I'd like to control for age in my SEM model - would I use WITH to do this? Also, if I do use WITH to control, is there one line of syntax that controls for age in the entire model or do I have to include a line in for each construct or variable?
1) I want to include Age as a covariate in my SEM models. Age only correlates with some variables in my model, so I'm only featuring it in some lines of my Mplus code. For example:
UDO ON SC Age; HS ON SC UDO; PD ON SC UDO HS Age;
This is okay to do, right? I see above you stated "You should include age as a covariate in all ON statements".
2) Also, even if MPlus automatically correlates Age with my main exogenous variable (here, SC), I would like to get the value of that path in the output. How do I ask for that? If I add a "WITH" line, some of the values (eg, the AIC) change...
I would recommend handling the covariates by the Mplus default, namely that they are all correlated. Their correlation values can be found by using SAMPSTAT. Those correlations are not part of the model. The only exception I would make in this regard is if you have strong theoretical reasons for zero correlations, for instance with a randomized study where the tx variable is uncorrelated with a pretest, but even then it is not necessary. What you should not do is to look at the sample correlations and for almost zero sample corr's fix those corrs at zero in the model. Including a correlation that is almost zero does not hurt.
When you start using WITH among covariates, they change status in Mplus and are include among the list of DVs and therefore BIC/AIC change.
Karen Kegel posted on Thursday, June 06, 2013 - 11:29 am
Thank you so much for your help. To clarify, you are saying that by default, MPlus includes covariance/correlation between a control variable and an exogenous variable. For one of my models, I absolutely need to this covariance/correlation to be contributing to model fit, path coefficient results, etc. This is because I am comparing it vs other models where I have a predictor path running from Age to UDO. When moving UDO to become the main exogenous variable for one model, this predictor path needs to get turned into an explicit covariance path.
If you are saying that such a covariance/correlation is NOT part of model results, is there a way to explicitly and accurately indicate covariance between a control variable and the exogenous variable--besides using a WITH statement?
Sorry if this sounds confusing. Thanks so much again for your time!