#Two group invariance tests
setwd("H:/data/User/Classes/multiv_13/Class13")
Wk13<- read_sav("class13_homework_2020.sav")
Wk13$Group<-as.numeric(Wk13$Group)
# Two group invariance
#Set up basic measurement model
Wk13.mod <-' Attention =~ Fixadur + Fixanum + Fixlocs
ZoomFatigue =~Energy + FAS + FSS
Mobility =~ SixMinWalk + TenMWalk + BalConf
'
#Configural invariance
Wk13.fit<- cfa(Wk13.mod, data=Wk13, group="Group", meanstructure = TRUE)
summary(Wk13.fit,fit.measures=TRUE)
## lavaan 0.6-7 ended normally after 762 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 60
##
## Number of observations per group:
## 0 200
## 1 200
##
## Model Test User Model:
##
## Test statistic 54.284
## Degrees of freedom 48
## P-value (Chi-square) 0.247
## Test statistic for each group:
## 0 24.130
## 1 30.154
##
## Model Test Baseline Model:
##
## Test statistic 15140.609
## Degrees of freedom 72
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 0.999
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5851.934
## Loglikelihood unrestricted model (H1) -5824.792
##
## Akaike (AIC) 11823.867
## Bayesian (BIC) 12063.355
## Sample-size adjusted Bayesian (BIC) 11872.971
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.026
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.055
## P-value RMSEA <= 0.05 0.908
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.002
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum 1.081 0.003 358.026 0.000
## Fixlocs 0.914 0.003 264.268 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS 1.073 0.003 332.062 0.000
## FSS 0.911 0.003 262.432 0.000
## Mobility =~
## SixMinWalk 1.000
## TenMWalk 1.078 0.003 352.551 0.000
## BalConf 0.908 0.003 277.102 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -14.640 7.152 -2.047 0.041
## Mobility 9.843 7.103 1.386 0.166
## ZoomFatigue ~~
## Mobility -35.324 7.518 -4.698 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur 67.024 0.707 94.860 0.000
## .Fixanum 71.991 0.763 94.313 0.000
## .Fixlocs 61.011 0.646 94.388 0.000
## .Energy 67.022 0.709 94.564 0.000
## .FAS 72.008 0.760 94.710 0.000
## .FSS 60.972 0.646 94.448 0.000
## .SixMinWalk 67.019 0.708 94.679 0.000
## .TenMWalk 72.037 0.763 94.420 0.000
## .BalConf 61.051 0.643 94.954 0.000
## Attention 0.000
## ZoomFatigue 0.000
## Mobility 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur 0.102 0.017 6.175 0.000
## .Fixanum 0.062 0.016 3.779 0.000
## .Fixlocs 0.153 0.019 8.153 0.000
## .Energy 0.118 0.018 6.458 0.000
## .FAS 0.073 0.018 4.148 0.000
## .FSS 0.144 0.018 7.785 0.000
## .SixMinWalk 0.070 0.015 4.603 0.000
## .TenMWalk 0.105 0.019 5.537 0.000
## .BalConf 0.157 0.019 8.135 0.000
## Attention 99.743 9.985 9.990 0.000
## ZoomFatigue 100.346 10.046 9.988 0.000
## Mobility 100.140 10.021 9.993 0.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum 1.072 0.003 330.538 0.000
## Fixlocs 0.915 0.003 265.546 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS 1.076 0.003 335.963 0.000
## FSS 0.908 0.003 265.016 0.000
## Mobility =~
## SixMinWalk 1.000
## TenMWalk 1.075 0.003 371.414 0.000
## BalConf 0.908 0.003 273.765 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -14.129 7.311 -1.933 0.053
## Mobility 12.783 7.283 1.755 0.079
## ZoomFatigue ~~
## Mobility -34.466 7.616 -4.525 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur 77.346 0.716 107.959 0.000
## .Fixanum 83.131 0.768 108.205 0.000
## .Fixlocs 70.459 0.656 107.463 0.000
## .Energy 60.113 0.715 84.032 0.000
## .FAS 64.644 0.770 83.988 0.000
## .FSS 54.765 0.650 84.255 0.000
## .SixMinWalk 73.801 0.714 103.408 0.000
## .TenMWalk 79.331 0.767 103.409 0.000
## .BalConf 67.149 0.648 103.602 0.000
## Attention 0.000
## ZoomFatigue 0.000
## Mobility 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur 0.105 0.018 5.808 0.000
## .Fixanum 0.095 0.019 4.876 0.000
## .Fixlocs 0.155 0.020 7.831 0.000
## .Energy 0.123 0.018 6.671 0.000
## .FAS 0.067 0.017 3.906 0.000
## .FSS 0.139 0.018 7.754 0.000
## .SixMinWalk 0.074 0.015 4.897 0.000
## .TenMWalk 0.085 0.017 4.882 0.000
## .BalConf 0.163 0.020 8.328 0.000
## Attention 102.551 10.266 9.990 0.000
## ZoomFatigue 102.224 10.235 9.988 0.000
## Mobility 101.797 10.187 9.993 0.000
d1<-as.data.frame(fitMeasures(Wk13.fit, fit.measures = "all"))
