_07 0.676 0.069 9.809 0.000 0.676 0.320
.GR01_08 0.638 0.069 9.302 0.000 0.638 0.340
.GR01_09 0.654 0.074 8.871 0.000 0.654 0.334
.GR01_10 0.776 0.070 11.086 0.000 0.776 0.368
.GR01_11 0.370 0.057 6.543 0.000 0.370 0.220
.GR01_12 0.818 0.077 10.591 0.000 0.818 0.421
.GR01_13 1.971 0.158 12.462 0.000 1.971 0.732
.GR01_14 2.073 0.123 16.911 0.000 2.073 0.739
.GR01_15 0.643 0.113 5.712 0.000 0.643 0.276
.COMM_log 4.975 0.206 24.175 0.000 4.975 0.932
pri_con 2.577 0.146 17.609 0.000 1.000 1.000
.grats_inf 0.299 0.045 6.618 0.000 0.321 0.321
.grats_rel 0.204 0.050 4.107 0.000 0.147 0.147
.grats_par 0.093 0.055 1.700 0.089 0.065 0.065
.grats_ide 0.181 0.048 3.742 0.000 0.136 0.136
.grats_ext 0.270 0.069 3.899 0.000 0.375 0.375
grats_spec 0.634 0.118 5.362 0.000 1.000 1.000rsquare_fit_simple <- inspect(fit_simple, what = "rsquare")["comm"]As stated in the preregistration, we also report the analyses including the deleted participants. Results don’t change meaningfully.
model_baseline <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
trust_gen =~ TR01_01 + TR01_05 + TR01_09
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
trust_spec =~ trust_community + trust_provider
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
comm_log =~ COMM_log
COMM_log ~~ a1*pri_con + b1*grats_gen + c1*pri_delib + d1*self_eff + e1*trust_spec + f1*trust_gen + g1*grats_spec
"
fit_baseline <- sem(model_baseline, data = d_all, missing = "ML")
summary(fit_baseline, standardized = TRUE, fit.measures = TRUE)lavaan 0.6-8 ended normally after 165 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 187
Number of equality constraints 1
Number of observations 590
Number of missing patterns 4
Model Test User Model:
Test statistic 3304.927
Degrees of freedom 1038
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 24074.746
Degrees of freedom 1128
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.901
Tucker-Lewis Index (TLI) 0.893
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -39838.640
Loglikelihood unrestricted model (H1) -38186.176
Akaike (AIC) 80049.279
Bayesian (BIC) 80863.982
Sample-size adjusted Bayesian (BIC) 80273.494
Root Mean Square Error of Approximation:
RMSEA 0.061
90 Percent confidence interval - lower 0.059
90 Percent confidence interval - upper 0.063
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.066
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.622 0.926
PC01_02 0.996 0.026 37.848 0.000 1.616 0.905
PC01_04 0.971 0.027 36.242 0.000 1.576 0.892
PC01_05 1.003 0.026 39.030 0.000 1.628 0.913
PC01_06 0.868 0.031 28.279 0.000 1.409 0.808
PC01_07 1.001 0.024 41.245 0.000 1.625 0.928
grats_gen =~
GR02_01 1.000 1.148 0.847
GR02_02 1.116 0.038 29.247 0.000 1.281 0.894
GR02_03 1.016 0.037 27.084 0.000 1.166 0.870
GR02_04 0.983 0.038 26.075 0.000 1.129 0.851
GR02_05 1.067 0.040 26.702 0.000 1.225 0.852
grats_inf =~
GR01_01 1.000 0.999 0.710
GR01_02 1.014 0.058 17.575 0.000 1.013 0.824
GR01_03 1.107 0.062 17.827 0.000 1.106 0.848
grats_rel =~
GR01_04 1.000 1.180 0.891
GR01_05 0.950 0.033 28.726 0.000 1.121 0.860
GR01_06 0.883 0.044 20.112 0.000 1.042 0.707
grats_par =~
GR01_07 1.000 1.204 0.825
GR01_08 0.924 0.040 22.913 0.000 1.113 0.814
GR01_09 0.956 0.041 23.344 0.000 1.151 0.822
grats_ide =~
GR01_10 1.000 1.152 0.797
GR01_11 1.004 0.041 24.422 0.000 1.157 0.887
GR01_12 0.939 0.046 20.337 0.000 1.082 0.767
grats_ext =~
GR01_13 1.000 0.883 0.540
GR01_14 1.012 0.101 10.051 0.000 0.894 0.525
GR01_15 1.468 0.128 11.448 0.000 1.297 0.848
grats_spec =~
grats_inf 1.000 0.850 0.850
grats_rel 1.292 0.082 15.697 0.000 0.930 0.930
grats_par 1.356 0.090 15.083 0.000 0.956 0.956
grats_ide 1.279 0.087 14.673 0.000 0.942 0.942
grats_ext 0.822 0.083 9.897 0.000 0.790 0.790
pri_delib =~
PD01_01 1.000 1.493 0.866
PD01_02 0.705 0.040 17.752 0.000 1.052 0.677
PD01_03 0.711 0.042 16.989 0.000 1.062 0.678
PD01_04 0.848 0.043 19.880 0.000 1.266 0.744
PD01_05 0.731 0.043 16.952 0.000 1.092 0.663
trust_gen =~
TR01_01 1.000 0.837 0.724
TR01_05 1.251 0.061 20.573 0.000 1.047 0.888
TR01_09 1.312 0.063 20.722 0.000 1.099 0.897
trust_community =~
TR01_02 1.000 1.033 0.808
TR01_03 0.832 0.042 19.593 0.000 0.860 0.767
TR01_04 0.947 0.043 22.184 0.000 0.979 0.835
trust_provider =~
TR01_06 1.000 1.056 0.861
TR01_07 0.880 0.037 23.587 0.000 0.929 0.779
TR01_08 0.880 0.035 25.150 0.000 0.929 0.813
TR01_10 0.771 0.040 19.457 0.000 0.814 0.686
TR01_11 0.827 0.044 18.792 0.000 0.874 0.674
TR01_12 1.076 0.040 26.847 0.000 1.136 0.844
trust_spec =~
trust_communty 1.000 0.865 0.865
trust_provider 1.154 0.058 19.742 0.000 0.977 0.977
self_eff =~
SE01_01 1.000 1.158 0.828
SE01_02 0.834 0.044 18.888 0.000 0.966 0.705
SE01_03 0.919 0.045 20.286 0.000 1.064 0.789
SE01_04 0.920 0.045 20.377 0.000 1.065 0.779
comm_log =~
COMM_log 1.000 2.283 1.000
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.106 0.028 3.820 0.000 0.106 0.140
.SE01_03 ~~
.SE01_04 (x) 0.106 0.028 3.820 0.000 0.106 0.150
pri_con ~~
.COMM_log (a1) -0.308 0.079 -3.905 0.000 -0.190 -Inf
grats_gen ~~
.COMM_log (b1) 0.126 0.056 2.251 0.024 0.110 Inf
pri_delib ~~
.COMM_log (c1) -0.338 0.077 -4.399 0.000 -0.226 -Inf
self_eff ~~
.COMM_log (d1) 0.509 0.064 7.912 0.000 0.439 Inf
trust_spec ~~
.COMM_log (e1) 0.163 0.045 3.598 0.000 0.183 Inf
trust_gen ~~
.COMM_log (f1) 0.158 0.042 3.727 0.000 0.189 Inf
grats_spec ~~
.COMM_log (g1) 0.191 0.043 4.402 0.000 0.225 Inf
pri_con ~~
grats_gen -0.180 0.081 -2.220 0.026 -0.097 -0.097
grats_spc -0.030 0.060 -0.504 0.614 -0.022 -0.022
pri_delib 1.430 0.131 10.950 0.000 0.590 0.590
trust_gen -0.500 0.067 -7.504 0.000 -0.368 -0.368
trust_spc -0.355 0.067 -5.263 0.000 -0.245 -0.245
self_eff -0.383 0.088 -4.340 0.000 -0.204 -0.204
comm_log -0.308 0.079 -3.905 0.000 -0.083 -0.083
grats_gen ~~
grats_spc 0.769 0.071 10.828 0.000 0.789 0.789
pri_delib 0.025 0.079 0.322 0.747 0.015 0.015
trust_gen 0.599 0.058 10.398 0.000 0.623 0.623
trust_spc 0.782 0.068 11.560 0.000 0.762 0.762
self_eff 0.473 0.066 7.120 0.000 0.356 0.356
comm_log 0.126 0.056 2.251 0.024 0.048 0.048
grats_spec ~~
pri_delib 0.078 0.059 1.324 0.185 0.061 0.061
trust_gen 0.479 0.050 9.494 0.000 0.673 0.673
trust_spc 0.598 0.059 10.073 0.000 0.788 0.788
self_eff 0.495 0.058 8.499 0.000 0.503 0.503
comm_log 0.191 0.043 4.402 0.000 0.099 0.099
pri_delib ~~
trust_gen -0.247 0.061 -4.048 0.000 -0.197 -0.197
trust_spc -0.083 0.063 -1.306 0.192 -0.062 -0.062
self_eff -0.309 0.085 -3.627 0.000 -0.179 -0.179
comm_log -0.338 0.077 -4.399 0.000 -0.099 -0.099
trust_gen ~~
trust_spc 0.719 0.062 11.631 0.000 0.961 0.961
self_eff 0.521 0.056 9.278 0.000 0.537 0.537
comm_log 0.158 0.042 3.727 0.000 0.083 0.083
trust_spec ~~
self_eff 0.579 0.060 9.580 0.000 0.560 0.560
comm_log 0.163 0.045 3.598 0.000 0.080 0.080
self_eff ~~
comm_log 0.509 0.064 7.912 0.000 0.192 0.192
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.353 0.072 46.459 0.000 3.353 1.913
.PC01_02 3.395 0.074 46.159 0.000 3.395 1.900
.PC01_04 3.286 0.073 45.171 0.000 3.286 1.860
.PC01_05 3.327 0.073 45.323 0.000 3.327 1.866
.PC01_06 3.078 0.072 42.870 0.000 3.078 1.765
.PC01_07 3.292 0.072 45.649 0.000 3.292 1.879
.GR02_01 4.300 0.056 77.060 0.000 4.300 3.173
.GR02_02 4.608 0.059 78.148 0.000 4.608 3.217
.GR02_03 5.120 0.055 92.770 0.000 5.120 3.819
.GR02_04 5.078 0.055 92.974 0.000 5.078 3.828
.GR02_05 4.698 0.059 79.418 0.000 4.698 3.270
.GR01_01 4.878 0.058 84.167 0.000 4.878 3.465
.GR01_02 5.412 0.051 106.975 0.000 5.412 4.404
.GR01_03 5.242 0.054 97.598 0.000 5.242 4.018
.GR01_04 4.922 0.055 90.292 0.000 4.922 3.717
.GR01_05 5.078 0.054 94.628 0.000 5.078 3.896
.GR01_06 4.669 0.061 76.928 0.000 4.669 3.167
.GR01_07 4.680 0.060 77.892 0.000 4.680 3.207
.GR01_08 5.054 0.056 89.819 0.000 5.054 3.698
.GR01_09 4.832 0.058 83.767 0.000 4.832 3.449
.GR01_10 4.554 0.060 76.499 0.000 4.554 3.149
.GR01_11 4.958 0.054 92.315 0.000 4.958 3.801
.GR01_12 4.766 0.058 82.079 0.000 4.766 3.379
.GR01_13 4.108 0.067 61.001 0.000 4.108 2.511
.GR01_14 3.129 0.070 44.664 0.000 3.129 1.839
.GR01_15 4.432 0.063 70.425 0.000 4.432 2.899
.PD01_01 3.715 0.071 52.336 0.000 3.715 2.155
.PD01_02 3.417 0.064 53.359 0.000 3.417 2.197
.PD01_03 4.220 0.064 65.450 0.000 4.220 2.695
.PD01_04 4.105 0.070 58.613 0.000 4.105 2.413
.PD01_05 4.378 0.068 64.560 0.000 4.378 2.658
.TR01_01 4.839 0.048 101.596 0.000 4.839 4.183
.TR01_05 5.334 0.049 109.817 0.000 5.334 4.521
.TR01_09 5.358 0.050 106.224 0.000 5.358 4.373
.TR01_02 4.746 0.053 90.189 0.000 4.746 3.713
.TR01_03 4.834 0.046 104.720 0.000 4.834 4.311
.TR01_04 4.605 0.048 95.408 0.000 4.605 3.928
.TR01_06 5.358 0.050 106.104 0.000 5.358 4.368
.TR01_07 5.166 0.049 105.184 0.000 5.166 4.330
.TR01_08 5.098 0.047 108.380 0.000 5.098 4.462
.TR01_10 5.688 0.049 116.332 0.000 5.688 4.792
.TR01_11 4.819 0.053 90.284 0.000 4.819 3.717
.TR01_12 5.159 0.055 93.101 0.000 5.159 3.833
.SE01_01 5.237 0.058 90.932 0.000 5.237 3.746
.SE01_02 5.472 0.057 96.846 0.000 5.472 3.992
.SE01_03 5.193 0.056 93.415 0.000 5.193 3.850
.SE01_04 5.111 0.056 90.727 0.000 5.111 3.739
.COMM_log 1.788 0.094 19.017 0.000 1.788 0.783
pri_con 0.000 0.000 0.000
grats_gen 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
grats_spec 0.000 0.000 0.000
pri_delib 0.000 0.000 0.000
trust_gen 0.000 0.000 0.000
.trust_communty 0.000 0.000 0.000
.trust_provider 0.000 0.000 0.000
trust_spec 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
comm_log 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.440 0.033 13.423 0.000 0.440 0.143
.PC01_02 0.579 0.040 14.320 0.000 0.579 0.181
.PC01_04 0.640 0.043 14.729 0.000 0.640 0.205
.PC01_05 0.530 0.038 14.053 0.000 0.530 0.167
.PC01_06 1.056 0.066 15.977 0.000 1.056 0.347
.PC01_07 0.428 0.032 13.303 0.000 0.428 0.140
.GR02_01 0.519 0.036 14.290 0.000 0.519 0.282
.GR02_02 0.410 0.032 12.716 0.000 0.410 0.200
.GR02_03 0.438 0.032 13.601 0.000 0.438 0.244
.GR02_04 0.486 0.034 14.250 0.000 0.486 0.276
.GR02_05 0.565 0.040 14.298 0.000 0.565 0.274
.GR01_01 0.984 0.068 14.368 0.000 0.984 0.496
.GR01_02 0.485 0.041 11.921 0.000 0.485 0.321
.GR01_03 0.479 0.044 10.812 0.000 0.479 0.281
.GR01_04 0.360 0.033 10.937 0.000 0.360 0.205
.GR01_05 0.442 0.035 12.624 0.000 0.442 0.260
.GR01_06 1.087 0.070 15.594 0.000 1.087 0.500
.GR01_07 0.680 0.049 13.752 0.000 0.680 0.319
.GR01_08 0.630 0.045 13.960 0.000 0.630 0.337
.GR01_09 0.637 0.047 13.699 0.000 0.637 0.325
.GR01_10 0.763 0.053 14.402 0.000 0.763 0.365
.GR01_11 0.363 0.033 10.995 0.000 0.363 0.213
.GR01_12 0.818 0.055 14.903 0.000 0.818 0.411
.GR01_13 1.897 0.125 15.217 0.000 1.897 0.709
.GR01_14 2.097 0.136 15.419 0.000 2.097 0.724
.GR01_15 0.655 0.090 7.255 0.000 0.655 0.280
.PD01_01 0.743 0.077 9.595 0.000 0.743 0.250
.PD01_02 1.312 0.087 15.127 0.000 1.312 0.542
.PD01_03 1.324 0.090 14.771 0.000 1.324 0.540
.PD01_04 1.291 0.092 14.076 0.000 1.291 0.446
.PD01_05 1.520 0.100 15.174 0.000 1.520 0.560
.TR01_01 0.637 0.042 15.232 0.000 0.637 0.476
.TR01_05 0.295 0.025 11.766 0.000 0.295 0.212
.TR01_09 0.294 0.026 11.250 0.000 0.294 0.196
.TR01_02 0.566 0.044 12.911 0.000 0.566 0.346
.TR01_03 0.518 0.037 13.974 0.000 0.518 0.412
.TR01_04 0.417 0.035 11.823 0.000 0.417 0.303
.TR01_06 0.389 0.028 13.872 0.000 0.389 0.259
.TR01_07 0.560 0.036 15.528 0.000 0.560 0.393
.TR01_08 0.442 0.029 15.137 0.000 0.442 0.338
.TR01_10 0.747 0.046 16.248 0.000 0.747 0.530
.TR01_11 0.917 0.056 16.352 0.000 0.917 0.546
.TR01_12 0.521 0.036 14.554 0.000 0.521 0.288
.SE01_01 0.613 0.052 11.892 0.000 0.613 0.314
.SE01_02 0.947 0.064 14.860 0.000 0.947 0.504
.SE01_03 0.687 0.053 12.906 0.000 0.687 0.378
.SE01_04 0.734 0.054 13.472 0.000 0.734 0.393
.COMM_log 0.000 0.000 0.000
pri_con 2.632 0.178 14.771 0.000 1.000 1.000
grats_gen 1.318 0.105 12.606 0.000 1.000 1.000
.grats_inf 0.277 0.036 7.648 0.000 0.277 0.277
.grats_rel 0.189 0.031 6.123 0.000 0.136 0.136
