I have developed a SEM model (latent growth model to be precise) that regresses a latent variable (the latent intercept) on several dichotomous predictors. Trouble is, I also test for interactions, which makes it all the more difficult to understand for the reader.
(I did the analysis with another software than Stata, this software has limited graphical capabilities (Mplus). In Stata I would need to use both -sem- and -gsem- (gsem due to categorical indicators in some analysis), at least that's what I believe. Another option is R/lavaan, but I prefer Stata over R.)
So, within Stata, how should I get plots for regression weights estimated in a SEM-model (-sem- and -gsem-)?
I have three main effects: a, b, and c. All these three variables are dichotomous, a and b are membership in religious groups, c is gender. (Nonreligios are scored zero on both a and b.) I then add two dichtomous variables representing interaction effects between religious affiliation and gender (a*c and b*c, both are dichotomous).
Thus:
The results are bound to be confusing for many people unless I use plots. I found Ben Jann's presentation of -ceofplot- interesting. Before I start digging into this on my own: I wonder which package/approach I should consider first while educating myself on how to use plots for regression weights obtained with -sem- and -gsem-.
(I did the analysis with another software than Stata, this software has limited graphical capabilities (Mplus). In Stata I would need to use both -sem- and -gsem- (gsem due to categorical indicators in some analysis), at least that's what I believe. Another option is R/lavaan, but I prefer Stata over R.)
So, within Stata, how should I get plots for regression weights estimated in a SEM-model (-sem- and -gsem-)?
I have three main effects: a, b, and c. All these three variables are dichotomous, a and b are membership in religious groups, c is gender. (Nonreligios are scored zero on both a and b.) I then add two dichtomous variables representing interaction effects between religious affiliation and gender (a*c and b*c, both are dichotomous).
Thus:
Code:
latent intercept <- religousgroup1 religiousgroup2 gender religiousgroup1female religiousgroup2female
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