random effects in mixed models

#1
Hello
I am not very familiar with mixed models --- I have been reviewing the various tutorials on the web but still am not sure if/how to specify my model, even to start with.
I have a relatively simple design:

- two weight groups (non-random; 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 over time, for the treatment vs. control and also between the two weight 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 REPEATED or RANDOM command (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 TIME as 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). So how does mixed models treat factors vs covariates differently?

Thanks -- I would really love some help.
Thanks so much.