US vs Mex CC Analyses
Andres Pinedo
2026-08-24
#Install packages
#read data
#read in data dile
USMexData <- read_spss("Merged_CriticalReflectionPaper.sav") #create a data dictionary
data_dictionary <- data.frame(
variable = names(USMexData),
item_stem = sapply(USMexData, function(x) attr(x, "label")),
row.names = NULL
)
#print(data_dictionary, row.names = FALSE)#Descriptives for CR Items
# Create matrix with cr vars
cr_items <- USMexData |>
dplyr::select(ShoCCS_Ch_1, ShoCCS_Ch_2, ShoCCS_Ch_3, ShoCCS_Ch_4, ShoCCS_Ch_5, IneqBelfs_Ch_1, IneqBelfs_Ch_2, IneqBelfs_Ch_3,IneqBelfs_Ch_4, IneqBelfs_Ch_5) |>
as.matrix()
# View the matrix
#cr_items
#descriptives for cr items
describe(cr_items) |> kable()| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ShoCCS_Ch_1 | 1 | 103 | 3.786408 | 1.273027 | 4.0 | 3.879518 | 1.4826 | 1 | 6 | 5 | -0.6168376 | 0.2403324 | 0.1254351 |
| ShoCCS_Ch_2 | 2 | 95 | 3.410526 | 1.432857 | 4.0 | 3.402597 | 1.4826 | 1 | 6 | 5 | -0.1547744 | -0.6071670 | 0.1470080 |
| ShoCCS_Ch_3 | 3 | 98 | 2.765306 | 1.241889 | 3.0 | 2.737500 | 1.4826 | 1 | 6 | 5 | 0.0617935 | -0.8544308 | 0.1254498 |
| ShoCCS_Ch_4 | 4 | 99 | 3.202020 | 1.370087 | 3.0 | 3.209877 | 1.4826 | 1 | 6 | 5 | -0.1033213 | -0.8441589 | 0.1376989 |
| ShoCCS_Ch_5 | 5 | 106 | 3.556604 | 1.568015 | 4.0 | 3.569767 | 1.4826 | 1 | 6 | 5 | -0.1623811 | -0.9077168 | 0.1522991 |
| IneqBelfs_Ch_1 | 6 | 98 | 3.051020 | 1.160985 | 3.0 | 3.037500 | 1.4826 | 1 | 6 | 5 | 0.0976476 | -0.3383210 | 0.1172772 |
| IneqBelfs_Ch_2 | 7 | 103 | 2.922330 | 1.177313 | 3.0 | 2.915663 | 1.4826 | 1 | 6 | 5 | 0.1843830 | -0.1371346 | 0.1160041 |
| IneqBelfs_Ch_3 | 8 | 109 | 3.082569 | 1.375234 | 3.0 | 3.056180 | 1.4826 | 1 | 6 | 5 | -0.0199544 | -0.7672483 | 0.1317235 |
| IneqBelfs_Ch_4 | 9 | 112 | 3.437500 | 1.367455 | 3.5 | 3.466667 | 0.7413 | 1 | 6 | 5 | -0.1005788 | -0.6994994 | 0.1292123 |
| IneqBelfs_Ch_5 | 10 | 103 | 2.844660 | 1.341329 | 3.0 | 2.746988 | 1.4826 | 1 | 6 | 5 | 0.4019343 | -0.3562579 | 0.1321650 |
#now describe to two composites
cr_comps <- USMexData |>
dplyr::select(CR_US_Ch, CR_MX_Ch) |>
as.matrix()
#descriptives for cr comps
describe(cr_comps) |> kable()| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CR_US_Ch | 1 | 115 | 3.357971 | 1.1569486 | 3.4 | 3.402509 | 1.08724 | 1 | 6 | 5 | -0.2563853 | -0.2403438 | 0.1078860 |
| CR_MX_Ch | 2 | 118 | 3.084322 | 0.9803804 | 3.0 | 3.113542 | 0.88956 | 1 | 6 | 5 | -0.2048551 | 0.0060922 | 0.0902513 |
##Cronbach’s Alpha for CR US
#create data matrix for alpha command
UScr_items <- USMexData %>%
dplyr::select(ShoCCS_Ch_1, ShoCCS_Ch_2, ShoCCS_Ch_3, ShoCCS_Ch_4, ShoCCS_Ch_5)
UScralpha <- psych::alpha(UScr_items)
# View the result
print(UScralpha$total$raw_alpha)## [1] 0.8642755##Cronbach’s Alpha for CR MX
#create data matrix for alpha command
MXcr_items <- USMexData %>%
dplyr::select(IneqBelfs_Ch_1, IneqBelfs_Ch_2, IneqBelfs_Ch_3,IneqBelfs_Ch_4, IneqBelfs_Ch_5)
MXcralpha <- psych::alpha(MXcr_items)
# View the result
print(MXcralpha$total$raw_alpha)## [1] 0.7814966#See correlation plot for US CR items
#create a correlation matrix for study vars
UScr_items_mat <-cor(UScr_items, use = "complete.obs")
ggcorrplot(UScr_items_mat, hc.order = TRUE, type = "lower", lab = TRUE, ggtheme = ggplot2::theme_gray,
colors = c("#6D9EC1", "white", "#E46726"))## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the ggcorrplot package.
