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Anonymous posted on Friday, August 27, 2004  12:16 pm



Hello. I am doing some analyses using the latent intercepts (i1, i2) and slopes (s1, s2) of two variables to predict some alcohol use outcomes. I used TYPE = RANDOM MEANSTRUCTURE MISSING. In this framework, I want to know whether the effects of i1, i2, s1, s2 on alcohol use are the same across two groups of adolescents: low & high emotionality. My initial thoughts are: I could do a twogroup analyses systematically constraining the paths from the latent variables (e.g., i1, s1) to the outcomes (e.g., early onset of alcohol use)in the two groups to be the same and then gradually release the constraints. The Chisquare difference test between the constrained and unconstrained models will tell me whether the effects of the latent variables on the outcomes are the same between the two groups. Is this an acceptable approach? I would appreciate any feedback or suggestion. Thanks a lot. 

bmuthen posted on Friday, August 27, 2004  12:22 pm



Yes, this is a good approach. And, probably the easiest way to study such interactions. 


I am doing growth curve analyses with two groups (GROUPING option) and I would like to test whether the intercepts and the slopes of the two groups are different. Can I do this by constraining like this: model group 1: [i] (1); model group 2: [i] (1); Then, can I compare the BIC value of this constrained model with the original model? Or do I need to look at other fit indices or maybe chisquare? So, how do I test whether two intercepts or slopes are different between groups? Another question: Is it a problem that one group has N=1358 whereas the other group has N=16038? Thanks in advance. 


You could compare those models using chisquare difference testing or loglikelihood difference testing. The large difference in the sample sizes may affect the results. 


Thank you. I read your instructions on this website about chisquare difference testing. Unfortunately in my output I don't see the scaled chisquare value, or the scaling correction factor, probably because I use ML now. However, if I use MLR I get the following error: THE STANDARD ERRORS FOR H1 ESTIMATED SAMPLE STATISTICS COULD NOT BE COMPUTED. THIS MAY BE DUE TO LOW COVARIANCE COVERAGE. THE ROBUST CHISQUARE COULD NOT BE COMPUTED. With ML, I get: ChiSquare Test of Model Fit Value 217.044 Degrees of Freedom 57 PValue 0.0000 ChiSquare Contributions From Each Group SING 65.367 TWIN 151.677 ChiSquare Test of Model Fit for the Baseline Model Value 4927.082 Degrees of Freedom 53 PValue 0.0000 Loglikelihood H0 Value 59972.855 H1 Value 59864.333 Also, I obtain CFI/TLI/BIC/AIC/RMSEA. What would you recommend me to do to test for group differences in i and s? 


You can use MODEL TEST or do a difference test with ML which does not require a scaling factor. 


Thank you for your swift response! 


I have a following question related to my previous posts. With the analysis I want to perform (see first post), you say that the large difference in group size may affect my results. Do the group sizes need to be exactly the same, or is there a range like maximum 1,5 times larger? Do you have a reference on multigroup comparisons in growth modeling with chisquare testing? Thanks in advance for your time. 


I don't know exactly what the proportion of the group sizes should be. I don't know of any papers that have examined this. I think the Bollen book probably covers multiple group comparison using chisquare difference testing. 

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