Pilot Study: CFA Invariance Testing
R. Noah Padgett
2020-11-16
Last updated: 2021-02-18
Checks: 6 1
Knit directory: pools-projects/
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Data
source("code/load_packages.R")-- Attaching packages --------------------------------------- tidyverse 1.3.0 --v ggplot2 3.3.3 v purrr 0.3.4
v tibble 3.0.5 v dplyr 1.0.3
v tidyr 1.1.2 v stringr 1.4.0
v readr 1.4.0 v forcats 0.5.0-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
Attaching package: 'data.table'The following objects are masked from 'package:dplyr':
between, first, lastThe following object is masked from 'package:purrr':
transposeThis is lavaan 0.6-7lavaan is BETA software! Please report any bugs. ###############################################################################This is semTools 0.5-4All users of R (or SEM) are invited to submit functions or ideas for functions.###############################################################################
Attaching package: 'semTools'The following object is masked from 'package:readr':
clipboardThis is MIIVsem 0.5.5MIIVsem is BETA software! Please report any bugs. #################################################################This is simsem 0.5-15simsem is BETA software! Please report any bugs.simsem was first developed at the University of Kansas Center forResearch Methods and Data Analysis, under NSF Grant 1053160.#################################################################
Attaching package: 'simsem'The following object is masked from 'package:lavaan':
inspectLoading required package: multilevelLoading required package: nlme
Attaching package: 'nlme'The following object is masked from 'package:dplyr':
collapseLoading required package: MASS
Attaching package: 'MASS'The following object is masked from 'package:patchwork':
areaThe following object is masked from 'package:dplyr':
select
Attaching package: 'psychometric'The following object is masked from 'package:ggplot2':
alpha
Attaching package: 'psych'The following object is masked from 'package:psychometric':
alphaThe following object is masked from 'package:simsem':
simThe following object is masked from 'package:semTools':
skewThe following object is masked from 'package:lavaan':
cor2covThe following objects are masked from 'package:ggplot2':
%+%, alphaLoading required package: lattice
Attaching package: 'nFactors'The following object is masked from 'package:lattice':
parallel
Attaching package: 'kableExtra'The following object is masked from 'package:dplyr':
group_rowsmydata1 <- read.table("data/data-2020-11-16/pools_data_split1_2020_11_16.txt", sep="\t", header=T)
mydata2 <- read.table("data/data-2020-11-16/pools_data_split2_2020_11_16.txt", sep="\t", header=T)