## Please report the issue at <https://github.com/kassambara/ggcorrplot/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#See correlation plot for MX CR items
#create a correlation matrix for study vars
MXcr_items_mat <-cor(MXcr_items, use = "complete.obs")
ggcorrplot(MXcr_items_mat, hc.order = TRUE, type = "lower", lab = TRUE, ggtheme = ggplot2::theme_gray,
colors = c("#6D9EC1", "white", "#E46726"))
#CFA for US critical reflection
#CFA for Critical Reflection Measures
us_cr_cfa_model <- 'US_CR =~ 1*ShoCCS_Ch_1 + ShoCCS_Ch_2 + ShoCCS_Ch_3 + ShoCCS_Ch_4 + ShoCCS_Ch_5'
us_cr_cfa <- sem(us_cr_cfa_model, data = USMexData, missing = "ML", estimator = "MLR") #will use MLR ## Warning: lavaan->lav_data_full():
## some cases are empty and will be ignored: 17 46 73 90 96.summary(us_cr_cfa, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-20 ended normally after 29 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 15
##
## Used Total
## Number of observations 115 120
## Number of missing patterns 20
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 5.654 4.433
## Degrees of freedom 5 5
## P-value (Chi-square) 0.341 0.489
## Scaling correction factor 1.275
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 204.595 142.842
## Degrees of freedom 10 10
## P-value 0.000 0.000
## Scaling correction factor 1.432
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.997 1.000
## Tucker-Lewis Index (TLI) 0.993 1.009
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.009
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -768.020 -768.020
## Scaling correction factor 1.105
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.148
## for the MLR correction
##
## Akaike (AIC) 1566.040 1566.040
## Bayesian (BIC) 1607.214 1607.214
## Sample-size adjusted Bayesian (SABIC) 1559.802 1559.802
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.034 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.137 0.111
## P-value H_0: RMSEA <= 0.050 0.500 0.675
## P-value H_0: RMSEA >= 0.080 0.309 0.160
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.170
## P-value H_0: Robust RMSEA <= 0.050 0.572
## P-value H_0: Robust RMSEA >= 0.080 0.321
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.027 0.027
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## US_CR =~
## ShoCCS_Ch_1 1.000 0.918 0.720
## ShoCCS_Ch_2 1.274 0.180 7.064 0.000 1.170 0.816
## ShoCCS_Ch_3 0.873 0.174 5.014 0.000 0.801 0.652
## ShoCCS_Ch_4 1.157 0.207 5.588 0.000 1.062 0.784
## ShoCCS_Ch_5 1.276 0.243 5.252 0.000 1.171 0.749
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 3.752 0.125 30.047 0.000 3.752 2.943
## .ShoCCS_Ch_2 3.373 0.139 24.330 0.000 3.373 2.354
## .ShoCCS_Ch_3 2.801 0.122 22.944 0.000 2.801 2.279
## .ShoCCS_Ch_4 3.251 0.131 24.760 0.000 3.251 2.402
## .ShoCCS_Ch_5 3.561 0.149 23.887 0.000 3.561 2.278
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 0.784 0.174 4.499 0.000 0.784 0.482
## .ShoCCS_Ch_2 0.684 0.228 3.004 0.003 0.684 0.333
## .ShoCCS_Ch_3 0.869 0.177 4.901 0.000 0.869 0.575
## .ShoCCS_Ch_4 0.704 0.161 4.374 0.000 0.704 0.385
## .ShoCCS_Ch_5 1.072 0.219 4.907 0.000 1.072 0.439
## US_CR 0.842 0.273 3.080 0.002 1.000 1.000
##
## R-Square:
## Estimate
## ShoCCS_Ch_1 0.518
## ShoCCS_Ch_2 0.667
## ShoCCS_Ch_3 0.425
## ShoCCS_Ch_4 0.615
## ShoCCS_Ch_5 0.561#CFA for MX critical reflection
#CFA for Critical Reflection Measures
mx_cr_cfa_model <- 'MX_CR =~ 1*IneqBelfs_Ch_1 + IneqBelfs_Ch_2 + IneqBelfs_Ch_3 + IneqBelfs_Ch_4 + IneqBelfs_Ch_5'
mx_cr_cfa <- sem(mx_cr_cfa_model, data = USMexData, missing = "ML", estimator = "MLR") ## Warning: lavaan->lav_data_full():
## some cases are empty and will be ignored: 17 45.summary(mx_cr_cfa, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-20 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 15
##
## Used Total
## Number of observations 118 120
## Number of missing patterns 17
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 12.140 10.374
## Degrees of freedom 5 5
## P-value (Chi-square) 0.033 0.065
## Scaling correction factor 1.170
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 145.444 110.012
## Degrees of freedom 10 10
## P-value 0.000 0.000
## Scaling correction factor 1.322
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.947 0.946
## Tucker-Lewis Index (TLI) 0.895 0.893
##
## Robust Comparative Fit Index (CFI) 0.951
## Robust Tucker-Lewis Index (TLI) 0.902
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -807.248 -807.248
## Scaling correction factor 1.090
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.110
## for the MLR correction
##
## Akaike (AIC) 1644.495 1644.495
## Bayesian (BIC) 1686.056 1686.056
## Sample-size adjusted Bayesian (SABIC) 1638.637 1638.637
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.110 0.095
