Linda:

I want to do a mixed model analysis on my data. I used both R and Spss to verify whether my R results where correct, but the results differ enormous for one variable. I can't figure out why there is such a large difference myself, your help would be appreciated! I already did various checks on the dataset.

DV: score on questionnaire (QUES)
IV: time (after intervention, 3 month follow-up, 9 month follow-up)
IV: group (two different interventions)
IV: score on questionnaire before the intervention (QUES_pre)
random intercept for participants

SPSS code:

MIXED QUES BY TIME GROUP WITH QUES_pre    
/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, 
        ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)    
/FIXED=TIME GROUP QUES_pre TIME*GROUP | SSTYPE(3)
/METHOD=REML
/PRINT=SOLUTION TESTCOV
/RANDOM=INTERCEPT | SUBJECT(ID) COVTYPE(AR1)
/REPEATED=Index1 | SUBJECT(ID) COVTYPE(AR1).

R code:

model1

The biggest difference lies in the effect of group. For the SPSS code, the p value is .045, for the R code the p-value is .28. Is there a mistake in my code, or has anyone a suggestion of something else that might go wrong?