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Collinearity

  • In: Encyclopedia of Epidemiology
  • Edited by: Sarah Boslaugh
  • Subject:Public Health (general), Public Health Research Methods , Epidemiology & Biostatistics

Collinearity is a concern when two regression covariates are associated with one another. In essence, because the covariates are correlated, their prediction of the outcome is no longer independent. As a result, when both covariates are included in the same regression model, each one becomes less statistically significant because they are explaining some of the same variance in the dependent variable. Cues that collinearity may be a concern are (1) high correlation or association between two potential covariates, (2) a dramatic increase in the p value (i.e., reduction in the significance level) of one covariate when another covariate is included in the regression model, or (3) high variance inflation factors. The variance inflation factor for each covariate is 1/(1 − R2 ∗), where R2 ∗ ...

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