library(pacman); p_load(psych, lavaan, sjmisc)
data$EduDi16 <- rec(data$educ5_2016, rec = "1, 2, 3 = 0; 4, 5 = 1")
data$ParDi16 <- rec(data$party7_2016, rec = "4, 5, 6, 7 = 0; 1, 2, 3 = 1")
data$EduDi20 <- rec(data$educ5_2020, rec = "1, 2, 3 = 0; 4, 5 = 1")
data$ParDi20 <- rec(data$party7_2020, rec = "4, 5, 6, 7 = 0; 1, 2, 3 = 1")
describe(data)ZMod <- '
party7_2020 ~ educ5_2016 + party7_2016
educ5_2020 ~ educ5_2016 + party7_2016
educ5_2016 ~~ party7_2016' #Redundant when estimator = "DWLS", but doesn't hurt to specify anyway
ZFit <- sem(ZMod, data, estimator = "DWLS")
summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 33 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 10
##
## Used Total
## Number of observations 2778 2839
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 4699.075
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.148 0.051 -2.935 0.003 -0.148 -0.072
## party7_2016 0.862 0.021 41.292 0.000 0.862 0.810
## educ5_2020 ~
## educ5_2016 0.881 0.028 31.350 0.000 0.881 0.877
## party7_2016 -0.009 0.013 -0.664 0.507 -0.009 -0.017
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.110 0.045 -2.438 0.015 -0.110 -0.046
## .party7_2020 ~~
## .educ5_2020 -0.048 0.083 -0.576 0.565 -0.048 -0.068
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.753 0.159 11.021 0.000 1.753 0.333
## .educ5_2020 0.284 0.056 5.096 0.000 0.284 0.230
## educ5_2016 1.227 0.026 46.405 0.000 1.227 1.000
## party7_2016 4.657 0.069 67.559 0.000 4.657 1.000ZFit <- sem(ZMod, data, estimator = "DWLS", sampling.weights = "weight_panel_pre")
summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 31 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 10
##
## Used Total
## Number of observations 2778 2839
## Sampling weights variable weight_panel_pre
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 4668.490
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.107 0.045 -2.371 0.018 -0.107 -0.055
## party7_2016 0.845 0.021 40.262 0.000 0.845 0.795
## educ5_2020 ~
## educ5_2016 0.840 0.025 34.061 0.000 0.840 0.846
## party7_2016 -0.007 0.013 -0.555 0.579 -0.007 -0.013
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.025 0.045 -0.562 0.574 -0.025 -0.010
## .party7_2020 ~~
## .educ5_2020 -0.090 0.081 -1.112 0.266 -0.090 -0.105
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.884 0.156 12.090 0.000 1.884 0.364
## .educ5_2020 0.383 0.053 7.163 0.000 0.383 0.284
## educ5_2016 1.365 0.026 51.647 0.000 1.365 1.000
## party7_2016 4.583 0.069 66.487 0.000 4.583 1.000ZFit <- sem(ZMod, data, cluster = "state_2020") #Identical to state_2016 results, and both can be used with long data
summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Used Total
## Number of observations 2778 2839
## Number of clusters [state_2020] 50
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
## Information Observed
##
## Model Test Baseline Model:
##
## Test statistic 7162.736 4924.066
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 1.455
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17208.793 -17208.793
## Loglikelihood unrestricted model (H1) -17208.793 -17208.793
##
## Akaike (AIC) 34445.585 34445.585
## Bayesian (BIC) 34528.598 34528.598
## Sample-size adjusted Bayesian (BIC) 34484.115 34484.115
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.148 0.025 -5.853 0.000 -0.148 -0.072
## party7_2016 0.862 0.012 71.612 0.000 0.862 0.810
## educ5_2020 ~
## educ5_2016 0.881 0.009 99.424 0.000 0.881 0.877
## party7_2016 -0.009 0.005 -1.863 0.062 -0.009 -0.017
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.110 0.057 -1.928 0.054 -0.110 -0.046
## .party7_2020 ~~
## .educ5_2020 -0.048 0.014 -3.391 0.001 -0.048 -0.068
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.122 0.112 10.033 0.000 1.122 0.489
## .educ5_2020 0.486 0.038 12.796 0.000 0.486 0.436
## educ5_2016 3.351 0.036 91.996 0.000 3.351 3.026
## party7_2016 3.821 0.075 51.195 0.000 3.821 1.771
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.752 0.087 20.224 0.000 1.752 0.333
## .educ5_2020 0.284 0.017 16.982 0.000 0.284 0.230
## educ5_2016 1.226 0.028 44.035 0.000 1.226 1.000
## party7_2016 4.655 0.085 54.731 0.000 4.655 1.000ZFit <- sem(ZMod, data, sampling.weights = "weight_panel_pre", cluster = "state_2020")
summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Used Total
## Number of observations 2778 2839
## Number of clusters [state_2020] 50
## Sampling weights variable weight_panel_pre
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 6333.348 2302.690
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 2.750
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17844.681 -17844.681
## Loglikelihood unrestricted model (H1) -17844.681 -17844.681
##
## Akaike (AIC) 35717.361 35717.361
## Bayesian (BIC) 35800.374 35800.374
## Sample-size adjusted Bayesian (BIC) 35755.892 35755.892
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.107 0.031 -3.443 0.001 -0.107 -0.055
## party7_2016 0.845 0.016 52.117 0.000 0.845 0.795
## educ5_2020 ~
## educ5_2016 0.840 0.015 56.644 0.000 0.840 0.846
## party7_2016 -0.007 0.008 -0.865 0.387 -0.007 -0.013
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.025 0.070 -0.362 0.717 -0.025 -0.010
## .party7_2020 ~~
## .educ5_2020 -0.090 0.028 -3.232 0.001 -0.090 -0.105
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.020 0.140 7.269 0.000 1.020 0.448
## .educ5_2020 0.643 0.060 10.707 0.000 0.643 0.554
## educ5_2016 2.987 0.039 76.412 0.000 2.987 2.556
## party7_2016 3.796 0.089 42.481 0.000 3.796 1.773
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.884 0.108 17.422 0.000 1.884 0.364
## .educ5_2020 0.383 0.026 14.893 0.000 0.383 0.284
## educ5_2016 1.365 0.041 33.587 0.000 1.365 1.000
## party7_2016 4.583 0.092 49.657 0.000 4.583 1.000ZModBi <- '
ParDi20 ~ EduDi16 + ParDi16
EduDi20 ~ EduDi16 + ParDi16
EduDi16 ~~ ParDi16'
ZFit <- sem(ZModBi, data, estimator = "DWLS")
summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 32 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 10
##
## Used Total
## Number of observations 2778 2839
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 10989.154
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ParDi20 ~