#Weak invariance (+ loading invariance)
weak <- cfa(Wk13.mod, data=Wk13, group="Group", meanstructure = TRUE,
group.equal=c("loadings") )
summary(weak,fit.measures=TRUE)
## lavaan 0.6-7 ended normally after 709 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 60
## Number of equality constraints 6
##
## Number of observations per group:
## 0 200
## 1 200
##
## Model Test User Model:
##
## Test statistic 60.641
## Degrees of freedom 54
## P-value (Chi-square) 0.249
## Test statistic for each group:
## 0 27.139
## 1 33.502
##
## Model Test Baseline Model:
##
## Test statistic 15140.609
## Degrees of freedom 72
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 0.999
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5855.112
## Loglikelihood unrestricted model (H1) -5824.792
##
## Akaike (AIC) 11818.225
## Bayesian (BIC) 12033.764
## Sample-size adjusted Bayesian (BIC) 11862.418
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.025
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.053
## P-value RMSEA <= 0.05 0.927
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.003
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 485.752 0.000
## Fixlocs (.p3.) 0.915 0.002 374.621 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.075 0.002 472.137 0.000
## FSS (.p6.) 0.909 0.002 372.913 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 511.933 0.000
## BalConf (.p9.) 0.908 0.002 389.508 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -14.662 7.162 -2.047 0.041
## Mobility 9.858 7.119 1.385 0.166
## ZoomFatigue ~~
## Mobility -35.324 7.518 -4.698 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur 67.024 0.708 94.690 0.000
## .Fixanum 71.991 0.762 94.463 0.000
## .Fixlocs 61.011 0.648 94.199 0.000
## .Energy 67.022 0.708 94.605 0.000
## .FAS 72.008 0.761 94.604 0.000
## .FSS 60.972 0.644 94.606 0.000
## .SixMinWalk 67.019 0.708 94.636 0.000
## .TenMWalk 72.037 0.762 94.506 0.000
## .BalConf 61.051 0.643 94.918 0.000
## Attention 0.000
## ZoomFatigue 0.000
## Mobility 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur 0.101 0.017 6.124 0.000
## .Fixanum 0.064 0.016 3.902 0.000
## .Fixlocs 0.153 0.019 8.129 0.000
## .Energy 0.118 0.018 6.454 0.000
## .FAS 0.073 0.018 4.131 0.000
## .FSS 0.144 0.018 7.799 0.000
## .SixMinWalk 0.070 0.015 4.587 0.000
## .TenMWalk 0.105 0.019 5.562 0.000
## .BalConf 0.157 0.019 8.131 0.000
## Attention 100.102 10.017 9.993 0.000
## ZoomFatigue 100.260 10.034 9.992 0.000
## Mobility 100.233 10.028 9.995 0.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 485.752 0.000
## Fixlocs (.p3.) 0.915 0.002 374.621 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.075 0.002 472.137 0.000
## FSS (.p6.) 0.909 0.002 372.913 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 511.933 0.000
## BalConf (.p9.) 0.908 0.002 389.508 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -14.106 7.301 -1.932 0.053
## Mobility 12.748 7.266 1.755 0.079
## ZoomFatigue ~~
## Mobility -34.450 7.615 -4.524 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur 77.346 0.715 108.160 0.000
## .Fixanum 83.131 0.770 107.957 0.000
## .Fixlocs 70.459 0.654 107.681 0.000
## .Energy 60.113 0.716 83.989 0.000
## .FAS 64.644 0.769 84.069 0.000
## .FSS 54.765 0.651 84.116 0.000
## .SixMinWalk 73.801 0.713 103.468 0.000
## .TenMWalk 79.331 0.768 103.343 0.000
## .BalConf 67.149 0.648 103.652 0.000
## Attention 0.000
## ZoomFatigue 0.000
## Mobility 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur 0.106 0.018 5.818 0.000
## .Fixanum 0.096 0.020 4.874 0.000
## .Fixlocs 0.156 0.020 7.828 0.000
## .Energy 0.122 0.018 6.653 0.000
## .FAS 0.068 0.017 3.959 0.000
## .FSS 0.139 0.018 7.742 0.000
## .SixMinWalk 0.074 0.015 4.911 0.000
## .TenMWalk 0.085 0.017 4.869 0.000
## .BalConf 0.163 0.020 8.328 0.000
## Attention 102.170 10.224 9.993 0.000
## ZoomFatigue 102.329 10.241 9.992 0.000
## Mobility 101.678 10.173 9.995 0.000
d2<-as.data.frame(fitMeasures(weak, fit.measures = "all"))
#Strong invariance (+ intercept invariance)
strong <- cfa(Wk13.mod, data=Wk13, group="Group", meanstructure = TRUE,