.grats_par 0.125 0.030 4.124 0.000 0.086 0.086
.grats_ide 0.149 0.029 5.130 0.000 0.112 0.112
.grats_ext 0.293 0.053 5.492 0.000 0.376 0.376
grats_spec 0.721 0.092 7.847 0.000 1.000 1.000
pri_delib 2.231 0.179 12.427 0.000 1.000 1.000
trust_gen 0.701 0.071 9.846 0.000 1.000 1.000
.trust_communty 0.269 0.034 7.812 0.000 0.252 0.252
.trust_provider 0.052 0.021 2.430 0.015 0.046 0.046
trust_spec 0.798 0.082 9.690 0.000 1.000 1.000
self_eff 1.341 0.115 11.619 0.000 1.000 1.000
comm_log 5.213 0.304 17.175 0.000 1.000 1.000# extract model predicted values for items & calc means
d_fs <- lavPredict(fit_baseline, type = "ov") %>% as.data.frame() %>% mutate(version = d_all$version, grats_gen_fs = rowMeans(select(.,
starts_with("GR02"))), grats_spec_fs = rowMeans(select(., starts_with("GR01"))), pri_con_fs = rowMeans(select(., starts_with("PC01"))),
trust_gen_fs = rowMeans(select(., TR01_01, TR01_05, TR01_09)), trust_spec_fs = rowMeans(select(., TR01_02:TR01_04, TR01_06:TR01_12)),
pri_del_fs = rowMeans(select(., starts_with("PD01"))), self_eff_fs = rowMeans(select(., starts_with("SE01")))) %>% select(version,
pri_con_fs, grats_gen_fs, grats_spec_fs, pri_del_fs, self_eff_fs, trust_gen_fs, trust_spec_fs, COMM_log)
# combine d with d factor scores
d_all %<>% cbind(select(d_fs, -version, -COMM_log))model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
trust_spec =~ trust_community + trust_provider
COMM_log ~ a1*pri_con + b1*grats_gen + c1*pri_delib + d1*self_eff + e1*trust_spec
# Covariates
COMM_log + GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit_prereg <- sem(model, data = d_all, estimator = "MLR", missing = "ML")
summary(fit_prereg, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 328 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 198
Number of equality constraints 1
Used Total
Number of observations 589 590
Number of missing patterns 4
Model Test User Model:
Standard Robust
Test Statistic 1258.211 943.255
Degrees of freedom 388 388
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.334
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 14204.010 10449.124
Degrees of freedom 525 525
P-value 0.000 0.000
Scaling correction factor 1.359
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.936 0.944
Tucker-Lewis Index (TLI) 0.914 0.924
Robust Comparative Fit Index (CFI) 0.945
Robust Tucker-Lewis Index (TLI) 0.926
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -25255.003 -25255.003
Scaling correction factor 1.277
for the MLR correction
Loglikelihood unrestricted model (H1) -24625.898 -24625.898
Scaling correction factor 1.317
for the MLR correction
Akaike (AIC) 50904.006 50904.006
Bayesian (BIC) 51766.556 51766.556
Sample-size adjusted Bayesian (BIC) 51141.147 51141.147
Root Mean Square Error of Approximation:
RMSEA 0.062 0.049
90 Percent confidence interval - lower 0.058 0.046
90 Percent confidence interval - upper 0.066 0.053
P-value RMSEA <= 0.05 0.000 0.627
Robust RMSEA 0.057
90 Percent confidence interval - lower 0.052
90 Percent confidence interval - upper 0.062
Standardized Root Mean Square Residual:
SRMR 0.049 0.049
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.613 0.921
PC01_02 0.993 0.027 37.163 0.000 1.601 0.897
PC01_04 0.965 0.026 36.523 0.000 1.557 0.882
PC01_05 1.003 0.023 43.213 0.000 1.619 0.908
PC01_06 0.867 0.036 24.367 0.000 1.399 0.802
PC01_07 1.000 0.023 44.338 0.000 1.613 0.922
grats_gen =~
GR02_01 1.000 1.152 0.849
GR02_02 1.116 0.031 35.566 0.000 1.286 0.897
GR02_03 1.003 0.044 22.797 0.000 1.156 0.862
GR02_04 0.976 0.045 21.730 0.000 1.125 0.848
GR02_05 1.066 0.037 28.721 0.000 1.228 0.854
pri_delib =~
PD01_01 1.000 1.468 0.851
PD01_02 0.696 0.046 15.073 0.000 1.023 0.657
PD01_03 0.718 0.053 13.675 0.000 1.055 0.673
PD01_04 0.845 0.045 18.724 0.000 1.241 0.729
PD01_05 0.731 0.048 15.305 0.000 1.073 0.651
self_eff =~
SE01_01 1.000 1.138 0.815
SE01_02 0.838 0.054 15.644 0.000 0.954 0.696
SE01_03 0.925 0.042 22.062 0.000 1.053 0.782
SE01_04 0.931 0.041 22.940 0.000 1.060 0.776
trust_community =~
TR01_02 1.000 1.039 0.812
TR01_03 0.817 0.054 15.254 0.000 0.849 0.757
TR01_04 0.929 0.043 21.682 0.000 0.965 0.824
trust_provider =~
TR01_06 1.000 1.069 0.871
TR01_07 0.876 0.036 24.096 0.000 0.937 0.786
TR01_08 0.856 0.037 22.945 0.000 0.916 0.803
TR01_10 0.751 0.048 15.780 0.000 0.803 0.677
TR01_11 0.818 0.048 17.116 0.000 0.875 0.674
TR01_12 1.058 0.044 24.178 0.000 1.131 0.840
trust_spec =~
trust_communty 1.000 0.885 0.885
trust_provider 1.111 0.072 15.464 0.000 0.956 0.956
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
COMM_log ~
pri_con (a1) -0.069 0.077 -0.894 0.371 -0.111 -0.049
grats_gen (b1) 0.098 0.157 0.624 0.532 0.113 0.049
pri_delib (c1) -0.159 0.092 -1.725 0.084 -0.233 -0.102
self_eff (d1) 0.815 0.145 5.607 0.000 0.928 0.406
trust_spc (e1) -0.329 0.255 -1.289 0.198 -0.303 -0.133
male 0.008 0.191 0.042 0.966 0.008 0.002
age 0.007 0.006 1.206 0.228 0.007 0.049
edu 0.198 0.113 1.756 0.079 0.198 0.073
GR02_01 ~
male -0.124 0.113 -1.098 0.272 -0.124 -0.046
age -0.001 0.004 -0.328 0.743 -0.001 -0.014
edu 0.002 0.067 0.035 0.972 0.002 0.001
GR02_02 ~
male -0.058 0.119 -0.490 0.624 -0.058 -0.020
age 0.004 0.004 1.005 0.315 0.004 0.043
edu -0.081 0.070 -1.166 0.244 -0.081 -0.048
GR02_03 ~
male -0.028 0.112 -0.252 0.801 -0.028 -0.010
age 0.001 0.004 0.198 0.843 0.001 0.008
edu -0.099 0.065 -1.515 0.130 -0.099 -0.062
GR02_04 ~
male 0.012 0.111 0.109 0.913 0.012 0.005
age 0.004 0.004 1.237 0.216 0.004 0.053
edu -0.089 0.066 -1.340 0.180 -0.089 -0.057
GR02_05 ~
male -0.134 0.120 -1.119 0.263 -0.134 -0.047
age -0.005 0.004 -1.191 0.234 -0.005 -0.051
edu 0.001 0.071 0.007 0.994 0.001 0.000
PC01_01 ~
male -0.114 0.148 -0.769 0.442 -0.114 -0.033
age -0.007 0.005 -1.510 0.131 -0.007 -0.065
edu 0.128 0.087 1.485 0.137 0.128 0.062
PC01_02 ~
male -0.255 0.150 -1.696 0.090 -0.255 -0.071
age -0.011 0.005 -2.336 0.020 -0.011 -0.098
edu 0.052 0.088 0.589 0.556 0.052 0.025
PC01_04 ~
male -0.189 0.148 -1.271 0.204 -0.189 -0.053
age -0.012 0.005 -2.441 0.015 -0.012 -0.102
edu 0.127 0.087 1.462 0.144 0.127 0.061
PC01_05 ~
male -0.055 0.150 -0.367 0.714 -0.055 -0.015
age -0.008 0.005 -1.719 0.086 -0.008 -0.073
edu 0.103 0.088 1.172 0.241 0.103 0.049
PC01_06 ~
male -0.098 0.147 -0.664 0.507 -0.098 -0.028
age -0.008 0.005 -1.675 0.094 -0.008 -0.071
edu 0.064 0.086 0.751 0.453 0.064 0.031
PC01_07 ~
male -0.143 0.147 -0.971 0.331 -0.143 -0.041
age -0.009 0.005 -1.910 0.056 -0.009 -0.081
edu 0.095 0.086 1.108 0.268 0.095 0.046
TR01_02 ~
male -0.275 0.106 -2.596 0.009 -0.275 -0.107
age -0.005 0.004 -1.332 0.183 -0.005 -0.058
edu 0.005 0.061 0.087 0.931 0.005 0.003
TR01_03 ~
male -0.152 0.094 -1.623 0.105 -0.152 -0.068
age -0.002 0.003 -0.528 0.597 -0.002 -0.023
edu 0.017 0.053 0.313 0.755 0.017 0.012
TR01_04 ~
male -0.117 0.098 -1.200 0.230 -0.117 -0.050
age -0.005 0.003 -1.443 0.149 -0.005 -0.064
edu 0.003 0.059 0.051 0.959 0.003 0.002
TR01_06 ~
male -0.086 0.102 -0.837 0.403 -0.086 -0.035
age 0.000 0.003 0.097 0.923 0.000 0.004
edu -0.052 0.058 -0.897 0.370 -0.052 -0.036
TR01_07 ~
male -0.034 0.099 -0.339 0.735 -0.034 -0.014
age 0.001 0.003 0.221 0.825 0.001 0.009
edu 0.019 0.058 0.326 0.745 0.019 0.013
TR01_08 ~
male 0.046 0.095 0.484 0.628 0.046 0.020
age -0.004 0.003 -1.289 0.197 -0.004 -0.054
edu 0.017 0.056 0.300 0.764 0.017 0.012
TR01_10 ~
male 0.068 0.098 0.691 0.489 0.068 0.029
age -0.001 0.003 -0.316 0.752 -0.001 -0.013
edu -0.060 0.057 -1.037 0.300 -0.060 -0.042
TR01_11 ~
male 0.035 0.108 0.327 0.743 0.035 0.014
age 0.002 0.003 0.643 0.520 0.002 0.027
edu -0.090 0.064 -1.410 0.159 -0.090 -0.058
TR01_12 ~
male -0.117 0.111 -1.053 0.292 -0.117 -0.044
age -0.001 0.004 -0.351 0.726 -0.001 -0.015
edu -0.151 0.066 -2.276 0.023 -0.151 -0.094
PD01_01 ~
male -0.137 0.143 -0.959 0.338 -0.137 -0.040
age -0.018 0.005 -3.928 0.000 -0.018 -0.160
edu -0.020 0.083 -0.244 0.807 -0.020 -0.010
PD01_02 ~
male -0.103 0.129 -0.798 0.425 -0.103 -0.033
age -0.016 0.004 -4.090 0.000 -0.016 -0.164
edu 0.033 0.076 0.429 0.668 0.033 0.018
PD01_03 ~
male -0.294 0.130 -2.268 0.023 -0.294 -0.094
age -0.005 0.004 -1.277 0.202 -0.005 -0.053
edu 0.095 0.079 1.203 0.229 0.095 0.051
PD01_04 ~
male -0.394 0.139 -2.826 0.005 -0.394 -0.116
age -0.010 0.005 -2.236 0.025 -0.010 -0.094
edu 0.109 0.083 1.319 0.187 0.109 0.054
PD01_05 ~
male -0.184 0.137 -1.341 0.180 -0.184 -0.056
age -0.013 0.004 -3.125 0.002 -0.013 -0.125
edu -0.003 0.082 -0.043 0.966 -0.003 -0.002
SE01_01 ~
male 0.136 0.116 1.177 0.239 0.136 0.049
age 0.001 0.004 0.237 0.812 0.001 0.010
edu 0.212 0.067 3.159 0.002 0.212 0.128
SE01_02 ~
male 0.056 0.111 0.500 0.617 0.056 0.020
age -0.012 0.004 -3.213 0.001 -0.012 -0.132
edu 0.192 0.066 2.929 0.003 0.192 0.118
SE01_03 ~
male 0.210 0.112 1.877 0.061 0.210 0.078
age 0.001 0.004 0.365 0.715 0.001 0.015
edu 0.151 0.066 2.307 0.021 0.151 0.095
SE01_04 ~
male 0.059 0.113 0.523 0.601 0.059 0.022
age 0.006 0.004 1.548 0.122 0.006 0.063
edu 0.107 0.066 1.615 0.106 0.107 0.066
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.102 0.043 2.344 0.019 0.102 0.137
.SE01_03 ~~
.SE01_04 (x) 0.102 0.043 2.344 0.019 0.102 0.145
pri_con ~~
grats_gen -0.178 0.097 -1.835 0.067 -0.096 -0.096
pri_delib 1.384 0.129 10.762 0.000 0.584 0.584
self_eff -0.380 0.093 -4.067 0.000 -0.207 -0.207
trust_spec -0.355 0.076 -4.672 0.000 -0.239 -0.239
grats_gen ~~
pri_delib 0.029 0.102 0.287 0.774 0.017 0.017
self_eff 0.481 0.068 7.091 0.000 0.367 0.367
trust_spec 0.823 0.084 9.773 0.000 0.777 0.777
pri_delib ~~
self_eff -0.303 0.096 -3.162 0.002 -0.181 -0.181
trust_spec -0.085 0.085 -1.000 0.317 -0.063 -0.063
self_eff ~~
trust_spec 0.598 0.062 9.718 0.000 0.571 0.571
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.506 0.285 12.292 0.000 3.506 2.002
.PC01_02 3.940 0.297 13.268 0.000 3.940 2.207
.PC01_04 3.673 0.288 12.743 0.000 3.673 2.079
.PC01_05 3.544 0.296 11.992 0.000 3.544 1.989
.PC01_06 3.371 0.282 11.941 0.000 3.371 1.933
.PC01_07 3.602 0.286 12.592 0.000 3.602 2.058
.GR02_01 4.413 0.222 19.917 0.000 4.413 3.254
.GR02_02 4.610 0.240 19.170 0.000 4.610 3.216
.GR02_03 5.288 0.218 24.259 0.000 5.288 3.943
.GR02_04 5.036 0.219 23.037 0.000 5.036 3.795
.GR02_05 4.981 0.248 20.123 0.000 4.981 3.464
.PD01_01 4.626 0.281 16.486 0.000 4.626 2.681
.PD01_02 4.153 0.243 17.065 0.000 4.153 2.668
.PD01_03 4.435 0.264 16.813 0.000 4.435 2.829
.PD01_04 4.565 0.286 15.975 0.000 4.565 2.681
.PD01_05 5.082 0.267 19.031 0.000 5.082 3.083
.SE01_01 4.730 0.242 19.517 0.000 4.730 3.386
.SE01_02 5.612 0.231 24.320 0.000 5.612 4.096
.SE01_03 4.742 0.220 21.590 0.000 4.742 3.520
.SE01_04 4.626 0.231 20.015 0.000 4.626 3.388
.TR01_02 5.088 0.215 23.693 0.000 5.088 3.978
.TR01_03 4.953 0.189 26.211 0.000 4.953 4.418
.TR01_04 4.875 0.199 24.554 0.000 4.875 4.159
.TR01_06 5.482 0.203 26.948 0.000 5.482 4.467
.TR01_07 5.111 0.199 25.716 0.000 5.111 4.289
.TR01_08 5.221 0.189 27.584 0.000 5.221 4.577
.TR01_10 5.809 0.190 30.547 0.000 5.809 4.894
.TR01_11 4.866 0.215 22.674 0.000 4.866 3.753
.TR01_12 5.560 0.227 24.445 0.000 5.560 4.128
.COMM_log 1.086 0.366 2.965 0.003 1.086 0.475
pri_con 0.000 0.000 0.000
grats_gen 0.000 0.000 0.000
pri_delib 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
.trust_communty 0.000 0.000 0.000
.trust_provider 0.000 0.000 0.000
trust_spec 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.436 0.056 7.803 0.000 0.436 0.142
.PC01_02 0.571 0.097 5.871 0.000 0.571 0.179
.PC01_04 0.638 0.074 8.606 0.000 0.638 0.205
.PC01_05 0.529 0.061 8.709 0.000 0.529 0.167
.PC01_06 1.061 0.113 9.386 0.000 1.061 0.349
.PC01_07 0.428 0.063 6.829 0.000 0.428 0.140
.GR02_01 0.508 0.051 9.919 0.000 0.508 0.276
.GR02_02 0.391 0.039 10.097 0.000 0.391 0.190
.GR02_03 0.454 0.070 6.521 0.000 0.454 0.252
.GR02_04 0.484 0.046 10.484 0.000 0.484 0.275
.GR02_05 0.549 0.059 9.304 0.000 0.549 0.265
