I have twelve binary latent class indicators to identifying my subgroups. I observed 3-class model fits the data well. I want to use gender "gend" as a measurement invariance to examine differences in the latent classes. Below is my model to accomplish the task.
I have few questions: 1. Is the model correct to accomplish the task?
2. Which is correct when using a restricted model to compare model indices where I constrained the parameters across groups (i.e., I will not include c ON cg in my model)?
classes = cg(2) c(3); vs. classes = c(3) cg(2);
Thank you and hope to hear from you! KB
............................... !Model 1: (Unrestricted model: All parameters allowed to vary across group)
... variable: names bk a b c d e f g h i j k l gend; usevariables = a-l; categorical = a-l; knownclass = cg(gend = 0 gend =1); classes = cg(2) c(3); missing = all(-999); cluster = bk; analysis: type = mixture complex; processor = 10; starts = 1000 50; model: %overall% c ON cg;
i have a LCA model with 9 indicators, 5 classes gives the best fit, after adding 2 association parameters. I want to check measurement invariance for the countries (6). *** ERROR in MODEL command Unknown class label in MODEL : %C#1%
what is wrong with my code?
knownclass= co (country1=1 country1=2 country1=3 country1=4 country1=5 country1=6); CLASSES =co(6) c (5); CATEGORICAL =pasxipv weapfght gang tsany swsex ea2 fa2 pstyr2p nprap ;
missing are all (-9999); ANALYSIS: TYPE = MIXTURE; algo=int; param=rescov; starts=0;
MODEL: %OVERALL% c on co; tsany with fa2; %c#1% %c#2% %c#3% tsany with fa2; %c#4% %c#5% tsany with fa2;