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Cluster-level Correlated Error Variance and the Estimation of Parameters in Linear Mixed Models

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dc.contributor.advisor Cortina, Jose M. Luchman, Joseph N.
dc.creator Luchman, Joseph N. en_US 2014-09-18T01:53:10Z 2014-09-18T01:53:10Z 2014-05 en_US
dc.description.abstract Multilevel theory is extended primarily through the evaluation of cross-level effects, or how some between-cluster predictor explains a within-cluster outcome. Cross-level effects are often estimated using linear mixed models (LMMs). LMMs are susceptible to a bias from correlated error variance, resulting from omitted predictors and correlated error variance or common method variance. The effects of correlated error variance are well known in linear regression, but are relatively less understood in LMMs, an extension of LMM. The current study extends previous research on correlated error variance on cross-level effect LMM parameter estimation by applying a tracing rule methodology to demonstrate the mathematical structure of the bias produced by correlated error variance. The current study shows that bias is mainly produced by omitted variable-between-cluster predictor relationships paired with common method variance in the between-cluster predictor. In particular, both parameters can produce attenuation or accentuation of parameter estimates, depending on the magnitude and direction of the effects. The
dc.format.extent 57 pages en_US
dc.language.iso en en_US
dc.rights Copyright 2014 Joseph N. Luchman en_US
dc.subject Quantitative psychology and psychometrics en_US
dc.subject Organizational behavior en_US
dc.subject Psychology en_US
dc.subject Common Method bias en_US
dc.subject Endogeneity Bias en_US
dc.subject Linear Mixed Model en_US
dc.subject Multilevel en_US
dc.title Cluster-level Correlated Error Variance and the Estimation of Parameters in Linear Mixed Models en_US
dc.type Dissertation en Doctoral en Psychology, Industrial/Organizational Concentration en George Mason University en

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