mydata <- full_join(mydata1, mydata2)Joining, by = c("ID", "Progress", "DurationSeconds", "Finished", "class", "teach", "Q4_1", "Q4_2", "Q4_3", "Q4_4", "Q4_5", "Q4_6", "Q4_7", "Q4_8", "Q4_9", "Q4_10", "Q4_11", "Q4_12", "Q4_13", "Q4_14", "Q4_15", "Q4_16", "Q4_17", "Q4_18", "Q4_19", "Q5_1", "Q5_2", "Q5_3", "Q5_4", "Q5_5", "Q5_6", "Q5_7", "Q5_8", "Q5_9", "Q5_10", "Q5_11", "Q5_12", "Q6_1", "Q6_2", "Q6_3", "Q6_4", "Q6_5", "Q6_6", "Q6_7", "Q6_8", "Q6_9", "Q6_10", "Q6_11", "Q7_1", "Q7_2", "Q7_3", "Q7_4", "Q7_5", "Q7_6", "Q7_7", "Q7_8", "Q7_9", "Q7_10", "Q7_11", "Q7_12", "Q7_13", "Q7_14", "Q7_15", "version", "random.split")# transform responses to (-2, 2) scale
mydata[, 7:63] <- apply(mydata[,7:63], 2, function(x){x-3})
mydata$teach <- factor(mydata$teach, levels=c(1, 2), labels=c("No to Online Teaching Experience", "Yes to Online Teaching Experience"))Data Summary
use.var <- c(paste0("Q4_",c(3:5,9, 11, 15, 18)), #13
paste0("Q5_",c(1:3,5:6, 12)), #8-> 14- 21
paste0("Q6_",c(2, 5:8, 11)), #9 -> 22-30
paste0("Q7_",c(2, 4:5, 7:8, 14))) #31-38
psych::describe(
mydata[, use.var]
) vars n mean sd median trimmed mad min max range skew kurtosis se
Q4_3 1 640 -0.50 0.84 0 -0.50 1.48 -2 2 4 0.25 0.30 0.03
Q4_4 2 640 -0.41 0.83 0 -0.39 0.00 -2 2 4 -0.08 0.22 0.03
Q4_5 3 640 -0.72 0.88 -1 -0.77 1.48 -2 2 4 0.55 0.18 0.03
Q4_9 4 640 -0.57 0.99 -1 -0.62 1.48 -2 2 4 0.50 -0.20 0.04
Q4_11 5 640 -0.51 0.95 -1 -0.54 1.48 -2 2 4 0.25 -0.24 0.04
Q4_15 6 640 -0.66 0.90 -1 -0.71 1.48 -2 2 4 0.39 -0.08 0.04
Q4_18 7 640 -0.68 0.81 -1 -0.70 1.48 -2 2 4 0.45 0.32 0.03
Q5_1 8 640 -0.42 0.95 0 -0.42 1.48 -2 2 4 0.18 -0.38 0.04
Q5_2 9 640 -0.04 1.02 0 0.01 1.48 -2 2 4 -0.21 -0.52 0.04
Q5_3 10 640 -0.37 1.04 0 -0.39 1.48 -2 2 4 0.18 -0.57 0.04
Q5_5 11 640 0.49 1.05 1 0.56 1.48 -2 2 4 -0.75 -0.09 0.04
Q5_6 12 640 -0.11 0.91 0 -0.07 1.48 -2 2 4 -0.19 0.00 0.04
Q5_12 13 640 -0.16 0.98 0 -0.12 1.48 -2 2 4 -0.17 -0.16 0.04
Q6_2 14 640 -0.94 0.91 -1 -1.04 1.48 -2 2 4 0.88 0.64 0.04
Q6_5 15 640 -0.63 1.07 -1 -0.71 1.48 -2 2 4 0.66 -0.19 0.04
Q6_6 16 640 -1.18 0.80 -1 -1.28 1.48 -2 2 4 1.12 1.83 0.03
Q6_7 17 640 -0.86 0.88 -1 -0.93 1.48 -2 2 4 0.64 0.24 0.03
Q6_8 18 640 -0.87 0.87 -1 -0.94 1.48 -2 2 4 0.71 0.53 0.03
Q6_11 19 640 -0.25 0.97 0 -0.23 1.48 -2 2 4 -0.06 -0.13 0.04
Q7_2 20 640 -0.33 0.87 0 -0.31 0.00 -2 2 4 -0.32 0.10 0.03
Q7_4 21 640 -0.19 0.96 0 -0.16 1.48 -2 2 4 -0.05 -0.15 0.04
Q7_5 22 640 -0.17 0.95 0 -0.14 0.00 -2 2 4 -0.14 0.09 0.04
Q7_7 23 640 0.61 1.04 1 0.71 0.00 -2 2 4 -0.90 0.29 0.04
Q7_8 24 640 -0.19 0.91 0 -0.14 0.00 -2 2 4 -0.21 0.12 0.04
Q7_14 25 640 0.59 1.01 1 0.67 1.48 -2 2 4 -0.75 0.22 0.04psych::describeBy(
mydata[, use.var],group = mydata$teach
)
Descriptive statistics by group
group: No to Online Teaching Experience
vars n mean sd median trimmed mad min max range skew kurtosis se
Q4_3 1 379 -0.39 0.86 0 -0.39 1.48 -2 2 4 0.00 0.19 0.04
Q4_4 2 379 -0.38 0.83 0 -0.37 0.00 -2 2 4 -0.06 0.23 0.04
Q4_5 3 379 -0.66 0.88 -1 -0.72 1.48 -2 2 4 0.43 -0.09 0.05
Q4_9 4 379 -0.50 1.02 -1 -0.54 1.48 -2 2 4 0.43 -0.44 0.05
Q4_11 5 379 -0.42 0.97 0 -0.44 1.48 -2 2 4 0.08 -0.25 0.05
