Hi, I am running a two group model and am testing for group difference on a fixed effect (e.g., slope on gender). I am using WLS as my estimator for the chi-square difference test. However, when I constrain paths to equality, I keep on getting a 2 df difference when I should only get a 1 df difference, isn't that correct? Is there something I'm missing? In other words, shouldn't the degrees of freedom only differ by 1 for constraining one path to equality across groups?
Never mind, I found the problem. I was a dope! I didn't realize I used one of the same numbers in parens to constrain my paths to equality as was used for threshold constraining previously. Sorry to bother you.
*** ERROR in Analysis command ALGORITHM = INTEGRATION is not available for multiple group analysis. Try using the KNOWNCLASS option for TYPE = MIXTURE.
I then switched to a default estimator instead of MLR, and I switched to theta paramaterization, and got the following error:
*** ERROR Cluster ID cannot appear in more than one group. Problem with cluster ID: 371
There is not a problem with the cluster variable. Before and after I got this error warning, I used these same data and the cluster command in my MG LGM with continiuos outcomes and it worked fine. Second, I'm not sure what this error means... by "group" I assume it does not mean the two groups I am analyzing since it is fine if individuals from these different groups are in the same cluster ID.
Then, just to peel back the onion layers, I got rid of the cluster command and dropped the "complex" from the analysis command, and received the following error:
*** ERROR The following MODEL statements are ignored: * Statements in the GENERAL group: [ H1SU1 ] [ H2SU1 ] [ H3TO130 ]
Perhaps the strategy I'm using now is a close to correct and just needs a few changes, or perhaps I am way off. Either way, can you suggest a way to conduct multiple group LGM with a continous outcome. Also, ideally I'd rather do it in "meanstructure" opposed to "mixture" because I am more familiar with analyses using meanstructure, and I don't think it is even possible for me to carry-out my planned subsequent analyses when using mixture.
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gibbon lab posted on Wednesday, October 12, 2011 - 7:57 am
Hi Professor Muthen,
For longitudinal binary outcomes (1/0), if the proportion of 1's increases along study waves and assume 1 does not change back to 0, (e.g., suppose the outcome is yes/no smoking and no one quit smoking during the study), is latent growth model still applicable in this case? Thanks.