group.equal=c("loadings", "intercepts") )
summary(strong,fit.measures=TRUE)
## lavaan 0.6-7 ended normally after 698 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 63
## Number of equality constraints 15
##
## Number of observations per group:
## 0 200
## 1 200
##
## Model Test User Model:
##
## Test statistic 64.887
## Degrees of freedom 60
## P-value (Chi-square) 0.310
## Test statistic for each group:
## 0 28.736
## 1 36.151
##
## Model Test Baseline Model:
##
## Test statistic 15140.609
## Degrees of freedom 72
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5857.235
## Loglikelihood unrestricted model (H1) -5824.792
##
## Akaike (AIC) 11810.470
## Bayesian (BIC) 12002.061
## Sample-size adjusted Bayesian (BIC) 11849.754
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.020
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.049
## P-value RMSEA <= 0.05 0.959
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.003
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 545.531 0.000
## Fixlocs (.p3.) 0.915 0.002 421.016 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.074 0.002 498.142 0.000
## FSS (.p6.) 0.908 0.002 392.973 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 540.392 0.000
## BalConf (.p9.) 0.907 0.002 410.136 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -14.662 7.165 -2.046 0.041
## Mobility 9.856 7.119 1.385 0.166
## ZoomFatigue ~~
## Mobility -35.348 7.524 -4.698 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.25.) 67.019 0.708 94.715 0.000
## .Fixanum (.26.) 71.995 0.762 94.448 0.000
## .Fixlocs (.27.) 61.009 0.647 94.227 0.000
## .Energy (.28.) 67.001 0.709 94.535 0.000
## .FAS (.29.) 72.013 0.761 94.641 0.000
## .FSS (.30.) 60.988 0.644 94.724 0.000
## .SxMnWlk (.31.) 67.024 0.708 94.641 0.000
## .TenMWlk (.32.) 72.041 0.762 94.521 0.000
## .BalConf (.33.) 61.030 0.642 95.000 0.000
## Attentn 0.000
## ZoomFtg 0.000
## Mobilty 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur 0.102 0.017 6.132 0.000
## .Fixanum 0.064 0.016 3.885 0.000
## .Fixlocs 0.153 0.019 8.133 0.000
## .Energy 0.119 0.018 6.464 0.000
## .FAS 0.073 0.018 4.098 0.000
## .FSS 0.145 0.019 7.810 0.000
## .SixMinWalk 0.070 0.015 4.585 0.000
## .TenMWalk 0.105 0.019 5.546 0.000
## .BalConf 0.157 0.019 8.141 0.000
## Attention 100.062 10.012 9.994 0.000
## ZoomFatigue 100.384 10.046 9.993 0.000
## Mobility 100.255 10.030 9.995 0.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 545.531 0.000
## Fixlocs (.p3.) 0.915 0.002 421.016 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.074 0.002 498.142 0.000
## FSS (.p6.) 0.908 0.002 392.973 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 540.392 0.000
## BalConf (.p9.) 0.907 0.002 410.136 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -14.118 7.303 -1.933 0.053
## Mobility 12.753 7.266 1.755 0.079
## ZoomFatigue ~~
## Mobility -34.530 7.627 -4.528 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.25.) 67.019 0.708 94.715 0.000
## .Fixanum (.26.) 71.995 0.762 94.448 0.000
## .Fixlocs (.27.) 61.009 0.647 94.227 0.000
## .Energy (.28.) 67.001 0.709 94.535 0.000
## .FAS (.29.) 72.013 0.761 94.641 0.000
## .FSS (.30.) 60.988 0.644 94.724 0.000
## .SxMnWlk (.31.) 67.024 0.708 94.641 0.000
## .TenMWlk (.32.) 72.041 0.762 94.521 0.000
## .BalConf (.33.) 61.030 0.642 95.000 0.000
## Attentn 10.332 1.006 10.275 0.000
## ZoomFtg -6.867 1.007 -6.817 0.000
## Mobilty 6.771 1.005 6.735 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur 0.106 0.018 5.821 0.000
## .Fixanum 0.096 0.020 4.880 0.000
## .Fixlocs 0.156 0.020 7.823 0.000
## .Energy 0.123 0.018 6.668 0.000
## .FAS 0.068 0.017 3.944 0.000
## .FSS 0.139 0.018 7.739 0.000
## .SixMinWalk 0.074 0.015 4.897 0.000
## .TenMWalk 0.085 0.017 4.860 0.000
## .BalConf 0.164 0.020 8.339 0.000
## Attention 102.065 10.213 9.994 0.000
## ZoomFatigue 102.498 10.257 9.993 0.000
## Mobility 101.793 10.184 9.995 0.000
d3<-as.data.frame(fitMeasures(strong, fit.measures = "all"))
#Strict invariance (+ residual invariance)
strict <- cfa(Wk13.mod, data=Wk13, group="Group", meanstructure = TRUE,
group.equal=c("loadings", "intercepts", "residuals") )
