Handling Missing Data in Growth Mixture Models

Daniel Y. Lee and Jeffrey R. Harring
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Daniel Y. Lee: College Board
Jeffrey R. Harring: University of Maryland

Journal of Educational and Behavioral Statistics, 2023, vol. 48, issue 3, 320-348

Abstract: A Monte Carlo simulation was performed to compare methods for handling missing data in growth mixture models. The methods considered in the current study were (a) a fully Bayesian approach using a Gibbs sampler, (b) full information maximum likelihood using the expectation–maximization algorithm, (c) multiple imputation, (d) a two-stage multiple imputation method, and (e) listwise deletion. Of the five methods, it was found that the Bayesian approach and two-stage multiple imputation methods generally produce less biased parameter estimates compared to maximum likelihood or single imputation methods, although key differences were observed. Similarities and disparities among methods are highlighted and general recommendations articulated.

Keywords: growth mixture models; missing data; multiple imputation; Bayesian (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:48:y:2023:i:3:p:320-348

DOI: 10.3102/10769986221149140

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