Factor analysis on mixed (continuous/ordinal/nominal) data?

by Figaro   Last Updated May 15, 2019 16:19 PM

What approaches are there to perform FA on data that is clearly ordinal (or nominal for that matter) by nature? Should the data be transformed our are there readily available R packages that can handle this format? What if the data is of a mixed nature, containing both numerical, ordinal and nominal data?

The data is from a survey where subjects have answered questions of many types: yes/no; continuous; scales. My aim is to use FA as a method for analyzing the underlying factors. I do not yet know what factors I'm looking for. However, condensing the underlying factors into a manageable number of factors is important.

EDIT: Also, can I approximate a survey question answered on the Likert-type scale as a continuous variable?

Thank you.

Answers 3

Particularly if you have nominal indicators along with the ordinal & continuous ones, this is probably a good candidate for latent class factor analysis.

Take a look at this -- http://web.archive.org/web/20130502181643/http://www.statisticalinnovations.com/articles/bozdogan.pdf

June 14, 2011 22:34 PM

FactoMineR is a nice package for Factor Analysis on mixed variables.

June 15, 2011 01:22 AM

Is there an update on this question? I have a similar situation. I have mixed data types with the variables I want to use for the FA. I have some dichotomous variables, some ordinal and some continuous. Any idea, how/ what to do in this case. All the references I'm looking at specify that FA should be done with continuous data. There's the catPCA option, but that won't take care of my dichotomous variables. I found an MPlus command that will create factors with mixed data, but then what are the assumptions for mixed data FA? Any help will be much appreciated. I'm stumped right now.

Nadia Koyratty
Nadia Koyratty
May 15, 2019 16:16 PM

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