.PD01_01 0.739 0.107 6.906 0.000 0.739 0.248
.PD01_02 1.306 0.125 10.414 0.000 1.306 0.539
.PD01_03 1.309 0.123 10.615 0.000 1.309 0.533
.PD01_04 1.283 0.140 9.190 0.000 1.283 0.443
.PD01_05 1.512 0.122 12.346 0.000 1.512 0.556
.SE01_01 0.615 0.083 7.389 0.000 0.615 0.315
.SE01_02 0.901 0.114 7.890 0.000 0.901 0.480
.SE01_03 0.675 0.090 7.475 0.000 0.675 0.372
.SE01_04 0.725 0.084 8.612 0.000 0.725 0.389
.TR01_02 0.531 0.064 8.264 0.000 0.531 0.324
.TR01_03 0.529 0.063 8.359 0.000 0.529 0.421
.TR01_04 0.432 0.044 9.754 0.000 0.432 0.315
.TR01_06 0.359 0.036 9.837 0.000 0.359 0.238
.TR01_07 0.542 0.052 10.447 0.000 0.542 0.381
.TR01_08 0.459 0.041 11.230 0.000 0.459 0.352
.TR01_10 0.761 0.066 11.575 0.000 0.761 0.540
.TR01_11 0.909 0.076 12.000 0.000 0.909 0.541
.TR01_12 0.513 0.065 7.914 0.000 0.513 0.283
.COMM_log 4.318 0.212 20.372 0.000 4.318 0.828
pri_con 2.602 0.146 17.837 0.000 1.000 1.000
grats_gen 1.328 0.113 11.795 0.000 1.000 1.000
pri_delib 2.156 0.153 14.128 0.000 1.000 1.000
self_eff 1.296 0.113 11.493 0.000 1.000 1.000
.trust_communty 0.233 0.044 5.345 0.000 0.216 0.216
.trust_provider 0.098 0.043 2.283 0.022 0.086 0.086
trust_spec 0.847 0.100 8.505 0.000 1.000 1.000Building on the preregistered model, instead of general gratifications and specific trust, we now use specific gratifications and general trust.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
trust_gen =~ TR01_01 + TR01_05 + TR01_09
COMM_log ~ a1*pri_con + b1*grats_spec + c1*pri_delib + d1*self_eff + e1*trust_gen
# Covariates
COMM_log + GR01_01 + GR01_02 + GR01_03 + GR01_04 + GR01_05 + GR01_06 + GR01_07 + GR01_08 + GR01_09 + GR01_10 + GR01_11 + GR01_12 + GR01_13 + GR01_14 + GR01_15 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_01 + TR01_05 + TR01_09 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit_adapted <- sem(model, data = d_all, estimator = "MLR", missing = "ML", missing = "ML")
summary(fit_adapted, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 357 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 225
Number of equality constraints 1
Used Total
Number of observations 589 590
Number of missing patterns 3
Model Test User Model:
Standard Robust
Test Statistic 1566.360 1180.294
Degrees of freedom 507 507
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.327
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 15398.674 11439.794
Degrees of freedom 663 663
P-value 0.000 0.000
Scaling correction factor 1.346
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.928 0.938
Tucker-Lewis Index (TLI) 0.906 0.918
Robust Comparative Fit Index (CFI) 0.938
Robust Tucker-Lewis Index (TLI) 0.919
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -29544.515 -29544.515
Scaling correction factor 1.266
for the MLR correction
Loglikelihood unrestricted model (H1) -28761.335 -28761.335
Scaling correction factor 1.310
for the MLR correction
Akaike (AIC) 59537.030 59537.030
Bayesian (BIC) 60517.797 60517.797
Sample-size adjusted Bayesian (BIC) 59806.672 59806.672
Root Mean Square Error of Approximation:
RMSEA 0.060 0.047
90 Percent confidence interval - lower 0.056 0.044
90 Percent confidence interval - upper 0.063 0.051
P-value RMSEA <= 0.05 0.000 0.911
Robust RMSEA 0.055
90 Percent confidence interval - lower 0.051
90 Percent confidence interval - upper 0.059
Standardized Root Mean Square Residual:
SRMR 0.061 0.061
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.613 0.921
PC01_02 0.993 0.027 37.310 0.000 1.601 0.897
PC01_04 0.965 0.026 36.712 0.000 1.557 0.882
PC01_05 1.004 0.023 43.198 0.000 1.619 0.909
PC01_06 0.868 0.036 24.437 0.000 1.400 0.803
PC01_07 1.000 0.023 44.445 0.000 1.613 0.922
grats_inf =~
GR01_01 1.000 0.973 0.691
GR01_02 1.038 0.070 14.856 0.000 1.010 0.822
GR01_03 1.136 0.072 15.805 0.000 1.106 0.848
grats_rel =~
GR01_04 1.000 1.183 0.893
GR01_05 0.946 0.035 26.672 0.000 1.120 0.859
GR01_06 0.879 0.043 20.349 0.000 1.040 0.705
grats_par =~
GR01_07 1.000 1.199 0.821
GR01_08 0.927 0.037 24.769 0.000 1.112 0.813
GR01_09 0.958 0.036 26.563 0.000 1.149 0.819
grats_ide =~
GR01_10 1.000 1.145 0.791
GR01_11 1.013 0.039 25.868 0.000 1.160 0.889
GR01_12 0.940 0.040 23.774 0.000 1.077 0.763
grats_ext =~
GR01_13 1.000 0.858 0.524
GR01_14 1.025 0.096 10.718 0.000 0.879 0.516
GR01_15 1.507 0.165 9.105 0.000 1.293 0.845
grats_spec =~
grats_inf 1.000 0.852 0.852
grats_rel 1.327 0.098 13.520 0.000 0.931 0.931
grats_par 1.399 0.109 12.888 0.000 0.968 0.968
grats_ide 1.287 0.100 12.885 0.000 0.932 0.932
grats_ext 0.811 0.099 8.190 0.000 0.784 0.784
pri_delib =~
PD01_01 1.000 1.476 0.855
PD01_02 0.694 0.046 15.052 0.000 1.024 0.658
PD01_03 0.710 0.052 13.606 0.000 1.048 0.669
PD01_04 0.841 0.045 18.741 0.000 1.241 0.729
PD01_05 0.722 0.048 15.134 0.000 1.066 0.646
self_eff =~
SE01_01 1.000 1.143 0.818
SE01_02 0.834 0.054 15.389 0.000 0.953 0.696
SE01_03 0.917 0.042 22.056 0.000 1.048 0.778
SE01_04 0.930 0.041 22.638 0.000 1.063 0.778
trust_gen =~
TR01_01 1.000 0.780 0.674
TR01_05 1.347 0.071 18.989 0.000 1.051 0.892
TR01_09 1.438 0.079 18.137 0.000 1.122 0.917
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
COMM_log ~
pri_con (a1) -0.106 0.079 -1.347 0.178 -0.171 -0.075
grats_spc (b1) 0.377 0.192 1.959 0.050 0.313 0.137
pri_delib (c1) -0.199 0.092 -2.160 0.031 -0.294 -0.129
self_eff (d1) 0.717 0.135 5.329 0.000 0.820 0.359
trust_gen (e1) -0.500 0.216 -2.315 0.021 -0.390 -0.171
male 0.008 0.191 0.042 0.967 0.008 0.002
age 0.007 0.006 1.206 0.228 0.007 0.049
edu 0.198 0.113 1.755 0.079 0.198 0.073
GR01_01 ~
male -0.329 0.117 -2.815 0.005 -0.329 -0.117
age -0.006 0.004 -1.453 0.146 -0.006 -0.063
edu -0.001 0.070 -0.018 0.986 -0.001 -0.001
GR01_02 ~
male -0.156 0.101 -1.554 0.120 -0.156 -0.064
age -0.006 0.003 -1.820 0.069 -0.006 -0.074
edu -0.037 0.060 -0.620 0.535 -0.037 -0.025
GR01_03 ~
male -0.212 0.107 -1.987 0.047 -0.212 -0.081
age -0.004 0.004 -1.239 0.215 -0.004 -0.053
edu -0.089 0.063 -1.412 0.158 -0.089 -0.057
GR01_04 ~
male 0.000 0.110 0.003 0.998 0.000 0.000
age 0.001 0.004 0.169 0.866 0.001 0.007
edu -0.022 0.065 -0.347 0.729 -0.022 -0.014
GR01_05 ~
male -0.074 0.109 -0.681 0.496 -0.074 -0.029
age -0.000 0.004 -0.119 0.906 -0.000 -0.005
edu 0.017 0.063 0.272 0.786 0.017 0.011
GR01_06 ~
male 0.031 0.120 0.257 0.797 0.031 0.010
age -0.011 0.004 -2.785 0.005 -0.011 -0.118
edu -0.081 0.073 -1.111 0.267 -0.081 -0.046
GR01_07 ~
male 0.047 0.121 0.385 0.700 0.047 0.016
age -0.006 0.004 -1.418 0.156 -0.006 -0.059
edu 0.006 0.071 0.082 0.935 0.006 0.003
GR01_08 ~
male 0.005 0.112 0.049 0.961 0.005 0.002
age -0.004 0.004 -1.131 0.258 -0.004 -0.050
edu 0.098 0.066 1.482 0.138 0.098 0.061
GR01_09 ~
male 0.058 0.116 0.505 0.614 0.058 0.021
age -0.003 0.004 -0.728 0.467 -0.003 -0.031
edu 0.107 0.067 1.588 0.112 0.107 0.064
GR01_10 ~
male -0.028 0.120 -0.234 0.815 -0.028 -0.010
age -0.008 0.004 -2.110 0.035 -0.008 -0.091
edu -0.023 0.072 -0.322 0.747 -0.023 -0.014
GR01_11 ~
male -0.086 0.108 -0.798 0.425 -0.086 -0.033
age -0.001 0.004 -0.368 0.713 -0.001 -0.016
edu -0.058 0.064 -0.910 0.363 -0.058 -0.037
GR01_12 ~
male -0.196 0.117 -1.667 0.095 -0.196 -0.069
age -0.004 0.004 -1.129 0.259 -0.004 -0.047
edu -0.065 0.070 -0.938 0.348 -0.065 -0.039
GR01_13 ~
male -0.188 0.133 -1.415 0.157 -0.188 -0.058
age -0.023 0.004 -5.610 0.000 -0.023 -0.219
edu 0.080 0.078 1.022 0.307 0.080 0.041
GR01_14 ~
male -0.248 0.143 -1.741 0.082 -0.248 -0.073
age -0.011 0.004 -2.418 0.016 -0.011 -0.099
edu 0.053 0.083 0.638 0.524 0.053 0.026
GR01_15 ~
male 0.041 0.128 0.321 0.748 0.041 0.013
age -0.006 0.004 -1.491 0.136 -0.006 -0.063
edu 0.041 0.076 0.543 0.587 0.041 0.023
PC01_01 ~
male -0.114 0.148 -0.769 0.442 -0.114 -0.033
age -0.007 0.005 -1.510 0.131 -0.007 -0.065
edu 0.128 0.087 1.485 0.138 0.128 0.062
PC01_02 ~
male -0.255 0.150 -1.696 0.090 -0.255 -0.071
age -0.011 0.005 -2.336 0.020 -0.011 -0.098
edu 0.052 0.088 0.589 0.556 0.052 0.025
PC01_04 ~
male -0.189 0.148 -1.271 0.204 -0.189 -0.053
age -0.012 0.005 -2.441 0.015 -0.012 -0.102
edu 0.127 0.087 1.462 0.144 0.127 0.061
PC01_05 ~
male -0.055 0.150 -0.367 0.714 -0.055 -0.015
age -0.008 0.005 -1.719 0.086 -0.008 -0.073
edu 0.103 0.088 1.172 0.241 0.103 0.049
PC01_06 ~
male -0.098 0.147 -0.664 0.507 -0.098 -0.028
age -0.008 0.005 -1.675 0.094 -0.008 -0.071
edu 0.064 0.086 0.751 0.453 0.064 0.031
PC01_07 ~
male -0.143 0.147 -0.971 0.331 -0.143 -0.041
age -0.009 0.005 -1.910 0.056 -0.009 -0.081
edu 0.095 0.086 1.108 0.268 0.095 0.046
TR01_01 ~
male -0.149 0.096 -1.555 0.120 -0.149 -0.065
age -0.004 0.003 -1.201 0.230 -0.004 -0.054
edu 0.014 0.059 0.231 0.817 0.014 0.010
TR01_05 ~
male 0.047 0.099 0.475 0.635 0.047 0.020
age -0.003 0.003 -0.850 0.395 -0.003 -0.036
edu 0.074 0.059 1.240 0.215 0.074 0.053
TR01_09 ~
male 0.049 0.102 0.480 0.631 0.049 0.020
age -0.005 0.004 -1.496 0.135 -0.005 -0.067
edu -0.034 0.059 -0.571 0.568 -0.034 -0.023
PD01_01 ~
male -0.137 0.143 -0.958 0.338 -0.137 -0.040
age -0.018 0.005 -3.928 0.000 -0.018 -0.160
edu -0.020 0.083 -0.244 0.807 -0.020 -0.010
PD01_02 ~
male -0.103 0.129 -0.798 0.425 -0.103 -0.033
age -0.016 0.004 -4.090 0.000 -0.016 -0.164
edu 0.033 0.076 0.429 0.668 0.033 0.018
PD01_03 ~
male -0.294 0.130 -2.268 0.023 -0.294 -0.094
age -0.005 0.004 -1.277 0.202 -0.005 -0.053
edu 0.095 0.079 1.203 0.229 0.095 0.051
PD01_04 ~
male -0.394 0.139 -2.826 0.005 -0.394 -0.116
age -0.010 0.005 -2.236 0.025 -0.010 -0.094
edu 0.109 0.083 1.319 0.187 0.109 0.054
PD01_05 ~
male -0.184 0.137 -1.341 0.180 -0.184 -0.056
age -0.013 0.004 -3.125 0.002 -0.013 -0.125
edu -0.004 0.082 -0.043 0.966 -0.004 -0.002
SE01_01 ~
male 0.135 0.116 1.168 0.243 0.135 0.048
age 0.001 0.004 0.228 0.820 0.001 0.010
edu 0.211 0.067 3.141 0.002 0.211 0.127
SE01_02 ~
male 0.054 0.111 0.490 0.624 0.054 0.020
age -0.012 0.004 -3.219 0.001 -0.012 -0.132
edu 0.191 0.066 2.917 0.004 0.191 0.117
SE01_03 ~
male 0.208 0.112 1.865 0.062 0.208 0.077
age 0.001 0.004 0.360 0.719 0.001 0.015
edu 0.150 0.066 2.293 0.022 0.150 0.094
SE01_04 ~
male 0.058 0.113 0.511 0.609 0.058 0.021
age 0.006 0.004 1.542 0.123 0.006 0.063
edu 0.106 0.066 1.602 0.109 0.106 0.066
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.103 0.043 2.372 0.018 0.103 0.139
.SE01_03 ~~
.SE01_04 (x) 0.103 0.043 2.372 0.018 0.103 0.146
pri_con ~~
grats_spec -0.043 0.069 -0.629 0.530 -0.032 -0.032
pri_delib 1.392 0.128 10.880 0.000 0.585 0.585
self_eff -0.384 0.094 -4.092 0.000 -0.209 -0.209
trust_gen -0.474 0.063 -7.478 0.000 -0.377 -0.377
grats_spec ~~
pri_delib 0.058 0.073 0.798 0.425 0.048 0.048
self_eff 0.490 0.060 8.211 0.000 0.517 0.517
trust_gen 0.432 0.061 7.094 0.000 0.667 0.667
pri_delib ~~
self_eff -0.307 0.097 -3.176 0.001 -0.182 -0.182
trust_gen -0.253 0.070 -3.602 0.000 -0.220 -0.220
self_eff ~~
trust_gen 0.476 0.055 8.675 0.000 0.534 0.534
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.506 0.285 12.292 0.000 3.506 2.002
.PC01_02 3.940 0.297 13.268 0.000 3.940 2.207
.PC01_04 3.673 0.288 12.743 0.000 3.673 2.079
.PC01_05 3.544 0.296 11.992 0.000 3.544 1.989
.PC01_06 3.371 0.282 11.941 0.000 3.371 1.933
.PC01_07 3.602 0.286 12.592 0.000 3.602 2.058
.GR01_01 5.305 0.241 21.998 0.000 5.305 3.767
.GR01_02 5.829 0.205 28.369 0.000 5.829 4.745
.GR01_03 5.715 0.221 25.876 0.000 5.715 4.383
.GR01_04 4.938 0.208 23.706 0.000 4.938 3.728
.GR01_05 5.104 0.215 23.716 0.000 5.104 3.915
.GR01_06 5.313 0.246 21.637 0.000 5.313 3.601
.GR01_07 4.899 0.231 21.165 0.000 4.899 3.355
.GR01_08 5.067 0.233 21.706 0.000 5.067 3.706
.GR01_09 4.731 0.233 20.274 0.000 4.731 3.374
.GR01_10 4.997 0.248 20.168 0.000 4.997 3.453
.GR01_11 5.171 0.223 23.181 0.000 5.171 3.962
.GR01_12 5.180 0.230 22.518 0.000 5.180 3.671
.GR01_13 5.100 0.253 20.182 0.000 5.100 3.115
.GR01_14 3.646 0.274 13.318 0.000 3.646 2.141
.GR01_15 4.615 0.259 17.811 0.000 4.615 3.017
.PD01_01 4.626 0.281 16.486 0.000 4.626 2.681