## 90 Percent confidence interval - lower 0.029 0.000
## 90 Percent confidence interval - upper 0.190 0.172
## P-value H_0: RMSEA <= 0.050 0.091 0.141
## P-value H_0: RMSEA >= 0.080 0.780 0.686
##
## Robust RMSEA 0.115
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.215
## P-value H_0: Robust RMSEA <= 0.050 0.121
## P-value H_0: Robust RMSEA >= 0.080 0.772
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.049 0.049
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## MX_CR =~
## IneqBelfs_Ch_1 1.000 0.787 0.680
## IneqBelfs_Ch_2 1.275 0.200 6.370 0.000 1.003 0.861
## IneqBelfs_Ch_3 1.042 0.236 4.420 0.000 0.820 0.602
## IneqBelfs_Ch_4 0.965 0.275 3.512 0.000 0.759 0.558
## IneqBelfs_Ch_5 0.973 0.242 4.028 0.000 0.766 0.569
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .IneqBelfs_Ch_1 3.054 0.114 26.876 0.000 3.054 2.637
## .IneqBelfs_Ch_2 2.928 0.112 26.228 0.000 2.928 2.512
## .IneqBelfs_Ch_3 3.091 0.129 24.053 0.000 3.091 2.269
## .IneqBelfs_Ch_4 3.430 0.128 26.830 0.000 3.430 2.520
## .IneqBelfs_Ch_5 2.882 0.131 21.998 0.000 2.882 2.143
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .IneqBelfs_Ch_1 0.722 0.170 4.241 0.000 0.722 0.538
## .IneqBelfs_Ch_2 0.352 0.147 2.394 0.017 0.352 0.259
## .IneqBelfs_Ch_3 1.184 0.234 5.072 0.000 1.184 0.638
## .IneqBelfs_Ch_4 1.276 0.209 6.101 0.000 1.276 0.689
## .IneqBelfs_Ch_5 1.223 0.230 5.310 0.000 1.223 0.676
## MX_CR 0.619 0.207 2.993 0.003 1.000 1.000
##
## R-Square:
## Estimate
## IneqBelfs_Ch_1 0.462
## IneqBelfs_Ch_2 0.741
## IneqBelfs_Ch_3 0.362
## IneqBelfs_Ch_4 0.311
## IneqBelfs_Ch_5 0.324modindices(mx_cr_cfa, sort. = TRUE)## lhs op rhs mi epc sepc.lv sepc.all sepc.nox
## 18 IneqBelfs_Ch_1 ~~ IneqBelfs_Ch_2 8.464 0.392 0.392 0.777 0.777
## 20 IneqBelfs_Ch_1 ~~ IneqBelfs_Ch_4 6.565 -0.316 -0.316 -0.330 -0.330
## 27 IneqBelfs_Ch_4 ~~ IneqBelfs_Ch_5 4.494 0.302 0.302 0.242 0.242
## 22 IneqBelfs_Ch_2 ~~ IneqBelfs_Ch_3 2.781 -0.236 -0.236 -0.365 -0.365
## 25 IneqBelfs_Ch_3 ~~ IneqBelfs_Ch_4 2.168 0.209 0.209 0.170 0.170
## 21 IneqBelfs_Ch_1 ~~ IneqBelfs_Ch_5 1.473 -0.150 -0.150 -0.159 -0.159
## 24 IneqBelfs_Ch_2 ~~ IneqBelfs_Ch_5 0.682 -0.112 -0.112 -0.170 -0.170
## 23 IneqBelfs_Ch_2 ~~ IneqBelfs_Ch_4 0.227 -0.064 -0.064 -0.096 -0.096
## 26 IneqBelfs_Ch_3 ~~ IneqBelfs_Ch_5 0.199 0.063 0.063 0.053 0.053
## 19 IneqBelfs_Ch_1 ~~ IneqBelfs_Ch_3 0.060 0.031 0.031 0.033 0.033lavInspect(mx_cr_cfa, "cor.lv")## MX_CR
## MX_CR 0.619#Measurement Configural invariance for CR across US and MX
#CFA for Critical Reflection Measures
mx_cr_conf_model <- 'US_CR =~ 1*ShoCCS_Ch_1 + ShoCCS_Ch_2 + ShoCCS_Ch_3 + ShoCCS_Ch_4 + ShoCCS_Ch_5
MX_CR =~ 1*IneqBelfs_Ch_1 + IneqBelfs_Ch_2 + IneqBelfs_Ch_3 + IneqBelfs_Ch_4 + IneqBelfs_Ch_5
ShoCCS_Ch_1 ~~ IneqBelfs_Ch_1
ShoCCS_Ch_2 ~~ IneqBelfs_Ch_2
ShoCCS_Ch_3 ~~ IneqBelfs_Ch_3
ShoCCS_Ch_4 ~~ IneqBelfs_Ch_4
ShoCCS_Ch_5 ~~ IneqBelfs_Ch_5'
mx_cr_conf_fit <- sem(mx_cr_conf_model, data = USMexData, missing = "ML", estimator = "MLR", meanstructure = TRUE) ## Warning: lavaan->lav_data_full():
## some cases are empty and will be ignored: 17.summary(mx_cr_conf_fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-20 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 36
##
## Used Total
## Number of observations 119 120
## Number of missing patterns 40
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 45.672 38.238
## Degrees of freedom 29 29
## P-value (Chi-square) 0.025 0.117
## Scaling correction factor 1.194
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 426.299 329.448
## Degrees of freedom 45 45
## P-value 0.000 0.000
## Scaling correction factor 1.294
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.956 0.968
## Tucker-Lewis Index (TLI) 0.932 0.950
##
## Robust Comparative Fit Index (CFI) 0.969
## Robust Tucker-Lewis Index (TLI) 0.951
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1551.077 -1551.077
## Scaling correction factor 1.144
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.167
## for the MLR correction
##
## Akaike (AIC) 3174.154 3174.154
## Bayesian (BIC) 3274.203 3274.203
## Sample-size adjusted Bayesian (SABIC) 3160.393 3160.393
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.070 0.052
## 90 Percent confidence interval - lower 0.025 0.000
## 90 Percent confidence interval - upper 0.106 0.089
## P-value H_0: RMSEA <= 0.050 0.193 0.443
## P-value H_0: RMSEA >= 0.080 0.348 0.116
##
## Robust RMSEA 0.065
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.118
## P-value H_0: Robust RMSEA <= 0.050 0.323
## P-value H_0: Robust RMSEA >= 0.080 0.355
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.069 0.069
