Topic: Regression with 1 control and 2 categorical variables on a postexperimental DV (Time 2 Mood)?
This is more of an issue about the appropriateness of using an analysis than a "how-to do on SPSS" issue but I am using IBM Statistics SPSS 19. I'm in desperate need of help as these analyses for my thesis so any input would be greatly appreciated.
I have pretest and post test measures on the DV (mood) and I am interested in determining mood change. There were two experimental manipulations. Participants either interacted with a negative or neutral actor (actor mood condition) and were given an independence prime, interdependent prime or no prime at all (prime cnodition). Time 2 mood was measured after an experimental manipulation. In the design, there are two between-subject factors (actor mood and prime) and the first time I analyzed the data, I considered time (pre vs. post) as a within-subjects factor and analyzed the data using a split plot/mixed design ANOVA. Some of the assumptions were violated when I did this (e.g., Box's Test of Equality of Covariance matrices). I also tried analyzing the data using an ANCOVA and inputted the pretest mood measure as a covariate but I think there are problems with doing this because it looks as though the covariate is not independent of experimental treatment groups (on one of the between subject factors). I think the split plot/mixed design ANOVA method is preferred but given that some of the assumptions were violated, I was wondering if I could analyze the data in terms of a hierarchical linear regression (with two categorical variables for the between subjects factors and one control variable i.e., the pre-test measure)? Does it make sense to do it this way? Would this add anything to using the ANCOVA given that I've read the ANCOVA has many similarities to doing a regression?
Thank you!! Any input would be helpful!