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I have to analyse 8 independent variables (IV) toward 8 dependent variables (DV) in my research. The IV is nominal while DV are ordinal (likert-scale). I run the normality test and the result is zero (p<.05) which lead me to perform non-parametric statistical test to the data. I choose the Kruskal Wallis test at first, because i want to test the differences among the groups (involved lots of groups) in influencing the DV. Since my hypothesis is just want to know either the IV are significant or not toward the DV, I neglect the role of post hoc analysis (for instance Mann-Whitney U test), because my aim is not to know which group causes the differences.

My question:

  1. Is it a must to do post hoc analysis for Kruskal-Wallis test? i'm afraid my analysis go wrong if I just report the significant value of Kruskal Wallis result. I found some article that just report the result of mean rank, rather using mann whitney u test. Is it acceptable if I just report the significant value and mean rank of the result? If I proceed with Mann-Whitney U test, I gonna end up producing lots of result (possibly more than 1000 tables for that purpose).

  2. I consult with several people and they suggest me to use MANOVA because my study involved lots of variables. Therefore, they suggest MANOVA which assist in producing result based on the whole data, not segregated like Kruskal Wallis analysis. However, I found that the data shall be in normally distributed in order to proceed with it. My data are totally non-normally distributed. How can I proceed with it? is it wrong of I used MANOVA for non-normally distributed data?

I confuse in choosing the appropriate analysis. which one should i use for my research? Thank you so much for your help! Please let me know if there is any other information I should provide.

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For 8 input variables and 8 outcome variables, you need multivariate multiple regression or MANCOVA.

MANOVA is used in case of one input and multiple outcomes.

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    $\begingroup$ It might be worth making a comment about the normality requirements, since that is a large part of the OP's concern. $\endgroup$ – Silverfish Mar 16 '16 at 11:29
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Can I ask what your sample size is? Are you planning on running all those tests?

Is there no other way of reorganizing your data?

If not, why not use the following multinomial model:

DV = IV1 + IV2 + IV3 + IV4 + IV5 + IV6 + IV7

where each of your nominal categories are dummy variables (1,0) and your IV 8 is your baseline?

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