Re: st: collinearity in categorical variables

["Mitchell F. Berman" <mfb1@columbia.edu>]

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



*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/