.PD01_02 4.153 0.243 17.065 0.000 4.153 2.668
.PD01_03 4.435 0.264 16.813 0.000 4.435 2.829
.PD01_04 4.565 0.286 15.975 0.000 4.565 2.681
.PD01_05 5.082 0.267 19.031 0.000 5.082 3.083
.SE01_01 4.733 0.242 19.522 0.000 4.733 3.387
.SE01_02 5.614 0.231 24.327 0.000 5.614 4.095
.SE01_03 4.744 0.220 21.587 0.000 4.744 3.521
.SE01_04 4.628 0.231 20.020 0.000 4.628 3.389
.TR01_01 5.067 0.205 24.664 0.000 5.067 4.380
.TR01_05 5.294 0.203 26.113 0.000 5.294 4.491
.TR01_09 5.633 0.214 26.264 0.000 5.633 4.601
.COMM_log 1.086 0.366 2.965 0.003 1.086 0.475
pri_con 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
grats_spec 0.000 0.000 0.000
pri_delib 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
trust_gen 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.438 0.056 7.819 0.000 0.438 0.143
.PC01_02 0.573 0.097 5.903 0.000 0.573 0.180
.PC01_04 0.639 0.074 8.618 0.000 0.639 0.205
.PC01_05 0.527 0.061 8.681 0.000 0.527 0.166
.PC01_06 1.058 0.113 9.392 0.000 1.058 0.348
.PC01_07 0.428 0.063 6.790 0.000 0.428 0.140
.GR01_01 0.999 0.103 9.669 0.000 0.999 0.504
.GR01_02 0.472 0.063 7.467 0.000 0.472 0.313
.GR01_03 0.453 0.065 6.995 0.000 0.453 0.266
.GR01_04 0.354 0.041 8.552 0.000 0.354 0.202
.GR01_05 0.444 0.054 8.256 0.000 0.444 0.261
.GR01_06 1.062 0.103 10.314 0.000 1.062 0.488
.GR01_07 0.688 0.068 10.063 0.000 0.688 0.323
.GR01_08 0.621 0.065 9.523 0.000 0.621 0.332
.GR01_09 0.634 0.067 9.411 0.000 0.634 0.323
.GR01_10 0.765 0.068 11.275 0.000 0.765 0.365
.GR01_11 0.353 0.052 6.805 0.000 0.353 0.207
.GR01_12 0.814 0.074 10.984 0.000 0.814 0.409
.GR01_13 1.795 0.144 12.451 0.000 1.795 0.670
.GR01_14 2.077 0.125 16.641 0.000 2.077 0.716
.GR01_15 0.657 0.107 6.157 0.000 0.657 0.281
.PD01_01 0.718 0.105 6.851 0.000 0.718 0.241
.PD01_02 1.303 0.125 10.399 0.000 1.303 0.538
.PD01_03 1.323 0.124 10.671 0.000 1.323 0.539
.PD01_04 1.283 0.139 9.216 0.000 1.283 0.442
.PD01_05 1.528 0.124 12.325 0.000 1.528 0.562
.SE01_01 0.607 0.083 7.328 0.000 0.607 0.311
.SE01_02 0.905 0.115 7.898 0.000 0.905 0.482
.SE01_03 0.687 0.090 7.632 0.000 0.687 0.378
.SE01_04 0.720 0.084 8.613 0.000 0.720 0.386
.TR01_01 0.720 0.060 11.990 0.000 0.720 0.538
.TR01_05 0.278 0.032 8.634 0.000 0.278 0.200
.TR01_09 0.232 0.040 5.790 0.000 0.232 0.155
.COMM_log 4.283 0.202 21.173 0.000 4.283 0.821
pri_con 2.601 0.146 17.842 0.000 1.000 1.000
.grats_inf 0.259 0.040 6.501 0.000 0.274 0.274
.grats_rel 0.188 0.044 4.229 0.000 0.134 0.134
.grats_par 0.090 0.048 1.882 0.060 0.062 0.062
.grats_ide 0.171 0.043 3.968 0.000 0.131 0.131
.grats_ext 0.284 0.067 4.205 0.000 0.385 0.385
grats_spec 0.688 0.117 5.892 0.000 1.000 1.000
pri_delib 2.177 0.152 14.313 0.000 1.000 1.000
self_eff 1.306 0.114 11.501 0.000 1.000 1.000
trust_gen 0.609 0.073 8.304 0.000 1.000 1.000We now use only variables, that is specific gratifications and privacy concerns.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
grats_spec =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext
COMM_log ~ a1*pri_con + b1*grats_spec
# Covariates
COMM_log + GR01_01 + GR01_02 + GR01_03 + GR01_04 + GR01_05 + GR01_06 + GR01_07 + GR01_08 + GR01_09 + GR01_10 + GR01_11 + GR01_12 + GR01_13 + GR01_14 + GR01_15 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 ~ male + age + edu
"
fit_simple <- sem(model, data = d_all, estimator = "MLR", missing = "ML")
summary(fit_simple, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 255 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 139
Used Total
Number of observations 589 590
Number of missing patterns 1
Model Test User Model:
Standard Robust
Test Statistic 753.381 518.260
Degrees of freedom 202 202
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.454
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 10395.964 7245.225
Degrees of freedom 297 297
P-value 0.000 0.000
Scaling correction factor 1.435
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.945 0.954
Tucker-Lewis Index (TLI) 0.920 0.933
Robust Comparative Fit Index (CFI) 0.954
Robust Tucker-Lewis Index (TLI) 0.932
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -19123.917 -19123.917
Scaling correction factor 1.256
for the MLR correction
Loglikelihood unrestricted model (H1) -18747.226 -18747.226
Scaling correction factor 1.373
for the MLR correction
Akaike (AIC) 38525.834 38525.834
Bayesian (BIC) 39134.436 39134.436
Sample-size adjusted Bayesian (BIC) 38693.157 38693.157
Root Mean Square Error of Approximation:
RMSEA 0.068 0.052
90 Percent confidence interval - lower 0.063 0.047
90 Percent confidence interval - upper 0.073 0.056
P-value RMSEA <= 0.05 0.000 0.281
Robust RMSEA 0.062
90 Percent confidence interval - lower 0.056
90 Percent confidence interval - upper 0.069
Standardized Root Mean Square Residual:
SRMR 0.054 0.054
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.616 0.922
PC01_02 0.991 0.027 36.965 0.000 1.601 0.897
PC01_04 0.962 0.026 36.419 0.000 1.555 0.880
PC01_05 1.001 0.023 43.210 0.000 1.617 0.907
PC01_06 0.863 0.036 24.263 0.000 1.395 0.800
PC01_07 1.000 0.023 44.019 0.000 1.615 0.923
grats_inf =~
GR01_01 1.000 0.971 0.689
GR01_02 1.039 0.072 14.519 0.000 1.009 0.822
GR01_03 1.142 0.074 15.351 0.000 1.109 0.850
grats_rel =~
GR01_04 1.000 1.185 0.895
GR01_05 0.941 0.035 26.575 0.000 1.115 0.855
GR01_06 0.881 0.044 20.235 0.000 1.045 0.708
grats_par =~
GR01_07 1.000 1.207 0.827
GR01_08 0.916 0.038 24.126 0.000 1.106 0.809
GR01_09 0.949 0.037 25.835 0.000 1.146 0.817
grats_ide =~
GR01_10 1.000 1.153 0.796
GR01_11 1.003 0.040 25.145 0.000 1.156 0.886
GR01_12 0.931 0.040 23.307 0.000 1.073 0.760
grats_ext =~
GR01_13 1.000 0.848 0.518
GR01_14 1.036 0.097 10.664 0.000 0.879 0.516
GR01_15 1.532 0.167 9.175 0.000 1.299 0.849
grats_spec =~
grats_inf 1.000 0.835 0.835
grats_rel 1.360 0.105 12.911 0.000 0.931 0.931
grats_par 1.448 0.120 12.056 0.000 0.973 0.973
grats_ide 1.327 0.110 12.062 0.000 0.934 0.934
grats_ext 0.833 0.104 8.013 0.000 0.796 0.796
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
COMM_log ~
pri_con (a1) -0.225 0.058 -3.859 0.000 -0.363 -0.159
grats_spc (b1) 0.560 0.133 4.224 0.000 0.455 0.199
male 0.008 0.191 0.042 0.966 0.008 0.002
age 0.007 0.006 1.206 0.228 0.007 0.049
edu 0.198 0.113 1.755 0.079 0.198 0.073
GR01_01 ~
male -0.329 0.117 -2.815 0.005 -0.329 -0.117
age -0.006 0.004 -1.453 0.146 -0.006 -0.063
edu -0.001 0.070 -0.018 0.986 -0.001 -0.001
GR01_02 ~
male -0.156 0.101 -1.554 0.120 -0.156 -0.064
age -0.006 0.003 -1.820 0.069 -0.006 -0.074
edu -0.037 0.060 -0.620 0.535 -0.037 -0.025
GR01_03 ~
male -0.212 0.107 -1.987 0.047 -0.212 -0.081
age -0.004 0.004 -1.239 0.215 -0.004 -0.053
edu -0.089 0.063 -1.412 0.158 -0.089 -0.057
GR01_04 ~
male 0.000 0.110 0.003 0.997 0.000 0.000
age 0.001 0.004 0.169 0.866 0.001 0.007
edu -0.022 0.065 -0.347 0.729 -0.022 -0.014
GR01_05 ~
male -0.074 0.109 -0.681 0.496 -0.074 -0.028
age -0.000 0.004 -0.119 0.906 -0.000 -0.005
edu 0.017 0.063 0.272 0.786 0.017 0.011
GR01_06 ~
male 0.031 0.120 0.257 0.797 0.031 0.011
age -0.011 0.004 -2.785 0.005 -0.011 -0.118
edu -0.081 0.073 -1.111 0.267 -0.081 -0.046
GR01_07 ~
male 0.047 0.121 0.385 0.700 0.047 0.016
age -0.006 0.004 -1.418 0.156 -0.006 -0.059
edu 0.006 0.071 0.082 0.935 0.006 0.003
GR01_08 ~
male 0.006 0.112 0.050 0.961 0.006 0.002
age -0.004 0.004 -1.131 0.258 -0.004 -0.050
edu 0.098 0.066 1.482 0.138 0.098 0.061
GR01_09 ~
male 0.058 0.116 0.505 0.614 0.058 0.021
age -0.003 0.004 -0.728 0.467 -0.003 -0.031
edu 0.107 0.067 1.588 0.112 0.107 0.064
GR01_10 ~
male -0.028 0.120 -0.234 0.815 -0.028 -0.010
age -0.008 0.004 -2.110 0.035 -0.008 -0.091
edu -0.023 0.072 -0.322 0.747 -0.023 -0.014
GR01_11 ~
male -0.086 0.108 -0.798 0.425 -0.086 -0.033
age -0.001 0.004 -0.368 0.713 -0.001 -0.016
edu -0.058 0.064 -0.910 0.363 -0.058 -0.037
GR01_12 ~
male -0.196 0.117 -1.667 0.096 -0.196 -0.069
age -0.004 0.004 -1.129 0.259 -0.004 -0.047
edu -0.065 0.070 -0.938 0.348 -0.065 -0.039
GR01_13 ~
male -0.188 0.133 -1.415 0.157 -0.188 -0.058
age -0.023 0.004 -5.610 0.000 -0.023 -0.219
edu 0.080 0.078 1.022 0.307 0.080 0.041
GR01_14 ~
male -0.248 0.143 -1.740 0.082 -0.248 -0.073
age -0.011 0.004 -2.418 0.016 -0.011 -0.099
edu 0.053 0.083 0.638 0.524 0.053 0.026
GR01_15 ~
male 0.041 0.128 0.322 0.748 0.041 0.013
age -0.006 0.004 -1.491 0.136 -0.006 -0.063
edu 0.041 0.076 0.543 0.587 0.041 0.023
PC01_01 ~
male -0.114 0.148 -0.769 0.442 -0.114 -0.033
age -0.007 0.005 -1.510 0.131 -0.007 -0.065
edu 0.128 0.087 1.485 0.138 0.128 0.062
PC01_02 ~
male -0.255 0.150 -1.696 0.090 -0.255 -0.071
age -0.011 0.005 -2.336 0.020 -0.011 -0.098
edu 0.052 0.088 0.589 0.556 0.052 0.025
PC01_04 ~
male -0.189 0.148 -1.271 0.204 -0.189 -0.053
age -0.012 0.005 -2.441 0.015 -0.012 -0.102
edu 0.127 0.087 1.462 0.144 0.127 0.061
PC01_05 ~
male -0.055 0.150 -0.367 0.714 -0.055 -0.015
age -0.008 0.005 -1.719 0.086 -0.008 -0.073
edu 0.103 0.088 1.172 0.241 0.103 0.049
PC01_06 ~
male -0.098 0.147 -0.664 0.507 -0.098 -0.028
age -0.008 0.005 -1.675 0.094 -0.008 -0.071
edu 0.064 0.086 0.751 0.453 0.064 0.031
PC01_07 ~
male -0.143 0.147 -0.972 0.331 -0.143 -0.041
age -0.009 0.005 -1.910 0.056 -0.009 -0.081
edu 0.095 0.086 1.108 0.268 0.095 0.046
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con ~~
grats_spec -0.042 0.067 -0.624 0.533 -0.032 -0.032
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.506 0.285 12.292 0.000 3.506 2.002
.PC01_02 3.940 0.297 13.268 0.000 3.940 2.207
.PC01_04 3.673 0.288 12.743 0.000 3.673 2.079
.PC01_05 3.544 0.296 11.992 0.000 3.544 1.989
.PC01_06 3.371 0.282 11.941 0.000 3.371 1.933
.PC01_07 3.602 0.286 12.592 0.000 3.602 2.058
.GR01_01 5.305 0.241 21.998 0.000 5.305 3.767
.GR01_02 5.829 0.205 28.368 0.000 5.829 4.745
.GR01_03 5.715 0.221 25.876 0.000 5.715 4.383
.GR01_04 4.938 0.208 23.706 0.000 4.938 3.728
.GR01_05 5.104 0.215 23.716 0.000 5.104 3.915
.GR01_06 5.313 0.246 21.637 0.000 5.313 3.601
.GR01_07 4.899 0.231 21.165 0.000 4.899 3.355
.GR01_08 5.067 0.233 21.706 0.000 5.067 3.706
.GR01_09 4.730 0.233 20.274 0.000 4.730 3.374
.GR01_10 4.997 0.248 20.168 0.000 4.997 3.453
.GR01_11 5.171 0.223 23.181 0.000 5.171 3.962
.GR01_12 5.180 0.230 22.517 0.000 5.180 3.671
.GR01_13 5.100 0.253 20.182 0.000 5.100 3.115
.GR01_14 3.646 0.274 13.318 0.000 3.646 2.141
.GR01_15 4.615 0.259 17.810 0.000 4.615 3.017
.COMM_log 1.086 0.366 2.965 0.003 1.086 0.475
pri_con 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
grats_spec 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.428 0.056 7.652 0.000 0.428 0.139
.PC01_02 0.571 0.098 5.797 0.000 0.571 0.179
.PC01_04 0.646 0.076 8.542 0.000 0.646 0.207
.PC01_05 0.535 0.062 8.608 0.000 0.535 0.168
.PC01_06 1.071 0.114 9.359 0.000 1.071 0.352
.PC01_07 0.422 0.061 6.860 0.000 0.422 0.138
.GR01_01 1.004 0.105 9.558 0.000 1.004 0.506
.GR01_02 0.474 0.063 7.521 0.000 0.474 0.314
.GR01_03 0.447 0.065 6.836 0.000 0.447 0.263
.GR01_04 0.349 0.041 8.535 0.000 0.349 0.199
.GR01_05 0.455 0.054 8.386 0.000 0.455 0.268
.GR01_06 1.053 0.103 10.243 0.000 1.053 0.484
.GR01_07 0.667 0.066 10.036 0.000 0.667 0.313
.GR01_08 0.635 0.065 9.732 0.000 0.635 0.339
.GR01_09 0.641 0.068 9.383 0.000 0.641 0.326
.GR01_10 0.748 0.066 11.284 0.000 0.748 0.357
.GR01_11 0.361 0.053 6.810 0.000 0.361 0.212
.GR01_12 0.822 0.075 10.983 0.000 0.822 0.413
.GR01_13 1.812 0.142 12.726 0.000 1.812 0.676
.GR01_14 2.077 0.123 16.826 0.000 2.077 0.717
.GR01_15 0.641 0.105 6.131 0.000 0.641 0.274
.COMM_log 4.832 0.203 23.835 0.000 4.832 0.926
pri_con 2.611 0.146 17.921 0.000 1.000 1.000
.grats_inf 0.285 0.043 6.651 0.000 0.302 0.302
.grats_rel 0.188 0.046 4.080 0.000 0.134 0.134
.grats_par 0.078 0.050 1.576 0.115 0.054 0.054
.grats_ide 0.170 0.044 3.827 0.000 0.128 0.128
.grats_ext 0.263 0.064 4.134 0.000 0.366 0.366
grats_spec 0.658 0.118 5.585 0.000 1.000 1.000In what follows, you can find slightly different models that were also explored.