Q4_15 6 379 -0.63 0.93 -1 -0.68 1.48 -2 2 4 0.33 -0.33 0.05
Q4_18 7 379 -0.64 0.81 -1 -0.66 1.48 -2 2 4 0.32 0.07 0.04
Q5_1 8 379 -0.31 0.99 0 -0.29 1.48 -2 2 4 -0.07 -0.51 0.05
Q5_2 9 379 -0.01 1.03 0 0.04 1.48 -2 2 4 -0.24 -0.48 0.05
Q5_3 10 379 -0.26 1.06 0 -0.26 1.48 -2 2 4 0.02 -0.59 0.05
Q5_5 11 379 0.47 1.10 1 0.55 1.48 -2 2 4 -0.69 -0.25 0.06
Q5_6 12 379 -0.08 0.95 0 -0.03 1.48 -2 2 4 -0.24 -0.14 0.05
Q5_12 13 379 -0.15 0.99 0 -0.11 1.48 -2 2 4 -0.18 -0.22 0.05
Q6_2 14 379 -0.91 0.92 -1 -1.01 1.48 -2 2 4 0.76 0.20 0.05
Q6_5 15 379 -0.58 1.11 -1 -0.66 1.48 -2 2 4 0.64 -0.29 0.06
Q6_6 16 379 -1.16 0.84 -1 -1.27 1.48 -2 2 4 1.16 1.80 0.04
Q6_7 17 379 -0.83 0.91 -1 -0.90 1.48 -2 2 4 0.57 -0.04 0.05
Q6_8 18 379 -0.82 0.90 -1 -0.90 1.48 -2 2 4 0.72 0.41 0.05
Q6_11 19 379 -0.20 1.00 0 -0.18 1.48 -2 2 4 -0.14 -0.28 0.05
Q7_2 20 379 -0.30 0.88 0 -0.28 0.00 -2 2 4 -0.23 0.16 0.05
Q7_4 21 379 -0.16 1.00 0 -0.13 1.48 -2 2 4 -0.11 -0.26 0.05
Q7_5 22 379 -0.16 0.99 0 -0.13 1.48 -2 2 4 -0.09 -0.16 0.05
Q7_7 23 379 0.57 1.10 1 0.68 1.48 -2 2 4 -0.82 -0.03 0.06
Q7_8 24 379 -0.17 0.94 0 -0.14 0.00 -2 2 4 -0.12 0.06 0.05
Q7_14 25 379 0.63 1.02 1 0.71 1.48 -2 2 4 -0.74 0.10 0.05
------------------------------------------------------------
group: Yes to Online Teaching Experience
vars n mean sd median trimmed mad min max range skew kurtosis se
Q4_3 1 261 -0.66 0.78 -1 -0.66 0.00 -2 2 4 0.62 0.97 0.05
Q4_4 2 261 -0.46 0.84 0 -0.43 0.00 -2 2 4 -0.11 0.16 0.05
Q4_5 3 261 -0.79 0.86 -1 -0.85 0.00 -2 2 4 0.73 0.67 0.05
Q4_9 4 261 -0.67 0.92 -1 -0.74 1.48 -2 2 4 0.58 0.22 0.06
Q4_11 5 261 -0.64 0.91 -1 -0.70 1.48 -2 2 4 0.49 -0.05 0.06
Q4_15 6 261 -0.72 0.85 -1 -0.74 1.48 -2 2 4 0.47 0.36 0.05
Q4_18 7 261 -0.74 0.80 -1 -0.76 0.00 -2 2 4 0.63 0.78 0.05
Q5_1 8 261 -0.58 0.88 -1 -0.62 1.48 -2 2 4 0.55 0.24 0.05
Q5_2 9 261 -0.09 1.02 0 -0.03 1.48 -2 2 4 -0.17 -0.59 0.06
Q5_3 10 261 -0.53 1.00 -1 -0.57 1.48 -2 2 4 0.41 -0.37 0.06
Q5_5 11 261 0.52 0.97 1 0.58 0.00 -2 2 4 -0.84 0.11 0.06
Q5_6 12 261 -0.15 0.86 0 -0.14 0.00 -2 2 4 -0.10 0.24 0.05
Q5_12 13 261 -0.17 0.98 0 -0.14 0.00 -2 2 4 -0.15 -0.10 0.06
Q6_2 14 261 -0.98 0.89 -1 -1.10 0.00 -2 2 4 1.08 1.41 0.06
Q6_5 15 261 -0.71 1.00 -1 -0.79 1.48 -2 2 4 0.64 -0.14 0.06
Q6_6 16 261 -1.20 0.75 -1 -1.30 0.00 -2 2 4 1.00 1.59 0.05
Q6_7 17 261 -0.91 0.82 -1 -0.97 0.00 -2 2 4 0.73 0.73 0.05
Q6_8 18 261 -0.93 0.82 -1 -0.98 0.00 -2 2 4 0.64 0.54 0.05
Q6_11 19 261 -0.31 0.93 0 -0.31 0.00 -2 2 4 0.05 0.17 0.06
Q7_2 20 261 -0.38 0.85 0 -0.34 0.00 -2 2 4 -0.46 -0.08 0.05
Q7_4 21 261 -0.23 0.90 0 -0.22 1.48 -2 2 4 0.03 0.01 0.06
Q7_5 22 261 -0.18 0.88 0 -0.14 0.00 -2 2 4 -0.24 0.49 0.05
Q7_7 23 261 0.67 0.94 1 0.75 0.00 -2 2 4 -1.01 0.77 0.06
Q7_8 24 261 -0.20 0.85 0 -0.15 0.00 -2 2 4 -0.39 0.11 0.05
Q7_14 25 261 0.52 1.00 1 0.60 1.48 -2 2 4 -0.77 0.40 0.06CFA
The hypothesized four-factor solution is shown below.