## EduDi16 0.083 0.024 3.526 0.000 0.083 0.083
## ParDi16 0.733 0.013 55.578 0.000 0.733 0.734
## EduDi20 ~
## EduDi16 0.875 0.011 77.033 0.000 0.875 0.870
## ParDi16 0.024 0.025 0.936 0.349 0.024 0.024
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EduDi16 ~~
## ParDi16 0.021 0.005 4.386 0.000 0.021 0.083
## .ParDi20 ~~
## .EduDi20 0.004 0.008 0.533 0.594 0.004 0.051
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.110 0.005 22.569 0.000 0.110 0.444
## .EduDi20 0.059 0.005 13.119 0.000 0.059 0.239
## EduDi16 0.245 0.001 187.856 0.000 0.245 1.000
## ParDi16 0.249 0.001 462.686 0.000 0.249 1.000ZFit <- sem(ZModBi, data, estimator = "DWLS", sampling.weights = "weight_panel_pre")
summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 30 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 10
##
## Used Total
## Number of observations 2778 2839
## Sampling weights variable weight_panel_pre
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 8961.013
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ParDi20 ~
## EduDi16 0.068 0.026 2.582 0.010 0.068 0.064
## ParDi16 0.715 0.013 54.487 0.000 0.715 0.717
## EduDi20 ~
## EduDi16 0.860 0.013 68.307 0.000 0.860 0.832
## ParDi16 0.032 0.025 1.269 0.204 0.032 0.033
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EduDi16 ~~
## ParDi16 0.014 0.005 3.064 0.002 0.014 0.062
## .ParDi20 ~~
## .EduDi20 0.007 0.008 0.871 0.384 0.007 0.072
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.118 0.005 24.913 0.000 0.118 0.476
## .EduDi20 0.071 0.004 15.815 0.000 0.071 0.303
## EduDi16 0.218 0.001 167.177 0.000 0.218 1.000
## ParDi16 0.249 0.001 462.162 0.000 0.249 1.000ZFit <- sem(ZModBi, data, cluster = "state_2020") ## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -2.173504e-19) is smaller than zero. This may be a symptom that
## the model is not identified.summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Used Total
## Number of observations 2778 2839
## Number of clusters [state_2020] 50
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
## Information Observed
##
## Model Test Baseline Model:
##
## Test statistic 6254.698 2724.243
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 2.296
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4887.287 -4887.287
## Loglikelihood unrestricted model (H1) -4887.287 -4887.287
##
## Akaike (AIC) 9802.574 9802.574
## Bayesian (BIC) 9885.587 9885.587
## Sample-size adjusted Bayesian (BIC) 9841.104 9841.104
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ParDi20 ~
## EduDi16 0.083 0.012 6.738 0.000 0.083 0.083
## ParDi16 0.733 0.014 51.592 0.000 0.733 0.734
## EduDi20 ~
## EduDi16 0.875 0.010 83.736 0.000 0.875 0.870
## ParDi16 0.024 0.011 2.223 0.026 0.024 0.024
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EduDi16 ~~
## ParDi16 0.021 0.005 4.012 0.000 0.021 0.083
## .ParDi20 ~~
## .EduDi20 0.004 0.001 3.075 0.002 0.004 0.051
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.078 0.009 8.295 0.000 0.078 0.156
## .EduDi20 0.071 0.007 9.685 0.000 0.071 0.142
## EduDi16 0.431 0.015 28.658 0.000 0.431 0.869
## ParDi16 0.472 0.017 27.734 0.000 0.472 0.945
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.110 0.005 21.496 0.000 0.110 0.444
## .EduDi20 0.059 0.005 13.084 0.000 0.059 0.239
## EduDi16 0.245 0.002 117.455 0.000 0.245 1.000
## ParDi16 0.249 0.001 257.681 0.000 0.249 1.000ZFit <- sem(ZModBi, data, sampling.weights = "weight_panel_pre", cluster = "state_2020")## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -4.263925e-18) is smaller than zero. This may be a symptom that
## the model is not identified.summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 14
##
## Used Total
## Number of observations 2778 2839
## Number of clusters [state_2020] 50
## Sampling weights variable weight_panel_pre
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 5404.084 1521.471
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 3.552
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5058.191 -5058.191
## Loglikelihood unrestricted model (H1) -5058.191 -5058.191
##
## Akaike (AIC) 10144.382 10144.382
## Bayesian (BIC) 10227.394 10227.394
## Sample-size adjusted Bayesian (BIC) 10182.912 10182.912
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ParDi20 ~
## EduDi16 0.068 0.015 4.495 0.000 0.068 0.064
## ParDi16 0.715 0.016 43.798 0.000 0.715 0.717
## EduDi20 ~
## EduDi16 0.860 0.014 63.572 0.000 0.860 0.832
## ParDi16 0.032 0.019 1.708 0.088 0.032 0.033
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EduDi16 ~~
## ParDi16 0.014 0.005 2.614 0.009 0.014 0.062
## .ParDi20 ~~
## .EduDi20 0.007 0.002 3.147 0.002 0.007 0.072
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.096 0.015 6.303 0.000 0.096 0.192
## .EduDi20 0.078 0.009 8.325 0.000 0.078 0.161
## EduDi16 0.322 0.014 23.423 0.000 0.322 0.689
## ParDi16 0.468 0.020 23.161 0.000 0.468 0.939
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.118 0.006 18.885 0.000 0.118 0.476
## .EduDi20 0.071 0.006 11.248 0.000 0.071 0.303
## EduDi16 0.218 0.005 44.582 0.000 0.218 1.000
## ParDi16 0.249 0.001 194.574 0.000 0.249 1.000ZFit <- sem(ZMod, data, ordered = T)
summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 14 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 2778 2839
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 118954.137 78564.625
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 1.514
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.081 0.012 -6.532 0.000 -0.081 -0.081
## party7_2016 0.855 0.006 149.789 0.000 0.855 0.855
## educ5_2020 ~
## educ5_2016 0.911 0.003 300.554 0.000 0.911 0.911
## party7_2016 -0.017 0.009 -1.783 0.075 -0.017 -0.017
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.051 0.020 -2.478 0.013 -0.051 -0.051
## .party7_2020 ~~
## .educ5_2020 -0.018 0.007 -2.609 0.009 -0.018 -0.088
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.000 0.000 0.000