Here, we combine both privacy measures into a single one. Likewise, we combine both gratifications and trust.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
pri_cau =~ pri_con + pri_delib
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
grats_meta =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext + trust_community + trust_provider
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
self_dis_log ~ a1*pri_cau + b1*grats_meta + c1*self_eff
# Covariates
self_dis_log + GR01_01 + GR01_02 + GR01_03 + GR01_04 + GR01_05 + GR01_06 + GR01_07 + GR01_08 + GR01_09 + GR01_10 + GR01_11 + GR01_12 + GR01_13 + GR01_14 + GR01_15 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 398 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 256
Number of equality constraints 1
Used Total
Number of observations 558 559
Number of missing patterns 3
Model Test User Model:
Standard Robust
Test Statistic 2305.334 1744.842
Degrees of freedom 725 725
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.321
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 17141.198 12836.227
Degrees of freedom 900 900
P-value 0.000 0.000
Scaling correction factor 1.335
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.903 0.915
Tucker-Lewis Index (TLI) 0.879 0.894
Robust Comparative Fit Index (CFI) 0.915
Robust Tucker-Lewis Index (TLI) 0.895
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -32387.951 -32387.951
Scaling correction factor 1.254
for the MLR correction
Loglikelihood unrestricted model (H1) -31235.284 -31235.284
Scaling correction factor 1.305
for the MLR correction
Akaike (AIC) 65285.902 65285.902
Bayesian (BIC) 66388.614 66388.614
Sample-size adjusted Bayesian (BIC) 65579.122 65579.122
Root Mean Square Error of Approximation:
RMSEA 0.063 0.050
90 Percent confidence interval - lower 0.060 0.048
90 Percent confidence interval - upper 0.065 0.053
P-value RMSEA <= 0.05 0.000 0.444
Robust RMSEA 0.058
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.061
Standardized Root Mean Square Residual:
SRMR 0.073 0.073
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.595 0.926
PC01_02 0.990 0.027 36.234 0.000 1.579 0.895
PC01_04 0.971 0.027 35.465 0.000 1.550 0.883
PC01_05 1.002 0.024 42.298 0.000 1.598 0.907
PC01_06 0.855 0.038 22.665 0.000 1.364 0.796
PC01_07 0.995 0.023 43.661 0.000 1.587 0.921
pri_delib =~
PD01_01 1.000 1.483 0.859
PD01_02 0.665 0.048 13.708 0.000 0.985 0.641
PD01_03 0.698 0.054 12.937 0.000 1.035 0.665
PD01_04 0.835 0.047 17.673 0.000 1.238 0.725
PD01_05 0.710 0.050 14.335 0.000 1.053 0.636
pri_cau =~
pri_con 1.000 0.704 0.704
pri_delib 1.059 0.321 3.300 0.001 0.801 0.801
grats_inf =~
GR01_01 1.000 0.963 0.684
GR01_02 1.030 0.076 13.580 0.000 0.991 0.813
GR01_03 1.123 0.077 14.554 0.000 1.081 0.841
grats_rel =~
GR01_04 1.000 1.176 0.891
GR01_05 0.945 0.038 25.049 0.000 1.111 0.859
GR01_06 0.874 0.046 19.073 0.000 1.027 0.694
grats_par =~
GR01_07 1.000 1.183 0.812
GR01_08 0.950 0.039 24.066 0.000 1.123 0.819
GR01_09 0.962 0.039 24.532 0.000 1.138 0.813
grats_ide =~
GR01_10 1.000 1.141 0.785
GR01_11 1.007 0.041 24.435 0.000 1.149 0.885
GR01_12 0.933 0.040 23.106 0.000 1.065 0.763
grats_ext =~
GR01_13 1.000 0.833 0.507
GR01_14 1.015 0.103 9.890 0.000 0.845 0.505
GR01_15 1.558 0.188 8.276 0.000 1.297 0.849
trust_community =~
TR01_02 1.000 1.040 0.821
TR01_03 0.785 0.055 14.314 0.000 0.817 0.743
TR01_04 0.907 0.051 17.760 0.000 0.943 0.818
trust_provider =~
TR01_06 1.000 1.051 0.875
TR01_07 0.859 0.039 22.260 0.000 0.903 0.781
TR01_08 0.827 0.038 21.728 0.000 0.869 0.786
TR01_10 0.791 0.038 20.646 0.000 0.831 0.706
TR01_11 0.802 0.052 15.424 0.000 0.843 0.650
TR01_12 1.088 0.039 27.767 0.000 1.143 0.850
grats_meta =~
grats_inf 1.000 0.856 0.856
grats_rel 1.309 0.102 12.794 0.000 0.918 0.918
grats_par 1.365 0.110 12.395 0.000 0.951 0.951
grats_ide 1.282 0.103 12.456 0.000 0.926 0.926
grats_ext 0.773 0.102 7.556 0.000 0.765 0.765
trust_communty 0.959 0.085 11.231 0.000 0.760 0.760
trust_provider 1.018 0.088 11.549 0.000 0.799 0.799
self_eff =~
SE01_01 1.000 1.116 0.809
SE01_02 0.809 0.058 13.917 0.000 0.902 0.669
SE01_03 0.930 0.047 19.906 0.000 1.037 0.774
SE01_04 0.958 0.044 21.963 0.000 1.069 0.792
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
self_dis_log ~
pri_cau (a1) -0.297 0.133 -2.232 0.026 -0.333 -0.146
grats_met (b1) 0.105 0.166 0.633 0.527 0.087 0.038
self_eff (c1) 0.635 0.140 4.553 0.000 0.709 0.310
male -0.022 0.197 -0.110 0.913 -0.022 -0.005
age 0.003 0.006 0.570 0.569 0.003 0.024
edu 0.213 0.116 1.836 0.066 0.213 0.078
GR01_01 ~
male -0.342 0.121 -2.834 0.005 -0.342 -0.122
age -0.005 0.004 -1.333 0.183 -0.005 -0.059
edu 0.000 0.072 0.006 0.995 0.000 0.000
GR01_02 ~
male -0.142 0.103 -1.378 0.168 -0.142 -0.058
age -0.007 0.003 -2.106 0.035 -0.007 -0.088
edu -0.037 0.060 -0.617 0.537 -0.037 -0.026
GR01_03 ~
male -0.187 0.109 -1.714 0.087 -0.187 -0.073
age -0.006 0.004 -1.704 0.088 -0.006 -0.075
edu -0.076 0.063 -1.204 0.229 -0.076 -0.050
GR01_04 ~
male -0.004 0.113 -0.037 0.970 -0.004 -0.002
age 0.001 0.004 0.262 0.793 0.001 0.011
edu -0.021 0.066 -0.319 0.750 -0.021 -0.013
GR01_05 ~
male -0.069 0.112 -0.614 0.539 -0.069 -0.027
age -0.001 0.004 -0.198 0.843 -0.001 -0.009
edu 0.025 0.064 0.388 0.698 0.025 0.016
GR01_06 ~
male 0.030 0.125 0.241 0.810 0.030 0.010
age -0.011 0.004 -2.567 0.010 -0.011 -0.111
edu -0.087 0.074 -1.176 0.240 -0.087 -0.050
GR01_07 ~
male 0.075 0.125 0.602 0.547 0.075 0.026
age -0.006 0.004 -1.391 0.164 -0.006 -0.060
edu 0.004 0.072 0.057 0.955 0.004 0.002
GR01_08 ~
male 0.006 0.116 0.051 0.959 0.006 0.002
age -0.004 0.004 -1.121 0.262 -0.004 -0.051
edu 0.113 0.068 1.675 0.094 0.113 0.070
GR01_09 ~
male 0.090 0.119 0.757 0.449 0.090 0.032
age -0.004 0.004 -0.952 0.341 -0.004 -0.041
edu 0.115 0.069 1.667 0.096 0.115 0.069
GR01_10 ~
male -0.019 0.125 -0.156 0.876 -0.019 -0.007
age -0.008 0.004 -1.993 0.046 -0.008 -0.089
edu -0.021 0.074 -0.277 0.782 -0.021 -0.012
GR01_11 ~
male -0.083 0.111 -0.749 0.454 -0.083 -0.032
age -0.001 0.004 -0.313 0.755 -0.001 -0.014
edu -0.053 0.065 -0.822 0.411 -0.053 -0.034
GR01_12 ~
male -0.218 0.120 -1.821 0.069 -0.218 -0.078
age -0.004 0.004 -1.009 0.313 -0.004 -0.043
edu -0.049 0.071 -0.685 0.493 -0.049 -0.029
GR01_13 ~
male -0.181 0.138 -1.311 0.190 -0.181 -0.055
age -0.023 0.004 -5.515 0.000 -0.023 -0.219
edu 0.078 0.081 0.959 0.337 0.078 0.040
GR01_14 ~
male -0.303 0.145 -2.092 0.036 -0.303 -0.090
age -0.007 0.005 -1.540 0.124 -0.007 -0.065
edu 0.030 0.084 0.361 0.718 0.030 0.015
GR01_15 ~
male 0.048 0.132 0.359 0.719 0.048 0.016
age -0.005 0.004 -1.214 0.225 -0.005 -0.053
edu 0.024 0.078 0.301 0.764 0.024 0.013
PC01_01 ~
male -0.182 0.151 -1.205 0.228 -0.182 -0.053
age -0.004 0.005 -0.820 0.412 -0.004 -0.036
edu 0.110 0.087 1.255 0.210 0.110 0.054
PC01_02 ~
male -0.302 0.154 -1.966 0.049 -0.302 -0.085
age -0.008 0.005 -1.664 0.096 -0.008 -0.072
edu 0.047 0.089 0.522 0.602 0.047 0.022
PC01_04 ~
male -0.225 0.152 -1.475 0.140 -0.225 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.269 0.204 0.113 0.054
PC01_05 ~
male -0.098 0.154 -0.636 0.525 -0.098 -0.028
age -0.006 0.005 -1.164 0.244 -0.006 -0.051
edu 0.090 0.090 0.996 0.319 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.721 0.471 -0.108 -0.032
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.491 0.623 0.043 0.021
PC01_07 ~
male -0.174 0.150 -1.160 0.246 -0.174 -0.050
age -0.006 0.005 -1.337 0.181 -0.006 -0.058
edu 0.081 0.087 0.933 0.351 0.081 0.040
TR01_02 ~
male -0.297 0.108 -2.744 0.006 -0.297 -0.117
age -0.004 0.004 -1.103 0.270 -0.004 -0.049
edu 0.005 0.062 0.086 0.931 0.005 0.004
TR01_03 ~
male -0.140 0.095 -1.480 0.139 -0.140 -0.064
age -0.002 0.003 -0.566 0.571 -0.002 -0.025
edu 0.023 0.053 0.434 0.664 0.023 0.018
TR01_04 ~
male -0.135 0.099 -1.362 0.173 -0.135 -0.058
age -0.004 0.003 -1.211 0.226 -0.004 -0.055
edu -0.003 0.060 -0.045 0.964 -0.003 -0.002
TR01_06 ~
male -0.086 0.104 -0.831 0.406 -0.086 -0.036
age 0.000 0.003 0.110 0.912 0.000 0.005
edu -0.051 0.058 -0.880 0.379 -0.051 -0.036
TR01_07 ~
male -0.045 0.099 -0.450 0.653 -0.045 -0.019
age 0.001 0.003 0.344 0.731 0.001 0.015
edu 0.018 0.058 0.309 0.757 0.018 0.013
TR01_08 ~
male 0.046 0.095 0.479 0.632 0.046 0.021
age -0.004 0.003 -1.250 0.211 -0.004 -0.053
edu 0.025 0.056 0.445 0.656 0.025 0.019
TR01_10 ~
male 0.091 0.100 0.909 0.364 0.091 0.039
age -0.004 0.003 -1.178 0.239 -0.004 -0.050
edu -0.055 0.058 -0.941 0.347 -0.055 -0.039
TR01_11 ~
male 0.027 0.112 0.245 0.807 0.027 0.011
age 0.003 0.004 0.824 0.410 0.003 0.035
edu -0.093 0.065 -1.435 0.151 -0.093 -0.061
TR01_12 ~
male -0.121 0.115 -1.045 0.296 -0.121 -0.045
age -0.002 0.004 -0.406 0.685 -0.002 -0.018
edu -0.146 0.068 -2.156 0.031 -0.146 -0.091
PD01_01 ~
male -0.177 0.148 -1.197 0.231 -0.177 -0.051
age -0.015 0.005 -3.275 0.001 -0.015 -0.137
edu -0.026 0.085 -0.310 0.756 -0.026 -0.013
PD01_02 ~
male -0.119 0.131 -0.906 0.365 -0.119 -0.039
age -0.014 0.004 -3.443 0.001 -0.014 -0.142
edu 0.031 0.077 0.405 0.686 0.031 0.017
PD01_03 ~
male -0.321 0.132 -2.424 0.015 -0.321 -0.103
age -0.004 0.004 -1.024 0.306 -0.004 -0.044
edu 0.065 0.080 0.807 0.420 0.065 0.035
PD01_04 ~
male -0.412 0.145 -2.847 0.004 -0.412 -0.121
age -0.009 0.005 -1.904 0.057 -0.009 -0.082
edu 0.103 0.085 1.207 0.227 0.103 0.051
PD01_05 ~
male -0.205 0.142 -1.438 0.150 -0.205 -0.062
age -0.012 0.004 -2.696 0.007 -0.012 -0.111
edu -0.002 0.084 -0.025 0.980 -0.002 -0.001
SE01_01 ~
male 0.118 0.118 0.996 0.319 0.118 0.043
age -0.000 0.004 -0.006 0.995 -0.000 -0.000
edu 0.209 0.068 3.083 0.002 0.209 0.128
SE01_02 ~
male 0.057 0.112 0.514 0.607 0.057 0.021
age -0.013 0.004 -3.608 0.000 -0.013 -0.152
edu 0.197 0.066 2.976 0.003 0.197 0.123
SE01_03 ~
male 0.192 0.114 1.682 0.093 0.192 0.072
age 0.001 0.004 0.233 0.816 0.001 0.010
edu 0.141 0.067 2.107 0.035 0.141 0.089
SE01_04 ~
male 0.051 0.115 0.447 0.655 0.051 0.019
age 0.007 0.004 2.033 0.042 0.007 0.085
edu 0.125 0.066 1.880 0.060 0.125 0.078
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.107 0.044 2.415 0.016 0.107 0.141
.SE01_03 ~~
.SE01_04 (x) 0.107 0.044 2.415 0.016 0.107 0.159
pri_cau ~~
grats_meta -0.087 0.097 -0.901 0.368 -0.094 -0.094
self_eff -0.340 0.117 -2.899 0.004 -0.271 -0.271
grats_meta ~~
self_eff 0.518 0.061 8.465 0.000 0.563 0.563
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.557 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.398 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.035
.PC01_05 3.414 0.304 11.229 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.155 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.780 0.000 3.461 2.008
.PD01_01 4.493 0.290 15.507 0.000 4.493 2.602
.PD01_02 3.997 0.248 16.102 0.000 3.997 2.601
.PD01_03 4.432 0.270 16.438 0.000 4.432 2.845
.PD01_04 4.506 0.295 15.283 0.000 4.506 2.640
.PD01_05 5.000 0.276 18.089 0.000 5.000 3.019
.GR01_01 5.296 0.247 21.424 0.000 5.296 3.763
.GR01_02 5.895 0.205 28.694 0.000 5.895 4.835
.GR01_03 5.800 0.221 26.272 0.000 5.800 4.511
.GR01_04 4.923 0.211 23.378 0.000 4.923 3.730
.GR01_05 5.109 0.217 23.539 0.000 5.109 3.952
.GR01_06 5.297 0.252 21.013 0.000 5.297 3.581
.GR01_07 4.896 0.236 20.768 0.000 4.896 3.363
.GR01_08 5.062 0.238 21.233 0.000 5.062 3.691
.GR01_09 4.755 0.237 20.070 0.000 4.755 3.395
.GR01_10 4.977 0.255 19.492 0.000 4.977 3.425
.GR01_11 5.158 0.227 22.705 0.000 5.158 3.974
.GR01_12 5.136 0.233 22.001 0.000 5.136 3.679
.GR01_13 5.091 0.259 19.644 0.000 5.091 3.101
.GR01_14 3.454 0.281 12.310 0.000 3.454 2.061
.GR01_15 4.582 0.266 17.224 0.000 4.582 2.998
.TR01_02 5.083 0.219 23.224 0.000 5.083 4.010
.TR01_03 4.951 0.189 26.178 0.000 4.951 4.505
.TR01_04 4.871 0.200 24.361 0.000 4.871 4.223
.TR01_06 5.521 0.205 26.991 0.000 5.521 4.600
.TR01_07 5.135 0.197 26.024 0.000 5.135 4.440
.TR01_08 5.232 0.188 27.764 0.000 5.232 4.728
.TR01_10 5.955 0.193 30.910 0.000 5.955 5.060
.TR01_11 4.855 0.218 22.270 0.000 4.855 3.745
.TR01_12 5.581 0.231 24.175 0.000 5.581 4.149
.SE01_01 4.829 0.250 19.353 0.000 4.829 3.499
.SE01_02 5.734 0.234 24.459 0.000 5.734 4.251
.SE01_03 4.823 0.226 21.322 0.000 4.823 3.598
.SE01_04 4.538 0.234 19.373 0.000 4.538 3.362
.self_dis_log 1.376 0.375 3.673 0.000 1.376 0.602
.pri_con 0.000 0.000 0.000
.pri_delib 0.000 0.000 0.000
pri_cau 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
.trust_communty 0.000 0.000 0.000
.trust_provider 0.000 0.000 0.000