summary(strict,fit.measures=TRUE)
## lavaan 0.6-7 ended normally after 507 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 63
## Number of equality constraints 24
##
## Number of observations per group:
## 0 200
## 1 200
##
## Model Test User Model:
##
## Test statistic 68.173
## Degrees of freedom 69
## P-value (Chi-square) 0.506
## Test statistic for each group:
## 0 30.828
## 1 37.345
##
## Model Test Baseline Model:
##
## Test statistic 15140.609
## Degrees of freedom 72
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5858.878
## Loglikelihood unrestricted model (H1) -5824.792
##
## Akaike (AIC) 11795.756
## Bayesian (BIC) 11951.423
## Sample-size adjusted Bayesian (BIC) 11827.674
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.040
## P-value RMSEA <= 0.05 0.991
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.003
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 543.547 0.000
## Fixlocs (.p3.) 0.915 0.002 421.079 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.074 0.002 498.110 0.000
## FSS (.p6.) 0.908 0.002 392.935 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 539.566 0.000
## BalConf (.p9.) 0.907 0.002 410.022 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -14.666 7.164 -2.047 0.041
## Mobility 9.840 7.119 1.382 0.167
## ZoomFatigue ~~
## Mobility -35.354 7.524 -4.699 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.25.) 67.018 0.708 94.719 0.000
## .Fixanum (.26.) 71.996 0.762 94.484 0.000
## .Fixlocs (.27.) 61.009 0.647 94.234 0.000
## .Energy (.28.) 67.001 0.709 94.538 0.000
## .FAS (.29.) 72.012 0.761 94.647 0.000
## .FSS (.30.) 60.987 0.644 94.724 0.000
## .SxMnWlk (.31.) 67.025 0.708 94.629 0.000
## .TenMWlk (.32.) 72.040 0.762 94.505 0.000
## .BalConf (.33.) 61.030 0.643 94.988 0.000
## Attentn 0.000
## ZoomFtg 0.000
## Mobilty 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.10.) 0.104 0.012 8.432 0.000
## .Fixanum (.11.) 0.080 0.013 6.267 0.000
## .Fixlocs (.12.) 0.154 0.014 11.274 0.000
## .Energy (.13.) 0.121 0.013 9.284 0.000
## .FAS (.14.) 0.070 0.012 5.686 0.000
## .FSS (.15.) 0.142 0.013 10.994 0.000
## .SxMnWlk (.16.) 0.072 0.011 6.698 0.000
## .TenMWlk (.17.) 0.095 0.013 7.379 0.000
## .BalConf (.18.) 0.161 0.014 11.653 0.000
## Attentn 100.049 10.011 9.994 0.000
## ZoomFtg 100.376 10.045 9.993 0.000
## Mobilty 100.279 10.033 9.995 0.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 543.547 0.000
## Fixlocs (.p3.) 0.915 0.002 421.079 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.074 0.002 498.110 0.000
## FSS (.p6.) 0.908 0.002 392.935 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 539.566 0.000
## BalConf (.p9.) 0.907 0.002 410.022 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -14.121 7.306 -1.933 0.053
## Mobility 12.749 7.265 1.755 0.079
## ZoomFatigue ~~
## Mobility -34.489 7.623 -4.525 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.25.) 67.018 0.708 94.719 0.000
## .Fixanum (.26.) 71.996 0.762 94.484 0.000
## .Fixlocs (.27.) 61.009 0.647 94.234 0.000
## .Energy (.28.) 67.001 0.709 94.538 0.000
## .FAS (.29.) 72.012 0.761 94.647 0.000
## .FSS (.30.) 60.987 0.644 94.724 0.000
## .SxMnWlk (.31.) 67.025 0.708 94.629 0.000
## .TenMWlk (.32.) 72.040 0.762 94.505 0.000
## .BalConf (.33.) 61.030 0.643 94.988 0.000
## Attentn 10.333 1.006 10.275 0.000
## ZoomFtg -6.867 1.007 -6.817 0.000
## Mobilty 6.771 1.005 6.737 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.10.) 0.104 0.012 8.432 0.000
## .Fixanum (.11.) 0.080 0.013 6.267 0.000
## .Fixlocs (.12.) 0.154 0.014 11.274 0.000
## .Energy (.13.) 0.121 0.013 9.284 0.000
## .FAS (.14.) 0.070 0.012 5.686 0.000
## .FSS (.15.) 0.142 0.013 10.994 0.000
## .SxMnWlk (.16.) 0.072 0.011 6.698 0.000
## .TenMWlk (.17.) 0.095 0.013 7.379 0.000
## .BalConf (.18.) 0.161 0.014 11.653 0.000
## Attentn 102.144 10.221 9.994 0.000
## ZoomFtg 102.503 10.258 9.993 0.000
## Mobilty 101.692 10.174 9.995 0.000
d4<-as.data.frame(fitMeasures(strict, fit.measures = "all"))
# + Factor means invariance
facmean <- cfa(Wk13.mod, data=Wk13, group="Group", meanstructure = TRUE,
group.equal=c("loadings", "intercepts", "residuals", "means") )
summary(facmean,fit.measures=TRUE)