## .educ5_2020 0.000 0.000 0.000
## educ5_2016 0.000 0.000 0.000
## party7_2016 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020|t1 -0.681 0.026 -26.285 0.000 -0.681 -0.681
## party7_2020|t2 -0.378 0.024 -15.479 0.000 -0.378 -0.378
## party7_2020|t3 -0.102 0.024 -4.286 0.000 -0.102 -0.102
## party7_2020|t4 0.171 0.024 7.167 0.000 0.171 0.171
## party7_2020|t5 0.456 0.025 18.444 0.000 0.456 0.456
## party7_2020|t6 0.768 0.027 28.949 0.000 0.768 0.768
## educ5_2020|t1 -1.692 0.041 -40.866 0.000 -1.692 -1.692
## educ5_2020|t2 -0.813 0.027 -30.258 0.000 -0.813 -0.813
## educ5_2020|t3 0.104 0.024 4.362 0.000 0.104 0.104
## educ5_2020|t4 0.842 0.027 31.062 0.000 0.842 0.842
## educ5_2016|t1 -1.669 0.041 -40.948 0.000 -1.669 -1.669
## educ5_2016|t2 -0.778 0.027 -29.269 0.000 -0.778 -0.778
## educ5_2016|t3 0.175 0.024 7.319 0.000 0.175 0.175
## educ5_2016|t4 0.892 0.028 32.371 0.000 0.892 0.892
## party7_2016|t1 -0.788 0.027 -29.553 0.000 -0.788 -0.788
## party7_2016|t2 -0.384 0.024 -15.705 0.000 -0.384 -0.384
## party7_2016|t3 -0.071 0.024 -2.997 0.003 -0.071 -0.071
## party7_2016|t4 0.234 0.024 9.742 0.000 0.234 0.234
## party7_2016|t5 0.557 0.025 22.133 0.000 0.557 0.557
## party7_2016|t6 0.983 0.028 34.537 0.000 0.983 0.983
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.256 0.256 0.256
## .educ5_2020 0.168 0.168 0.168
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 1.000 1.000 1.000
## educ5_2020 1.000 1.000 1.000
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000ZFit <- sem(ZMod, data, ordered = T, sampling.weights = "weight_panel_pre")
summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 16 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 2778 2839
## Sampling weights variable weight_panel_pre
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 42209.644 28908.857
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 1.460
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.067 0.017 -3.813 0.000 -0.067 -0.067
## party7_2016 0.844 0.008 111.444 0.000 0.844 0.844
## educ5_2020 ~
## educ5_2016 0.882 0.005 170.076 0.000 0.882 0.882
## party7_2016 -0.014 0.014 -0.970 0.332 -0.014 -0.014
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.003 0.027 -0.129 0.897 -0.003 -0.003
## .party7_2020 ~~
## .educ5_2020 -0.036 0.012 -2.884 0.004 -0.036 -0.144
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.000 0.000 0.000
## .educ5_2020 0.000 0.000 0.000
## educ5_2016 0.000 0.000 0.000
## party7_2016 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020|t1 -0.704 0.032 -21.698 0.000 -0.704 -0.704
## party7_2020|t2 -0.378 0.030 -12.401 0.000 -0.378 -0.378
## party7_2020|t3 -0.120 0.030 -4.054 0.000 -0.120 -0.120
## party7_2020|t4 0.206 0.029 7.048 0.000 0.206 0.206
## party7_2020|t5 0.473 0.030 15.753 0.000 0.473 0.473
## party7_2020|t6 0.771 0.032 23.988 0.000 0.771 0.771
## educ5_2020|t1 -1.466 0.051 -28.590 0.000 -1.466 -1.466
## educ5_2020|t2 -0.452 0.033 -13.516 0.000 -0.452 -0.452
## educ5_2020|t3 0.333 0.028 11.733 0.000 0.333 0.333
## educ5_2020|t4 1.034 0.031 33.508 0.000 1.034 1.034
## educ5_2016|t1 -1.346 0.052 -26.049 0.000 -1.346 -1.346
## educ5_2016|t2 -0.309 0.033 -9.466 0.000 -0.309 -0.309
## educ5_2016|t3 0.463 0.027 16.863 0.000 0.463 0.463
## educ5_2016|t4 1.114 0.030 36.561 0.000 1.114 1.114
## party7_2016|t1 -0.788 0.034 -23.115 0.000 -0.788 -0.788
## party7_2016|t2 -0.375 0.030 -12.305 0.000 -0.375 -0.375
## party7_2016|t3 -0.079 0.029 -2.697 0.007 -0.079 -0.079
## party7_2016|t4 0.259 0.029 8.771 0.000 0.259 0.259
## party7_2016|t5 0.583 0.031 18.871 0.000 0.583 0.583
## party7_2016|t6 1.011 0.035 29.217 0.000 1.011 1.011
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.283 0.283 0.283
## .educ5_2020 0.222 0.222 0.222
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 1.000 1.000 1.000
## educ5_2020 1.000 1.000 1.000
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000ZFit <- sem(ZMod, data, ordered = T, cluster = "state_2020") #Identical to state_2016 results, and both can be used with long data
summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 14 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 2778 2839
## Number of clusters [state_2020] 50
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 118954.137 118954.137
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 1.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.081 0.012 -6.532 0.000 -0.081 -0.081
## party7_2016 0.855 0.006 149.789 0.000 0.855 0.855
## educ5_2020 ~
## educ5_2016 0.911 0.003 300.554 0.000 0.911 0.911
## party7_2016 -0.017 0.009 -1.783 0.075 -0.017 -0.017
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.051 0.020 -2.478 0.013 -0.051 -0.051
## .party7_2020 ~~
## .educ5_2020 -0.018 0.007 -2.609 0.009 -0.018 -0.088
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.000 0.000 0.000
## .educ5_2020 0.000 0.000 0.000
## educ5_2016 0.000 0.000 0.000
## party7_2016 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020|t1 -0.681 0.026 -26.285 0.000 -0.681 -0.681
## party7_2020|t2 -0.378 0.024 -15.479 0.000 -0.378 -0.378
## party7_2020|t3 -0.102 0.024 -4.286 0.000 -0.102 -0.102
## party7_2020|t4 0.171 0.024 7.167 0.000 0.171 0.171
## party7_2020|t5 0.456 0.025 18.444 0.000 0.456 0.456
## party7_2020|t6 0.768 0.027 28.949 0.000 0.768 0.768
## educ5_2020|t1 -1.692 0.041 -40.866 0.000 -1.692 -1.692
## educ5_2020|t2 -0.813 0.027 -30.258 0.000 -0.813 -0.813
## educ5_2020|t3 0.104 0.024 4.362 0.000 0.104 0.104
## educ5_2020|t4 0.842 0.027 31.062 0.000 0.842 0.842
## educ5_2016|t1 -1.669 0.041 -40.948 0.000 -1.669 -1.669
## educ5_2016|t2 -0.778 0.027 -29.269 0.000 -0.778 -0.778
## educ5_2016|t3 0.175 0.024 7.319 0.000 0.175 0.175
## educ5_2016|t4 0.892 0.028 32.371 0.000 0.892 0.892
## party7_2016|t1 -0.788 0.027 -29.553 0.000 -0.788 -0.788
## party7_2016|t2 -0.384 0.024 -15.705 0.000 -0.384 -0.384
## party7_2016|t3 -0.071 0.024 -2.997 0.003 -0.071 -0.071
## party7_2016|t4 0.234 0.024 9.742 0.000 0.234 0.234
## party7_2016|t5 0.557 0.025 22.133 0.000 0.557 0.557
## party7_2016|t6 0.983 0.028 34.537 0.000 0.983 0.983
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.256 0.256 0.256
## .educ5_2020 0.168 0.168 0.168
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 1.000 1.000 1.000
## educ5_2020 1.000 1.000 1.000
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000ZFit <- sem(ZMod, data, ordered = T, sampling.weights = "weight_panel_pre", cluster = "state_2020")
summary(ZFit, stand = T, fit = T)## lavaan 0.6-12 ended normally after 16 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 2778 2839
## Number of clusters [state_2020] 50
## Sampling weights variable weight_panel_pre
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 42209.644 42209.644
