Analysis of Repeated Measures Study with Unequal Time Between Measurements

#1
Hello Everyone,

I have inherited the analysis of a study from a previous research student. I am having trouble determining what type of analysis would be most appropriate to use.

I am trying to assess whether or not a significant change in measurement scores (FMD) occurred comparing before and after an intervention.

~25 participants were measured at Time 1, then re-tested under near identical circumstances 1 week later at Time 2. Then a 6 month intervention was implemented and the participants were re-tested after the intervention (at Time 3 and Time 4). Time 3 and Time 4 were also conducted under near identical circumstances 1 week apart.

I have been struggling to find an appropriate way to analyze all of these measurement points while also attributing the dependence between them appropriately.

I do not feel that a repeated measures ANOVA is appropriate as it will assume that all the measurements are equally dependent (which is not the case - Time 1 vs Time 2 are more related than Time 1 vs Times 3 or 4)

I have considered using a Multilevel Linear Model, however I am unfamiliar with the process and cannot find an appropriate covariance structure in the predefined SPSS options (I also know that SPSS is not the best program for multi-level modelling).

My understanding of statistical analysis and theory is fairly limited (not a stats or extensive research background). If Multilevel Modelling is the way to go, is there a way to model this scenario appropriately in SPSS or would I have to learn another program? Any advice would be deeply appreciated.

Thanks,

Rafreaki
 
#2
My interpretation of your description is that scores at times 1 and 2 measure the same thing, namely the pre intervention score. The scores at times 3 and 4 also measure the same thing, the post intervention score. If this is the case, then I can't see any reason why you shouldn't just average the two pre scores into one, and the two post scores into another, and use a paired t test.