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## US_CR =~
## ShoCCS_Ch_1 1.000 0.918 0.716
## ShoCCS_Ch_2 1.301 0.190 6.860 0.000 1.195 0.822
## ShoCCS_Ch_3 0.908 0.179 5.082 0.000 0.834 0.672
## ShoCCS_Ch_4 1.089 0.200 5.451 0.000 1.000 0.752
## ShoCCS_Ch_5 1.291 0.255 5.069 0.000 1.185 0.761
## MX_CR =~
## IneqBelfs_Ch_1 1.000 0.787 0.673
## IneqBelfs_Ch_2 1.229 0.193 6.354 0.000 0.967 0.824
## IneqBelfs_Ch_3 1.075 0.263 4.086 0.000 0.846 0.621
## IneqBelfs_Ch_4 1.059 0.306 3.459 0.001 0.833 0.608
## IneqBelfs_Ch_5 0.979 0.268 3.648 0.000 0.770 0.576
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 ~~
## .IneqBelfs_Ch_1 0.050 0.110 0.460 0.646 0.050 0.065
## .ShoCCS_Ch_2 ~~
## .IneqBelfs_Ch_2 0.034 0.090 0.373 0.709 0.034 0.061
## .ShoCCS_Ch_3 ~~
## .IneqBelfs_Ch_3 0.201 0.127 1.578 0.114 0.201 0.204
## .ShoCCS_Ch_4 ~~
## .IneqBelfs_Ch_4 0.383 0.130 2.942 0.003 0.383 0.403
## .ShoCCS_Ch_5 ~~
## .IneqBelfs_Ch_5 0.125 0.163 0.763 0.445 0.125 0.113
## US_CR ~~
## MX_CR 0.438 0.117 3.758 0.000 0.606 0.606
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 3.746 0.126 29.817 0.000 3.746 2.918
## .ShoCCS_Ch_2 3.371 0.139 24.198 0.000 3.371 2.319
## .ShoCCS_Ch_3 2.813 0.122 22.995 0.000 2.813 2.266
## .ShoCCS_Ch_4 3.248 0.130 24.962 0.000 3.248 2.445
## .ShoCCS_Ch_5 3.564 0.149 23.955 0.000 3.564 2.289
## .IneqBelfs_Ch_1 3.062 0.115 26.716 0.000 3.062 2.619
## .IneqBelfs_Ch_2 2.951 0.113 26.179 0.000 2.951 2.514
## .IneqBelfs_Ch_3 3.115 0.128 24.417 0.000 3.115 2.286
## .IneqBelfs_Ch_4 3.426 0.128 26.790 0.000 3.426 2.503
## .IneqBelfs_Ch_5 2.885 0.131 21.964 0.000 2.885 2.158
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 0.804 0.173 4.637 0.000 0.804 0.488
## .ShoCCS_Ch_2 0.685 0.232 2.952 0.003 0.685 0.324
## .ShoCCS_Ch_3 0.845 0.176 4.815 0.000 0.845 0.549
## .ShoCCS_Ch_4 0.766 0.160 4.779 0.000 0.766 0.434
## .ShoCCS_Ch_5 1.019 0.219 4.661 0.000 1.019 0.420
## .IneqBelfs_Ch_1 0.748 0.192 3.893 0.000 0.748 0.547
## .IneqBelfs_Ch_2 0.442 0.164 2.693 0.007 0.442 0.321
## .IneqBelfs_Ch_3 1.141 0.237 4.821 0.000 1.141 0.615
## .IneqBelfs_Ch_4 1.180 0.216 5.456 0.000 1.180 0.630
## .IneqBelfs_Ch_5 1.195 0.231 5.174 0.000 1.195 0.668
## US_CR 0.843 0.274 3.074 0.002 1.000 1.000
## MX_CR 0.619 0.230 2.691 0.007 1.000 1.000
##
## R-Square:
## Estimate
## ShoCCS_Ch_1 0.512
## ShoCCS_Ch_2 0.676
## ShoCCS_Ch_3 0.451
## ShoCCS_Ch_4 0.566
## ShoCCS_Ch_5 0.580
## IneqBelfs_Ch_1 0.453
## IneqBelfs_Ch_2 0.679
## IneqBelfs_Ch_3 0.385
## IneqBelfs_Ch_4 0.370
## IneqBelfs_Ch_5 0.332#inspect correlation between the CR factors
#lavInspect(mx_cr_conf_fit, "cor.lv")
#inspect the covariance between the factors
#lavInspect(mx_cr_conf_fit, "cov.lv")
#inspect the observed covariance
#lavInspect(mx_cr_conf_fit, "cov.ov")
#inspect the residual (theta) matrix
#lavInspect(mx_cr_conf_fit, "theta")
#insect the theta values
#eigen(lavInspect(mx_cr_conf_fit, "theta"))$values
#eigen(lavInspect(mx_cr_conf_fit, "cov.lv"))$values
#eigen(lavInspect(mx_cr_conf_fit, "cov.ov"))$values
#vcov_matrix <- vcov(mx_cr_conf_fit)
#eigen(vcov_matrix)$values
#lavInspect(mx_cr_conf_fit, "vcov")
#lavInspect(mx_cr_conf_fit, "information")
#inspect(mx_cr_conf_fit, "post.check")
#lavInspect(mx_cr_conf_fit, "estimates")#Metric Invariance for CR across US and MX
#CFA for Critical Reflection Measures
mx_cr_metric_model <- 'US_CR =~ 1*ShoCCS_Ch_1 + l2*ShoCCS_Ch_2 + l3*ShoCCS_Ch_3 + l4*ShoCCS_Ch_4 + l5*ShoCCS_Ch_5
MX_CR =~ 1*IneqBelfs_Ch_1 + l2*IneqBelfs_Ch_2 + l3*IneqBelfs_Ch_3 + l4*IneqBelfs_Ch_4 + l5*IneqBelfs_Ch_5
ShoCCS_Ch_1 ~~ IneqBelfs_Ch_1
ShoCCS_Ch_2 ~~ IneqBelfs_Ch_2
ShoCCS_Ch_3 ~~ IneqBelfs_Ch_3
ShoCCS_Ch_4 ~~ IneqBelfs_Ch_4
ShoCCS_Ch_5 ~~ IneqBelfs_Ch_5'
mx_cr_metric_fit <- sem(mx_cr_metric_model, data = USMexData, missing = "ML", estimator = "MLR", meanstructure = TRUE) ## Warning: lavaan->lav_data_full():
## some cases are empty and will be ignored: 17.summary(mx_cr_metric_fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-20 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 36
## Number of equality constraints 4
##
## Used Total
## Number of observations 119 120
## Number of missing patterns 40
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 48.866 40.921
## Degrees of freedom 33 33
## P-value (Chi-square) 0.037 0.162
## Scaling correction factor 1.194
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 426.299 329.448
## Degrees of freedom 45 45
## P-value 0.000 0.000
## Scaling correction factor 1.294
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.958 0.972
## Tucker-Lewis Index (TLI) 0.943 0.962
##
## Robust Comparative Fit Index (CFI) 0.973
## Robust Tucker-Lewis Index (TLI) 0.963
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1552.674 -1552.674