## Degrees of freedom 6 6
## P-value 0.000 0.000
## Scaling correction factor 1.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.067 0.017 -3.813 0.000 -0.067 -0.067
## party7_2016 0.844 0.008 111.444 0.000 0.844 0.844
## educ5_2020 ~
## educ5_2016 0.882 0.005 170.076 0.000 0.882 0.882
## party7_2016 -0.014 0.014 -0.970 0.332 -0.014 -0.014
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.003 0.027 -0.129 0.897 -0.003 -0.003
## .party7_2020 ~~
## .educ5_2020 -0.036 0.012 -2.884 0.004 -0.036 -0.144
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.000 0.000 0.000
## .educ5_2020 0.000 0.000 0.000
## educ5_2016 0.000 0.000 0.000
## party7_2016 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020|t1 -0.704 0.032 -21.698 0.000 -0.704 -0.704
## party7_2020|t2 -0.378 0.030 -12.401 0.000 -0.378 -0.378
## party7_2020|t3 -0.120 0.030 -4.054 0.000 -0.120 -0.120
## party7_2020|t4 0.206 0.029 7.048 0.000 0.206 0.206
## party7_2020|t5 0.473 0.030 15.753 0.000 0.473 0.473
## party7_2020|t6 0.771 0.032 23.988 0.000 0.771 0.771
## educ5_2020|t1 -1.466 0.051 -28.590 0.000 -1.466 -1.466
## educ5_2020|t2 -0.452 0.033 -13.516 0.000 -0.452 -0.452
## educ5_2020|t3 0.333 0.028 11.733 0.000 0.333 0.333
## educ5_2020|t4 1.034 0.031 33.508 0.000 1.034 1.034
## educ5_2016|t1 -1.346 0.052 -26.049 0.000 -1.346 -1.346
## educ5_2016|t2 -0.309 0.033 -9.466 0.000 -0.309 -0.309
## educ5_2016|t3 0.463 0.027 16.863 0.000 0.463 0.463
## educ5_2016|t4 1.114 0.030 36.561 0.000 1.114 1.114
## party7_2016|t1 -0.788 0.034 -23.115 0.000 -0.788 -0.788
## party7_2016|t2 -0.375 0.030 -12.305 0.000 -0.375 -0.375
## party7_2016|t3 -0.079 0.029 -2.697 0.007 -0.079 -0.079
## party7_2016|t4 0.259 0.029 8.771 0.000 0.259 0.259
## party7_2016|t5 0.583 0.031 18.871 0.000 0.583 0.583
## party7_2016|t6 1.011 0.035 29.217 0.000 1.011 1.011
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.283 0.283 0.283
## .educ5_2020 0.222 0.222 0.222
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 1.000 1.000 1.000
## educ5_2020 1.000 1.000 1.000
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000FITM <- c("chisq", "df", "nPar", "cfi", "rmsea", "rmsea.ci.lower", "rmsea.ci.upper", "aic", "bic")
ZFitR <- sem(ZMod, data, estimator = "DWLS", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesZFitRE <- sem(ZMod, data, estimator = "DWLS", group = "white_2020", group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesround(cbind(Free = fitMeasures(ZFitR, FITM), #Saturated vs not-saturated
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0 3.399
## df 0 4.000
## npar 28 24.000
## cfi 1 1.000
## rmsea 0 0.000
## rmsea.ci.lower 0 0.000
## rmsea.ci.upper 0 0.038
## aic NA NA
## bic NA NAZFitR <- sem(ZMod, data, estimator = "DWLS", sampling.weights = "weight_panel_pre", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesZFitRE <- sem(ZMod, data, estimator = "DWLS", sampling.weights = "weight_panel_pre", group = "white_2020",
group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesround(cbind(Free = fitMeasures(ZFitR, FITM),
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0 4.627
## df 0 4.000
## npar 28 24.000
## cfi 1 1.000
## rmsea 0 0.011
## rmsea.ci.lower 0 0.000
## rmsea.ci.upper 0 0.043
## aic NA NA
## bic NA NAZFitR <- sem(ZMod, data, cluster = "state_2020", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesZFitRE <- sem(ZMod, data, cluster = "state_2020", group = "white_2020",
group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesround(cbind(Free = fitMeasures(ZFitR, FITM),
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0.00 17.823
## df 0.00 4.000
## npar 28.00 24.000
## cfi 1.00 0.998
## rmsea 0.00 0.050
## rmsea.ci.lower 0.00 0.028
## rmsea.ci.upper 0.00 0.075
## aic 33916.49 33926.317
## bic 34082.33 34068.460ZFitR <- sem(ZMod, data, sampling.weights = "weight_panel_pre", cluster = "state_2020", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesZFitRE <- sem(ZMod, data, sampling.weights = "weight_panel_pre", cluster = "state_2020", group = "white_2020",
group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesround(cbind(Free = fitMeasures(ZFitR, FITM),
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0.00 24.520
## df 0.00 4.000
## npar 28.00 24.000
## cfi 1.00 0.997
## rmsea 0.00 0.061
## rmsea.ci.lower 0.00 0.039
## rmsea.ci.upper 0.00 0.085
## aic 35071.88 35088.401
## bic 35237.71 35230.544There was racial equivalence for the first model and the second, but not the third and fourth, unless we scale the threshold \(\alpha\).
ZFitR <- sem(ZMod, data, ordered = T, group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesZFitRE <- sem(ZMod, data, ordered = T, group = "white_2020", group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesround(cbind(Free = fitMeasures(ZFitR, FITM),
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0 11.094
## df 0 4.000
## npar 52 48.000
## cfi 1 1.000
## rmsea 0 0.036
## rmsea.ci.lower 0 0.011
## rmsea.ci.upper 0 0.062
## aic NA NA
## bic NA NAZFitR <- sem(ZMod, data, ordered = T, sampling.weights = "weight_panel_pre", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesZFitRE <- sem(ZMod, data, ordered = T, sampling.weights = "weight_panel_pre", group = "white_2020",
group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesround(cbind(Free = fitMeasures(ZFitR, FITM),
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0 5.763
## df 0 4.000
## npar 52 48.000
## cfi 1 1.000
## rmsea 0 0.018
## rmsea.ci.lower 0 0.000
## rmsea.ci.upper 0 0.047
## aic NA NA
## bic NA NAZFitR <- sem(ZMod, data, ordered = T, cluster = "state_2020", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesZFitRE <- sem(ZMod, data, ordered = T, cluster = "state_2020", group = "white_2020",
group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesround(cbind(Free = fitMeasures(ZFitR, FITM),
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0 11.094
## df 0 4.000
## npar 52 48.000
## cfi 1 1.000
## rmsea 0 0.036
## rmsea.ci.lower 0 0.011
## rmsea.ci.upper 0 0.062
## aic NA NA
## bic NA NAZFitR <- sem(ZMod, data, ordered = T, sampling.weights = "weight_panel_pre", cluster = "state_2020",
group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesZFitRE <- sem(ZMod, data, ordered = T, sampling.weights = "weight_panel_pre", cluster = "state_2020",
group = "white_2020",
group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesround(cbind(Free = fitMeasures(ZFitR, FITM),
