I have a dataset of n=3000 nested within 8 countries with approximately 200 or 400 responses in each country. I originally planned to perform multilevel modelling with 4 dependent variables (DV) as fixed effects in SPSS.

The DV variables are responses in a scale of 1-100 and this kind of variables is treated as metric in psychology.

However, all my DV and the error terms are clearly skewed or clearly curtotic. My questions are:

1. I have read that in some cases the size of the dataset or the number of nesting groups allow to use the general linear model. Does it make sense, however, to do so if the dataset clearly shows extreme tendencies? It looks to me like clearly different distributions, but I am not sure how to define them. Should I regard them as continuous distributions?

2. Am I right to think that data transformation is not a good option if there is a different form of distribution?

3. What would be the advantages and disadvantages of bootstrapping or simulation?

4. What would be good reasons for using a generalized linear or a mixed model?

5. Would it be appropriate to perform a factor analysis of the four DV. If not, are there alternatives?

I would appreciate if someone can answer any of these questions or suggest some not very technical references !

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