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I have a data set with a categorical dependent (14 categories - party choices), and three independent variables - age (continuous), education (8 categories) and gender (male or female).

Online I understood that I need to run a multiple regression analysis. However, advice online is conflicting, with some proposing the use of dummies while others do not.

Could you please help me with some guidance? The software I work in is RStudio.

Thanks in advance.

Regards, Vlad

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  • $\begingroup$ Was my answer helpful? $\endgroup$ – Gabriel J. Odom Dec 13 '17 at 23:22
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I'm worried that this question is a duplicate, but I'll answer it anyway. What you are describing can be called "supervised classification". There are a wide range of tools that you can use to solve such a problem, and each tool has its own strengths and weaknesses. Here is a brief summary of your options, borrowed shamelessly

  • Multinomial logistic regression: the crux of what is discussed here
  • Multinomial probit regression: similar to multinomial logistic regression with independent normal error terms. This is a nice R package for that.
  • Multiple-group discriminant analysis: My personal favorite, but it does have some limitations (assuming multivariate normality of the errors for one). See this post on how to perform it in R.

The article lists a few other options for you to explore. Beyond their options, Random Forests via Classification Trees is also very powerful, but can be too "black box" for some people.

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