Multicollinearity definition

Multicollinearity means that there is a high correla- tion between one of the independent variables and some linear combination of the remaining ones (Glantz and Slinker, 2001), although many erro- neously interpret multicollinearity as a high pairwise correlation between the independent variables. Multicollinearity always challenges interpretation of effects, but, of course, the stronger the multicollinear- ity the more difficult to meet the challenge.
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Examples of Multicollinearity in a sentence

  • Multicollinearity tests indicated that 2 variables were collinear—age (VIF=4.5; data not shown) and grade (VIF=4.5; data not shown).

  • Multicollinearity was assessed by examining condition indices (greater than 35) and variance decomposition factors (two or more greater than 0.5).

  • Multilevel analyses provide a more accurate estimate when dealing with hierarchically/nested structured data than traditional logistic regression analyses since it accounts for a dependency of patients within hospitals.20,21 Multicollinearity was assessed with the variance of inflation factor (VIF), and a VIF of >2.5 was considered multicollinear, resulting in the exclusion of one of the variables.

  • Multicollinearity diagnostics were conducted to ensure that the variables in the adjusted models were not highly associated with each other.

  • Multicollinearity will only become an issue should VIF exceeded the value of 10I.

  • Multicollinearity could not be detected; the variance inflation factors for all three regressions were just slightly above 1.

  • Multicollinearity was assessed for each model then multiple linear regression was performed for the association between poverty and LF and STH, while logistic regression was performed for poverty and trachoma.

  • Multicollinearity among the predictors was evaluated from scatterplots and the correlation coefficient r values.

  • Multicollinearity analysis resulted in the removal of the interaction between enrollment status and enrollment age because it was collinear with enrollment status and enrollment age.