grats_meta 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.403 0.050 8.008 0.000 0.403 0.136
.PC01_02 0.580 0.102 5.677 0.000 0.580 0.186
.PC01_04 0.630 0.078 8.100 0.000 0.630 0.205
.PC01_05 0.534 0.064 8.328 0.000 0.534 0.172
.PC01_06 1.067 0.115 9.245 0.000 1.067 0.363
.PC01_07 0.430 0.065 6.624 0.000 0.430 0.145
.PD01_01 0.715 0.107 6.661 0.000 0.715 0.240
.PD01_02 1.335 0.128 10.454 0.000 1.335 0.565
.PD01_03 1.321 0.127 10.363 0.000 1.321 0.544
.PD01_04 1.308 0.147 8.876 0.000 1.308 0.449
.PD01_05 1.586 0.128 12.413 0.000 1.586 0.578
.GR01_01 1.015 0.105 9.636 0.000 1.015 0.512
.GR01_02 0.485 0.067 7.212 0.000 0.485 0.327
.GR01_03 0.460 0.069 6.709 0.000 0.460 0.279
.GR01_04 0.360 0.045 7.955 0.000 0.360 0.206
.GR01_05 0.435 0.055 7.860 0.000 0.435 0.260
.GR01_06 1.103 0.109 10.139 0.000 1.103 0.504
.GR01_07 0.712 0.072 9.855 0.000 0.712 0.336
.GR01_08 0.604 0.066 9.151 0.000 0.604 0.321
.GR01_09 0.649 0.072 9.060 0.000 0.649 0.331
.GR01_10 0.794 0.072 10.995 0.000 0.794 0.376
.GR01_11 0.362 0.053 6.763 0.000 0.362 0.215
.GR01_12 0.796 0.074 10.690 0.000 0.796 0.409
.GR01_13 1.853 0.150 12.384 0.000 1.853 0.687
.GR01_14 2.054 0.124 16.508 0.000 2.054 0.732
.GR01_15 0.645 0.117 5.528 0.000 0.645 0.276
.TR01_02 0.497 0.068 7.290 0.000 0.497 0.309
.TR01_03 0.535 0.058 9.190 0.000 0.535 0.443
.TR01_04 0.431 0.050 8.641 0.000 0.431 0.324
.TR01_06 0.333 0.035 9.627 0.000 0.333 0.231
.TR01_07 0.522 0.051 10.312 0.000 0.522 0.390
.TR01_08 0.464 0.041 11.365 0.000 0.464 0.379
.TR01_10 0.688 0.056 12.287 0.000 0.688 0.497
.TR01_11 0.962 0.082 11.690 0.000 0.962 0.572
.TR01_12 0.481 0.061 7.891 0.000 0.481 0.266
.SE01_01 0.623 0.088 7.060 0.000 0.623 0.327
.SE01_02 0.930 0.118 7.894 0.000 0.930 0.511
.SE01_03 0.695 0.094 7.359 0.000 0.695 0.387
.SE01_04 0.655 0.078 8.418 0.000 0.655 0.359
.self_dis_log 4.360 0.206 21.146 0.000 4.360 0.836
.pri_con 1.285 0.406 3.169 0.002 0.505 0.505
.pri_delib 0.787 0.444 1.772 0.076 0.358 0.358
pri_cau 1.260 0.409 3.077 0.002 1.000 1.000
.grats_inf 0.247 0.039 6.376 0.000 0.267 0.267
.grats_rel 0.218 0.047 4.636 0.000 0.157 0.157
.grats_par 0.134 0.051 2.596 0.009 0.096 0.096
.grats_ide 0.186 0.044 4.206 0.000 0.143 0.143
.grats_ext 0.288 0.072 4.012 0.000 0.415 0.415
.trust_communty 0.458 0.054 8.401 0.000 0.423 0.423
.trust_provider 0.400 0.050 8.045 0.000 0.362 0.362
grats_meta 0.679 0.119 5.712 0.000 1.000 1.000
self_eff 1.245 0.114 10.933 0.000 1.000 1.000Here, we combine both privacy measures into a single one. Likewise, we combine both gratifications and trust.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
pri_cau =~ pri_con + pri_delib
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
grats_meta =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext + trust_community + trust_provider
self_eff =~ SE01_01 + SE01_02 + SE01_03 + SE01_04
SE01_01 ~~ x*SE01_02
SE01_03 ~~ x*SE01_04
self_dis_log ~ a1*pri_cau + b1*grats_meta + c1*self_eff
# Covariates
self_dis_log + GR01_01 + GR01_02 + GR01_03 + GR01_04 + GR01_05 + GR01_06 + GR01_07 + GR01_08 + GR01_09 + GR01_10 + GR01_11 + GR01_12 + GR01_13 + GR01_14 + GR01_15 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 + SE01_01 + SE01_02 + SE01_03 + SE01_04 ~ male + age + edu
"
fit <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 398 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 256
Number of equality constraints 1
Used Total
Number of observations 558 559
Number of missing patterns 3
Model Test User Model:
Standard Robust
Test Statistic 2305.334 1744.842
Degrees of freedom 725 725
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.321
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 17141.198 12836.227
Degrees of freedom 900 900
P-value 0.000 0.000
Scaling correction factor 1.335
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.903 0.915
Tucker-Lewis Index (TLI) 0.879 0.894
Robust Comparative Fit Index (CFI) 0.915
Robust Tucker-Lewis Index (TLI) 0.895
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -32387.951 -32387.951
Scaling correction factor 1.254
for the MLR correction
Loglikelihood unrestricted model (H1) -31235.284 -31235.284
Scaling correction factor 1.305
for the MLR correction
Akaike (AIC) 65285.902 65285.902
Bayesian (BIC) 66388.614 66388.614
Sample-size adjusted Bayesian (BIC) 65579.122 65579.122
Root Mean Square Error of Approximation:
RMSEA 0.063 0.050
90 Percent confidence interval - lower 0.060 0.048
90 Percent confidence interval - upper 0.065 0.053
P-value RMSEA <= 0.05 0.000 0.444
Robust RMSEA 0.058
90 Percent confidence interval - lower 0.054
90 Percent confidence interval - upper 0.061
Standardized Root Mean Square Residual:
SRMR 0.073 0.073
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.595 0.926
PC01_02 0.990 0.027 36.234 0.000 1.579 0.895
PC01_04 0.971 0.027 35.465 0.000 1.550 0.883
PC01_05 1.002 0.024 42.298 0.000 1.598 0.907
PC01_06 0.855 0.038 22.665 0.000 1.364 0.796
PC01_07 0.995 0.023 43.661 0.000 1.587 0.921
pri_delib =~
PD01_01 1.000 1.483 0.859
PD01_02 0.665 0.048 13.708 0.000 0.985 0.641
PD01_03 0.698 0.054 12.937 0.000 1.035 0.665
PD01_04 0.835 0.047 17.673 0.000 1.238 0.725
PD01_05 0.710 0.050 14.335 0.000 1.053 0.636
pri_cau =~
pri_con 1.000 0.704 0.704
pri_delib 1.059 0.321 3.300 0.001 0.801 0.801
grats_inf =~
GR01_01 1.000 0.963 0.684
GR01_02 1.030 0.076 13.580 0.000 0.991 0.813
GR01_03 1.123 0.077 14.554 0.000 1.081 0.841
grats_rel =~
GR01_04 1.000 1.176 0.891
GR01_05 0.945 0.038 25.049 0.000 1.111 0.859
GR01_06 0.874 0.046 19.073 0.000 1.027 0.694
grats_par =~
GR01_07 1.000 1.183 0.812
GR01_08 0.950 0.039 24.066 0.000 1.123 0.819
GR01_09 0.962 0.039 24.532 0.000 1.138 0.813
grats_ide =~
GR01_10 1.000 1.141 0.785
GR01_11 1.007 0.041 24.435 0.000 1.149 0.885
GR01_12 0.933 0.040 23.106 0.000 1.065 0.763
grats_ext =~
GR01_13 1.000 0.833 0.507
GR01_14 1.015 0.103 9.890 0.000 0.845 0.505
GR01_15 1.558 0.188 8.276 0.000 1.297 0.849
trust_community =~
TR01_02 1.000 1.040 0.821
TR01_03 0.785 0.055 14.314 0.000 0.817 0.743
TR01_04 0.907 0.051 17.760 0.000 0.943 0.818
trust_provider =~
TR01_06 1.000 1.051 0.875
TR01_07 0.859 0.039 22.260 0.000 0.903 0.781
TR01_08 0.827 0.038 21.728 0.000 0.869 0.786
TR01_10 0.791 0.038 20.646 0.000 0.831 0.706
TR01_11 0.802 0.052 15.424 0.000 0.843 0.650
TR01_12 1.088 0.039 27.767 0.000 1.143 0.850
grats_meta =~
grats_inf 1.000 0.856 0.856
grats_rel 1.309 0.102 12.794 0.000 0.918 0.918
grats_par 1.365 0.110 12.395 0.000 0.951 0.951
grats_ide 1.282 0.103 12.456 0.000 0.926 0.926
grats_ext 0.773 0.102 7.556 0.000 0.765 0.765
trust_communty 0.959 0.085 11.231 0.000 0.760 0.760
trust_provider 1.018 0.088 11.549 0.000 0.799 0.799
self_eff =~
SE01_01 1.000 1.116 0.809
SE01_02 0.809 0.058 13.917 0.000 0.902 0.669
SE01_03 0.930 0.047 19.906 0.000 1.037 0.774
SE01_04 0.958 0.044 21.963 0.000 1.069 0.792
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
self_dis_log ~
pri_cau (a1) -0.297 0.133 -2.232 0.026 -0.333 -0.146
grats_met (b1) 0.105 0.166 0.633 0.527 0.087 0.038
self_eff (c1) 0.635 0.140 4.553 0.000 0.709 0.310
male -0.022 0.197 -0.110 0.913 -0.022 -0.005
age 0.003 0.006 0.570 0.569 0.003 0.024
edu 0.213 0.116 1.836 0.066 0.213 0.078
GR01_01 ~
male -0.342 0.121 -2.834 0.005 -0.342 -0.122
age -0.005 0.004 -1.333 0.183 -0.005 -0.059
edu 0.000 0.072 0.006 0.995 0.000 0.000
GR01_02 ~
male -0.142 0.103 -1.378 0.168 -0.142 -0.058
age -0.007 0.003 -2.106 0.035 -0.007 -0.088
edu -0.037 0.060 -0.617 0.537 -0.037 -0.026
GR01_03 ~
male -0.187 0.109 -1.714 0.087 -0.187 -0.073
age -0.006 0.004 -1.704 0.088 -0.006 -0.075
edu -0.076 0.063 -1.204 0.229 -0.076 -0.050
GR01_04 ~
male -0.004 0.113 -0.037 0.970 -0.004 -0.002
age 0.001 0.004 0.262 0.793 0.001 0.011
edu -0.021 0.066 -0.319 0.750 -0.021 -0.013
GR01_05 ~
male -0.069 0.112 -0.614 0.539 -0.069 -0.027
age -0.001 0.004 -0.198 0.843 -0.001 -0.009
edu 0.025 0.064 0.388 0.698 0.025 0.016
GR01_06 ~
male 0.030 0.125 0.241 0.810 0.030 0.010
age -0.011 0.004 -2.567 0.010 -0.011 -0.111
edu -0.087 0.074 -1.176 0.240 -0.087 -0.050
GR01_07 ~
male 0.075 0.125 0.602 0.547 0.075 0.026
age -0.006 0.004 -1.391 0.164 -0.006 -0.060
edu 0.004 0.072 0.057 0.955 0.004 0.002
GR01_08 ~
male 0.006 0.116 0.051 0.959 0.006 0.002
age -0.004 0.004 -1.121 0.262 -0.004 -0.051
edu 0.113 0.068 1.675 0.094 0.113 0.070
GR01_09 ~
male 0.090 0.119 0.757 0.449 0.090 0.032
age -0.004 0.004 -0.952 0.341 -0.004 -0.041
edu 0.115 0.069 1.667 0.096 0.115 0.069
GR01_10 ~
male -0.019 0.125 -0.156 0.876 -0.019 -0.007
age -0.008 0.004 -1.993 0.046 -0.008 -0.089
edu -0.021 0.074 -0.277 0.782 -0.021 -0.012
GR01_11 ~
male -0.083 0.111 -0.749 0.454 -0.083 -0.032
age -0.001 0.004 -0.313 0.755 -0.001 -0.014
edu -0.053 0.065 -0.822 0.411 -0.053 -0.034
GR01_12 ~
male -0.218 0.120 -1.821 0.069 -0.218 -0.078
age -0.004 0.004 -1.009 0.313 -0.004 -0.043
edu -0.049 0.071 -0.685 0.493 -0.049 -0.029
GR01_13 ~
male -0.181 0.138 -1.311 0.190 -0.181 -0.055
age -0.023 0.004 -5.515 0.000 -0.023 -0.219
edu 0.078 0.081 0.959 0.337 0.078 0.040
GR01_14 ~
male -0.303 0.145 -2.092 0.036 -0.303 -0.090
age -0.007 0.005 -1.540 0.124 -0.007 -0.065
edu 0.030 0.084 0.361 0.718 0.030 0.015
GR01_15 ~
male 0.048 0.132 0.359 0.719 0.048 0.016
age -0.005 0.004 -1.214 0.225 -0.005 -0.053
edu 0.024 0.078 0.301 0.764 0.024 0.013
PC01_01 ~
male -0.182 0.151 -1.205 0.228 -0.182 -0.053
age -0.004 0.005 -0.820 0.412 -0.004 -0.036
edu 0.110 0.087 1.255 0.210 0.110 0.054
PC01_02 ~
male -0.302 0.154 -1.966 0.049 -0.302 -0.085
age -0.008 0.005 -1.664 0.096 -0.008 -0.072
edu 0.047 0.089 0.522 0.602 0.047 0.022
PC01_04 ~
male -0.225 0.152 -1.475 0.140 -0.225 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.269 0.204 0.113 0.054
PC01_05 ~
male -0.098 0.154 -0.636 0.525 -0.098 -0.028
age -0.006 0.005 -1.164 0.244 -0.006 -0.051
edu 0.090 0.090 0.996 0.319 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.721 0.471 -0.108 -0.032
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.491 0.623 0.043 0.021
PC01_07 ~
male -0.174 0.150 -1.160 0.246 -0.174 -0.050
age -0.006 0.005 -1.337 0.181 -0.006 -0.058
edu 0.081 0.087 0.933 0.351 0.081 0.040
TR01_02 ~
male -0.297 0.108 -2.744 0.006 -0.297 -0.117
age -0.004 0.004 -1.103 0.270 -0.004 -0.049
edu 0.005 0.062 0.086 0.931 0.005 0.004
TR01_03 ~
male -0.140 0.095 -1.480 0.139 -0.140 -0.064
age -0.002 0.003 -0.566 0.571 -0.002 -0.025
edu 0.023 0.053 0.434 0.664 0.023 0.018
TR01_04 ~
male -0.135 0.099 -1.362 0.173 -0.135 -0.058
age -0.004 0.003 -1.211 0.226 -0.004 -0.055
edu -0.003 0.060 -0.045 0.964 -0.003 -0.002
TR01_06 ~
male -0.086 0.104 -0.831 0.406 -0.086 -0.036
age 0.000 0.003 0.110 0.912 0.000 0.005
edu -0.051 0.058 -0.880 0.379 -0.051 -0.036
TR01_07 ~
male -0.045 0.099 -0.450 0.653 -0.045 -0.019
age 0.001 0.003 0.344 0.731 0.001 0.015
edu 0.018 0.058 0.309 0.757 0.018 0.013
TR01_08 ~
male 0.046 0.095 0.479 0.632 0.046 0.021
age -0.004 0.003 -1.250 0.211 -0.004 -0.053
edu 0.025 0.056 0.445 0.656 0.025 0.019
TR01_10 ~
male 0.091 0.100 0.909 0.364 0.091 0.039
age -0.004 0.003 -1.178 0.239 -0.004 -0.050
edu -0.055 0.058 -0.941 0.347 -0.055 -0.039
TR01_11 ~
male 0.027 0.112 0.245 0.807 0.027 0.011
age 0.003 0.004 0.824 0.410 0.003 0.035
edu -0.093 0.065 -1.435 0.151 -0.093 -0.061
TR01_12 ~
male -0.121 0.115 -1.045 0.296 -0.121 -0.045
age -0.002 0.004 -0.406 0.685 -0.002 -0.018
edu -0.146 0.068 -2.156 0.031 -0.146 -0.091
PD01_01 ~
male -0.177 0.148 -1.197 0.231 -0.177 -0.051
age -0.015 0.005 -3.275 0.001 -0.015 -0.137
edu -0.026 0.085 -0.310 0.756 -0.026 -0.013
PD01_02 ~
male -0.119 0.131 -0.906 0.365 -0.119 -0.039
age -0.014 0.004 -3.443 0.001 -0.014 -0.142
edu 0.031 0.077 0.405 0.686 0.031 0.017
PD01_03 ~
male -0.321 0.132 -2.424 0.015 -0.321 -0.103
age -0.004 0.004 -1.024 0.306 -0.004 -0.044
edu 0.065 0.080 0.807 0.420 0.065 0.035
PD01_04 ~
male -0.412 0.145 -2.847 0.004 -0.412 -0.121
age -0.009 0.005 -1.904 0.057 -0.009 -0.082
edu 0.103 0.085 1.207 0.227 0.103 0.051
PD01_05 ~
male -0.205 0.142 -1.438 0.150 -0.205 -0.062
age -0.012 0.004 -2.696 0.007 -0.012 -0.111
edu -0.002 0.084 -0.025 0.980 -0.002 -0.001
SE01_01 ~
male 0.118 0.118 0.996 0.319 0.118 0.043
age -0.000 0.004 -0.006 0.995 -0.000 -0.000
edu 0.209 0.068 3.083 0.002 0.209 0.128
SE01_02 ~
male 0.057 0.112 0.514 0.607 0.057 0.021
age -0.013 0.004 -3.608 0.000 -0.013 -0.152
edu 0.197 0.066 2.976 0.003 0.197 0.123
SE01_03 ~
male 0.192 0.114 1.682 0.093 0.192 0.072
age 0.001 0.004 0.233 0.816 0.001 0.010
edu 0.141 0.067 2.107 0.035 0.141 0.089