The above model can be convert to code using the below model.
mod1 <- "
EL =~ Q4_3 + Q4_4 + Q4_5 + Q4_9 + Q4_11 + Q4_15 + Q4_18
SC =~ Q5_1 + Q5_2 + Q5_3 + Q5_5 + Q5_6 + Q5_12
IN =~ Q6_2 + Q6_5 + Q6_6 + Q6_7 + Q6_8 + Q6_11
EN =~ Q7_2 + Q7_4 + Q7_5 + Q7_7 + Q7_8 + Q7_14
EL ~~ EL + SC + IN + EN
SC ~~ SC + IN + EN
IN ~~ IN + EN
EN ~~ EN
Q4_3 ~~ Q4_4
Q5_5 + Q5_2 ~~ Q5_6
Q6_2 ~~ Q6_8
Q7_7 ~~ Q7_8
"
fit0 <- lavaan::cfa(mod1, data=mydata, estimator = "MLM",group = "teach")
summary(fit0, standardized=T, fit.measures=T)lavaan 0.6-7 ended normally after 81 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 172
Number of observations per group:
No to Online Teaching Experience 379
Yes to Online Teaching Experience 261
Model Test User Model:
Standard Robust
Test Statistic 1353.467 1009.595
Degrees of freedom 528 528
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.341
Satorra-Bentler correction
Test statistic for each group:
No to Online Teaching Experience 679.691 507.004
Yes to Online Teaching Experience 673.775 502.591
Model Test Baseline Model:
Test statistic 9685.276 6600.281
Degrees of freedom 600 600
P-value 0.000 0.000
Scaling correction factor 1.467
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.909 0.920
Tucker-Lewis Index (TLI) 0.897 0.909
Robust Comparative Fit Index (CFI) 0.927
Robust Tucker-Lewis Index (TLI) 0.917
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -17344.367 -17344.367
Loglikelihood unrestricted model (H1) -16667.634 -16667.634
Akaike (AIC) 35032.734 35032.734
Bayesian (BIC) 35800.106 35800.106
Sample-size adjusted Bayesian (BIC) 35254.018 35254.018
Root Mean Square Error of Approximation:
RMSEA 0.070 0.053
90 Percent confidence interval - lower 0.065 0.049
90 Percent confidence interval - upper 0.075 0.058
P-value RMSEA <= 0.05 0.000 0.097
Robust RMSEA 0.062
90 Percent confidence interval - lower 0.056
90 Percent confidence interval - upper 0.068
Standardized Root Mean Square Residual:
SRMR 0.061 0.061
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Structured
Group 1 [No to Online Teaching Experience]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL =~
Q4_3 1.000 0.633 0.733
Q4_4 1.054 0.050 21.047 0.000 0.667 0.806
Q4_5 1.044 0.067 15.602 0.000 0.661 0.750
Q4_9 1.118 0.082 13.709 0.000 0.707 0.691
Q4_11 1.211 0.072 16.759 0.000 0.766 0.790
Q4_15 1.052 0.064 16.487 0.000 0.665 0.715
Q4_18 1.044 0.062 16.710 0.000 0.660 0.812
SC =~
Q5_1 1.000 0.674 0.683
Q5_2 1.085 0.080 13.556 0.000 0.731 0.713
Q5_3 1.185 0.086 13.752 0.000 0.799 0.754
Q5_5 0.982 0.104 9.414 0.000 0.662 0.604
Q5_6 1.059 0.086 12.326 0.000 0.714 0.753
