I am not very familiar with mixed models --- I have been reviewing the various tutorials on the web but still am not sure how to specify my model, even as a starting point for comparison. I have a relatively simple design:
- two non-random groups (normal/obese) -- randomized within each group to two conditions (control/treatment). And the DV measured at pre and post-test.
So I am interested in looking at differences between pre and post, for the treatment vs. control and also between the two groups.
I am using SPSS -- this is the syntax I've come up with so far.
MIXED Score BY Group Treatment Time /CRITERIA=CIN(95) MXITER(200) MXSTEP(10) SCORING(5) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE) /FIXED=Group Treatment Group*Treatment Time Group*Time Treatment*Time Group*Treatment*Time | SSTYPE(3) /METHOD=REML /PRINT=G R SOLUTION TESTCOV /RANDOM=INTERCEPT | SUBJECT(ID) COVTYPE(VC) /REPEATED=Time | SUBJECT(ID) COVTYPE(UN)
Some of my questions --
I have specified a random intercept for subject ID, but do I need to account somehow that these are random effects from TWO separate populations (normal weight and obese)? If so, how would I do this?
It seems that I could estimate the time effect using either the
RANDOMcommand (by adding time to the
RANDOM). I can't figure out the difference of what that means conceptually and how to decide which fits my data or design better.
Is it correct to list
TIMEas a fixed factor? I have also seen in one of the books I'm looking at that variables have been coded as 0/1 indicators and entered as covariates. I have no idea why this was done -- I can't seem to find an explanation. However, If I do this, it changes my fixed effects (even with all the same effects and interactions specified). And changes on whether the pre or post is specified as 0/1. So how does mixed models treat factors vs covariates differently? What could be the advantage of treating a variable as a covariate rather than a factor? I noted that although the tests of fixed effects changed, the estimates using
TESTremained the same, which I also thought was weird.
Does one need to account for baseline group differences, and if so how would this be done? Since my data is in long format I no longer have a pre-test measurement that I can put in as a covariate in post-test scores. Sorry if these are dumb questions -- I am much more used to ANCOVA and GLM.