SE01_04 ~
male 0.051 0.115 0.447 0.655 0.051 0.019
age 0.007 0.004 2.033 0.042 0.007 0.085
edu 0.125 0.066 1.880 0.060 0.125 0.078
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.SE01_01 ~~
.SE01_02 (x) 0.107 0.044 2.415 0.016 0.107 0.141
.SE01_03 ~~
.SE01_04 (x) 0.107 0.044 2.415 0.016 0.107 0.159
pri_cau ~~
grats_meta -0.087 0.097 -0.901 0.368 -0.094 -0.094
self_eff -0.340 0.117 -2.899 0.004 -0.271 -0.271
grats_meta ~~
self_eff 0.518 0.061 8.465 0.000 0.563 0.563
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.557 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.398 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.035
.PC01_05 3.414 0.304 11.229 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.155 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.780 0.000 3.461 2.008
.PD01_01 4.493 0.290 15.507 0.000 4.493 2.602
.PD01_02 3.997 0.248 16.102 0.000 3.997 2.601
.PD01_03 4.432 0.270 16.438 0.000 4.432 2.845
.PD01_04 4.506 0.295 15.283 0.000 4.506 2.640
.PD01_05 5.000 0.276 18.089 0.000 5.000 3.019
.GR01_01 5.296 0.247 21.424 0.000 5.296 3.763
.GR01_02 5.895 0.205 28.694 0.000 5.895 4.835
.GR01_03 5.800 0.221 26.272 0.000 5.800 4.511
.GR01_04 4.923 0.211 23.378 0.000 4.923 3.730
.GR01_05 5.109 0.217 23.539 0.000 5.109 3.952
.GR01_06 5.297 0.252 21.013 0.000 5.297 3.581
.GR01_07 4.896 0.236 20.768 0.000 4.896 3.363
.GR01_08 5.062 0.238 21.233 0.000 5.062 3.691
.GR01_09 4.755 0.237 20.070 0.000 4.755 3.395
.GR01_10 4.977 0.255 19.492 0.000 4.977 3.425
.GR01_11 5.158 0.227 22.705 0.000 5.158 3.974
.GR01_12 5.136 0.233 22.001 0.000 5.136 3.679
.GR01_13 5.091 0.259 19.644 0.000 5.091 3.101
.GR01_14 3.454 0.281 12.310 0.000 3.454 2.061
.GR01_15 4.582 0.266 17.224 0.000 4.582 2.998
.TR01_02 5.083 0.219 23.224 0.000 5.083 4.010
.TR01_03 4.951 0.189 26.178 0.000 4.951 4.505
.TR01_04 4.871 0.200 24.361 0.000 4.871 4.223
.TR01_06 5.521 0.205 26.991 0.000 5.521 4.600
.TR01_07 5.135 0.197 26.024 0.000 5.135 4.440
.TR01_08 5.232 0.188 27.764 0.000 5.232 4.728
.TR01_10 5.955 0.193 30.910 0.000 5.955 5.060
.TR01_11 4.855 0.218 22.270 0.000 4.855 3.745
.TR01_12 5.581 0.231 24.175 0.000 5.581 4.149
.SE01_01 4.829 0.250 19.353 0.000 4.829 3.499
.SE01_02 5.734 0.234 24.459 0.000 5.734 4.251
.SE01_03 4.823 0.226 21.322 0.000 4.823 3.598
.SE01_04 4.538 0.234 19.373 0.000 4.538 3.362
.self_dis_log 1.376 0.375 3.673 0.000 1.376 0.602
.pri_con 0.000 0.000 0.000
.pri_delib 0.000 0.000 0.000
pri_cau 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
.trust_communty 0.000 0.000 0.000
.trust_provider 0.000 0.000 0.000
grats_meta 0.000 0.000 0.000
self_eff 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.403 0.050 8.008 0.000 0.403 0.136
.PC01_02 0.580 0.102 5.677 0.000 0.580 0.186
.PC01_04 0.630 0.078 8.100 0.000 0.630 0.205
.PC01_05 0.534 0.064 8.328 0.000 0.534 0.172
.PC01_06 1.067 0.115 9.245 0.000 1.067 0.363
.PC01_07 0.430 0.065 6.624 0.000 0.430 0.145
.PD01_01 0.715 0.107 6.661 0.000 0.715 0.240
.PD01_02 1.335 0.128 10.454 0.000 1.335 0.565
.PD01_03 1.321 0.127 10.363 0.000 1.321 0.544
.PD01_04 1.308 0.147 8.876 0.000 1.308 0.449
.PD01_05 1.586 0.128 12.413 0.000 1.586 0.578
.GR01_01 1.015 0.105 9.636 0.000 1.015 0.512
.GR01_02 0.485 0.067 7.212 0.000 0.485 0.327
.GR01_03 0.460 0.069 6.709 0.000 0.460 0.279
.GR01_04 0.360 0.045 7.955 0.000 0.360 0.206
.GR01_05 0.435 0.055 7.860 0.000 0.435 0.260
.GR01_06 1.103 0.109 10.139 0.000 1.103 0.504
.GR01_07 0.712 0.072 9.855 0.000 0.712 0.336
.GR01_08 0.604 0.066 9.151 0.000 0.604 0.321
.GR01_09 0.649 0.072 9.060 0.000 0.649 0.331
.GR01_10 0.794 0.072 10.995 0.000 0.794 0.376
.GR01_11 0.362 0.053 6.763 0.000 0.362 0.215
.GR01_12 0.796 0.074 10.690 0.000 0.796 0.409
.GR01_13 1.853 0.150 12.384 0.000 1.853 0.687
.GR01_14 2.054 0.124 16.508 0.000 2.054 0.732
.GR01_15 0.645 0.117 5.528 0.000 0.645 0.276
.TR01_02 0.497 0.068 7.290 0.000 0.497 0.309
.TR01_03 0.535 0.058 9.190 0.000 0.535 0.443
.TR01_04 0.431 0.050 8.641 0.000 0.431 0.324
.TR01_06 0.333 0.035 9.627 0.000 0.333 0.231
.TR01_07 0.522 0.051 10.312 0.000 0.522 0.390
.TR01_08 0.464 0.041 11.365 0.000 0.464 0.379
.TR01_10 0.688 0.056 12.287 0.000 0.688 0.497
.TR01_11 0.962 0.082 11.690 0.000 0.962 0.572
.TR01_12 0.481 0.061 7.891 0.000 0.481 0.266
.SE01_01 0.623 0.088 7.060 0.000 0.623 0.327
.SE01_02 0.930 0.118 7.894 0.000 0.930 0.511
.SE01_03 0.695 0.094 7.359 0.000 0.695 0.387
.SE01_04 0.655 0.078 8.418 0.000 0.655 0.359
.self_dis_log 4.360 0.206 21.146 0.000 4.360 0.836
.pri_con 1.285 0.406 3.169 0.002 0.505 0.505
.pri_delib 0.787 0.444 1.772 0.076 0.358 0.358
pri_cau 1.260 0.409 3.077 0.002 1.000 1.000
.grats_inf 0.247 0.039 6.376 0.000 0.267 0.267
.grats_rel 0.218 0.047 4.636 0.000 0.157 0.157
.grats_par 0.134 0.051 2.596 0.009 0.096 0.096
.grats_ide 0.186 0.044 4.206 0.000 0.143 0.143
.grats_ext 0.288 0.072 4.012 0.000 0.415 0.415
.trust_communty 0.458 0.054 8.401 0.000 0.423 0.423
.trust_provider 0.400 0.050 8.045 0.000 0.362 0.362
grats_meta 0.679 0.119 5.712 0.000 1.000 1.000
self_eff 1.245 0.114 10.933 0.000 1.000 1.000We now use also delete self-efficacy.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
pri_delib =~ PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05
pri_cau =~ pri_con + pri_delib
grats_inf =~ GR01_01 + GR01_02 + GR01_03
grats_rel =~ GR01_04 + GR01_05 + GR01_06
grats_par =~ GR01_07 + GR01_08 + GR01_09
grats_ide =~ GR01_10 + GR01_11 + GR01_12
grats_ext =~ GR01_13 + GR01_14 + GR01_15
trust_community =~ TR01_02 + TR01_03 + TR01_04
trust_provider =~ TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12
grats_meta =~ grats_inf + grats_rel + grats_par + grats_ide + grats_ext + trust_community + trust_provider
self_dis_log ~ a1*pri_cau + b1*grats_meta
# Covariates
self_dis_log + GR01_01 + GR01_02 + GR01_03 + GR01_04 + GR01_05 + GR01_06 + GR01_07 + GR01_08 + GR01_09 + GR01_10 + GR01_11 + GR01_12 + GR01_13 + GR01_14 + GR01_15 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 + TR01_02 + TR01_03 + TR01_04 + TR01_06 + TR01_07 + TR01_08 + TR01_10 + TR01_11 + TR01_12 + PD01_01 + PD01_02 + PD01_03 + PD01_04 + PD01_05 ~ male + age + edu
"
fit <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 383 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 227
Used Total
Number of observations 558 559
Number of missing patterns 2
Model Test User Model:
Standard Robust
Test Statistic 2017.827 1506.031
Degrees of freedom 583 583
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.340
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 15551.219 11557.604
Degrees of freedom 738 738
P-value 0.000 0.000
Scaling correction factor 1.346
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.903 0.915
Tucker-Lewis Index (TLI) 0.877 0.892
Robust Comparative Fit Index (CFI) 0.915
Robust Tucker-Lewis Index (TLI) 0.892
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -29202.378 -29202.378
Scaling correction factor 1.236
for the MLR correction
Loglikelihood unrestricted model (H1) -28193.464 -28193.464
Scaling correction factor 1.311
for the MLR correction
Akaike (AIC) 58858.756 58858.756
Bayesian (BIC) 59840.385 59840.385
Sample-size adjusted Bayesian (BIC) 59119.779 59119.779
Root Mean Square Error of Approximation:
RMSEA 0.066 0.053
90 Percent confidence interval - lower 0.063 0.050
90 Percent confidence interval - upper 0.070 0.056
P-value RMSEA <= 0.05 0.000 0.030
Robust RMSEA 0.062
90 Percent confidence interval - lower 0.058
90 Percent confidence interval - upper 0.065
Standardized Root Mean Square Residual:
SRMR 0.074 0.074
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.595 0.926
PC01_02 0.990 0.027 36.128 0.000 1.579 0.894
PC01_04 0.971 0.027 35.382 0.000 1.550 0.883
PC01_05 1.002 0.024 42.276 0.000 1.598 0.907
PC01_06 0.855 0.038 22.619 0.000 1.364 0.795
PC01_07 0.995 0.023 43.626 0.000 1.587 0.921
pri_delib =~
PD01_01 1.000 1.482 0.858
PD01_02 0.665 0.049 13.468 0.000 0.985 0.641
PD01_03 0.700 0.054 12.919 0.000 1.037 0.666
PD01_04 0.836 0.047 17.644 0.000 1.239 0.726
PD01_05 0.710 0.050 14.276 0.000 1.053 0.636
pri_cau =~
pri_con 1.000 0.731 0.731
pri_delib 0.980 0.450 2.176 0.030 0.771 0.771
grats_inf =~
GR01_01 1.000 0.967 0.687
GR01_02 1.023 0.076 13.446 0.000 0.989 0.811
GR01_03 1.116 0.078 14.387 0.000 1.079 0.839
grats_rel =~
GR01_04 1.000 1.176 0.891
GR01_05 0.944 0.038 25.041 0.000 1.110 0.859
GR01_06 0.874 0.046 19.097 0.000 1.028 0.695
grats_par =~
GR01_07 1.000 1.186 0.815
GR01_08 0.945 0.040 23.649 0.000 1.120 0.817
GR01_09 0.960 0.039 24.523 0.000 1.139 0.813
grats_ide =~
GR01_10 1.000 1.142 0.786
GR01_11 1.005 0.041 24.249 0.000 1.147 0.884
GR01_12 0.933 0.041 23.004 0.000 1.065 0.763
grats_ext =~
GR01_13 1.000 0.830 0.506
GR01_14 1.017 0.103 9.870 0.000 0.844 0.504
GR01_15 1.564 0.187 8.369 0.000 1.299 0.850
trust_community =~
TR01_02 1.000 1.041 0.821
TR01_03 0.784 0.055 14.262 0.000 0.816 0.743
TR01_04 0.906 0.051 17.611 0.000 0.943 0.817
trust_provider =~
TR01_06 1.000 1.051 0.876
TR01_07 0.858 0.039 22.173 0.000 0.902 0.780
TR01_08 0.827 0.038 21.644 0.000 0.869 0.786
TR01_10 0.789 0.038 20.599 0.000 0.830 0.705
TR01_11 0.802 0.052 15.370 0.000 0.843 0.650
TR01_12 1.088 0.039 27.695 0.000 1.144 0.850
grats_meta =~
grats_inf 1.000 0.854 0.854
grats_rel 1.306 0.104 12.591 0.000 0.917 0.917
grats_par 1.364 0.113 12.099 0.000 0.950 0.950
grats_ide 1.286 0.106 12.101 0.000 0.931 0.931
grats_ext 0.778 0.104 7.494 0.000 0.774 0.774
trust_communty 0.956 0.087 11.048 0.000 0.758 0.758
trust_provider 1.011 0.089 11.410 0.000 0.794 0.794
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
self_dis_log ~
pri_cau (a1) -0.416 0.199 -2.088 0.037 -0.485 -0.212
grats_met (b1) 0.563 0.140 4.032 0.000 0.465 0.204
male -0.021 0.197 -0.109 0.913 -0.021 -0.005
age 0.003 0.006 0.570 0.569 0.003 0.024
edu 0.213 0.116 1.836 0.066 0.213 0.078
GR01_01 ~
male -0.342 0.121 -2.833 0.005 -0.342 -0.122
age -0.005 0.004 -1.333 0.183 -0.005 -0.059
edu 0.000 0.072 0.006 0.995 0.000 0.000
GR01_02 ~
male -0.142 0.103 -1.378 0.168 -0.142 -0.058
age -0.007 0.003 -2.106 0.035 -0.007 -0.088
edu -0.037 0.060 -0.617 0.537 -0.037 -0.026
GR01_03 ~
male -0.186 0.109 -1.713 0.087 -0.186 -0.073
age -0.006 0.004 -1.704 0.088 -0.006 -0.075
edu -0.076 0.063 -1.204 0.229 -0.076 -0.050
GR01_04 ~
male -0.004 0.113 -0.037 0.971 -0.004 -0.002
age 0.001 0.004 0.262 0.793 0.001 0.011
edu -0.021 0.066 -0.319 0.750 -0.021 -0.013
GR01_05 ~
male -0.069 0.112 -0.614 0.539 -0.069 -0.027
age -0.001 0.004 -0.198 0.843 -0.001 -0.009
edu 0.025 0.064 0.387 0.698 0.025 0.016
GR01_06 ~
male 0.030 0.125 0.241 0.809 0.030 0.010
age -0.011 0.004 -2.567 0.010 -0.011 -0.111
edu -0.087 0.074 -1.176 0.240 -0.087 -0.050
GR01_07 ~
male 0.075 0.125 0.602 0.547 0.075 0.026
age -0.006 0.004 -1.391 0.164 -0.006 -0.060
edu 0.004 0.072 0.057 0.955 0.004 0.002
GR01_08 ~
male 0.006 0.116 0.052 0.959 0.006 0.002
age -0.004 0.004 -1.121 0.262 -0.004 -0.051
edu 0.113 0.068 1.675 0.094 0.113 0.070
GR01_09 ~
male 0.090 0.119 0.757 0.449 0.090 0.032
age -0.004 0.004 -0.951 0.341 -0.004 -0.041
edu 0.115 0.069 1.667 0.096 0.115 0.069
GR01_10 ~
male -0.019 0.125 -0.156 0.876 -0.019 -0.007
age -0.008 0.004 -1.993 0.046 -0.008 -0.089
edu -0.021 0.074 -0.277 0.782 -0.021 -0.012
GR01_11 ~
male -0.083 0.111 -0.749 0.454 -0.083 -0.032
age -0.001 0.004 -0.312 0.755 -0.001 -0.014
edu -0.053 0.065 -0.822 0.411 -0.053 -0.034
GR01_12 ~
male -0.218 0.120 -1.820 0.069 -0.218 -0.078
age -0.004 0.004 -1.009 0.313 -0.004 -0.043
edu -0.049 0.071 -0.685 0.493 -0.049 -0.029
GR01_13 ~
male -0.181 0.138 -1.311 0.190 -0.181 -0.055
age -0.023 0.004 -5.515 0.000 -0.023 -0.219
edu 0.078 0.081 0.959 0.337 0.078 0.040
GR01_14 ~
male -0.303 0.145 -2.091 0.036 -0.303 -0.090
age -0.007 0.005 -1.540 0.124 -0.007 -0.065
edu 0.030 0.084 0.361 0.718 0.030 0.015
GR01_15 ~
male 0.048 0.132 0.360 0.719 0.048 0.016
age -0.005 0.004 -1.213 0.225 -0.005 -0.053
edu 0.024 0.078 0.301 0.764 0.024 0.013
PC01_01 ~
male -0.182 0.151 -1.206 0.228 -0.182 -0.053
age -0.004 0.005 -0.820 0.412 -0.004 -0.036
edu 0.110 0.087 1.255 0.210 0.110 0.054
PC01_02 ~
male -0.302 0.154 -1.966 0.049 -0.302 -0.085
age -0.008 0.005 -1.664 0.096 -0.008 -0.072
edu 0.047 0.089 0.522 0.602 0.047 0.022
PC01_04 ~
male -0.225 0.152 -1.475 0.140 -0.225 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.269 0.204 0.113 0.054
PC01_05 ~
male -0.098 0.154 -0.636 0.525 -0.098 -0.028
age -0.006 0.005 -1.164 0.244 -0.006 -0.051