## Scaling correction factor 1.012
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.167
## for the MLR correction
##
## Akaike (AIC) 3169.348 3169.348
## Bayesian (BIC) 3258.280 3258.280
## Sample-size adjusted Bayesian (SABIC) 3157.115 3157.115
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.064 0.045
## 90 Percent confidence interval - lower 0.016 0.000
## 90 Percent confidence interval - upper 0.099 0.082
## P-value H_0: RMSEA <= 0.050 0.261 0.553
## P-value H_0: RMSEA >= 0.080 0.246 0.061
##
## Robust RMSEA 0.056
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.108
## P-value H_0: Robust RMSEA <= 0.050 0.404
## P-value H_0: Robust RMSEA >= 0.080 0.259
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.076 0.076
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## US_CR =~
## ShCCS_C_1 1.000 0.933 0.722
## ShCCS_C_2 (l2) 1.276 0.134 9.497 0.000 1.191 0.823
## ShCCS_C_3 (l3) 0.963 0.148 6.490 0.000 0.898 0.702
## ShCCS_C_4 (l4) 1.079 0.185 5.826 0.000 1.007 0.755
## ShCCS_C_5 (l5) 1.183 0.215 5.495 0.000 1.104 0.729
## MX_CR =~
## InqBl_C_1 1.000 0.763 0.659
## InqBl_C_2 (l2) 1.276 0.134 9.497 0.000 0.974 0.827
## InqBl_C_3 (l3) 0.963 0.148 6.490 0.000 0.734 0.558
## InqBl_C_4 (l4) 1.079 0.185 5.826 0.000 0.823 0.604
## InqBl_C_5 (l5) 1.183 0.215 5.495 0.000 0.902 0.644
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 ~~
## .IneqBelfs_Ch_1 0.051 0.108 0.469 0.639 0.051 0.065
## .ShoCCS_Ch_2 ~~
## .IneqBelfs_Ch_2 0.027 0.087 0.306 0.759 0.027 0.049
## .ShoCCS_Ch_3 ~~
## .IneqBelfs_Ch_3 0.207 0.130 1.588 0.112 0.207 0.208
## .ShoCCS_Ch_4 ~~
## .IneqBelfs_Ch_4 0.386 0.130 2.968 0.003 0.386 0.406
## .ShoCCS_Ch_5 ~~
## .IneqBelfs_Ch_5 0.097 0.160 0.603 0.546 0.097 0.087
## US_CR ~~
## MX_CR 0.431 0.116 3.701 0.000 0.605 0.605
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 3.745 0.125 30.013 0.000 3.745 2.896
## .ShoCCS_Ch_2 3.370 0.139 24.181 0.000 3.370 2.327
## .ShoCCS_Ch_3 2.818 0.123 22.986 0.000 2.818 2.203
## .ShoCCS_Ch_4 3.249 0.130 24.990 0.000 3.249 2.437
## .ShoCCS_Ch_5 3.564 0.149 23.955 0.000 3.564 2.355
## .IneqBelfs_Ch_1 3.062 0.114 26.840 0.000 3.062 2.643
## .IneqBelfs_Ch_2 2.950 0.112 26.290 0.000 2.950 2.506
## .IneqBelfs_Ch_3 3.111 0.129 24.124 0.000 3.111 2.364
## .IneqBelfs_Ch_4 3.426 0.128 26.830 0.000 3.426 2.512
## .IneqBelfs_Ch_5 2.894 0.132 21.956 0.000 2.894 2.064
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 0.801 0.170 4.720 0.000 0.801 0.479
## .ShoCCS_Ch_2 0.678 0.223 3.046 0.002 0.678 0.323
## .ShoCCS_Ch_3 0.829 0.170 4.869 0.000 0.829 0.507
## .ShoCCS_Ch_4 0.764 0.161 4.755 0.000 0.764 0.429
## .ShoCCS_Ch_5 1.072 0.218 4.927 0.000 1.072 0.468
## .IneqBelfs_Ch_1 0.759 0.170 4.476 0.000 0.759 0.566
## .IneqBelfs_Ch_2 0.437 0.143 3.060 0.002 0.437 0.316
## .IneqBelfs_Ch_3 1.193 0.207 5.765 0.000 1.193 0.689
## .IneqBelfs_Ch_4 1.183 0.180 6.556 0.000 1.183 0.636
## .IneqBelfs_Ch_5 1.152 0.223 5.165 0.000 1.152 0.586
## US_CR 0.871 0.229 3.812 0.000 1.000 1.000
## MX_CR 0.582 0.166 3.516 0.000 1.000 1.000
##
## R-Square:
## Estimate
## ShoCCS_Ch_1 0.521
## ShoCCS_Ch_2 0.677
## ShoCCS_Ch_3 0.493
## ShoCCS_Ch_4 0.571
## ShoCCS_Ch_5 0.532
## IneqBelfs_Ch_1 0.434
## IneqBelfs_Ch_2 0.684
## IneqBelfs_Ch_3 0.311
## IneqBelfs_Ch_4 0.364
## IneqBelfs_Ch_5 0.414#Full Scalar Invariance for CR across US and MX
#CFA for Critical Reflection Measures
mx_cr_scalar_model <- 'US_CR =~ 1*ShoCCS_Ch_1 + l2*ShoCCS_Ch_2 + l3*ShoCCS_Ch_3 + l4*ShoCCS_Ch_4 + l5*ShoCCS_Ch_5
MX_CR =~ 1*IneqBelfs_Ch_1 + l2*IneqBelfs_Ch_2 + l3*IneqBelfs_Ch_3 + l4*IneqBelfs_Ch_4 + l5*IneqBelfs_Ch_5
ShoCCS_Ch_1 ~~ IneqBelfs_Ch_1
ShoCCS_Ch_2 ~~ IneqBelfs_Ch_2
ShoCCS_Ch_3 ~~ IneqBelfs_Ch_3
ShoCCS_Ch_4 ~~ IneqBelfs_Ch_4
ShoCCS_Ch_5 ~~ IneqBelfs_Ch_5
ShoCCS_Ch_1 ~ i1*1
IneqBelfs_Ch_1 ~ i1*1
ShoCCS_Ch_2 ~ i2*1
IneqBelfs_Ch_2 ~ i2*1
ShoCCS_Ch_3 ~ i3*1
IneqBelfs_Ch_3 ~ i3*1
ShoCCS_Ch_4 ~ i4*1
IneqBelfs_Ch_4 ~ i4*1
ShoCCS_Ch_5 ~ i5*1
IneqBelfs_Ch_5 ~ i5*1'
mx_cr_scalar_fit <- sem(mx_cr_scalar_model, data = USMexData, missing = "ML", estimator = "MLR", meanstructure = TRUE) ## Warning: lavaan->lav_data_full():
## some cases are empty and will be ignored: 17.summary(mx_cr_scalar_fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-20 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 36
## Number of equality constraints 9
##
## Used Total
## Number of observations 119 120
## Number of missing patterns 40
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 105.890 89.178
## Degrees of freedom 38 38
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 1.187
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 426.299 329.448
## Degrees of freedom 45 45
## P-value 0.000 0.000
## Scaling correction factor 1.294
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.822 0.820