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0 5.763
## df 0 4.000
## npar 52 48.000
## cfi 1 1.000
## rmsea 0 0.018
## rmsea.ci.lower 0 0.000
## rmsea.ci.upper 0 0.047
## aic NA NA
## bic NA NASignificant, not significant, significant, not significant.
ZFitR <- sem(ZMod, data, cluster = "state_2020", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuessummary(ZFitR, stand = T, fit = T)## lavaan 0.6-12 ended normally after 59 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Number of observations per group: Used Total
## 1 2018 2059
## 0 741 756
## Number of clusters [state_2020]:
## 1 50
## 0 49
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
## Information Observed
## Test statistic for each group:
## 1 0.000 0.000
## 0 0.000 0.000
##
## Model Test Baseline Model:
##
## Test statistic 7033.481 4362.910
## Degrees of freedom 12 12
## P-value 0.000 0.000
## Scaling correction factor 1.612
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -16930.247 -16930.247
## Loglikelihood unrestricted model (H1) -16930.247 -16930.247
##
## Akaike (AIC) 33916.494 33916.494
## Bayesian (BIC) 34082.327 34082.327
## Sample-size adjusted Bayesian (BIC) 33993.362 33993.362
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [1]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.207 0.027 -7.762 0.000 -0.207 -0.097
## party7_2016 0.857 0.014 59.996 0.000 0.857 0.802
## educ5_2020 ~
## educ5_2016 0.888 0.010 85.039 0.000 0.888 0.884
## party7_2016 -0.010 0.004 -2.436 0.015 -0.010 -0.019
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.262 0.079 -3.313 0.001 -0.262 -0.112
## .party7_2020 ~~
## .educ5_2020 -0.064 0.018 -3.495 0.000 -0.064 -0.096
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.423 0.136 10.446 0.000 1.423 0.617
## .educ5_2020 0.455 0.043 10.683 0.000 0.455 0.417
## educ5_2016 3.419 0.046 74.874 0.000 3.419 3.149
## party7_2016 4.117 0.096 43.045 0.000 4.117 1.908
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.754 0.101 17.435 0.000 1.754 0.330
## .educ5_2020 0.254 0.017 14.967 0.000 0.254 0.214
## educ5_2016 1.179 0.033 35.376 0.000 1.179 1.000
## party7_2016 4.656 0.100 46.460 0.000 4.656 1.000
##
##
## Group 2 [0]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.040 0.048 -0.825 0.409 -0.040 -0.023
## party7_2016 0.802 0.025 32.527 0.000 0.802 0.775
## educ5_2020 ~
## educ5_2016 0.864 0.019 44.748 0.000 0.864 0.851
## party7_2016 0.006 0.014 0.414 0.679 0.006 0.009
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 0.094 0.088 1.074 0.283 0.094 0.043
## .party7_2020 ~~
## .educ5_2020 0.012 0.039 0.310 0.757 0.012 0.016
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.723 0.171 4.229 0.000 0.723 0.361
## .educ5_2020 0.525 0.084 6.268 0.000 0.525 0.453
## educ5_2016 3.166 0.045 69.965 0.000 3.166 2.778
## party7_2016 3.009 0.084 35.689 0.000 3.009 1.555
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.603 0.112 14.321 0.000 1.603 0.400
## .educ5_2020 0.369 0.036 10.199 0.000 0.369 0.275
## educ5_2016 1.299 0.051 25.325 0.000 1.299 1.000
## party7_2016 3.748 0.125 30.002 0.000 3.748 1.000ZFitR <- sem(ZMod, data, sampling.weights = "weight_panel_pre", cluster = "state_2020", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuessummary(ZFitR, stand = T, fit = T)## lavaan 0.6-12 ended normally after 58 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Number of observations per group: Used Total
## 1 2018 2059
## 0 741 756
## Number of clusters [state_2020]:
## 1 50
## 0 49
## Sampling weights variable weight_panel_pre
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
## Test statistic for each group:
## 1 0.000 0.000
## 0 0.000 0.000
##
## Model Test Baseline Model:
##
## Test statistic 6153.172 2063.727
## Degrees of freedom 12 12
## P-value 0.000 0.000
## Scaling correction factor 2.982
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17507.941 -17507.941
## Loglikelihood unrestricted model (H1) -17507.941 -17507.941
##
## Akaike (AIC) 35071.882 35071.882
## Bayesian (BIC) 35237.715 35237.715
## Sample-size adjusted Bayesian (BIC) 35148.750 35148.750
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.174 0.034 -5.075 0.000 -0.174 -0.089
## party7_2016 0.837 0.020 42.146 0.000 0.837 0.782
## educ5_2020 ~
## educ5_2016 0.840 0.019 44.913 0.000 0.840 0.858
## party7_2016 -0.013 0.008 -1.663 0.096 -0.013 -0.024
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.215 0.093 -2.313 0.021 -0.215 -0.087
## .party7_2020 ~~
## .educ5_2020 -0.113 0.037 -3.080 0.002 -0.113 -0.141
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.386 0.173 8.023 0.000 1.386 0.609
## .educ5_2020 0.652 0.073 8.908 0.000 0.652 0.572
## educ5_2016 3.075 0.053 58.471 0.000 3.075 2.643
## party7_2016 4.177 0.102 40.758 0.000 4.177 1.963
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.909 0.129 14.748 0.000 1.909 0.368
## .educ5_2020 0.338 0.026 12.933 0.000 0.338 0.260
## educ5_2016 1.354 0.047 28.567 0.000 1.354 1.000
## party7_2016 4.527 0.096 47.170 0.000 4.527 1.000
##
##
## Group 2 [0]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.005 0.052 -0.098 0.922 -0.005 -0.003
## party7_2016 0.764 0.033 22.892 0.000 0.764 0.753
## educ5_2020 ~
## educ5_2016 0.835 0.030 28.142 0.000 0.835 0.808
## party7_2016 0.019 0.018 1.032 0.302 0.019 0.030
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 0.145 0.108 1.343 0.179 0.145 0.066
## .party7_2020 ~~
## .educ5_2020 -0.017 0.061 -0.276 0.782 -0.017 -0.019
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.685 0.193 3.556 0.000 0.685 0.354
## .educ5_2020 0.610 0.125 4.873 0.000 0.610 0.513
## educ5_2016 2.789 0.049 56.408 0.000 2.789 2.425
## party7_2016 2.948 0.113 26.164 0.000 2.948 1.543
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 1.620 0.120 13.544 0.000 1.620 0.433