edu 0.090 0.090 0.996 0.319 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.722 0.470 -0.108 -0.032
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.491 0.623 0.043 0.021
PC01_07 ~
male -0.174 0.150 -1.160 0.246 -0.174 -0.050
age -0.006 0.005 -1.337 0.181 -0.006 -0.058
edu 0.081 0.087 0.934 0.351 0.081 0.040
TR01_02 ~
male -0.297 0.108 -2.744 0.006 -0.297 -0.117
age -0.004 0.004 -1.103 0.270 -0.004 -0.049
edu 0.005 0.062 0.086 0.931 0.005 0.004
TR01_03 ~
male -0.140 0.095 -1.480 0.139 -0.140 -0.064
age -0.002 0.003 -0.566 0.571 -0.002 -0.025
edu 0.023 0.053 0.434 0.664 0.023 0.018
TR01_04 ~
male -0.134 0.099 -1.361 0.173 -0.134 -0.058
age -0.004 0.003 -1.211 0.226 -0.004 -0.055
edu -0.003 0.060 -0.046 0.964 -0.003 -0.002
TR01_06 ~
male -0.086 0.104 -0.831 0.406 -0.086 -0.036
age 0.000 0.003 0.110 0.912 0.000 0.005
edu -0.051 0.058 -0.880 0.379 -0.051 -0.036
TR01_07 ~
male -0.045 0.099 -0.450 0.653 -0.045 -0.019
age 0.001 0.003 0.344 0.731 0.001 0.015
edu 0.018 0.058 0.309 0.757 0.018 0.013
TR01_08 ~
male 0.046 0.095 0.480 0.631 0.046 0.021
age -0.004 0.003 -1.250 0.211 -0.004 -0.053
edu 0.025 0.056 0.445 0.656 0.025 0.019
TR01_10 ~
male 0.091 0.100 0.909 0.363 0.091 0.039
age -0.004 0.003 -1.179 0.239 -0.004 -0.050
edu -0.055 0.058 -0.941 0.347 -0.055 -0.039
TR01_11 ~
male 0.027 0.112 0.245 0.806 0.027 0.011
age 0.003 0.004 0.824 0.410 0.003 0.035
edu -0.093 0.065 -1.435 0.151 -0.093 -0.061
TR01_12 ~
male -0.121 0.115 -1.045 0.296 -0.121 -0.045
age -0.002 0.004 -0.405 0.685 -0.002 -0.018
edu -0.146 0.068 -2.156 0.031 -0.146 -0.091
PD01_01 ~
male -0.177 0.148 -1.197 0.231 -0.177 -0.051
age -0.015 0.005 -3.275 0.001 -0.015 -0.137
edu -0.026 0.085 -0.310 0.756 -0.026 -0.013
PD01_02 ~
male -0.119 0.131 -0.906 0.365 -0.119 -0.039
age -0.014 0.004 -3.443 0.001 -0.014 -0.142
edu 0.031 0.077 0.405 0.686 0.031 0.017
PD01_03 ~
male -0.321 0.132 -2.424 0.015 -0.321 -0.103
age -0.004 0.004 -1.024 0.306 -0.004 -0.044
edu 0.065 0.080 0.807 0.420 0.065 0.035
PD01_04 ~
male -0.412 0.145 -2.846 0.004 -0.412 -0.121
age -0.009 0.005 -1.904 0.057 -0.009 -0.082
edu 0.103 0.085 1.207 0.227 0.103 0.051
PD01_05 ~
male -0.205 0.142 -1.438 0.150 -0.205 -0.062
age -0.012 0.004 -2.696 0.007 -0.012 -0.111
edu -0.002 0.084 -0.025 0.980 -0.002 -0.001
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_cau ~~
grats_meta -0.099 0.124 -0.795 0.427 -0.102 -0.102
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.557 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.399 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.036
.PC01_05 3.414 0.304 11.229 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.155 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.780 0.000 3.461 2.008
.PD01_01 4.493 0.290 15.507 0.000 4.493 2.602
.PD01_02 3.997 0.248 16.102 0.000 3.997 2.601
.PD01_03 4.432 0.270 16.438 0.000 4.432 2.845
.PD01_04 4.506 0.295 15.283 0.000 4.506 2.640
.PD01_05 5.000 0.276 18.089 0.000 5.000 3.019
.GR01_01 5.296 0.247 21.424 0.000 5.296 3.763
.GR01_02 5.895 0.205 28.694 0.000 5.895 4.835
.GR01_03 5.800 0.221 26.272 0.000 5.800 4.511
.GR01_04 4.923 0.211 23.377 0.000 4.923 3.730
.GR01_05 5.109 0.217 23.539 0.000 5.109 3.952
.GR01_06 5.297 0.252 21.013 0.000 5.297 3.581
.GR01_07 4.896 0.236 20.767 0.000 4.896 3.363
.GR01_08 5.062 0.238 21.233 0.000 5.062 3.691
.GR01_09 4.754 0.237 20.070 0.000 4.754 3.395
.GR01_10 4.977 0.255 19.492 0.000 4.977 3.425
.GR01_11 5.158 0.227 22.705 0.000 5.158 3.973
.GR01_12 5.136 0.233 22.000 0.000 5.136 3.679
.GR01_13 5.091 0.259 19.644 0.000 5.091 3.101
.GR01_14 3.454 0.281 12.310 0.000 3.454 2.061
.GR01_15 4.582 0.266 17.223 0.000 4.582 2.998
.TR01_02 5.083 0.219 23.224 0.000 5.083 4.010
.TR01_03 4.951 0.189 26.178 0.000 4.951 4.505
.TR01_04 4.871 0.200 24.361 0.000 4.871 4.223
.TR01_06 5.521 0.205 26.991 0.000 5.521 4.600
.TR01_07 5.134 0.197 26.024 0.000 5.134 4.440
.TR01_08 5.232 0.188 27.764 0.000 5.232 4.728
.TR01_10 5.955 0.193 30.909 0.000 5.955 5.060
.TR01_11 4.855 0.218 22.270 0.000 4.855 3.745
.TR01_12 5.581 0.231 24.175 0.000 5.581 4.149
.self_dis_log 1.376 0.375 3.672 0.000 1.376 0.602
.pri_con 0.000 0.000 0.000
.pri_delib 0.000 0.000 0.000
pri_cau 0.000 0.000 0.000
.grats_inf 0.000 0.000 0.000
.grats_rel 0.000 0.000 0.000
.grats_par 0.000 0.000 0.000
.grats_ide 0.000 0.000 0.000
.grats_ext 0.000 0.000 0.000
.trust_communty 0.000 0.000 0.000
.trust_provider 0.000 0.000 0.000
grats_meta 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.403 0.050 7.990 0.000 0.403 0.136
.PC01_02 0.580 0.102 5.666 0.000 0.580 0.186
.PC01_04 0.630 0.078 8.100 0.000 0.630 0.205
.PC01_05 0.534 0.064 8.326 0.000 0.534 0.172
.PC01_06 1.068 0.116 9.245 0.000 1.068 0.363
.PC01_07 0.430 0.065 6.609 0.000 0.430 0.145
.PD01_01 0.718 0.109 6.565 0.000 0.718 0.241
.PD01_02 1.336 0.128 10.401 0.000 1.336 0.566
.PD01_03 1.317 0.127 10.359 0.000 1.317 0.543
.PD01_04 1.306 0.147 8.883 0.000 1.306 0.449
.PD01_05 1.588 0.128 12.371 0.000 1.588 0.579
.GR01_01 1.006 0.105 9.541 0.000 1.006 0.508
.GR01_02 0.489 0.068 7.200 0.000 0.489 0.329
.GR01_03 0.465 0.070 6.632 0.000 0.465 0.281
.GR01_04 0.358 0.045 7.937 0.000 0.358 0.206
.GR01_05 0.438 0.055 7.903 0.000 0.438 0.262
.GR01_06 1.101 0.109 10.135 0.000 1.101 0.503
.GR01_07 0.705 0.072 9.762 0.000 0.705 0.332
.GR01_08 0.611 0.067 9.153 0.000 0.611 0.325
.GR01_09 0.648 0.071 9.106 0.000 0.648 0.331
.GR01_10 0.791 0.072 11.054 0.000 0.791 0.375
.GR01_11 0.365 0.054 6.810 0.000 0.365 0.216
.GR01_12 0.795 0.074 10.688 0.000 0.795 0.408
.GR01_13 1.856 0.149 12.490 0.000 1.856 0.688
.GR01_14 2.056 0.123 16.681 0.000 2.056 0.732
.GR01_15 0.640 0.115 5.587 0.000 0.640 0.274
.TR01_02 0.496 0.068 7.272 0.000 0.496 0.308
.TR01_03 0.536 0.058 9.204 0.000 0.536 0.444
.TR01_04 0.432 0.050 8.611 0.000 0.432 0.325
.TR01_06 0.331 0.035 9.577 0.000 0.331 0.230
.TR01_07 0.523 0.051 10.297 0.000 0.523 0.391
.TR01_08 0.464 0.041 11.335 0.000 0.464 0.379
.TR01_10 0.690 0.056 12.303 0.000 0.690 0.498
.TR01_11 0.962 0.082 11.670 0.000 0.962 0.572
.TR01_12 0.480 0.061 7.877 0.000 0.480 0.265
.self_dis_log 4.687 0.223 20.987 0.000 4.687 0.898
.pri_con 1.185 0.645 1.838 0.066 0.466 0.466
.pri_delib 0.890 0.624 1.426 0.154 0.405 0.405
pri_cau 1.360 0.654 2.081 0.037 1.000 1.000
.grats_inf 0.253 0.040 6.309 0.000 0.271 0.271
.grats_rel 0.220 0.047 4.675 0.000 0.159 0.159
.grats_par 0.138 0.052 2.637 0.008 0.098 0.098
.grats_ide 0.175 0.044 3.997 0.000 0.134 0.134
.grats_ext 0.277 0.069 4.027 0.000 0.401 0.401
.trust_communty 0.460 0.055 8.356 0.000 0.425 0.425
.trust_provider 0.409 0.051 8.083 0.000 0.370 0.370
grats_meta 0.682 0.121 5.623 0.000 1.000 1.000Here, we exchange specific gratitudes for general gratitudes.
model <- "
pri_con =~ PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07
grats_gen =~ GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05
self_dis_log ~ a1*pri_con + b1*grats_gen
# Covariates
self_dis_log + GR02_01 + GR02_02 + GR02_03 + GR02_04 + GR02_05 + PC01_01 + PC01_02 + PC01_04 + PC01_05 + PC01_06 + PC01_07 ~ male + age + edu
"
fit <- sem(model, data = d, estimator = "MLR", missing = "ML")
summary(fit, fit = TRUE, std = TRUE)lavaan 0.6-8 ended normally after 172 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 74
Used Total
Number of observations 558 559
Number of missing patterns 1
Model Test User Model:
Standard Robust
Test Statistic 195.732 134.370
Degrees of freedom 52 52
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.457
Yuan-Bentler correction (Mplus variant)
Model Test Baseline Model:
Test statistic 6224.965 4243.430
Degrees of freedom 102 102
P-value 0.000 0.000
Scaling correction factor 1.467
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.977 0.980
Tucker-Lewis Index (TLI) 0.954 0.961
Robust Comparative Fit Index (CFI) 0.980
Robust Tucker-Lewis Index (TLI) 0.961
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -9678.380 -9678.380
Scaling correction factor 1.290
for the MLR correction
Loglikelihood unrestricted model (H1) -9580.514 -9580.514
Scaling correction factor 1.359
for the MLR correction
Akaike (AIC) 19504.761 19504.761
Bayesian (BIC) 19824.763 19824.763
Sample-size adjusted Bayesian (BIC) 19589.852 19589.852
Root Mean Square Error of Approximation:
RMSEA 0.070 0.053
90 Percent confidence interval - lower 0.060 0.044
90 Percent confidence interval - upper 0.081 0.063
P-value RMSEA <= 0.05 0.001 0.267
Robust RMSEA 0.064
90 Percent confidence interval - lower 0.051
90 Percent confidence interval - upper 0.078
Standardized Root Mean Square Residual:
SRMR 0.030 0.030
Parameter Estimates:
Standard errors Sandwich
Information bread Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con =~
PC01_01 1.000 1.598 0.928
PC01_02 0.988 0.027 36.123 0.000 1.580 0.895
PC01_04 0.968 0.027 35.680 0.000 1.548 0.882
PC01_05 0.999 0.024 42.430 0.000 1.597 0.906
PC01_06 0.851 0.038 22.618 0.000 1.360 0.793
PC01_07 0.994 0.023 43.564 0.000 1.588 0.921
grats_gen =~
GR02_01 1.000 1.144 0.852
GR02_02 1.121 0.033 33.587 0.000 1.283 0.905
GR02_03 0.996 0.046 21.598 0.000 1.139 0.851
GR02_04 0.962 0.047 20.475 0.000 1.100 0.837
GR02_05 1.064 0.039 27.084 0.000 1.217 0.849
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
self_dis_log ~
pri_con (a1) -0.204 0.061 -3.330 0.001 -0.327 -0.143
grats_gen (b1) 0.205 0.087 2.360 0.018 0.235 0.103
male -0.022 0.197 -0.109 0.913 -0.022 -0.005
age 0.003 0.006 0.570 0.569 0.003 0.024
edu 0.213 0.116 1.836 0.066 0.213 0.078
GR02_01 ~
male -0.127 0.116 -1.096 0.273 -0.127 -0.047
age 0.000 0.004 0.091 0.927 0.000 0.004
edu 0.005 0.068 0.073 0.942 0.005 0.003
GR02_02 ~
male -0.067 0.120 -0.559 0.576 -0.067 -0.024
age 0.006 0.004 1.542 0.123 0.006 0.068
edu -0.080 0.071 -1.127 0.260 -0.080 -0.047
GR02_03 ~
male -0.025 0.116 -0.219 0.826 -0.025 -0.009
age 0.001 0.004 0.310 0.756 0.001 0.014
edu -0.083 0.067 -1.237 0.216 -0.083 -0.052
GR02_04 ~
male 0.028 0.113 0.250 0.802 0.028 0.011
age 0.005 0.004 1.304 0.192 0.005 0.057
edu -0.072 0.067 -1.072 0.284 -0.072 -0.046
GR02_05 ~
male -0.140 0.124 -1.136 0.256 -0.140 -0.049
age -0.004 0.004 -0.874 0.382 -0.004 -0.039
edu 0.013 0.073 0.173 0.862 0.013 0.007
PC01_01 ~
male -0.182 0.151 -1.206 0.228 -0.182 -0.053
age -0.004 0.005 -0.820 0.412 -0.004 -0.036
edu 0.110 0.087 1.255 0.210 0.110 0.054
PC01_02 ~
male -0.302 0.154 -1.966 0.049 -0.302 -0.085
age -0.008 0.005 -1.663 0.096 -0.008 -0.072
edu 0.047 0.089 0.522 0.602 0.047 0.022
PC01_04 ~
male -0.225 0.152 -1.475 0.140 -0.225 -0.064
age -0.010 0.005 -1.980 0.048 -0.010 -0.085
edu 0.113 0.089 1.269 0.204 0.113 0.054
PC01_05 ~
male -0.098 0.154 -0.636 0.525 -0.098 -0.028
age -0.006 0.005 -1.164 0.244 -0.006 -0.051
edu 0.090 0.090 0.996 0.319 0.090 0.043
PC01_06 ~
male -0.108 0.150 -0.722 0.470 -0.108 -0.032
age -0.005 0.005 -1.055 0.291 -0.005 -0.046
edu 0.043 0.087 0.491 0.623 0.043 0.021
PC01_07 ~
male -0.174 0.150 -1.160 0.246 -0.174 -0.050
age -0.006 0.005 -1.337 0.181 -0.006 -0.058
edu 0.081 0.087 0.934 0.351 0.081 0.040
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
pri_con ~~
grats_gen -0.280 0.097 -2.887 0.004 -0.153 -0.153
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 3.369 0.292 11.557 0.000 3.369 1.955
.PC01_02 3.769 0.304 12.398 0.000 3.769 2.135
.PC01_04 3.571 0.297 12.020 0.000 3.571 2.035
.PC01_05 3.414 0.304 11.229 0.000 3.414 1.937
.PC01_06 3.215 0.288 11.155 0.000 3.215 1.875
.PC01_07 3.461 0.294 11.780 0.000 3.461 2.008
.GR02_01 4.319 0.224 19.252 0.000 4.319 3.215
.GR02_02 4.492 0.244 18.372 0.000 4.492 3.170
.GR02_03 5.244 0.222 23.667 0.000 5.244 3.917
.GR02_04 4.988 0.221 22.522 0.000 4.988 3.795
.GR02_05 4.905 0.254 19.323 0.000 4.905 3.422
.self_dis_log 1.376 0.375 3.673 0.000 1.376 0.602
pri_con 0.000 0.000 0.000
grats_gen 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PC01_01 0.394 0.050 7.939 0.000 0.394 0.133
.PC01_02 0.579 0.104 5.591 0.000 0.579 0.186
.PC01_04 0.636 0.078 8.103 0.000 0.636 0.206
.PC01_05 0.540 0.065 8.259 0.000 0.540 0.174
.PC01_06 1.079 0.117 9.228 0.000 1.079 0.367
.PC01_07 0.425 0.064 6.632 0.000 0.425 0.143
.GR02_01 0.492 0.052 9.433 0.000 0.492 0.273
.GR02_02 0.346 0.036 9.664 0.000 0.346 0.172
.GR02_03 0.490 0.074 6.639 0.000 0.490 0.273
.GR02_04 0.507 0.051 10.016 0.000 0.507 0.294
.GR02_05 0.564 0.062 9.122 0.000 0.564 0.274
.self_dis_log 4.999 0.208 23.979 0.000 4.999 0.958
pri_con 2.555 0.144 17.730 0.000 1.000 1.000
grats_gen 1.309 0.114 11.490 0.000 1.000 1.000