## Tucker-Lewis Index (TLI) 0.789 0.787
##
## Robust Comparative Fit Index (CFI) 0.837
## Robust Tucker-Lewis Index (TLI) 0.807
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1581.186 -1581.186
## Scaling correction factor 0.853
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.167
## for the MLR correction
##
## Akaike (AIC) 3216.372 3216.372
## Bayesian (BIC) 3291.409 3291.409
## Sample-size adjusted Bayesian (SABIC) 3206.051 3206.051
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.123 0.106
## 90 Percent confidence interval - lower 0.095 0.080
## 90 Percent confidence interval - upper 0.151 0.133
## P-value H_0: RMSEA <= 0.050 0.000 0.000
## P-value H_0: RMSEA >= 0.080 0.994 0.951
##
## Robust RMSEA 0.129
## 90 Percent confidence interval - lower 0.092
## 90 Percent confidence interval - upper 0.166
## P-value H_0: Robust RMSEA <= 0.050 0.001
## P-value H_0: Robust RMSEA >= 0.080 0.984
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.115 0.115
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## US_CR =~
## ShCCS_C_1 1.000 0.988 0.730
## ShCCS_C_2 (l2) 1.261 0.129 9.740 0.000 1.246 0.838
## ShCCS_C_3 (l3) 0.874 0.163 5.380 0.000 0.863 0.674
## ShCCS_C_4 (l4) 0.988 0.197 5.017 0.000 0.976 0.735
## ShCCS_C_5 (l5) 1.168 0.211 5.534 0.000 1.154 0.738
## MX_CR =~
## InqBl_C_1 1.000 0.792 0.659
## InqBl_C_2 (l2) 1.261 0.129 9.740 0.000 0.998 0.844
## InqBl_C_3 (l3) 0.874 0.163 5.380 0.000 0.692 0.519
## InqBl_C_4 (l4) 0.988 0.197 5.017 0.000 0.782 0.570
## InqBl_C_5 (l5) 1.168 0.211 5.534 0.000 0.924 0.639
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 ~~
## .IneqBelfs_Ch_1 0.012 0.118 0.104 0.917 0.012 0.015
## .ShoCCS_Ch_2 ~~
## .IneqBelfs_Ch_2 0.013 0.091 0.146 0.884 0.013 0.026
## .ShoCCS_Ch_3 ~~
## .IneqBelfs_Ch_3 0.170 0.136 1.252 0.210 0.170 0.157
## .ShoCCS_Ch_4 ~~
## .IneqBelfs_Ch_4 0.373 0.143 2.616 0.009 0.373 0.367
## .ShoCCS_Ch_5 ~~
## .IneqBelfs_Ch_5 0.007 0.151 0.049 0.961 0.007 0.006
## US_CR ~~
## MX_CR 0.448 0.119 3.766 0.000 0.573 0.573
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShCCS_C_1 (i1) 3.363 0.108 31.069 0.000 3.363 2.486
## .InqBl_C_1 (i1) 3.363 0.108 31.069 0.000 3.363 2.802
## .ShCCS_C_2 (i2) 3.079 0.107 28.753 0.000 3.079 2.072
## .InqBl_C_2 (i2) 3.079 0.107 28.753 0.000 3.079 2.603
## .ShCCS_C_3 (i3) 2.893 0.108 26.754 0.000 2.893 2.258
## .InqBl_C_3 (i3) 2.893 0.108 26.754 0.000 2.893 2.170
## .ShCCS_C_4 (i4) 3.255 0.118 27.542 0.000 3.255 2.450
## .InqBl_C_4 (i4) 3.255 0.118 27.542 0.000 3.255 2.373
## .ShCCS_C_5 (i5) 3.199 0.131 24.505 0.000 3.199 2.046
## .InqBl_C_5 (i5) 3.199 0.131 24.505 0.000 3.199 2.211
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 0.855 0.198 4.312 0.000 0.855 0.467
## .ShoCCS_Ch_2 0.656 0.236 2.779 0.005 0.656 0.297
## .ShoCCS_Ch_3 0.896 0.188 4.763 0.000 0.896 0.546
## .ShoCCS_Ch_4 0.813 0.179 4.546 0.000 0.813 0.460
## .ShoCCS_Ch_5 1.114 0.230 4.852 0.000 1.114 0.456
## .IneqBelfs_Ch_1 0.814 0.186 4.374 0.000 0.814 0.565
## .IneqBelfs_Ch_2 0.403 0.154 2.609 0.009 0.403 0.288
## .IneqBelfs_Ch_3 1.298 0.226 5.743 0.000 1.298 0.731
## .IneqBelfs_Ch_4 1.270 0.197 6.443 0.000 1.270 0.675
## .IneqBelfs_Ch_5 1.239 0.227 5.452 0.000 1.239 0.592
## US_CR 0.975 0.262 3.721 0.000 1.000 1.000
## MX_CR 0.627 0.176 3.552 0.000 1.000 1.000
##
## R-Square:
## Estimate
## ShoCCS_Ch_1 0.533
## ShoCCS_Ch_2 0.703
## ShoCCS_Ch_3 0.454
## ShoCCS_Ch_4 0.540
## ShoCCS_Ch_5 0.544
## IneqBelfs_Ch_1 0.435
## IneqBelfs_Ch_2 0.712
## IneqBelfs_Ch_3 0.269
## IneqBelfs_Ch_4 0.325
## IneqBelfs_Ch_5 0.408#Partial Scalar Invariance for CR across US and MX
#CFA for Critical Reflection Measures
mx_cr_scalar_model <- 'US_CR =~ 1*ShoCCS_Ch_1 + l2*ShoCCS_Ch_2 + l3*ShoCCS_Ch_3 + l4*ShoCCS_Ch_4 + l5*ShoCCS_Ch_5
MX_CR =~ 1*IneqBelfs_Ch_1 + l2*IneqBelfs_Ch_2 + l3*IneqBelfs_Ch_3 + l4*IneqBelfs_Ch_4 + l5*IneqBelfs_Ch_5
ShoCCS_Ch_1 ~~ IneqBelfs_Ch_1
ShoCCS_Ch_2 ~~ IneqBelfs_Ch_2
ShoCCS_Ch_3 ~~ IneqBelfs_Ch_3
ShoCCS_Ch_4 ~~ IneqBelfs_Ch_4
ShoCCS_Ch_5 ~~ IneqBelfs_Ch_5
ShoCCS_Ch_1 ~ 1 #free the first item intercepts due to fit
IneqBelfs_Ch_1 ~ 1
ShoCCS_Ch_2 ~ 1
IneqBelfs_Ch_2 ~ 1
ShoCCS_Ch_3 ~ i3*1
IneqBelfs_Ch_3 ~ i3*1
ShoCCS_Ch_4 ~ i4*1
IneqBelfs_Ch_4 ~ i4*1
ShoCCS_Ch_5 ~ 1
IneqBelfs_Ch_5 ~ 1 #free the last item intercepts due to fit'
mx_cr_scalar_fit <- sem(mx_cr_scalar_model, data = USMexData, missing = "ML", estimator = "MLR", meanstructure = TRUE) ## Warning: lavaan->lav_data_full():
## some cases are empty and will be ignored: 17.summary(mx_cr_scalar_fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-20 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 36
## Number of equality constraints 6
##
## Used Total
## Number of observations 119 120
## Number of missing patterns 40
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 53.502 45.518
