I ran a study, where a single variable was measured repeatedly for each participant, under different conditions. Analyzing this data with SPSS has been rather straightforward, by using the General Linear Model -> Repeated Measures ANOVA, and specifying each measure in the within-subject factors.
My problem arises when I try to include some post-experiment data I collected, like a test that all participants completed after the experiment, with integral scores ranging from 0-24. I want to include this to see how these scores relate to the within-subjects measures, and adding this data as a between-subject factor makes sense, but due to the nature of the test score variable I have up to 25 categories that make the data impossible to work with.
It seems like this should be a common issue, and my Googling has helped me a bit, but most of the examples I have seen use categorical variables like gender for the between-subject factors. What do I do if the between-subject factor isn't categorical?
I will be very grateful for any insight, comments and tips you have. Thanks!
Best Answer
You could treat the test score as an interval between subjects predictor in a full model of that and the within effects. You really should be doing multi-level modelling at this point and I'd strongly encourage you look at some tutorials on that for SPSS.
There is an alternative, that is not as good but might be simpler to understand. Let's say your primary question is whether the repeated measures effect is dependent on (interacts with) a linear effect of the test score. Calculate the repeated measures effect score for each participant and then perform a regression on the resulting effect scores. In general, once you have each subject with a single score you're interested in then you could analyze the continuous test score predictor how you'd handle any continuous variable.