We are examining the effect of within-patient cognitive change (CC) at Session N on within-patient depression symptoms at Session N+1 across 10 sessions. I would like to test if this repeated-measures effect is moderated by stable interpersonal problems (IP) that were assessed at one time point (i.e., prior to treatment).
I would like to know if there is a way to model the interaction between interpersonal problems and cognitive change across all sessions (i.e., CC_IP = CC1-CC10*IP), rather than at each session (i.e., CC_IP1 = CC1*IP; CC_IP2 = CC2*IP; etc.). For context, we are running our main effect using ML-SEM syntax.
I wanted to follow up on this thread with a similar question.
I want to test for moderation (W = negative daily events) at a within-person level in a DSEM with three variables (sleep = X, stress = Y, coping = Z) measured repeatedly. The moderator W can potentially act on all three variables. Of course, multicollinearity is a major concern.
What syntax would be recommended for this kind of moderation? Are there best practices suggested given multicollinearity (r of 0.2 to 0.3)?
My thinking was to try this: MODEL: %WITHIN% Y_t on Y_t-1 X_t-1 Z_t-1 W_t-1 YW_t-1 XW_t-1 ZW_t-1 X_t on X_t-1 Y_t-1 Z_t-1 W_t-1 YW_t-1 XW_t-1 ZW_t-1 Z_t on Z_t-1 Y_t-1 X_t-1 W_t-1 YW_t-1 XW_t-1 ZW_t-1 %BETWEEN% Y on X Z W XW ZW YW X on Y Z W XW ZW YW Z on X Y W ZW ZW YW
A critical factor for DSEM is the number of time points; I don't know what your T is. With large T, say at least 50, you can add random slopes, random variances, and random autoregressions.
I am guessing that what you are interested in is a model like:
y on x z xz
with appropriate lags added. I don't see why you want a y*z term. You can think about whether from a substantive point of view, you want y_t on y_t-1 which is DSEM, or if you want the residual of y_t regressed on the residual of y_t-1 which is RDSEM. The x and z variables can be "brought into the model" or not.