This feels like it should be a very straightforward stats analysis but I'm struggling to find a solution.
I have a data set made up of pre- and post-test continuous data values taken from multiple groups i.e.:
Group 1 (pre-test, post-test)
Group 2 (pre-test, post-test)
Group 3 (pre-test, post-test) etc..
I want to see in which groups there is a significant difference across the pre- and post-test measures. The most simple analysis would be a paired t-test, however I have many groups and am concerned that multiple paired t-tests will leave my results vulnerable to familywise errors.
An alternative might be to use a repeated measure GLM. I ran such an analysis on SPSS and sure enough came up with a between(group)*within(test-effect) interaction.
I'm now stuck as to how to find in which groups the within-subject comparison is significant. I can only think of splitting the groups up and running multiple comparisons in the estimated marginal means, with a Bonferroni correction. Only the Bonferroni correction does not work with just a single within-subject comparison.
Are there any kinds of family-wise error corrections I can do for the follow-up within-subject comparisons when there is only one-level of comparison?