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I am running a two-level model with one latent variable both with effects within and between (outcome variable is continuous). This variable is a dummy variable (4 categories, so I have 3 dummies).
As my latent variable is 0/1, a pearson correlation with the dependent variable would not be possible. So, where does the output of the correlations come from? I cannot reproduce these correlations in SAS or R (neither from the original variables nor from my proc mixed/lmer/stan_glmer multilevel regression outputs) but need to understand where they come from and why they are so different from the other regression outputs.
Thanks a lot for your help! |
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Sampstat gives correlations among observed variables (no latents included). It's unclear what your model is. You say
"this variable is a dummy variable" - it sounds like "this" refers to your latent variable, that is, you have a mixture model. |
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Hi,
That's right, my dummy variable is my latent variable. This variable actually causes some convergence problems (I'll have to take a look at that too).
My question is, why if it's the same dataset, do I see such different correlations between the observed variables than in SAS and R? I'm having a hard time understanding the calculations behind this output.
Thanks! |
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| Please send your Mplus output and that of SAS/R (using a pdf) to Support along with your license number. |
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Hi Linda and Bengt,
I have another question regarding the type of correlations in my output. I am using model estimator MLR in a model with covariates. Does this mean the correlations from the SAMPSTAT are probit residual correlations? (as suggested in the user's manual Ch.18?)
Thank you,
Carolina |
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| That depends on whether you specify some variables as Categorical. If you do, and you use WLSMV, then yes they are probit residual correlations. |
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