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Hi all,

I am working on a very complicated analysis and am quite lost!

I am analysing data from a randomised controlled trial. I have 3 groups (treatment 1, treatment 2, waitlist), and 3 repeated measures (baseline, time 1, time 2). In addition, I have 7 possible continuous moderators that I want to test, so I want to know whether the relationship between treatment group and time point changes at different values of the potential moderating variable.

I am running separate analyses for each moderator.

I created two dummy variables for both time (time 1, time 2) and treatment (treatment 1, treatment 2).

I entered those in a mixed model on SPSS:


MIXED Y with time1 time2 treatment1 treatment2 moderator
/PRINT R SOLUTION TESTCOV
/FIXED INTERCEPT
time1
time2
treatment1
treatment2
time1*treatment1
time1*treatment2
time2*treatment1
time2*treatment2
moderator
moderator*time1
moderator*time2
moderator*treatment1
moderator*treatment2
moderator*treatment1*time1
moderator*treatment2*time1
moderator*treatment1*time2
moderator*treatment2*time2
/METHOD = ML
/REPEATED Time | SUBJECT(ID) COVTYPE(UN) .


What I am interested in are the last 4 three-way interactions, because they would indicate that the treatments have a differential effect on the post-tests along different levels of the moderating variables (right?)

I adopted a model building strategy whereby I first enter the "main effectiveness" predictors (time1 time2 treatment1 treatment2 time1*treatment1 time1*treatment2
time2*treatment1 time2*treatment2), then the moderator main effects (moderator
moderator*time1 moderator*time2), and finally the rest of the interactions.
I only proceed if there is a significant improvement in model fit according to the -2LL value.
Unfortunately, the last step never seems to be significant...

I am wondering whether I am correct in using all these dummy variables, and whether I actually need them or not. I am very confused.

For instance, do I need the main effects of time 1 and time 2 dummies? If I exclude them I get better results...

I would very much appreciate your thoughts on how best to analyse my data! Please please please help me!!!!

It sounds to me like you might need repeated measures anova.

try to look into that!