Multicollinearity in Hierarchical Linear Modeling: A Monte Carlo Simulation Study

Boston, MA · May 2017

Poster Session · Methodology

Keke Schuler
University of North Texas
Camilo Ruggero
University of North Texas

Abstract

Multicollinearity is a well-documented problem in general linear models. However, little is known about the impacts of multicollinearity in Hierarchical Linear Modeling (HLM). The aim of the present study is to assess the impacts of multicollinearity on parameter estimates, intraclass correlation (ICC), and coverage probability in HLM.

Quantitative

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Multicollinearity in Hierarchical Linear Modeling: A Monte Carlo Simulation Study

Abstract

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