Document Type

Working/Discussion Paper

Publication Date

5-8-2018

DOI of Published Version

10.17077/67hl-x2l9

Total Pages

33 pages

Abstract

The linear mixed model is a commonly used model for longitudinal or nested data due to its ability to account for the dependency of nested data. Researchers typically rely on the random effects to adequately account for the dependency due to correlated data, however serial correlation can also be used. If the random effect structure is misspecified (perhaps due to convergence problems), can the addition of serial correlation overcome this misspecification and allow for unbiased estimation and accurate inferences? This study explored this question with a simulation. Simulation results show that the fixed effects are unbiased, however inflation of the empirical type I error rate occurs when a random effect is missing from the model. Implications for applied researchers are discussed.

Keywords

Linear Mixed Model, Random Effect Misspecification, Serial Correlation, Monte Carlo

Rights

Copyright © 2018 Brandon LeBeau.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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URL

https://ir.uiowa.edu/pq_pubs/2