I am trying to run a multigroup CFA using lavaan with the code below
fjcfa<-'fjqual=~firstjob_desired+firstjob_meaning+firstjob_grow+firstjob_mobility+firstjob_satisfy+firstjob_skills+firstjob_majr
'
fjcfa.fit.FNW<-cfa(model=fjcfa, data = HEDS, std.lv = T, group = "Race_White", ordered = c("firstjob_desired","firstjob_meaning","firstjob_grow","firstjob_mobility","firstjob_satisfy",
"firstjob_skills","firstjob_majr"))
summary(fjcfa.fit.FW,fit.measures = T, standardized = T, rsquare = T)
However the output is not giving me separate output for the different groups and I cannot figure out why. See below output. Does anyone know how I get get the different groups to show up? I've double checked that different groups and there are 1300 in the non-white and 12000 in the white
lavaan 0.6.16 ended normally after 53 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 21
Used Total
Number of observations 9483 12466
Model Test User Model:
Standard Scaled
Test Statistic 66.790 145.496
Degrees of freedom 7 7
P-value (Chi-square) 0.000 0.000
Scaling correction factor 0.460
Shift parameter 0.340
simple second-order correction
Model Test Baseline Model:
Test statistic 38580.165 25466.385
Degrees of freedom 21 21
P-value 0.000 0.000
Scaling correction factor 1.515
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.998 0.995
Tucker-Lewis Index (TLI) 0.995 0.984
Robust Comparative Fit Index (CFI) 0.998
Robust Tucker-Lewis Index (TLI) 0.995
Root Mean Square Error of Approximation:
RMSEA 0.030 0.046
90 Percent confidence interval - lower 0.024 0.039
90 Percent confidence interval - upper 0.037 0.052
P-value H_0: RMSEA <= 0.050 1.000 0.855
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.031
90 Percent confidence interval - lower 0.027
90 Percent confidence interval - upper 0.035
P-value H_0: Robust RMSEA <= 0.050 1.000
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.015 0.015
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
fjqual =~
firstjob_desrd 0.314 0.005 65.728 0.000 0.314 0.640
firstjob_menng 0.321 0.006 58.106 0.000 0.321 0.653
firstjob_grow 0.311 0.005 62.942 0.000 0.311 0.670
firstjob_mblty 0.188 0.005 36.706 0.000 0.188 0.385
firstjob_stsfy 0.285 0.006 51.293 0.000 0.285 0.578
firstjob_sklls 0.336 0.005 72.760 0.000 0.336 0.693
firstjob_majr 0.257 0.006 46.070 0.000 0.257 0.515
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.firstjob_meaning ~~
.firstjob_stsfy 0.049 0.003 17.690 0.000 0.049 0.329
.firstjob_grow 0.021 0.003 7.889 0.000 0.021 0.162
.firstjob_desired ~~
.firstjob_majr 0.052 0.002 21.164 0.000 0.052 0.321
.firstjob_skills ~~
.firstjob_majr 0.038 0.002 15.624 0.000 0.038 0.257
.firstjob_grow ~~
.firstjob_stsfy 0.028 0.003 10.726 0.000 0.028 0.199
.firstjob_mobility ~~
.firstjob_stsfy 0.019 0.002 9.261 0.000 0.019 0.105
.firstjob_desired ~~
.firstjob_menng 0.011 0.002 5.292 0.000 0.011 0.081
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.firstjob_desrd 0.142 0.003 47.594 0.000 0.142 0.590
.firstjob_menng 0.138 0.003 39.579 0.000 0.138 0.574
.firstjob_grow 0.119 0.003 41.937 0.000 0.119 0.551
.firstjob_mblty 0.203 0.002 100.247 0.000 0.203 0.852
.firstjob_stsfy 0.162 0.003 51.201 0.000 0.162 0.666
.firstjob_sklls 0.122 0.003 40.740 0.000 0.122 0.520
.firstjob_majr 0.183 0.003 63.471 0.000 0.183 0.734
fjqual 1.000 1.000 1.000
R-Square:
Estimate
firstjob_desrd 0.410
firstjob_menng 0.426
firstjob_grow 0.449
firstjob_mblty 0.148
firstjob_stsfy 0.334
firstjob_sklls 0.480
firstjob_majr 0.266