I am trying to model data from a diary survey (5 days cluster in person). I would like to know how to study lagged effects.There is a variable (Y) predicted by previous (X) and same day variables (Z) and all variables are continuous. As long as I understand, it would be an equation similar to next one: Y(ti+1) = B0j + B1J.X(ti)+ B2j.z(ti+1)+ Eij Based on this, is it correct the next model? i s | Y2@0Y3@1Y4@2Y5@3; y2 ON x1 z2; y3 ON x2 z3; y4 ON x3 z4; y5 ON x4 z5; Should I set y2 on x2[…] y5 on x5 same-day effects to study lagged effects? Since I expect that Y changes across time are due to the IV, should I include regressions on the random i and s? Thank you very much for your advice!
Your equation does not include the growth model i and s factors. But the Mplus input makes sense. Typically, time-varying covariates are not made to correlate with i and s (although one could argue that at least time 1 covariates could influence i and s).
Apart from this, the modeling choices are up to you; you may want to ask general modeling choice questions on SEMNET.