Solved – How to perform regression on a nominal variable using many binary explanatory variables

binary datacategorical datacount-data

I have a dataset:

  • Response: categorical, 9 levels.
  • 23 predictors: binary, with many 0's.
  • Number of samples: 64.

I'm interested to know the dependence of (each level of) the response on predictors.

Grateful for any suggestion to start.

Edit: The topic is related to the analysis of the scientific definitions regarding an ecological concept, for which there exist 9 major terms (the response, in 9 categories). I want to use a method proposed by Delong, 1996: to split the definitions according to some common ecological factors (the 23 binary predictors, which could appear or not in each definition). The predictors (ecological factors) could be combined, but it means also losing detail in the analysis, so if possible, I would like to keep them. The main objective is to find quantitative relationships between the different terms of the concept and the (presence of) ecological factors.

Best Answer

If the response is ordinal then use Ordinal logistic regression see for description here, and if response is nominal then use multinomial logistic regression see for description here.

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