## .educ5_2020 0.484 0.052 9.389 0.000 0.484 0.343
## educ5_2016 1.324 0.061 21.648 0.000 1.324 1.000
## party7_2016 3.647 0.132 27.534 0.000 3.647 1.000ZFitR <- sem(ZMod, data, ordered = T, group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuessummary(ZFitR, stand = T, fit = T)## lavaan 0.6-12 ended normally after 30 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 52
##
## Number of observations per group: Used Total
## 1 2018 2059
## 0 741 756
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
## Test statistic for each group:
## 1 0.000 0.000
## 0 0.000 0.000
##
## Model Test Baseline Model:
##
## Test statistic 135050.240 90311.071
## Degrees of freedom 12 12
## P-value 0.000 0.000
## Scaling correction factor 1.495
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [1]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.108 0.014 -7.506 0.000 -0.108 -0.108
## party7_2016 0.844 0.007 116.243 0.000 0.844 0.844
## educ5_2020 ~
## educ5_2016 0.925 0.003 278.796 0.000 0.925 0.925
## party7_2016 -0.019 0.011 -1.779 0.075 -0.019 -0.019
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.124 0.024 -5.231 0.000 -0.124 -0.124
## .party7_2020 ~~
## .educ5_2020 -0.023 0.008 -2.830 0.005 -0.023 -0.122
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.000 0.000 0.000
## .educ5_2020 0.000 0.000 0.000
## educ5_2016 0.000 0.000 0.000
## party7_2016 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020|t1 -0.786 0.031 -25.117 0.000 -0.786 -0.786
## party7_2020|t2 -0.525 0.029 -17.884 0.000 -0.525 -0.525
## party7_2020|t3 -0.256 0.028 -9.070 0.000 -0.256 -0.256
## party7_2020|t4 -0.011 0.028 -0.401 0.689 -0.011 -0.011
## party7_2020|t5 0.287 0.028 10.134 0.000 0.287 0.287
## party7_2020|t6 0.626 0.030 20.877 0.000 0.626 0.626
## educ5_2020|t1 -1.810 0.053 -34.226 0.000 -1.810 -1.810
## educ5_2020|t2 -0.855 0.032 -26.761 0.000 -0.855 -0.855
## educ5_2020|t3 0.052 0.028 1.869 0.062 0.052 0.052
## educ5_2020|t4 0.827 0.032 26.108 0.000 0.827 0.827
## educ5_2016|t1 -1.803 0.053 -34.268 0.000 -1.803 -1.803
## educ5_2016|t2 -0.844 0.032 -26.517 0.000 -0.844 -0.844
## educ5_2016|t3 0.108 0.028 3.871 0.000 0.108 0.108
## educ5_2016|t4 0.852 0.032 26.680 0.000 0.852 0.852
## party7_2016|t1 -0.920 0.033 -28.163 0.000 -0.920 -0.920
## party7_2016|t2 -0.535 0.029 -18.190 0.000 -0.535 -0.535
## party7_2016|t3 -0.213 0.028 -7.561 0.000 -0.213 -0.213
## party7_2016|t4 0.071 0.028 2.537 0.011 0.071 0.071
## party7_2016|t5 0.410 0.029 14.244 0.000 0.410 0.410
## party7_2016|t6 0.859 0.032 26.842 0.000 0.859 0.859
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.253 0.253 0.253
## .educ5_2020 0.140 0.140 0.140
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 1.000 1.000 1.000
## educ5_2020 1.000 1.000 1.000
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.024 0.027 -0.894 0.371 -0.024 -0.024
## party7_2016 0.823 0.013 64.177 0.000 0.823 0.823
## educ5_2020 ~
## educ5_2016 0.894 0.007 122.774 0.000 0.894 0.894
## party7_2016 0.016 0.021 0.768 0.442 0.016 0.016
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 0.058 0.040 1.441 0.150 0.058 0.058
## .party7_2020 ~~
## .educ5_2020 0.003 0.017 0.209 0.834 0.003 0.014
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.000 0.000 0.000
## .educ5_2020 0.000 0.000 0.000
## educ5_2016 0.000 0.000 0.000
## party7_2016 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020|t1 -0.420 0.048 -8.822 0.000 -0.420 -0.420
## party7_2020|t2 0.005 0.046 0.110 0.912 0.005 0.005
## party7_2020|t3 0.329 0.047 7.001 0.000 0.329 0.329
## party7_2020|t4 0.732 0.051 14.397 0.000 0.732 0.732
## party7_2020|t5 1.037 0.056 18.425 0.000 1.037 1.037
## party7_2020|t6 1.298 0.063 20.491 0.000 1.298 1.298
## educ5_2020|t1 -1.465 0.069 -21.111 0.000 -1.465 -1.465
## educ5_2020|t2 -0.710 0.051 -14.049 0.000 -0.710 -0.710
## educ5_2020|t3 0.255 0.047 5.466 0.000 0.255 0.255
## educ5_2020|t4 0.887 0.053 16.643 0.000 0.887 0.887
## educ5_2016|t1 -1.417 0.068 -20.989 0.000 -1.417 -1.417
## educ5_2016|t2 -0.617 0.049 -12.496 0.000 -0.617 -0.617
## educ5_2016|t3 0.365 0.047 7.731 0.000 0.365 0.365
## educ5_2016|t4 1.014 0.056 18.181 0.000 1.014 1.014
## party7_2016|t1 -0.483 0.048 -10.055 0.000 -0.483 -0.483
## party7_2016|t2 -0.002 0.046 -0.037 0.971 -0.002 -0.002
## party7_2016|t3 0.322 0.047 6.855 0.000 0.322 0.322
## party7_2016|t4 0.736 0.051 14.467 0.000 0.736 0.736
## party7_2016|t5 1.055 0.057 18.605 0.000 1.055 1.055
## party7_2016|t6 1.455 0.069 21.089 0.000 1.455 1.455
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.324 0.324 0.324
## .educ5_2020 0.199 0.199 0.199
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 1.000 1.000 1.000
## educ5_2020 1.000 1.000 1.000
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000ZFitR <- sem(ZMod, data, ordered = T, cluster = "state_2020", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuessummary(ZFitR, stand = T, fit = T)## lavaan 0.6-12 ended normally after 30 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 52
##
## Number of observations per group: Used Total
## 1 2018 2059
## 0 741 756
## Number of clusters [state_2020]:
## 1 50
## 0 49
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
## Test statistic for each group:
## 1 0.000 0.000
## 0 0.000 0.000
##
## Model Test Baseline Model:
##
## Test statistic 135050.240 135050.240
## Degrees of freedom 12 12
## P-value 0.000 0.000
## Scaling correction factor 1.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
##
## Group 1 [1]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.108 0.014 -7.506 0.000 -0.108 -0.108
## party7_2016 0.844 0.007 116.243 0.000 0.844 0.844
## educ5_2020 ~
## educ5_2016 0.925 0.003 278.796 0.000 0.925 0.925
## party7_2016 -0.019 0.011 -1.779 0.075 -0.019 -0.019
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 -0.124 0.024 -5.231 0.000 -0.124 -0.124
## .party7_2020 ~~
## .educ5_2020 -0.023 0.008 -2.830 0.005 -0.023 -0.122
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.000 0.000 0.000
