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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.

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    $\begingroup$ If you look at the Related threads on the right hand side there are several that appear to answer very similar (if not identical) questions. Such as this one and this one. $\endgroup$
    – Andy W
    Commented Jun 14, 2011 at 13:31
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    $\begingroup$ I've edited your title; the question about nominal data is important in that it's why this question is not a duplicate. $\endgroup$
    – JMS
    Commented Jun 14, 2011 at 13:53
  • $\begingroup$ @JMS Given that the nominal data would have to be represented as dummy indicators, doesn't that point us right back to the question about factor analysis of dyadic data? $\endgroup$
    – whuber ♦
    Commented Jun 14, 2011 at 14:18
  • $\begingroup$ For what it's worth, my vote would be to modify the question to extract the unique elements: i.e., (a) how to factor analyse data that is based on a mixture of different data types, and (b) how to factor analyse nominal data. @whuber I suppose approaches like optimal scaling of nominal data are an alternative to dummy indicators. (@Figaro are you able to update the question to make it a non-duplicate?) $\endgroup$
    – Jeromy Anglim
    Commented Jun 14, 2011 at 14:58
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    $\begingroup$ Also, an example of handling nominal data with a discrete-choice type specification is given here, for example. $\endgroup$
    – JMS
    Commented Jun 14, 2011 at 16:50

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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

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  • $\begingroup$ Link is broken. Would appreciate if you update it. Thanks $\endgroup$
    – MYaseen208
    Commented Nov 13, 2015 at 13:44
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    $\begingroup$ Here's an Internet Archive cache of the PDF linked in dmf38's answer (since I can't comment on answers yet): web.archive.org/web/20130502181643/http://… $\endgroup$
    – user98098
    Commented Dec 13, 2015 at 19:13
  • $\begingroup$ The link should work now, I have added an archived version. $\endgroup$
    – Silverfish
    Commented Dec 13, 2015 at 19:26
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FactoMineR is a nice package for Factor Analysis on mixed variables.

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