## lavaan 0.6-7 ended normally after 506 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 60
## Number of equality constraints 24
##
## Number of observations per group:
## 0 200
## 1 200
##
## Model Test User Model:
##
## Test statistic 196.461
## Degrees of freedom 72
## P-value (Chi-square) 0.000
## Test statistic for each group:
## 0 93.392
## 1 103.069
##
## Model Test Baseline Model:
##
## Test statistic 15140.609
## Degrees of freedom 72
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.992
## Tucker-Lewis Index (TLI) 0.992
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5923.022
## Loglikelihood unrestricted model (H1) -5824.792
##
## Akaike (AIC) 11918.044
## Bayesian (BIC) 12061.737
## Sample-size adjusted Bayesian (BIC) 11947.507
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.093
## 90 Percent confidence interval - lower 0.078
## 90 Percent confidence interval - upper 0.109
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.224
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 543.585 0.000
## Fixlocs (.p3.) 0.915 0.002 421.141 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.074 0.002 498.100 0.000
## FSS (.p6.) 0.908 0.002 392.859 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 539.586 0.000
## BalConf (.p9.) 0.907 0.002 410.080 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -31.880 8.685 -3.671 0.000
## Mobility 26.436 8.560 3.088 0.002
## ZoomFatigue ~~
## Mobility -46.546 8.548 -5.445 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.25.) 72.071 0.565 127.496 0.000
## .Fixanum (.26.) 77.438 0.609 127.206 0.000
## .Fixlocs (.27.) 65.630 0.517 126.877 0.000
## .Energy (.28.) 63.594 0.532 119.475 0.000
## .FAS (.29.) 68.354 0.571 119.631 0.000
## .FSS (.30.) 57.893 0.484 119.714 0.000
## .SxMnWlk (.31.) 70.309 0.530 132.584 0.000
## .TenMWlk (.32.) 75.575 0.571 132.415 0.000
## .BalConf (.33.) 64.009 0.481 133.033 0.000
## Attentn 0.000
## ZoomFtg 0.000
## Mobilty 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.10.) 0.104 0.012 8.427 0.000
## .Fixanum (.11.) 0.080 0.013 6.272 0.000
## .Fixlocs (.12.) 0.154 0.014 11.274 0.000
## .Energy (.13.) 0.121 0.013 9.287 0.000
## .FAS (.14.) 0.070 0.012 5.681 0.000
## .FSS (.15.) 0.142 0.013 10.995 0.000
## .SxMnWlk (.16.) 0.072 0.011 6.701 0.000
## .TenMWlk (.17.) 0.095 0.013 7.378 0.000
## .BalConf (.18.) 0.160 0.014 11.652 0.000
## Attentn 125.574 12.564 9.995 0.000
## ZoomFtg 111.985 11.206 9.993 0.000
## Mobilty 111.069 11.112 9.995 0.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 543.585 0.000
## Fixlocs (.p3.) 0.915 0.002 421.141 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.074 0.002 498.100 0.000
## FSS (.p6.) 0.908 0.002 392.859 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 539.586 0.000
## BalConf (.p9.) 0.907 0.002 410.080 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -32.388 8.927 -3.628 0.000
## Mobility 31.161 8.884 3.507 0.000
## ZoomFatigue ~~
## Mobility -46.558 8.721 -5.339 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.25.) 72.071 0.565 127.496 0.000
## .Fixanum (.26.) 77.438 0.609 127.206 0.000
## .Fixlocs (.27.) 65.630 0.517 126.877 0.000
## .Energy (.28.) 63.594 0.532 119.475 0.000
## .FAS (.29.) 68.354 0.571 119.631 0.000
## .FSS (.30.) 57.893 0.484 119.714 0.000
## .SxMnWlk (.31.) 70.309 0.530 132.584 0.000
## .TenMWlk (.32.) 75.575 0.571 132.415 0.000
## .BalConf (.33.) 64.009 0.481 133.033 0.000
## Attentn 0.000
## ZoomFtg 0.000
## Mobilty 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.10.) 0.104 0.012 8.427 0.000
## .Fixanum (.11.) 0.080 0.013 6.272 0.000
## .Fixlocs (.12.) 0.154 0.014 11.274 0.000
## .Energy (.13.) 0.121 0.013 9.287 0.000
## .FAS (.14.) 0.070 0.012 5.681 0.000
## .FSS (.15.) 0.142 0.013 10.995 0.000
## .SxMnWlk (.16.) 0.072 0.011 6.701 0.000
## .TenMWlk (.17.) 0.095 0.013 7.378 0.000
## .BalConf (.18.) 0.160 0.014 11.652 0.000
## Attentn 130.014 13.008 9.995 0.000
## ZoomFtg 114.460 11.454 9.993 0.000
## Mobilty 113.881 11.393 9.995 0.000
d5<-as.data.frame(fitMeasures(facmean, fit.measures = "all"))
# + Factor variance invariance
facvar <- cfa(Wk13.mod, data=Wk13, group="Group", meanstructure = TRUE,
