How to Deal With Multicollinearity VIF above 10

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What to do about multicollinearity (i.e., a variance inflation factor above 10) in a multiple regression? This video will show you multiple options for handling multicollinerity: • 0:00 Start • 0:43 Ignoring it (based on the paper by O'brien) • 3:29 Removing multicollinearity by removing predictors • 3:58 Removing multicollinearity by pooling predictors • 4:32 Adressing multicollinearity in moderation analysis and polynomial regression (structural multicollinearity) • 5:15 Using penalized regression (ridge regression, lasso regression) • 6:00 Avoiding specification errors • O'brien's paper: • O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality Quantity, 41(5), 673-690. • Paper about mean centering: • Dalal, D. K., Zickar, M. J. (2012). Some common myths about centering predictor variables in moderated multiple regression and polynomial regression. _Organizational Research Methods_, _15_(3), 339-362. • Papers about ridge regression: • Marquardt, D. W., Snee, R. D. (1975). Ridge regression in practice. _The American Statistician_, _29_(1), 3-20. • Wilcox, R. R. (2019). Multicolinearity and ridge regression: results on type I errors, power and heteroscedasticity. _Journal of Applied Statistics_, _46_(5), 946-957. • Videos about ridge regression and lasso regression: • • Regularization Part 1: Ridge (L2) Reg... • • Regularization Part 2: Lasso (L1) Reg...

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