## Degrees of freedom 35 35
## P-value (Chi-square) 0.023 0.110
## Scaling correction factor 1.175
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 426.299 329.448
## Degrees of freedom 45 45
## P-value 0.000 0.000
## Scaling correction factor 1.294
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.951 0.963
## Tucker-Lewis Index (TLI) 0.938 0.952
##
## Robust Comparative Fit Index (CFI) 0.966
## Robust Tucker-Lewis Index (TLI) 0.956
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1554.992 -1554.992
## Scaling correction factor 0.964
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.167
## for the MLR correction
##
## Akaike (AIC) 3169.984 3169.984
## Bayesian (BIC) 3253.358 3253.358
## Sample-size adjusted Bayesian (SABIC) 3158.516 3158.516
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.067 0.050
## 90 Percent confidence interval - lower 0.025 0.000
## 90 Percent confidence interval - upper 0.101 0.085
## P-value H_0: RMSEA <= 0.050 0.212 0.468
## P-value H_0: RMSEA >= 0.080 0.283 0.084
##
## Robust RMSEA 0.061
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.110
## P-value H_0: Robust RMSEA <= 0.050 0.347
## P-value H_0: Robust RMSEA >= 0.080 0.295
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.081 0.081
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## US_CR =~
## ShCCS_C_1 1.000 0.938 0.724
## ShCCS_C_2 (l2) 1.276 0.134 9.512 0.000 1.197 0.825
## ShCCS_C_3 (l3) 0.971 0.147 6.594 0.000 0.910 0.705
## ShCCS_C_4 (l4) 1.082 0.184 5.885 0.000 1.015 0.758
## ShCCS_C_5 (l5) 1.178 0.215 5.482 0.000 1.105 0.729
## MX_CR =~
## InqBl_C_1 1.000 0.763 0.659
## InqBl_C_2 (l2) 1.276 0.134 9.512 0.000 0.974 0.828
## InqBl_C_3 (l3) 0.971 0.147 6.594 0.000 0.741 0.560
## InqBl_C_4 (l4) 1.082 0.184 5.885 0.000 0.825 0.604
## InqBl_C_5 (l5) 1.178 0.215 5.482 0.000 0.899 0.642
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 ~~
## .IneqBelfs_Ch_1 0.053 0.108 0.489 0.625 0.053 0.068
## .ShoCCS_Ch_2 ~~
## .IneqBelfs_Ch_2 0.030 0.087 0.343 0.732 0.030 0.055
## .ShoCCS_Ch_3 ~~
## .IneqBelfs_Ch_3 0.198 0.129 1.535 0.125 0.198 0.197
## .ShoCCS_Ch_4 ~~
## .IneqBelfs_Ch_4 0.386 0.131 2.950 0.003 0.386 0.405
## .ShoCCS_Ch_5 ~~
## .IneqBelfs_Ch_5 0.097 0.161 0.605 0.545 0.097 0.087
## US_CR ~~
## MX_CR 0.423 0.116 3.648 0.000 0.591 0.591
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShCCS_C_1 3.818 0.120 31.944 0.000 3.818 2.948
## .InqBl_C_1 3.037 0.115 26.417 0.000 3.037 2.625
## .ShCCS_C_2 3.464 0.136 25.453 0.000 3.464 2.388
## .InqBl_C_2 2.917 0.108 27.119 0.000 2.917 2.481
## .ShCCS_C_3 (i3) 2.974 0.104 28.517 0.000 2.974 2.302
## .InqBl_C_3 (i3) 2.974 0.104 28.517 0.000 2.974 2.247
## .ShCCS_C_4 (i4) 3.356 0.113 29.594 0.000 3.356 2.507
## .InqBl_C_4 (i4) 3.356 0.113 29.594 0.000 3.356 2.455
## .ShCCS_C_5 3.650 0.152 24.072 0.000 3.650 2.408
## .InqBl_C_5 2.863 0.129 22.247 0.000 2.863 2.045
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 0.799 0.170 4.706 0.000 0.799 0.476
## .ShoCCS_Ch_2 0.672 0.220 3.052 0.002 0.672 0.319
## .ShoCCS_Ch_3 0.840 0.173 4.848 0.000 0.840 0.503
## .ShoCCS_Ch_4 0.763 0.161 4.742 0.000 0.763 0.426
## .ShoCCS_Ch_5 1.078 0.219 4.933 0.000 1.078 0.469
## .IneqBelfs_Ch_1 0.756 0.169 4.476 0.000 0.756 0.565
## .IneqBelfs_Ch_2 0.434 0.142 3.064 0.002 0.434 0.314
## .IneqBelfs_Ch_3 1.202 0.214 5.623 0.000 1.202 0.687
## .IneqBelfs_Ch_4 1.187 0.182 6.511 0.000 1.187 0.635
## .IneqBelfs_Ch_5 1.153 0.223 5.171 0.000 1.153 0.588
## US_CR 0.879 0.230 3.820 0.000 1.000 1.000
## MX_CR 0.582 0.166 3.517 0.000 1.000 1.000
##
## R-Square:
## Estimate
## ShoCCS_Ch_1 0.524
## ShoCCS_Ch_2 0.681
## ShoCCS_Ch_3 0.497
## ShoCCS_Ch_4 0.574
## ShoCCS_Ch_5 0.531
## IneqBelfs_Ch_1 0.435
## IneqBelfs_Ch_2 0.686
## IneqBelfs_Ch_3 0.313
## IneqBelfs_Ch_4 0.365
## IneqBelfs_Ch_5 0.412#For key analyses: run a cfa model with two cr factors (US vs MEX), check via wald/lrt if their mean is sig. dif #CFA with Wald Test for MX vs US critical reflection
#CFA for Critical Reflection Measures
mx_cr_key_model <- 'US_CR =~ 1*ShoCCS_Ch_1 + l2*ShoCCS_Ch_2 + l3*ShoCCS_Ch_3 + l4*ShoCCS_Ch_4 + l5*ShoCCS_Ch_5
MX_CR =~ 1*IneqBelfs_Ch_1 + l2*IneqBelfs_Ch_2 + l3*IneqBelfs_Ch_3 + l4*IneqBelfs_Ch_4 + l5*IneqBelfs_Ch_5
ShoCCS_Ch_1 ~~ IneqBelfs_Ch_1
ShoCCS_Ch_2 ~~ IneqBelfs_Ch_2
ShoCCS_Ch_3 ~~ IneqBelfs_Ch_3
ShoCCS_Ch_4 ~~ IneqBelfs_Ch_4
ShoCCS_Ch_5 ~~ IneqBelfs_Ch_5
ShoCCS_Ch_1 ~ 1
IneqBelfs_Ch_1 ~ 1
ShoCCS_Ch_2 ~ 1
IneqBelfs_Ch_2 ~ 1
ShoCCS_Ch_3 ~ i3*1
IneqBelfs_Ch_3 ~ i3*1
ShoCCS_Ch_4 ~ i4*1
IneqBelfs_Ch_4 ~ i4*1
ShoCCS_Ch_5 ~ 1
IneqBelfs_Ch_5 ~ 1