group.equal=c("loadings", "intercepts", "residuals", "means",
"lv.variances") )
summary(facvar,fit.measures=TRUE)
## lavaan 0.6-7 ended normally after 452 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 60
## Number of equality constraints 27
##
## Number of observations per group:
## 0 200
## 1 200
##
## Model Test User Model:
##
## Test statistic 196.514
## Degrees of freedom 75
## P-value (Chi-square) 0.000
## Test statistic for each group:
## 0 95.843
## 1 100.670
##
## Model Test Baseline Model:
##
## Test statistic 15140.609
## Degrees of freedom 72
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.992
## Tucker-Lewis Index (TLI) 0.992
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5923.049
## Loglikelihood unrestricted model (H1) -5824.792
##
## Akaike (AIC) 11912.097
## Bayesian (BIC) 12043.815
## Sample-size adjusted Bayesian (BIC) 11939.104
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.090
## 90 Percent confidence interval - lower 0.075
## 90 Percent confidence interval - upper 0.105
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.225
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 543.552 0.000
## Fixlocs (.p3.) 0.915 0.002 421.104 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.074 0.002 498.083 0.000
## FSS (.p6.) 0.908 0.002 392.850 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 539.612 0.000
## BalConf (.p9.) 0.907 0.002 410.098 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -33.135 8.216 -4.033 0.000
## Mobility 27.671 8.258 3.351 0.001
## ZoomFatigue ~~
## Mobility -47.660 7.460 -6.389 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.25.) 72.182 0.565 127.677 0.000
## .Fixanum (.26.) 77.558 0.609 127.387 0.000
## .Fixlocs (.27.) 65.733 0.517 127.058 0.000
## .Energy (.28.) 63.517 0.532 119.336 0.000
## .FAS (.29.) 68.272 0.571 119.492 0.000
## .FSS (.30.) 57.823 0.484 119.575 0.000
## .SxMnWlk (.31.) 70.389 0.530 132.709 0.000
## .TenMWlk (.32.) 75.661 0.571 132.540 0.000
## .BalConf (.33.) 64.081 0.481 133.158 0.000
## Attentn 0.000
## ZoomFtg 0.000
## Mobilty 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.10.) 0.104 0.012 8.428 0.000
## .Fixanum (.11.) 0.080 0.013 6.271 0.000
## .Fixlocs (.12.) 0.154 0.014 11.274 0.000
## .Energy (.13.) 0.121 0.013 9.287 0.000
## .FAS (.14.) 0.070 0.012 5.681 0.000
## .FSS (.15.) 0.142 0.013 10.995 0.000
## .SxMnWlk (.16.) 0.072 0.011 6.701 0.000
## .TenMWlk (.17.) 0.095 0.013 7.378 0.000
## .BalConf (.18.) 0.160 0.014 11.652 0.000
## Attentn (.19.) 127.778 9.042 14.131 0.000
## ZoomFtg (.20.) 113.215 8.014 14.128 0.000
## Mobilty (.21.) 112.487 7.959 14.134 0.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 543.552 0.000
## Fixlocs (.p3.) 0.915 0.002 421.104 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.074 0.002 498.083 0.000
## FSS (.p6.) 0.908 0.002 392.850 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 539.612 0.000
## BalConf (.p9.) 0.907 0.002 410.098 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFatigue -31.139 8.247 -3.776 0.000
## Mobility 29.885 8.232 3.630 0.000
## ZoomFatigue ~~
## Mobility -45.462 7.508 -6.055 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.25.) 72.182 0.565 127.677 0.000
## .Fixanum (.26.) 77.558 0.609 127.387 0.000
## .Fixlocs (.27.) 65.733 0.517 127.058 0.000
## .Energy (.28.) 63.517 0.532 119.336 0.000
## .FAS (.29.) 68.272 0.571 119.492 0.000
## .FSS (.30.) 57.823 0.484 119.575 0.000
## .SxMnWlk (.31.) 70.389 0.530 132.709 0.000
## .TenMWlk (.32.) 75.661 0.571 132.540 0.000
## .BalConf (.33.) 64.081 0.481 133.158 0.000
## Attentn 0.000
## ZoomFtg 0.000
## Mobilty 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.10.) 0.104 0.012 8.428 0.000
## .Fixanum (.11.) 0.080 0.013 6.271 0.000
## .Fixlocs (.12.) 0.154 0.014 11.274 0.000
## .Energy (.13.) 0.121 0.013 9.287 0.000
## .FAS (.14.) 0.070 0.012 5.681 0.000
## .FSS (.15.) 0.142 0.013 10.995 0.000
## .SxMnWlk (.16.) 0.072 0.011 6.701 0.000
## .TenMWlk (.17.) 0.095 0.013 7.378 0.000
## .BalConf (.18.) 0.160 0.014 11.652 0.000
## Attentn (.19.) 127.778 9.042 14.131 0.000
## ZoomFtg (.20.) 113.215 8.014 14.128 0.000
## Mobilty (.21.) 112.487 7.959 14.134 0.000
d6<-as.data.frame(fitMeasures(facvar, fit.measures = "all"))
