Path analysis with a binary outcome v... PreviousNext
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 Manon Burgat posted on Thursday, June 13, 2013 - 7:25 pm
Hi there,

I am using MPlus 6.12 to conduct a path analysis with 1 binary outcome variable (DV), a continuous mediating variable, and 5 continuous IVs. I have a couple of questions regarding this:

(1) I expect from past research that my 5 IVs will be correlated. Should I therefore specify this in the input syntax? If so, should I use WITH (i.e. v3 WITH v2), or something else? I read that Mplus assumes that exogenous variables are correlated but I have noticed that I am given different model fit results depending on whether or not I specify these correlations.

(2) At the moment I am using WLS estimator for this model, but I have noticed from previous posts that others have used WLSMV. Can you tell me which is best in my particular case?

(3) Lastly, I have a broad question regarding improper solutions for SEM and/or path analysis. Is it incorrect to have standardized estimates that are negative (less than 0)? Or does this just indicate a negative relationship between the variables?

Thanks in advance.
 Linda K. Muthen posted on Friday, June 14, 2013 - 9:00 am
1. You should not bring the covariates into the model. The model is estimated conditioned on the covariates. Their means, variances, and covariances are not model parameters. If you want to see their correlations, get their descriptive statistics. With WLSMV doing this changes the model.

2. WLSMV.

3. Factor loadings can be positive or negative. They are regression coefficients.
 Manon Burgat posted on Friday, June 14, 2013 - 6:53 pm
Thank you Linda. I have a follow-up question. I'm wondering what the advantage of using an ESTIMATOR=ML approach with my model may be? I do not have any missing data, so if I did employ ML would I need to use numerical integration? Lastly, can I obtain estimates of the indirect effects if I use ML? I know that MODEL INDIRECT cannot be used with ML but I'm wondering whether these can be obtained another way?
 Linda K. Muthen posted on Sunday, June 16, 2013 - 3:43 pm
You would need numerical integration for maximum likelihood and categorical outcomes. MODEL INDIRECT is not available in this case. You would need to use MODEL CONSTRAINT to define the indirect effects.
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