I am hoping to get some advice on my analysis for my MSc dissertation. I will try to keep it short but please let me know if there is more information I can provide to make the situation clearer.
Participants in this study were presented with information on women's issues in the four experimental conditions. These varied by presenting statistical information or personal narratives, and intersectional or non-intersectional issues (so a 2x2 design). In the control condition participants read statistics on various issues, such as the environment (I have been told that there are issues with the design of the control group because it doesn't follow the 2x2 structure. I am willing to drop it if necessary). I have 167 participants, fairly equally distributed across the five conditions. The aim of the study was to determine if the type of information presented would have an impact on attitudes towards policies promoting gender equality.
The four DVs are agreement scores with four different policies. Agreement was measured on 6-point Likert scales. The original plan was to combine them into one variable but there is very little correlation between the agreement scores to actually justify doing this.
One of these DVs follows a u-shaped distribution and I am wondering what to do with it.
I was advised by our stats supervisor to carry out a two-way MANOVA and infer conclusions about the control group from post-hoc tests(but had not discussed the issue of non-normal distribution with them). The back-up plan now is to carry out the MANOVA on the three DVs that are normally distributed (actually the kurtosis values are a bit problematic) and then run a non-parametric (Kruskal-Wallis) test on the other DV.
As far as I can tell there are no main effects, but significant interactions and that is the only interesting result I can report on.
Does this approach (MANOVA for three DVs, KW for the other) seem appropriate? It doesn't seem very elegant.
Thank you very much for your time