## .educ5_2020 0.000 0.000 0.000
## educ5_2016 0.000 0.000 0.000
## party7_2016 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020|t1 -0.786 0.031 -25.117 0.000 -0.786 -0.786
## party7_2020|t2 -0.525 0.029 -17.884 0.000 -0.525 -0.525
## party7_2020|t3 -0.256 0.028 -9.070 0.000 -0.256 -0.256
## party7_2020|t4 -0.011 0.028 -0.401 0.689 -0.011 -0.011
## party7_2020|t5 0.287 0.028 10.134 0.000 0.287 0.287
## party7_2020|t6 0.626 0.030 20.877 0.000 0.626 0.626
## educ5_2020|t1 -1.810 0.053 -34.226 0.000 -1.810 -1.810
## educ5_2020|t2 -0.855 0.032 -26.761 0.000 -0.855 -0.855
## educ5_2020|t3 0.052 0.028 1.869 0.062 0.052 0.052
## educ5_2020|t4 0.827 0.032 26.108 0.000 0.827 0.827
## educ5_2016|t1 -1.803 0.053 -34.268 0.000 -1.803 -1.803
## educ5_2016|t2 -0.844 0.032 -26.517 0.000 -0.844 -0.844
## educ5_2016|t3 0.108 0.028 3.871 0.000 0.108 0.108
## educ5_2016|t4 0.852 0.032 26.680 0.000 0.852 0.852
## party7_2016|t1 -0.920 0.033 -28.163 0.000 -0.920 -0.920
## party7_2016|t2 -0.535 0.029 -18.190 0.000 -0.535 -0.535
## party7_2016|t3 -0.213 0.028 -7.561 0.000 -0.213 -0.213
## party7_2016|t4 0.071 0.028 2.537 0.011 0.071 0.071
## party7_2016|t5 0.410 0.029 14.244 0.000 0.410 0.410
## party7_2016|t6 0.859 0.032 26.842 0.000 0.859 0.859
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.253 0.253 0.253
## .educ5_2020 0.140 0.140 0.140
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 1.000 1.000 1.000
## educ5_2020 1.000 1.000 1.000
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000
##
##
## Group 2 [0]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 ~
## educ5_2016 -0.024 0.027 -0.894 0.371 -0.024 -0.024
## party7_2016 0.823 0.013 64.177 0.000 0.823 0.823
## educ5_2020 ~
## educ5_2016 0.894 0.007 122.774 0.000 0.894 0.894
## party7_2016 0.016 0.021 0.768 0.442 0.016 0.016
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## educ5_2016 ~~
## party7_2016 0.058 0.040 1.441 0.150 0.058 0.058
## .party7_2020 ~~
## .educ5_2020 0.003 0.017 0.209 0.834 0.003 0.014
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.000 0.000 0.000
## .educ5_2020 0.000 0.000 0.000
## educ5_2016 0.000 0.000 0.000
## party7_2016 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020|t1 -0.420 0.048 -8.822 0.000 -0.420 -0.420
## party7_2020|t2 0.005 0.046 0.110 0.912 0.005 0.005
## party7_2020|t3 0.329 0.047 7.001 0.000 0.329 0.329
## party7_2020|t4 0.732 0.051 14.397 0.000 0.732 0.732
## party7_2020|t5 1.037 0.056 18.425 0.000 1.037 1.037
## party7_2020|t6 1.298 0.063 20.491 0.000 1.298 1.298
## educ5_2020|t1 -1.465 0.069 -21.111 0.000 -1.465 -1.465
## educ5_2020|t2 -0.710 0.051 -14.049 0.000 -0.710 -0.710
## educ5_2020|t3 0.255 0.047 5.466 0.000 0.255 0.255
## educ5_2020|t4 0.887 0.053 16.643 0.000 0.887 0.887
## educ5_2016|t1 -1.417 0.068 -20.989 0.000 -1.417 -1.417
## educ5_2016|t2 -0.617 0.049 -12.496 0.000 -0.617 -0.617
## educ5_2016|t3 0.365 0.047 7.731 0.000 0.365 0.365
## educ5_2016|t4 1.014 0.056 18.181 0.000 1.014 1.014
## party7_2016|t1 -0.483 0.048 -10.055 0.000 -0.483 -0.483
## party7_2016|t2 -0.002 0.046 -0.037 0.971 -0.002 -0.002
## party7_2016|t3 0.322 0.047 6.855 0.000 0.322 0.322
## party7_2016|t4 0.736 0.051 14.467 0.000 0.736 0.736
## party7_2016|t5 1.055 0.057 18.605 0.000 1.055 1.055
## party7_2016|t6 1.455 0.069 21.089 0.000 1.455 1.455
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .party7_2020 0.324 0.324 0.324
## .educ5_2020 0.199 0.199 0.199
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## party7_2020 1.000 1.000 1.000
## educ5_2020 1.000 1.000 1.000
## educ5_2016 1.000 1.000 1.000
## party7_2016 1.000 1.000 1.000So, there’s the difference: education only affects party among Whites, but not non-Whites. This is presumably due to range-restriction, since few non-Whites vote Republican.
ZFitR <- sem(ZModBi, data, estimator = "DWLS", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesZFitRE <- sem(ZModBi, data, estimator = "DWLS", group = "white_2020", group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesround(cbind(Free = fitMeasures(ZFitR, FITM), #Saturated vs not-saturated
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0 4.885
## df 0 4.000
## npar 28 24.000
## cfi 1 1.000
## rmsea 0 0.013
## rmsea.ci.lower 0 0.000
## rmsea.ci.upper 0 0.044
## aic NA NA
## bic NA NAZFitR <- sem(ZModBi, data, estimator = "DWLS", sampling.weights = "weight_panel_pre", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesZFitRE <- sem(ZModBi, data, estimator = "DWLS", sampling.weights = "weight_panel_pre", group = "white_2020",
group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing valuesround(cbind(Free = fitMeasures(ZFitR, FITM),
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0 7.135
## df 0 4.000
## npar 28 24.000
## cfi 1 1.000
## rmsea 0 0.024
## rmsea.ci.lower 0 0.000
## rmsea.ci.upper 0 0.052
## aic NA NA
## bic NA NAZFitR <- sem(ZModBi, data, cluster = "state_2020", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing values## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -7.934755e-18) is smaller than zero. This may be a symptom that
## the model is not identified.ZFitRE <- sem(ZModBi, data, cluster = "state_2020", group = "white_2020",
group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing values## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 1.034559e-18) is close to zero. This may be a symptom that the
## model is not identified.round(cbind(Free = fitMeasures(ZFitR, FITM),
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0.000 10.340
## df 0.000 4.000
## npar 28.000 24.000
## cfi 1.000 0.999
## rmsea 0.000 0.034
## rmsea.ci.lower 0.000 0.008
## rmsea.ci.upper 0.000 0.060
## aic 9508.995 9511.335
## bic 9674.828 9653.478ZFitR <- sem(ZModBi, data, sampling.weights = "weight_panel_pre", cluster = "state_2020", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing values## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -1.234322e-17) is smaller than zero. This may be a symptom that
## the model is not identified.ZFitRE <- sem(ZModBi, data, sampling.weights = "weight_panel_pre", cluster = "state_2020", group = "white_2020",
group.equal = "regressions")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing values## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= 7.584927e-20) is close to zero. This may be a symptom that the
