Multicollinearity occurs when two or more predictor variables are highly correlated in a regression model
In order to account for multicollinearity in regression modeling, researchers should conduct bivariate correlations between the predictor variables. If any of the variables are significantly correlated, then one of the variables should be dropped from the model. Researchers can make this decision based on the relative empirical or clinical context in which the model is being built.
In applied regression modeling, multicollinearity is assessed using the Variance Inflation Factor (VIF) and tolerance.
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