IBM Support

Multicollinearity Diagnostics for LOGISTIC REGRESSION, NOMREG, or PLUM

Troubleshooting


Problem

In the REGRESSION procedure for linear regression analysis, I can request statistics that are diagnostic for multicollinearity (or, simply, collinearity). How can I detect collinearity with the LOGISTIC REGRESSION, Nominal Regression (NOMREG), or Ordinal Regression (PLUM) procedures?

Resolving The Problem

The regression procedures for categorical dependent variables do not have collinearity diagnostics. However, you can use the linear Regression procedure for this purpose. Collinearity statistics in regression concern the relationships among the predictors, ignoring the dependent variable. So, you can run REGRESSION with the same list of predictors and dependent variable as you wish to use in LOGISTIC REGRESSION (for example) and request the collinearity diagnostics. Run Logistic Regression to get the proper coefficients, predicted probabilities, etc. after you've made any necessary decisions (dropping predictors, etc.) that result from the collinearity analysis.

If you have categorical predictors in your model, you will need to transform these to sets of dummy variables to run collinearity analysis in REGRESSION, which does not have a facility for declaring a predictor to be categorical. Technote #1476169, which is titled "Recoding a categorical SPSS variable into indicator (dummy) variables", discusses how to do this.

An enhancement request has been filed to request that collinearity diagnostics be added as options to other procedures, including Logistic Regression, NOMREG, and PLUM.

Internal Use Only

Resolution Status at Transfer: Published - External ; Products: Statistics,Modeler; Versions: ALL,13.0.3

[{"Product":{"code":"SS3RA7","label":"SPSS Modeler"},"Business Unit":{"code":"BU001","label":"Analytics Private Cloud"},"Component":"Modeler","Platform":[{"code":"PF025","label":"Platform Independent"}],"Version":"Not Applicable","Edition":""}]

Historical Number

19450

Document Information

Modified date:
16 June 2018

UID

swg21476696