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I am working with a dataset which consists of both categorical (14 vars) and continuous variables (5). Each categorical variable consists of a minimum of 2 categories up to 106 categories.

The aim of my study is to find those variables that are mostly important for my analysis, I was thinking of using Principal Component Analysis (PCA) how ever from my understanding this can only be used for continuous variables.

Anyone can help me what I can use for dimension reduction, I will be using R-studio to carry out analysis.

Thanks

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