Analyzing Multicollinearity in Multilevel Regression: A Simulation Study Using Ridge Regression

• In Event: 26.081 - Poster Session 3
  In Poster Session: 26.081-4 - Division D Section 2 Poster Session: Statistical Methods

Abstract

Multicollinearity is a problem with being able to separate the effects of two (or more) variables on an outcome variable. In the presence of multicollinearity, the estimation of parameters in multiple linear regression models by means of Ordinary Least Squares (OLS) is known to suffer severe distortion. The main objective in this paper, is to find out best method to tackle multicollinearity in multilevel regression model. The effect of multicollinearity will be analyzed and a Ridge regression processed will be used to solve the multicollinearity problem using simulated dataset using free R software.

Author

• Subir Hait, The University of Southern Mississippi