# + Factor covariance invariance
faccovar <- cfa(Wk13.mod, data=Wk13, group="Group", meanstructure = TRUE,
group.equal=c("loadings", "intercepts", "residuals", "means",
"lv.variances", "lv.covariances") )
summary(faccovar,fit.measures=TRUE)
## lavaan 0.6-7 ended normally after 438 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 60
## Number of equality constraints 30
##
## Number of observations per group:
## 0 200
## 1 200
##
## Model Test User Model:
##
## Test statistic 196.685
## Degrees of freedom 78
## P-value (Chi-square) 0.000
## Test statistic for each group:
## 0 95.807
## 1 100.878
##
## Model Test Baseline Model:
##
## Test statistic 15140.609
## Degrees of freedom 72
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.992
## Tucker-Lewis Index (TLI) 0.993
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5923.134
## Loglikelihood unrestricted model (H1) -5824.792
##
## Akaike (AIC) 11906.269
## Bayesian (BIC) 12026.013
## Sample-size adjusted Bayesian (BIC) 11930.821
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.087
## 90 Percent confidence interval - lower 0.072
## 90 Percent confidence interval - upper 0.102
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.225
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [0]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 543.560 0.000
## Fixlocs (.p3.) 0.915 0.002 421.103 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.074 0.002 498.100 0.000
## FSS (.p6.) 0.908 0.002 392.855 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 539.563 0.000
## BalConf (.p9.) 0.907 0.002 410.059 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFtg (.22.) -32.131 6.227 -5.160 0.000
## Mobilty (.23.) 28.787 6.166 4.669 0.000
## ZoomFatigue ~~
## Mobilty (.24.) -46.549 6.105 -7.624 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.25.) 72.185 0.565 127.663 0.000
## .Fixanum (.26.) 77.561 0.609 127.374 0.000
## .Fixlocs (.27.) 65.735 0.517 127.045 0.000
## .Energy (.28.) 63.567 0.532 119.417 0.000
## .FAS (.29.) 68.326 0.571 119.573 0.000
## .FSS (.30.) 57.869 0.484 119.656 0.000
## .SxMnWlk (.31.) 70.410 0.530 132.744 0.000
## .TenMWlk (.32.) 75.684 0.571 132.575 0.000
## .BalConf (.33.) 64.100 0.481 133.193 0.000
## Attentn 0.000
## ZoomFtg 0.000
## Mobilty 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.10.) 0.104 0.012 8.428 0.000
## .Fixanum (.11.) 0.080 0.013 6.271 0.000
## .Fixlocs (.12.) 0.154 0.014 11.274 0.000
## .Energy (.13.) 0.121 0.013 9.287 0.000
## .FAS (.14.) 0.070 0.012 5.681 0.000
## .FSS (.15.) 0.142 0.013 10.995 0.000
## .SxMnWlk (.16.) 0.072 0.011 6.701 0.000
## .TenMWlk (.17.) 0.095 0.013 7.378 0.000
## .BalConf (.18.) 0.160 0.014 11.652 0.000
## Attentn (.19.) 127.781 9.043 14.131 0.000
## ZoomFtg (.20.) 113.222 8.015 14.127 0.000
## Mobilty (.21.) 112.465 7.958 14.133 0.000
##
##
## Group 2 [1]:
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## Attention =~
## Fixadur 1.000
## Fixanum (.p2.) 1.077 0.002 543.560 0.000
## Fixlocs (.p3.) 0.915 0.002 421.103 0.000
## ZoomFatigue =~
## Energy 1.000
## FAS (.p5.) 1.074 0.002 498.100 0.000
## FSS (.p6.) 0.908 0.002 392.855 0.000
## Mobility =~
## SxMnWlk 1.000
## TenMWlk (.p8.) 1.076 0.002 539.563 0.000
## BalConf (.p9.) 0.907 0.002 410.059 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## Attention ~~
## ZoomFtg (.22.) -32.131 6.227 -5.160 0.000
## Mobilty (.23.) 28.787 6.166 4.669 0.000
## ZoomFatigue ~~
## Mobilty (.24.) -46.549 6.105 -7.624 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.25.) 72.185 0.565 127.663 0.000
## .Fixanum (.26.) 77.561 0.609 127.374 0.000
## .Fixlocs (.27.) 65.735 0.517 127.045 0.000
## .Energy (.28.) 63.567 0.532 119.417 0.000
## .FAS (.29.) 68.326 0.571 119.573 0.000
## .FSS (.30.) 57.869 0.484 119.656 0.000
## .SxMnWlk (.31.) 70.410 0.530 132.744 0.000
## .TenMWlk (.32.) 75.684 0.571 132.575 0.000
## .BalConf (.33.) 64.100 0.481 133.193 0.000
## Attentn 0.000
## ZoomFtg 0.000
## Mobilty 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Fixadur (.10.) 0.104 0.012 8.428 0.000
## .Fixanum (.11.) 0.080 0.013 6.271 0.000
## .Fixlocs (.12.) 0.154 0.014 11.274 0.000
## .Energy (.13.) 0.121 0.013 9.287 0.000
## .FAS (.14.) 0.070 0.012 5.681 0.000
## .FSS (.15.) 0.142 0.013 10.995 0.000
## .SxMnWlk (.16.) 0.072 0.011 6.701 0.000
## .TenMWlk (.17.) 0.095 0.013 7.378 0.000
## .BalConf (.18.) 0.160 0.014 11.652 0.000
## Attentn (.19.) 127.781 9.043 14.131 0.000
## ZoomFtg (.20.) 113.222 8.015 14.127 0.000
## Mobilty (.21.) 112.465 7.958 14.133 0.000
d7<-as.data.frame(fitMeasures(faccovar, fit.measures = "all"))
