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From |
Richard Williams <Richard.A.Williams.5@ND.edu> |

To |
statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Collinearity in svy |

Date |
Fri, 02 May 2008 11:13:09 -0500 |

At 08:21 AM 5/2/2008, Simon, Alan (CDC/CCHIS/NCHS) wrote:

Personally, I see no problem with that. Multicollinearity is a problem with the right hand side of the model, i.e. the Xs. It doesn't matter whether Y itself will be analyzed via ols regression, logistic regression, or whatever. For example, in a non-svy setting, if y was a dichotomy that you will be analyzing via logistic regression, it is nonetheless fine to do something likeThe website essentially suggests using each variable as a dependent variable in a separate regression using all other variables as independent variables, and then using the following command: display "tolerance = " 1-e(r2) " VIF = " 1/(1-e(r2)) to calculate the Variance inflation factor. However, this only seems to work if the dependent variable is continous and the regression is OLS. Is there a way to measure the variance inflation factor for categorical variables in a complex survey design? Or is there a better way to approach this problem?

regress y x1 x2 x3

vif

You are not interested in the coefficients from the regression, you are just interested in the collinearity diagnostics from vif.

One caveat: I am not sure if the use of svy somehow invalidates or complicates the usual collinearity diagnostics. But at the same time, it is not like these diagnostics have to be accurate down to 12 decimal places. You usually just want to get a ballpark estimate of whether or not collinearity is a problem in your data.

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Richard Williams, Notre Dame Dept of Sociology

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**References**:**st: Collinearity in svy***From:*"Simon, Alan (CDC/CCHIS/NCHS)" <fpa8@cdc.gov>

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