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| KB posted on Tuesday, June 13, 2017 - 12:28 am
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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
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!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;
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| Your input is correct. The order should be cg(2) c(2) when c is regressed on cg. |
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| KB posted on Tuesday, June 13, 2017 - 7:15 pm
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| 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? |
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| Imposing measurement invariance across groups amounts to control for group-effects in the latent classes? |
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| 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. |
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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; |
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| Send your output to Mplus Support along with your license number. |
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