Q5_12 1.053 0.084 12.525 0.000 0.710 0.721
IN =~
Q6_2 1.000 0.665 0.721
Q6_5 0.864 0.094 9.150 0.000 0.575 0.520
Q6_6 1.043 0.069 15.213 0.000 0.694 0.828
Q6_7 1.170 0.081 14.387 0.000 0.779 0.853
Q6_8 1.069 0.069 15.581 0.000 0.712 0.788
Q6_11 1.070 0.086 12.469 0.000 0.712 0.712
EN =~
Q7_2 1.000 0.671 0.760
Q7_4 1.104 0.074 14.926 0.000 0.741 0.742
Q7_5 1.159 0.076 15.323 0.000 0.778 0.787
Q7_7 1.160 0.090 12.867 0.000 0.778 0.706
Q7_8 1.067 0.068 15.704 0.000 0.716 0.760
Q7_14 0.886 0.093 9.485 0.000 0.594 0.581
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL ~~
SC 0.312 0.041 7.539 0.000 0.731 0.731
IN 0.324 0.042 7.650 0.000 0.771 0.771
EN 0.337 0.043 7.756 0.000 0.794 0.794
SC ~~
IN 0.314 0.041 7.641 0.000 0.700 0.700
EN 0.366 0.046 8.037 0.000 0.809 0.809
IN ~~
EN 0.337 0.044 7.655 0.000 0.755 0.755
.Q4_3 ~~
.Q4_4 0.131 0.022 5.906 0.000 0.131 0.454
.Q5_5 ~~
.Q5_6 0.176 0.040 4.392 0.000 0.176 0.324
.Q5_2 ~~
.Q5_6 -0.031 0.030 -1.037 0.300 -0.031 -0.069
.Q6_2 ~~
.Q6_8 0.080 0.027 2.945 0.003 0.080 0.224
.Q7_7 ~~
.Q7_8 0.118 0.043 2.747 0.006 0.118 0.246
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Q4_3 -0.391 0.044 -8.810 0.000 -0.391 -0.453
.Q4_4 -0.383 0.043 -9.001 0.000 -0.383 -0.462
.Q4_5 -0.665 0.045 -14.686 0.000 -0.665 -0.754
.Q4_9 -0.504 0.053 -9.589 0.000 -0.504 -0.493
.Q4_11 -0.420 0.050 -8.423 0.000 -0.420 -0.433
.Q4_15 -0.628 0.048 -13.130 0.000 -0.628 -0.674
.Q4_18 -0.644 0.042 -15.404 0.000 -0.644 -0.791
.Q5_1 -0.306 0.051 -6.043 0.000 -0.306 -0.310
.Q5_2 -0.013 0.053 -0.250 0.802 -0.013 -0.013
.Q5_3 -0.264 0.054 -4.847 0.000 -0.264 -0.249
.Q5_5 0.472 0.056 8.397 0.000 0.472 0.431
.Q5_6 -0.082 0.049 -1.680 0.093 -0.082 -0.086
.Q5_12 -0.148 0.051 -2.920 0.003 -0.148 -0.150
.Q6_2 -0.905 0.047 -19.097 0.000 -0.905 -0.981
.Q6_5 -0.575 0.057 -10.136 0.000 -0.575 -0.521
.Q6_6 -1.158 0.043 -26.891 0.000 -1.158 -1.381
.Q6_7 -0.828 0.047 -17.668 0.000 -0.828 -0.908
.Q6_8 -0.823 0.046 -17.745 0.000 -0.823 -0.911
.Q6_11 -0.203 0.051 -3.953 0.000 -0.203 -0.203
.Q7_2 -0.298 0.045 -6.576 0.000 -0.298 -0.338
.Q7_4 -0.156 0.051 -3.035 0.002 -0.156 -0.156
.Q7_5 -0.158 0.051 -3.117 0.002 -0.158 -0.160
.Q7_7 0.573 0.057 10.109 0.000 0.573 0.519
.Q7_8 -0.174 0.048 -3.598 0.000 -0.174 -0.185
.Q7_14 0.628 0.053 11.952 0.000 0.628 0.614
EL 0.000 0.000 0.000
SC 0.000 0.000 0.000
IN 0.000 0.000 0.000
EN 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL 0.400 0.057 7.076 0.000 1.000 1.000
SC 0.454 0.062 7.354 0.000 1.000 1.000
IN 0.443 0.062 7.180 0.000 1.000 1.000
EN 0.450 0.059 7.648 0.000 1.000 1.000
