Message/Author |
|
KB posted on Tuesday, June 13, 2017 - 12:28 am
|
|
|
Hello Dr. Muthen, 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; ......................................... |
|
|
Your input is correct. The order should be cg(2) c(2) when c is regressed on cg. |
|
KB posted on Tuesday, June 13, 2017 - 7:15 pm
|
|
|
Thank you, Dr. Muthen for your response. Does it mean either cg(2)c(2) or c(2)cg(2) can be used when c ON cg is not in the model? |
|
|
Imposing measurement invariance across groups amounts to control for group-effects in the latent classes? |
|
|
I would put it differently - if measurement invariance is appropriate it is more likely that you make the group comparison for latent classes with the same meaning. |
|
|
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; |
|
|
Send your output to Mplus Support along with your license number. |
|
Back to top |