setwd(mypath) # change it to the path of your own data folder
data <- read.table("proficiencyraw-female.csv", sep=",", header=F)
# give variable names
colnames(data) <- c(paste0("goals",1:6),
paste0("rsc",1:5),
paste0("hsc",1:5),
paste0("msc",1:5),
paste0("ssc",1:5),
"SATvoc",
"SATcomp",
"SATlang",
"SATmath",
"SATprob",
"SATproc")
cfa.Model <- '
# measurement model
RSC =~ rsc1 + rsc2 + rsc3 + rsc4 + rsc5
HSC =~ hsc1 + hsc2 + hsc3 + hsc4 + hsc5
MSC =~ msc1 + msc2 + msc3 + msc4 + msc5
SSC =~ ssc1 + ssc2 + ssc3 + ssc4 + ssc5
# mean structure
rsc1~1
rsc2~1
rsc3~1
rsc4~1
rsc5~1
hsc1~1
hsc2~1
hsc3~1
hsc4~1
hsc5~1
msc1~1
msc2~1
msc3~1
msc4~1
msc5~1
ssc1~1
ssc2~1
ssc3~1
ssc4~1
ssc5~1
# residual covariances
rsc1~~hsc1+msc1+ssc1
rsc2~~hsc2+msc2+ssc2
rsc3~~hsc3+msc3+ssc3
rsc4~~hsc4+msc4+ssc4
rsc5~~hsc5+msc5+ssc5
hsc1~~msc1+ssc1
hsc2~~msc2+ssc2
hsc3~~msc3+ssc3
hsc4~~msc4+ssc4
hsc5~~msc5+ssc5
msc1~~ssc1
msc2~~ssc2
msc3~~ssc3
msc4~~ssc4
msc5~~ssc5
# covariances between the latent variables
RSC ~~ HSC + MSC + SSC
HSC ~~ MSC + SSC
MSC ~~ SSC
'
cfa.Fit <- sem(cfa.Model, data = data)
summary(cfa.Fit, fit.measures = T, standardized = T)
## lavaan 0.6.15 ended normally after 42 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 96
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 167.327
## Degrees of freedom 134
## P-value (Chi-square) 0.027
##
## Model Test Baseline Model:
##
## Test statistic 10473.929
## Degrees of freedom 190
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.997
## Tucker-Lewis Index (TLI) 0.995
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -28759.567
## Loglikelihood unrestricted model (H1) -28675.904
##
## Akaike (AIC) 57711.135
## Bayesian (BIC) 58182.279
## Sample-size adjusted Bayesian (SABIC) 57877.378
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.016
## 90 Percent confidence interval - lower 0.006
## 90 Percent confidence interval - upper 0.023
## P-value H_0: RMSEA <= 0.050 1.000
## P-value H_0: RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.018
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RSC =~
## rsc1 1.000 0.827 0.649
## rsc2 1.246 0.064 19.549 0.000 1.031 0.764
## rsc3 1.002 0.058 17.171 0.000 0.828 0.646
## rsc4 1.207 0.061 19.878 0.000 0.998 0.790
## rsc5 1.045 0.059 17.615 0.000 0.864 0.663
## HSC =~
## hsc1 1.000 0.952 0.700
## hsc2 1.065 0.047 22.659 0.000 1.014 0.802
## hsc3 0.967 0.048 20.080 0.000 0.921 0.700
## hsc4 0.880 0.047 18.779 0.000 0.838 0.649
## hsc5 1.145 0.050 22.961 0.000 1.091 0.819
## MSC =~
## msc1 1.000 1.133 0.814
## msc2 0.947 0.032 29.280 0.000 1.073 0.821
## msc3 0.907 0.033 27.817 0.000 1.028 0.791
## msc4 0.964 0.034 28.020 0.000 1.092 0.795
## msc5 0.898 0.033 27.255 0.000 1.018 0.776
## SSC =~
## ssc1 1.000 1.169 0.842
## ssc2 0.985 0.029 33.743 0.000 1.152 0.868
## ssc3 0.819 0.029 28.012 0.000 0.957 0.765
## ssc4 0.940 0.031 29.918 0.000 1.099 0.799
## ssc5 0.838 0.031 27.138 0.000 0.980 0.744
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .rsc1 ~~
## .hsc1 0.120 0.034 3.505 0.000 0.120 0.127
## .msc1 -0.026 0.029 -0.883 0.377 -0.026 -0.033
## .ssc1 0.069 0.028 2.497 0.013 0.069 0.095
## .rsc2 ~~
## .hsc2 0.125 0.028 4.512 0.000 0.125 0.189
## .msc2 0.014 0.026 0.522 0.602 0.014 0.021
## .ssc2 0.034 0.024 1.412 0.158 0.034 0.059
## .rsc3 ~~
## .hsc3 0.094 0.033 2.832 0.005 0.094 0.103
## .msc3 0.031 0.029 1.096 0.273 0.031 0.040
## .ssc3 0.046 0.028 1.628 0.104 0.046 0.059
## .rsc4 ~~
## .hsc4 0.087 0.030 2.908 0.004 0.087 0.113
## .msc4 0.017 0.026 0.636 0.525 0.017 0.026
## .ssc4 