.Q4_3 0.344 0.032 10.819 0.000 0.344 0.463
.Q4_4 0.240 0.021 11.298 0.000 0.240 0.351
.Q4_5 0.340 0.036 9.537 0.000 0.340 0.438
.Q4_9 0.546 0.046 12.006 0.000 0.546 0.522
.Q4_11 0.354 0.033 10.572 0.000 0.354 0.376
.Q4_15 0.424 0.038 11.305 0.000 0.424 0.489
.Q4_18 0.226 0.023 9.807 0.000 0.226 0.341
.Q5_1 0.518 0.043 12.119 0.000 0.518 0.533
.Q5_2 0.518 0.053 9.768 0.000 0.518 0.492
.Q5_3 0.485 0.047 10.295 0.000 0.485 0.432
.Q5_5 0.761 0.065 11.673 0.000 0.761 0.635
.Q5_6 0.389 0.041 9.473 0.000 0.389 0.433
.Q5_12 0.466 0.047 9.965 0.000 0.466 0.481
.Q6_2 0.408 0.044 9.325 0.000 0.408 0.480
.Q6_5 0.890 0.080 11.073 0.000 0.890 0.729
.Q6_6 0.221 0.024 9.336 0.000 0.221 0.315
.Q6_7 0.227 0.026 8.723 0.000 0.227 0.273
.Q6_8 0.309 0.035 8.965 0.000 0.309 0.379
.Q6_11 0.494 0.044 11.143 0.000 0.494 0.493
.Q7_2 0.329 0.033 9.970 0.000 0.329 0.422
.Q7_4 0.448 0.046 9.799 0.000 0.448 0.450
.Q7_5 0.373 0.041 9.065 0.000 0.373 0.381
.Q7_7 0.610 0.056 10.941 0.000 0.610 0.502
.Q7_8 0.375 0.042 9.014 0.000 0.375 0.422
.Q7_14 0.693 0.054 12.899 0.000 0.693 0.662
Group 2 [Yes to Online Teaching Experience]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL =~
Q4_3 1.000 0.554 0.709
Q4_4 1.026 0.088 11.643 0.000 0.568 0.679
Q4_5 1.093 0.099 11.077 0.000 0.605 0.703
Q4_9 1.108 0.123 9.038 0.000 0.613 0.665
Q4_11 1.242 0.125 9.942 0.000 0.688 0.756
Q4_15 1.226 0.112 10.930 0.000 0.679 0.798
Q4_18 1.210 0.096 12.568 0.000 0.670 0.843
SC =~
Q5_1 1.000 0.598 0.684
Q5_2 1.266 0.125 10.128 0.000 0.758 0.747
Q5_3 1.152 0.117 9.867 0.000 0.689 0.692
Q5_5 0.901 0.127 7.099 0.000 0.539 0.556
Q5_6 1.049 0.108 9.682 0.000 0.628 0.732
Q5_12 0.929 0.110 8.433 0.000 0.556 0.569
IN =~
Q6_2 1.000 0.520 0.586
Q6_5 0.933 0.156 5.966 0.000 0.485 0.486
Q6_6 1.105 0.133 8.277 0.000 0.575 0.770
Q6_7 1.333 0.171 7.806 0.000 0.694 0.852
Q6_8 1.276 0.141 9.074 0.000 0.664 0.816
Q6_11 1.092 0.173 6.327 0.000 0.568 0.611
EN =~
Q7_2 1.000 0.628 0.738
Q7_4 0.931 0.080 11.649 0.000 0.585 0.649
Q7_5 1.189 0.098 12.093 0.000 0.747 0.851
Q7_7 0.849 0.131 6.470 0.000 0.533 0.568
Q7_8 0.918 0.108 8.518 0.000 0.577 0.679
Q7_14 0.749 0.136 5.530 0.000 0.471 0.473
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL ~~
SC 0.232 0.049 4.764 0.000 0.700 0.700
IN 0.178 0.035 5.137 0.000 0.616 0.616
EN 0.209 0.035 5.952 0.000 0.601 0.601
SC ~~
IN 0.164 0.038 4.363 0.000 0.526 0.526
EN 0.271 0.042 6.511 0.000 0.720 0.720
IN ~~
EN 0.205 0.040 5.141 0.000 0.627 0.627
.Q4_3 ~~
.Q4_4 0.086 0.027 3.219 0.001 0.086 0.253
.Q5_5 ~~
.Q5_6 0.131 0.039 3.380 0.001 0.131 0.277
.Q5_2 ~~
.Q5_6 -0.014 0.035 -0.387 0.699 -0.014 -0.034
