I'm working on a project where I have repeated measures of a (continuous) IV and DV. At each measurement "time" I would like to predict the DV from the IV. I also have a few categorical IVs that do not vary across measurements. My goal is to see if the strength of the relationship between the continuous IV and DV depends on either of the categorical IVs.

I've been trying to use SPSS's mixed procedure to accomplish this (more specifically, using /REPEATED and COVTYPE(AR1)). However, I've encountered several problems I was hoping someone here could help me with:

1: mixed only gives unstandardized coefficients, and my tests of the interactions between the continuous IV and categorical IVs seem to be based on these unstandardized coefficients. Therefore, these tests are not telling me anything about changes in the

*strength*of relationships, correct? My reasoning is: the unstandardized coefficient may very well be bigger in Condition A than in Condition B; however, if I swap the IV and the DV, the effect will be the opposite, i.e., a bigger coefficient in Condition B than in Condition A.

This leads me to my first general question: can you switch the predictor and predicted variables in a mixed model and expect the same results?

2: in an attempt to get around this and make sure the tests are based on standardized values, I converted my IV and DV values to Z scores and reran the analyses. Still, I get totally different results depending on which variable I use as the predictor and which I use as the predicted.

How is this possible? I'm thinking that it may have to do with the fact that the size of the continuous IV values is strongly related to the Conditions they are in. Could such a relationship cause this type of result?

Am I totally misunderstanding the mixed procedure?

I would extremely appreciate any help I can get here. I'm more convinced of my stupidity with each passing hour of going over these SPSS manual chapters. Thank you!