The Effect of Multicollinearity on Multilevel Modeling Parameter Estimates and Standard Errors

This study investigates the quality of multilevel model parameter estimates and standard errors as a function of varying magnitudes of correlation among Level 1 predictors and model characteristics. The results of the study showthat with multicollinearity presented at Level 1 of a two-level mixed-effects linear model, the fixed-effect parameter estimates produce relatively unbiased values; however, the variance and covariance component estimates produce downwardly biased values except for Level 1 variance (< 5%). The standard errors associated with the parameter estimates are also biased under varied magnitudes of Level 1 predictor correlation.