I measure a continuous variable prior and after treatment allocation in a 3x4 factorial experimental design . The pre-treatment measurements are all unaffected by the treatment. There is no counter-balancing.
As far as I know, generally there are several way to approach this kind of data. For example, paired t-test or Wilcoxon signed-rank test, repeated measures ANOVA, ANCOVA, mixed models (random or fixed effects), and change scores as dependent variable. Some of these are answering slightly different research questions but are generally suited to the data at hand.
My general interest is in differences in changes from pre- to post-treatment measurements between the different factor combinations (factor interactions).
In general, can anyone recommend a source where to read up on these approaches relative to each other, in order to assess the merits of them?
More specifically, I wonder about how, e.g. coding the pre- and post-treatment variables as change scores, and using these change scores as a dependent variable in e.g. an OLS regression with the treatment interaction term as independent variables, performs relative to testing the treatment interaction in a repeated-measures ANOVA (or random/fixed effects model). Is using change scores like this somehow inferior to the repeated-measures approach (possibly due to a loss of information due to reducing two variables to one)?