#Nested model comparisons
anova(Wk13.fit,weak,strong,strict,facmean,facvar,faccovar)
## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## Wk13.fit 48 11824 12063 54.284
## weak 54 11818 12034 60.641 6.357 6 0.3844
## strong 60 11810 12002 64.887 4.246 6 0.6435
## strict 69 11796 11951 68.173 3.286 9 0.9519
## facmean 72 11918 12062 196.461 128.288 3 <2e-16 ***
## facvar 75 11912 12044 196.514 0.053 3 0.9968
## faccovar 78 11906 12026 196.685 0.172 3 0.9820
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summtable<-cbind(d1,d2,d3,d4,d5,d6,d7)
names(summtable) <- c("config","weak","strong", "strict", "facmean","facvar","faccovar")
summtable<-as.data.frame(summtable)
is.num <- sapply(summtable, is.numeric)
summtable[is.num] <- lapply(summtable[is.num], round, 3)
print(summtable)
## config weak strong strict facmean facvar
## npar 60.000 54.000 48.000 39.000 36.000 33.000
## fmin 0.068 0.076 0.081 0.085 0.246 0.246
## chisq 54.284 60.641 64.887 68.173 196.461 196.514
## df 48.000 54.000 60.000 69.000 72.000 75.000
## pvalue 0.247 0.249 0.310 0.506 0.000 0.000
## baseline.chisq 15140.609 15140.609 15140.609 15140.609 15140.609 15140.609
## baseline.df 72.000 72.000 72.000 72.000 72.000 72.000
## baseline.pvalue 0.000 0.000 0.000 0.000 0.000 0.000
## cfi 1.000 1.000 1.000 1.000 0.992 0.992
## tli 0.999 0.999 1.000 1.000 0.992 0.992
## nnfi 0.999 0.999 1.000 1.000 0.992 0.992
## rfi 0.995 0.995 0.995 0.995 0.987 NA
## nfi 0.996 0.996 0.996 0.995 0.987 NA
## pnfi 0.664 0.747 0.830 0.954 0.987 1.028
## ifi 1.000 1.000 1.000 1.000 0.992 0.992
## rni 1.000 1.000 1.000 1.000 0.992 0.992
## logl -5851.934 -5855.112 -5857.235 -5858.878 -5923.022 -5923.049
## unrestricted.logl -5824.792 -5824.792 -5824.792 -5824.792 -5824.792 -5824.792
## aic 11823.867 11818.225 11810.470 11795.756 11918.044 11912.097
## bic 12063.355 12033.764 12002.061 11951.423 12061.737 12043.815
## ntotal 400.000 400.000 400.000 400.000 400.000 400.000
## bic2 11872.971 11862.418 11849.754 11827.674 11947.507 11939.104
## rmsea 0.026 0.025 0.020 0.000 0.093 0.090
## rmsea.ci.lower 0.000 0.000 0.000 0.000 0.078 0.075
## rmsea.ci.upper 0.055 0.053 0.049 0.040 0.109 0.105
## rmsea.pvalue 0.908 0.927 0.959 0.991 0.000 0.000
## rmr 0.209 0.247 0.253 0.250 15.412 15.408
## rmr_nomean 0.229 0.271 0.277 0.273 16.784 16.780
## srmr 0.002 0.003 0.003 0.003 0.224 0.225
## srmr_bentler 0.002 0.003 0.003 0.003 0.224 0.225
## srmr_bentler_nomean 0.002 0.003 0.003 0.003 0.166 0.166
## crmr 0.002 0.005 0.005 0.005 0.303 0.300
## crmr_nomean 0.003 0.003 0.003 0.003 0.096 0.096
## srmr_mplus 0.002 0.005 0.005 0.005 0.287 0.284
## srmr_mplus_nomean 0.002 0.003 0.003 0.003 0.117 0.118
## cn_05 481.221 476.935 488.505 525.497 189.960 196.847
## cn_01 543.942 535.743 545.820 583.211 210.337 217.561
## gfi 0.999 0.999 0.999 0.999 0.998 0.998
## agfi 0.999 0.999 0.999 0.999 0.996 0.996
## pgfi 0.444 0.500 0.555 0.638 0.665 0.693
## mfi 0.992 0.992 0.994 1.001 0.856 0.859
## faccovar
## npar 30.000
## fmin 0.246
## chisq 196.685
## df 78.000
## pvalue 0.000
## baseline.chisq 15140.609
## baseline.df 72.000
## baseline.pvalue 0.000
## cfi 0.992
## tli 0.993
## nnfi 0.993
## rfi NA
## nfi NA
## pnfi 1.069
## ifi 0.992
## rni 0.992
## logl -5923.134
## unrestricted.logl -5824.792
## aic 11906.269
## bic 12026.013
## ntotal 400.000
## bic2 11930.821
## rmsea 0.087
## rmsea.ci.lower 0.072
## rmsea.ci.upper 0.102
## rmsea.pvalue 0.000
## rmr 15.419
## rmr_nomean 16.792
## srmr 0.225
## srmr_bentler 0.225
## srmr_bentler_nomean 0.166
## crmr 0.299
## crmr_nomean 0.097
## srmr_mplus 0.284
## srmr_mplus_nomean 0.118
## cn_05 203.591
## cn_01 224.622
## gfi 0.998
## agfi 0.997
## pgfi 0.720
## mfi 0.862