.Q6_2 ~~
.Q6_8 0.079 0.041 1.901 0.057 0.079 0.232
.Q7_7 ~~
.Q7_8 0.104 0.045 2.278 0.023 0.104 0.215
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Q4_3 -0.655 0.048 -13.563 0.000 -0.655 -0.840
.Q4_4 -0.460 0.052 -8.875 0.000 -0.460 -0.549
.Q4_5 -0.789 0.053 -14.812 0.000 -0.789 -0.917
.Q4_9 -0.667 0.057 -11.678 0.000 -0.667 -0.723
.Q4_11 -0.644 0.056 -11.435 0.000 -0.644 -0.708
.Q4_15 -0.716 0.053 -13.602 0.000 -0.716 -0.842
.Q4_18 -0.736 0.049 -14.958 0.000 -0.736 -0.926
.Q5_1 -0.579 0.054 -10.687 0.000 -0.579 -0.662
.Q5_2 -0.088 0.063 -1.402 0.161 -0.088 -0.087
.Q5_3 -0.529 0.062 -8.574 0.000 -0.529 -0.531
.Q5_5 0.521 0.060 8.686 0.000 0.521 0.538
.Q5_6 -0.153 0.053 -2.888 0.004 -0.153 -0.179
.Q5_12 -0.169 0.060 -2.791 0.005 -0.169 -0.173
.Q6_2 -0.985 0.055 -17.909 0.000 -0.985 -1.109
.Q6_5 -0.709 0.062 -11.476 0.000 -0.709 -0.710
.Q6_6 -1.199 0.046 -25.936 0.000 -1.199 -1.605
.Q6_7 -0.912 0.050 -18.096 0.000 -0.912 -1.120
.Q6_8 -0.931 0.050 -18.488 0.000 -0.931 -1.144
.Q6_11 -0.310 0.058 -5.390 0.000 -0.310 -0.334
.Q7_2 -0.375 0.053 -7.125 0.000 -0.375 -0.441
.Q7_4 -0.230 0.056 -4.119 0.000 -0.230 -0.255
.Q7_5 -0.184 0.054 -3.384 0.001 -0.184 -0.209
.Q7_7 0.667 0.058 11.473 0.000 0.667 0.710
.Q7_8 -0.203 0.053 -3.863 0.000 -0.203 -0.239
.Q7_14 0.525 0.062 8.511 0.000 0.525 0.527
EL 0.000 0.000 0.000
SC 0.000 0.000 0.000
IN 0.000 0.000 0.000
EN 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
EL 0.306 0.062 4.975 0.000 1.000 1.000
SC 0.358 0.067 5.331 0.000 1.000 1.000
IN 0.271 0.073 3.730 0.000 1.000 1.000
EN 0.395 0.062 6.325 0.000 1.000 1.000
.Q4_3 0.303 0.038 8.035 0.000 0.303 0.497
.Q4_4 0.378 0.033 11.609 0.000 0.378 0.540
.Q4_5 0.375 0.051 7.302 0.000 0.375 0.506
.Q4_9 0.474 0.054 8.706 0.000 0.474 0.558
.Q4_11 0.354 0.050 7.020 0.000 0.354 0.428
.Q4_15 0.263 0.033 7.982 0.000 0.263 0.364
.Q4_18 0.183 0.026 6.991 0.000 0.183 0.290
.Q5_1 0.407 0.039 10.341 0.000 0.407 0.532
.Q5_2 0.456 0.059 7.705 0.000 0.456 0.443
.Q5_3 0.517 0.065 7.992 0.000 0.517 0.521
.Q5_5 0.648 0.062 10.506 0.000 0.648 0.690
.Q5_6 0.342 0.048 7.056 0.000 0.342 0.465
.Q5_12 0.644 0.071 9.059 0.000 0.644 0.676
.Q6_2 0.518 0.068 7.630 0.000 0.518 0.657
.Q6_5 0.760 0.096 7.925 0.000 0.760 0.763
.Q6_6 0.227 0.029 7.803 0.000 0.227 0.407
.Q6_7 0.182 0.034 5.379 0.000 0.182 0.274
.Q6_8 0.221 0.046 4.829 0.000 0.221 0.333
.Q6_11 0.542 0.061 8.932 0.000 0.542 0.627
.Q7_2 0.330 0.045 7.413 0.000 0.330 0.455
.Q7_4 0.471 0.055 8.621 0.000 0.471 0.579
.Q7_5 0.213 0.037 5.716 0.000 0.213 0.276
.Q7_7 0.597 0.067 8.843 0.000 0.597 0.677
