The Effects of Missing Time-Varying Covariates in Multilevel Models Public Deposited
- Last Modified
- March 22, 2019
- Creator
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Bainter, Sierra A.
- Affiliation: College of Arts and Sciences, Department of Psychology and Neuroscience
- Abstract
- Multilevel models are commonly used in psychological research to examine developmentally-motivated hypotheses concerning the within- and between- person effects of a time-varying covariate on some outcome. Whereas multilevel models are flexible to accommodate incomplete data on the outcome, missing time-varying covariates present significant challenges to researchers. Unless multiple imputation is used, missing time-varying covariates will lead to a loss of data. This project evaluated the effects of missing time-varying covariates and imputation of missing time-varying covariates in multilevel models using a multifaceted simulation study. My results showed that missing time-varying covariates can lead to biased parameter estimates. However this bias is likely minor compared to bias already present in complete data due to unreliable estimates of the person-mean of the time-varying covariate. The results presented here are clear motivation for researchers to choose alternative estimation strategies that can account for measurement error in the person mean, whether or not time-varying covariates are missing.
- Date of publication
- May 2013
- Keyword
- DOI
- Resource type
- Rights statement
- In Copyright
- Advisor
- Curran, Patrick
- Degree
- Master of Arts
- Degree granting institution
- University of North Carolina at Chapel Hill
- Graduation year
- 2013
- Language
- Publisher
- Parents:
This work has no parents.
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