The Effects of Misspecifying the Random Part of Multilevel Models

Authors

  • David M. LaHuis
    Department of Psychology, Wright State University, Dayton, OH, USA
  • Daniel R. Jenkins
    Department of Psychology, Wright State University, Dayton, OH, USA
  • Michael J. Hartman
    Infor, Dallas, TX, USA
  • Shotaro Hakoyama
    Infor, Dallas, TX, USA
  • Patrick C. Clark
    Infor, Dallas, TX, USA

Abstract

This paper examined the amount bias in standard errors for fixed effects when the random part of a multilevel model is misspecified. Study 1 examined the effects of misspecification for a model with one Level 1 predictor. Results indicated that misspecifying random slope variance as fixed had a moderate effect size on the standard errors of the fixed effects and had a greater effect than misspecifying fixed slopes as random. In Study 2, a second Level 1 predictor was added and allowed for the examination of the effects of misspecifying the slope variance of one predictor on the standard errors for the fixed effects of the other predictor. Results indicated that only the standard errors of coefficient relevant to that predictor were impacted and that the effect size for the bias could be considered moderate to large. These results suggest that researchers can use a piecemeal approach to testing multilevel models with random effects.