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Re: st: collinearity in categorical variables


From   "Mitchell F. Berman" <mfb1@columbia.edu>
To   Stata List Server <statalist@hsphsun2.harvard.edu>
Subject   Re: st: collinearity in categorical variables
Date   Fri, 26 Apr 2013 08:58:27 -0400

Thank you for the reply. Yes, I see that for a single categorical variable broken into dummy variables, collinearity between the dummy variables would be zero. But my question concerns correlation between related, similar, categorical variables.

If I have multiple similar categorical variables, for example: homebound, uses a walker, home-health aide, lives in nursing home, these categorical variables will move together though the data--- won't be identical for all patients, but correlated.

People mention standard VIF (which I know how to do), but the more thorough answers imply this is not correct.

This links suggests perturb (a module available for Stata, R, and SPSS) or polychoric correlation
http://stats.stackexchange.com/questions/35233/how-to-test-for-and-remedy-multicollinearity-in-optimal-scaling-ordinal-regressi

This link from talkstats suggests that polychoric correlations (available in R) are preferable, because correlations calculated using pearson product moment are invalid for categorical data.
http://www.talkstats.com/showthread.php/22996-Collinearity-Among-Categorical-Variables-in-Regression

someone else suggested spearman correlation coefficient
http://www.statisticsforums.com/showthread.php?t=802

factor analysis
http://www.talkstats.com/showthread.php/13264-Collinearity-in-Logistic-Regression

This is beyond my level of theoretical understanding. I was trying to get a sense of what the experts on the Stata List server use.

Thank you for any additional input.

Mitchell



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