SPSS Analysis for Pre-test & post-test, control group design

#1
Hi,

I am running an analysis for a experimental study and would like to get some opinions about the most appropriate method.

  • I have two groups: control (no treatment) and experimental groups (intervention)
  • I test all individuals before the experiment and after (pretest-posttest)
  • I collect 10 different measures from each participant before and after; 5 physical condition measures, 4 emotional state measures and 1 motivation/productivity measure, so the total of 20 measures for each participant.
I don't see fit to combine the sub-measures in each group to one value (one overall physical score, one emotional score and one motivational score), because for example the different physical aspects might respond differently to the emotional state and treatment (e.g. changes in strength and flexibility).

My research question goes: whether there is a significant difference between the control group and test group's post-test measures (significant change over time) on physical scores, emotional scores and motivational scores.

Probably no need to mention, but the pre-test and post-test values between subjects are not directly comparable, because of differences between groups' starting levels. I am only interested if the intervention has significant effect on the mean post-test difference between groups, and if so, what measures are affected.

I was quite confident to use repeated measures MANCOVA, according to similar study designs, but the more I research the more I am doubting whether ANOVAs or paired samples t-tests were more appropriate. I would love to receive some feedback and detailed advice about the assumption tests, analysis and what to input as dependent variables, fixed variables, covariates etc. I read that if the data is normally distributed I should use paired samples t-test, but if the normality is violated Wilkinson signed rank test and ANCOVA (in case the pre-test scores significantly differ across groups).

Should I run paired samples t-tests for normally distributed data and something else for the not normally distributed data? Is it not possible run one analysis only or do I need to repeat manually the tests for different components?

Thank you so much in advance, I am a bit confused because of the numerous measure pairs and assumptions for different tests. Would really appreciate some help!
-Emilia