0.014 0.026 0.563 0.574 0.014 0.023
## .rsc5 ~~
## .hsc5 0.095 0.030 3.167 0.002 0.095 0.127
## .msc5 0.066 0.030 2.226 0.026 0.066 0.082
## .ssc5 0.124 0.031 4.007 0.000 0.124 0.145
## .hsc1 ~~
## .msc1 0.043 0.030 1.467 0.142 0.043 0.055
## .ssc1 0.059 0.028 2.099 0.036 0.059 0.081
## .hsc2 ~~
## .msc2 0.040 0.023 1.751 0.080 0.040 0.071
## .ssc2 -0.006 0.021 -0.275 0.783 -0.006 -0.012
## .hsc3 ~~
## .msc3 -0.026 0.028 -0.926 0.354 -0.026 -0.034
## .ssc3 0.037 0.027 1.362 0.173 0.037 0.049
## .hsc4 ~~
## .msc4 0.055 0.030 1.822 0.069 0.055 0.067
## .ssc4 0.092 0.030 3.121 0.002 0.092 0.114
## .hsc5 ~~
## .msc5 0.026 0.025 1.030 0.303 0.026 0.041
## .ssc5 0.017 0.026 0.630 0.528 0.017 0.025
## .msc1 ~~
## .ssc1 0.053 0.024 2.187 0.029 0.053 0.088
## .msc2 ~~
## .ssc2 0.048 0.021 2.303 0.021 0.048 0.097
## .msc3 ~~
## .ssc3 0.039 0.024 1.625 0.104 0.039 0.060
## .msc4 ~~
## .ssc4 0.028 0.026 1.060 0.289 0.028 0.040
## .msc5 ~~
## .ssc5 0.123 0.027 4.594 0.000 0.123 0.169
## RSC ~~
## HSC 0.385 0.038 10.229 0.000 0.488 0.488
## MSC -0.226 0.036 -6.225 0.000 -0.242 -0.242
## SSC -0.150 0.036 -4.181 0.000 -0.155 -0.155
## HSC ~~
## MSC -0.148 0.039 -3.763 0.000 -0.137 -0.137
## SSC -0.045 0.040 -1.135 0.256 -0.041 -0.041
## MSC ~~
## SSC 0.831 0.059 14.111 0.000 0.627 0.627
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .rsc1 3.308 0.040 82.124 0.000 3.308 2.597
## .rsc2 3.657 0.043 85.727 0.000 3.657 2.711
## .rsc3 3.645 0.041 89.949 0.000 3.645 2.844
## .rsc4 3.230 0.040 80.828 0.000 3.230 2.556
## .rsc5 3.558 0.041 86.313 0.000 3.558 2.729
## .hsc1 3.636 0.043 84.520 0.000 3.636 2.673
## .hsc2 3.694 0.040 92.374 0.000 3.694 2.921
## .hsc3 3.310 0.042 79.593 0.000 3.310 2.517
## .hsc4 3.233 0.041 79.106 0.000 3.233 2.502
## .hsc5 3.633 0.042 86.261 0.000 3.633 2.728
## .msc1 3.349 0.044 76.060 0.000 3.349 2.405
## .msc2 3.162 0.041 76.494 0.000 3.162 2.419
## .msc3 3.528 0.041 85.819 0.000 3.528 2.714
## .msc4 3.608 0.043 83.070 0.000 3.608 2.627
## .msc5 3.651 0.041 88.024 0.000 3.651 2.784
## .ssc1 3.269 0.044 74.440 0.000 3.269 2.354
## .ssc2 3.130 0.042 74.538 0.000 3.130 2.357
## .ssc3 3.419 0.040 86.353 0.000 3.419 2.731
## .ssc4 3.531 0.043 81.217 0.000 3.531 2.568
## .ssc5 3.621 0.042 86.868 0.000 3.621 2.747
## RSC 0.000 0.000 0.000
## HSC 0.000 0.000 0.000
## MSC 0.000 0.000 0.000
## SSC 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .rsc1 0.939 0.049 19.356 0.000 0.939 0.579
## .rsc2 0.758 0.046 16.487 0.000 0.758 0.416
## .rsc3 0.956 0.049 19.397 0.000 0.956 0.582
## .rsc4 0.601 0.039 15.421 0.000 0.601 0.376
## .rsc5 0.952 0.050 19.101 0.000 0.952 0.560
## .hsc1 0.943 0.050 19.057 0.000 0.943 0.510
## .hsc2 0.571 0.036 16.052 0.000 0.571 0.357
## .hsc3 0.881 0.046 19.043 0.000 0.881 0.509
## .hsc4 0.967 0.049 19.865 0.000 0.967 0.579
## .hsc5 0.584 0.038 15.239 0.000 0.584 0.329
## .msc1 0.655 0.037 17.610 0.000 0.655 0.338
## .msc2 0.557 0.032 17.339 0.000 0.557 0.326
## .msc3 0.633 0.035 18.322 0.000 0.633 0.375
## .msc4 0.694 0.038 18.197 0.000 0.694 0.368
## .msc5 0.685 0.037 18.717 0.000 0.685 0.398
## .ssc1 0.561 0.033 16.910 0.000 0.561 0.291
## .ssc2 0.436 0.028 15.565 0.000 0.436 0.247
## .ssc3 0.651 0.034 19.253 0.000 0.651 0.415
## .ssc4 0.683 0.037 18.427 0.000 0.683 0.361
## .ssc5 0.777 0.040 19.647 0.000 0.777 0.447
## RSC 0.684 0.064 10.759 0.000 1.000 1.000
## HSC 0.907 0.075 12.043 0.000 1.000 1.000
## MSC 1.284 0.085 15.174 0.000 1.000 1.000
## SSC 1.367 0.085 16.085 0.000 1.000 1.000
© Copyright 2024
@Yi Feng
and
@Gregory R. Hancock.