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Mplus Discussion > Multilevel Data/Complex Sample >
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 CXN posted on Tuesday, October 31, 2017 - 5:49 am
Hi, professor, my data with a complex sample of multistage cluster random sampling, i want to do a latent class model , and use first approach : compute standard errors and chi-square tests of model fit taking into account complex sampling features. This is my Language:
DATA: file is "C:\Users\Administrator\Desktop\xwms.dat";
VARIABLE:
NAMES ARE x1-x5 urdsp code fweight;
USEVARIABLES =x1-x5;
CATEGORICAL=x1-x5;
STRATIFICATION=urdsp;
CLUSTER=code;
WEIGHT=fweight;
CLASSES = C(3);
ANALYSIS:
type= mixture complex;
OUTPUT: TECH1 tech11 ;
TWO QUSETIONS: 1) Is this language all right? Is the CLUSTER means primary sample unit ? WEIGHT means final sample weight ? 2) what would you consider to be the "best" estimator for this model? Can pseudo maximum likelihood estimation method do (i read it in some literatures, but it can not be used in mplus ) ? or some method else ?
Thank you very much!
 Bengt O. Muthen posted on Tuesday, October 31, 2017 - 12:09 pm
1) Yes and yes

2) ML
 CXN posted on Wednesday, November 01, 2017 - 8:01 pm
1)I tried to run my Language fore-mentioned, i find that even though delete the CLUSTER or STRATIFICATION from my Language,and just keep WEIGHT, the results showed nothing changed, does it means the CLUSTER or STRATIFICATION is insignificance for model, and why?
2)my data used a multistage sampling,should i take any statement about stage variable in this model ?
3)The "best" estimator for this model is ML or MLR ?

Looking forward to your reply and give my sincerely thanks to you!
 Bengt O. Muthen posted on Thursday, November 02, 2017 - 2:58 pm
1) Send both outputs and the data to Support along with your license number.

2) See the papers on our website page

http://www.statmodel.com/resrchpap.shtml

3) MLR.
 CXN posted on Sunday, November 05, 2017 - 6:22 pm
I think i made some mistakes, and need to do
more literature learning and thinking, i really appreciate your assistance.
 Bengt O. Muthen posted on Tuesday, November 07, 2017 - 2:13 pm
Ok.
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