## model is not identified.round(cbind(Free = fitMeasures(ZFitR, FITM),
Restricted = fitMeasures(ZFitRE, FITM)), 3)## Free Restricted
## chisq 0.000 15.852
## df 0.000 4.000
## npar 28.000 24.000
## cfi 1.000 0.998
## rmsea 0.000 0.046
## rmsea.ci.lower 0.000 0.024
## rmsea.ci.upper 0.000 0.071
## aic 9771.753 9779.605
## bic 9937.587 9921.748Not significant, not significant, significant, significant.
ZFitR <- sem(ZModBi, data, cluster = "state_2020", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing values## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -7.934755e-18) is smaller than zero. This may be a symptom that
## the model is not identified.summary(ZFitR, stand = T, fit = T)## lavaan 0.6-12 ended normally after 45 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Number of observations per group: Used Total
## 1 2018 2059
## 0 741 756
## Number of clusters [state_2020]:
## 1 50
## 0 49
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
## Information Observed
## Test statistic for each group:
## 1 0.000 0.000
## 0 0.000 0.000
##
## Model Test Baseline Model:
##
## Test statistic 6212.830 2833.240
## Degrees of freedom 12 12
## P-value 0.000 0.000
## Scaling correction factor 2.193
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4726.497 -4726.497
## Loglikelihood unrestricted model (H1) -4726.497 -4726.497
##
## Akaike (AIC) 9508.995 9508.995
## Bayesian (BIC) 9674.828 9674.828
## Sample-size adjusted Bayesian (BIC) 9585.863 9585.863
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Expected
## Information saturated (h1) model Structured
##
##
## Group 1 [1]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ParDi20 ~
## EduDi16 0.104 0.016 6.373 0.000 0.104 0.106
## ParDi16 0.731 0.016 45.413 0.000 0.731 0.735
## EduDi20 ~
## EduDi16 0.888 0.011 78.493 0.000 0.888 0.885
## ParDi16 0.024 0.011 2.226 0.026 0.024 0.024
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EduDi16 ~~
## ParDi16 0.034 0.007 4.865 0.000 0.034 0.139
## .ParDi20 ~~
## .EduDi20 0.005 0.002 2.806 0.005 0.005 0.065
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.048 0.009 5.274 0.000 0.048 0.097
## .EduDi20 0.063 0.008 7.823 0.000 0.063 0.127
## EduDi16 0.457 0.018 25.262 0.000 0.457 0.917
## ParDi16 0.416 0.022 19.317 0.000 0.416 0.844
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.102 0.005 21.669 0.000 0.102 0.426
## .EduDi20 0.052 0.005 10.756 0.000 0.052 0.210
## EduDi16 0.248 0.002 159.124 0.000 0.248 1.000
## ParDi16 0.243 0.004 66.984 0.000 0.243 1.000
##
##
## Group 2 [0]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ParDi20 ~
## EduDi16 0.051 0.024 2.079 0.038 0.051 0.050
## ParDi16 0.675 0.030 22.549 0.000 0.675 0.676
## EduDi20 ~
## EduDi16 0.841 0.021 39.225 0.000 0.841 0.823
## ParDi16 0.016 0.024 0.650 0.515 0.016 0.016
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EduDi16 ~~
## ParDi16 0.001 0.008 0.190 0.850 0.001 0.006
## .ParDi20 ~~
## .EduDi20 0.002 0.003 0.460 0.646 0.002 0.015
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.188 0.027 7.008 0.000 0.188 0.389
## .EduDi20 0.089 0.018 5.025 0.000 0.089 0.181
## EduDi16 0.358 0.019 19.231 0.000 0.358 0.746
## ParDi16 0.626 0.021 30.458 0.000 0.626 1.294
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.126 0.010 12.631 0.000 0.126 0.540
## .EduDi20 0.077 0.009 8.289 0.000 0.077 0.323
## EduDi16 0.230 0.005 43.384 0.000 0.230 1.000
## ParDi16 0.234 0.005 45.117 0.000 0.234 1.000ZFitR <- sem(ZModBi, data, sampling.weights = "weight_panel_pre", cluster = "state_2020", group = "white_2020")## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: group variable 'white_2020' contains missing values## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## The variance-covariance matrix of the estimated parameters (vcov)
## does not appear to be positive definite! The smallest eigenvalue
## (= -1.234322e-17) is smaller than zero. This may be a symptom that
## the model is not identified.summary(ZFitR, stand = T, fit = T)## lavaan 0.6-12 ended normally after 43 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Number of observations per group: Used Total
## 1 2018 2059
## 0 741 756
## Number of clusters [state_2020]:
## 1 50
## 0 49
## Sampling weights variable weight_panel_pre
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
## Test statistic for each group:
## 1 0.000 0.000
## 0 0.000 0.000
##
## Model Test Baseline Model:
##
## Test statistic 5360.319 1640.851
## Degrees of freedom 12 12
## P-value 0.000 0.000
## Scaling correction factor 3.267
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4857.877 -4857.877
## Loglikelihood unrestricted model (H1) -4857.877 -4857.877
##
## Akaike (AIC) 9771.753 9771.753
## Bayesian (BIC) 9937.587 9937.587
## Sample-size adjusted Bayesian (BIC) 9848.621 9848.621
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Observed
## Observed information based on Hessian
##
##
## Group 1 [1]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ParDi20 ~
## EduDi16 0.097 0.018 5.447 0.000 0.097 0.095
## ParDi16 0.714 0.020 34.953 0.000 0.714 0.722
## EduDi20 ~
## EduDi16 0.876 0.013 65.732 0.000 0.876 0.858
## ParDi16 0.036 0.019 1.850 0.064 0.036 0.036
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EduDi16 ~~
## ParDi16 0.029 0.008 3.866 0.000 0.029 0.126
## .ParDi20 ~~
## .EduDi20 0.008 0.003 3.247 0.001 0.008 0.102
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.058 0.011 5.168 0.000 0.058 0.120
## .EduDi20 0.067 0.009 7.360 0.000 0.067 0.137
## EduDi16 0.351 0.017 20.245 0.000 0.351 0.735
## ParDi16 0.396 0.024 16.329 0.000 0.396 0.810
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.106 0.006 17.156 0.000 0.106 0.453
## .EduDi20 0.061 0.006 10.398 0.000 0.061 0.255
## EduDi16 0.228 0.005 43.979 0.000 0.228 1.000
## ParDi16 0.239 0.005 47.550 0.000 0.239 1.000
##
##
## Group 2 [0]:
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ParDi20 ~
## EduDi16 0.022 0.033 0.673 0.501 0.022 0.020
## ParDi16 0.645 0.044 14.769 0.000 0.645 0.645
## EduDi20 ~
## EduDi16 0.819 0.027 30.366 0.000 0.819 0.763
## ParDi16 0.012 0.032 0.365 0.715 0.012 0.012
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EduDi16 ~~
## ParDi16 -0.002 0.008 -0.215 0.829 -0.002 -0.008
## .ParDi20 ~~
## .EduDi20 0.001 0.005 0.269 0.788 0.001 0.013
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.215 0.045 4.779 0.000 0.215 0.445
## .EduDi20 0.108 0.018 5.902 0.000 0.108 0.231
## EduDi16 0.256 0.018 13.922 0.000 0.256 0.587
## ParDi16 0.629 0.026 24.145 0.000 0.629 1.303
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ParDi20 0.137 0.012 11.309 0.000 0.137 0.584
## .EduDi20 0.092 0.013 7.228 0.000 0.092 0.418
## EduDi16 0.191 0.009 21.236 0.000 0.191 1.000
## ParDi16 0.233 0.007 34.606 0.000 0.233 1.000sessionInfo()## R version 4.2.1 (2022-06-23 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] sjmisc_2.8.9 lavaan_0.6-12 psych_2.2.9 pacman_0.5.1
##
## loaded via a namespace (and not attached):
## [1] pillar_1.8.1 bslib_0.4.0 compiler_4.2.1 jquerylib_0.1.4
## [5] tools_4.2.1 digest_0.6.29 tibble_3.1.8 lifecycle_1.0.3
## [9] jsonlite_1.8.2 evaluate_0.17 nlme_3.1-157 lattice_0.20-45
## [13] pkgconfig_2.0.3 rlang_1.0.6 cli_3.4.1 rstudioapi_0.14
## [17] yaml_2.3.5 parallel_4.2.1 pbivnorm_0.6.0 xfun_0.33
## [21] fastmap_1.1.0 stringr_1.4.1 dplyr_1.0.10 knitr_1.40
## [25] generics_0.1.3 sass_0.4.2 vctrs_0.4.2 tidyselect_1.2.0
## [29] stats4_4.2.1 sjlabelled_1.2.0 grid_4.2.1 snakecase_0.11.0
## [33] glue_1.6.2 R6_2.5.1 fansi_1.0.3 rmarkdown_2.17
## [37] purrr_0.3.5 magrittr_2.0.3 MASS_7.3-57 htmltools_0.5.3
## [41] mnormt_2.1.1 insight_0.18.5 utf8_1.2.2 stringi_1.7.8
## [45] cachem_1.0.6