.Q7_8 0.388 0.053 7.316 0.000 0.388 0.539
.Q7_14 0.771 0.072 10.743 0.000 0.771 0.777library(semTools)
## fit indices of interest for multiparameter omnibus test
myAFIs <- c("chisq","cfi","rmsea","srmr","aic", "bic")
## Use only 20 permutations for a demo. In practice,
## use > 1000 to reduce sampling variability of estimated p values
## test configural invariance
set.seed(12345)
out.config <- permuteMeasEq(nPermute = 1000, con = fit0,AFIs = myAFIs)
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|==================================================| 100%summary(out.config)Omnibus p value based on parametric chi-squared difference test:
Chisq diff Df diff Pr(>Chisq)
1009.595 528.000 0.000
Omnibus p values based on nonparametric permutation method:
AFI.Difference p.value
chisq 1353.467 0.248
cfi 0.909 0.241
rmsea 0.070 0.248
srmr 0.061 0.065
aic 35032.734 0.999
bic 35800.106 0.999hist(out.config, AFI = "chisq", nd = 2, alpha = .01,
legendArgs = list(x = "top"))
hist(out.config, AFI = "cfi", legendArgs = list(x = "topright"))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] xtable_1.8-4 kableExtra_1.3.1 readxl_1.3.1 coda_0.19-4
[5] nFactors_2.4.1 lattice_0.20-41 psych_2.0.12 psychometric_2.2
[9] multilevel_2.6 MASS_7.3-53 nlme_3.1-151 mvtnorm_1.1-1
[13] ggcorrplot_0.1.3 naniar_0.6.0 simsem_0.5-15 lslx_0.6.10
[17] MIIVsem_0.5.5 lavaanPlot_0.5.1 semTools_0.5-4 lavaan_0.6-7
[21] data.table_1.13.6 patchwork_1.1.1 forcats_0.5.0 stringr_1.4.0
[25] dplyr_1.0.3 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
[29] tibble_3.0.5 ggplot2_3.3.3 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.9.2 webshot_0.5.2 RColorBrewer_1.1-2
[5] httr_1.4.2 rprojroot_2.0.2 tools_4.0.3 backports_1.2.0
[9] R6_2.5.0 DBI_1.1.1 colorspace_2.0-0 withr_2.4.0
[13] tidyselect_1.1.0 mnormt_2.0.2 compiler_4.0.3 git2r_0.28.0
[17] cli_2.2.0 rvest_0.3.6 xml2_1.3.2 scales_1.1.1
[21] digest_0.6.27 pbivnorm_0.6.0 rmarkdown_2.6 pkgconfig_2.0.3
[25] htmltools_0.5.1 dbplyr_2.0.0 htmlwidgets_1.5.3 rlang_0.4.10
[29] rstudioapi_0.13 visNetwork_2.0.9 generics_0.1.0 jsonlite_1.7.2
[33] magrittr_2.0.1 Rcpp_1.0.6 munsell_0.5.0 fansi_0.4.2
[37] lifecycle_0.2.0 visdat_0.5.3 stringi_1.5.3 yaml_2.2.1
[41] grid_4.0.3 parallel_4.0.3 promises_1.1.1 crayon_1.3.4
[45] haven_2.3.1 hms_1.0.0 tmvnsim_1.0-2 knitr_1.30
[49] ps_1.5.0 pillar_1.4.7 stats4_4.0.3 reprex_0.3.0
[53] glue_1.4.2 evaluate_0.14 modelr_0.1.8 vctrs_0.3.6
[57] httpuv_1.5.5 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[61] xfun_0.20 broom_0.7.3 later_1.1.0.1 viridisLite_0.3.0
[65] workflowr_1.6.2 DiagrammeR_1.0.6.1 ellipsis_0.3.1