Hello,
I ran latent profile analysis on mplus. Now I would like to add two covariates - gender and socioeconomic status. I have couple of questions:
- Can I just regress latent variable (c) on covariate(s) or should I add as in some example also direct effect (regress observed item(s) on covariate(s)?
- What can I obtain as a result, for example for gender? How can this addition influence the results?
- is it add to add two covariates in one model?
Please help if you can. If there is something not clear in my description , please let me know and I will amend it.
Thanks again.
I think the current consensus is to use the "R3STEP" command in Mplus.
See: Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329-341.
Hi Witold, this webnote may also be helpful for you as it includes Mplus syntax:
https://www.statmodel.com/examples/LTAwebnote.pdf
Kind regards, Karin
Hi Witold,
I am conducting now a LCA with a continuous covariate (PIPRE) using the new three-steps approach manually in MPlus. I managed to run it, but now I am not sure about how to interpret the output and then how to report the results.
As I read that you worked in something similar, I was wondering if you could give me some guidance or refer me to some article(s) where such results had been reported. I have only found articles with simulations studies.
My output looks like this:
Estimate S.E. Est./S.E. P-Value
Categorical Latent Variables
C#1 ON
PIPRE -0.244 0.420 -0.581 0.561
C#2 ON
PIPRE 1.442 0.627 2.301 0.021
Intercepts
C#1 0.684 0.268 2.556 0.011
C#2 -0.985 0.488 -2.018 0.044
LOGISTIC REGRESSION ODDS RATIO RESULTS
Categorical Latent Variables
C#1 ON
PIPRE 0.784
C#2 ON
PIPRE 4.229
ALTERNATIVE PARAMETERIZATIONS FOR THE CATEGORICAL LATENT VARIABLE REGRESSION
Parameterization using Reference Class 1
C#2 ON
PIPRE 1.686 0.535 3.150 0.002
C#3 ON
PIPRE 0.244 0.420 0.581 0.561
Intercepts
C#2 -1.670 0.461 -3.625 0.000
C#3 -0.684 0.268 -2.556 0.011
Parameterization using Reference Class 2
C#1 ON
PIPRE -1.686 0.535 -3.150 0.002
C#3 ON
PIPRE -1.442 0.627 -2.301 0.021
Intercepts
C#1 1.670 0.461 3.625 0.000
C#3 0.985 0.488 2.018 0.044
I would really appreciate some help.
Thanks!
Hi David,
Thank you very much for your quick response.
I am still not sure if I can interpret the results the way you suggest because they are log odds ratios and not probabilities. Shouldn't I first calculate the probabilities in some way in order to be able to make statements such as "Pipre is x times more likely to predict membership to class 2 than the reference class"?
Kind regards,
Macarena
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