US_CR ~ 0*1
MX_CR ~ m1*1'
mx_cr_key_fit <- sem(mx_cr_key_model, data = USMexData, missing = "ML", estimator = "MLR", meanstructure = TRUE) ## Warning: lavaan->lav_data_full():
## some cases are empty and will be ignored: 17.summary(mx_cr_key_fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-20 ended normally after 39 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 37
## Number of equality constraints 6
##
## Used Total
## Number of observations 119 120
## Number of missing patterns 40
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 49.553 41.736
## Degrees of freedom 34 34
## P-value (Chi-square) 0.041 0.170
## Scaling correction factor 1.187
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 426.299 329.448
## Degrees of freedom 45 45
## P-value 0.000 0.000
## Scaling correction factor 1.294
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.959 0.973
## Tucker-Lewis Index (TLI) 0.946 0.964
##
## Robust Comparative Fit Index (CFI) 0.974
## Robust Tucker-Lewis Index (TLI) 0.965
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1553.017 -1553.017
## Scaling correction factor 0.959
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 1.167
## for the MLR correction
##
## Akaike (AIC) 3168.035 3168.035
## Bayesian (BIC) 3254.188 3254.188
## Sample-size adjusted Bayesian (SABIC) 3156.185 3156.185
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.062 0.044
## 90 Percent confidence interval - lower 0.013 0.000
## 90 Percent confidence interval - upper 0.097 0.081
## P-value H_0: RMSEA <= 0.050 0.282 0.573
## P-value H_0: RMSEA >= 0.080 0.222 0.053
##
## Robust RMSEA 0.055
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.106
## P-value H_0: Robust RMSEA <= 0.050 0.420
## P-value H_0: Robust RMSEA >= 0.080 0.240
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.076 0.076
##
## Parameter Estimates:
##
## Standard errors Sandwich
## Information bread Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## US_CR =~
## ShCCS_C_1 1.000 0.934 0.722
## ShCCS_C_2 (l2) 1.276 0.134 9.498 0.000 1.192 0.823
## ShCCS_C_3 (l3) 0.973 0.148 6.587 0.000 0.908 0.706
## ShCCS_C_4 (l4) 1.067 0.184 5.793 0.000 0.997 0.751
## ShCCS_C_5 (l5) 1.182 0.215 5.502 0.000 1.104 0.729
## MX_CR =~
## InqBl_C_1 1.000 0.763 0.659
## InqBl_C_2 (l2) 1.276 0.134 9.498 0.000 0.973 0.827
## InqBl_C_3 (l3) 0.973 0.148 6.587 0.000 0.742 0.562
## InqBl_C_4 (l4) 1.067 0.184 5.793 0.000 0.814 0.598
## InqBl_C_5 (l5) 1.182 0.215 5.502 0.000 0.901 0.643
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 ~~
## .IneqBelfs_Ch_1 0.050 0.108 0.466 0.641 0.050 0.064
## .ShoCCS_Ch_2 ~~
## .IneqBelfs_Ch_2 0.027 0.087 0.304 0.761 0.027 0.049
## .ShoCCS_Ch_3 ~~
## .IneqBelfs_Ch_3 0.205 0.129 1.583 0.113 0.205 0.206
## .ShoCCS_Ch_4 ~~
## .IneqBelfs_Ch_4 0.387 0.130 2.967 0.003 0.387 0.405
## .ShoCCS_Ch_5 ~~
## .IneqBelfs_Ch_5 0.098 0.161 0.612 0.541 0.098 0.088
## US_CR ~~
## MX_CR 0.431 0.116 3.701 0.000 0.605 0.605
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShCCS_C_1 3.745 0.125 30.009 0.000 3.745 2.895
## .InqBl_C_1 2.844 0.151 18.801 0.000 2.844 2.457
## .ShCCS_C_2 3.370 0.139 24.174 0.000 3.370 2.327
## .InqBl_C_2 2.672 0.155 17.200 0.000 2.672 2.271
## .ShCCS_C_3 (i3) 2.852 0.117 24.465 0.000 2.852 2.218
## .InqBl_C_3 (i3) 2.852 0.117 24.465 0.000 2.852 2.161
## .ShCCS_C_4 (i4) 3.228 0.126 25.583 0.000 3.228 2.431
## .InqBl_C_4 (i4) 3.228 0.126 25.583 0.000 3.228 2.372
## .ShCCS_C_5 3.563 0.149 23.952 0.000 3.563 2.355
## .InqBl_C_5 2.637 0.179 14.769 0.000 2.637 1.881
## US_CR 0.000 0.000 0.000
## MX_CR (m1) 0.218 0.112 1.934 0.053 0.285 0.285
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ShoCCS_Ch_1 0.801 0.170 4.723 0.000 0.801 0.479
## .ShoCCS_Ch_2 0.678 0.223 3.039 0.002 0.678 0.323
## .ShoCCS_Ch_3 0.829 0.172 4.828 0.000 0.829 0.501
## .ShoCCS_Ch_4 0.769 0.160 4.814 0.000 0.769 0.436
## .ShoCCS_Ch_5 1.072 0.218 4.914 0.000 1.072 0.468
## .IneqBelfs_Ch_1 0.758 0.169 4.474 0.000 0.758 0.566
## .IneqBelfs_Ch_2 0.437 0.143 3.053 0.002 0.437 0.315
## .IneqBelfs_Ch_3 1.191 0.208 5.733 0.000 1.191 0.684
## .IneqBelfs_Ch_4 1.188 0.181 6.574 0.000 1.188 0.642
## .IneqBelfs_Ch_5 1.152 0.223 5.162 0.000 1.152 0.586
## US_CR 0.872 0.228 3.821 0.000 1.000 1.000
## MX_CR 0.582 0.165 3.518 0.000 1.000 1.000
##
## R-Square:
## Estimate
## ShoCCS_Ch_1 0.521
## ShoCCS_Ch_2 0.677
## ShoCCS_Ch_3 0.499
## ShoCCS_Ch_4 0.564
## ShoCCS_Ch_5 0.532
## IneqBelfs_Ch_1 0.434
## IneqBelfs_Ch_2 0.685
## IneqBelfs_Ch_3 0.316
## IneqBelfs_Ch_4 0.358
## IneqBelfs_Ch_5 0.414#Wald test the means on cr
lavTestWald(mx_cr_key_fit, constraints = "m1 == 0")## $stat
## [1] 3.741744
##
## $df
## [1] 1
##
## $p.value
## [1] 0.05306904
##
## $se
## [1] "robust.huber.white"