Condition indexes and variance decompositions for diagnosing collinearity in linear model analysis of survey data

Collinearities among explanatory variables in linear regression models affect estimates from survey data just as t hey do in non-survey data. Unde sirable effects are unnecessarily inflated standard err ors, spuriously low or high t-statistics, and parameter estimates with illogical signs. The available collinearity diagnostics are not generally appropriate for survey data because the variance estimators they incorporate do not properly account for stratification, clustering, and survey weights. In this article, we derive condition indexe s and variance decompositions to diagnose collinearity problems in complex survey data. The adapted diagnostics are illustrated with data based on a survey of health characteristics.