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Handling Missing Data in Growth Mixture Models

Author

Listed:
  • Daniel Y. Lee

    (College Board)

  • Jeffrey R. Harring

    (University of Maryland)

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.

Suggested Citation

  • Daniel Y. Lee & Jeffrey R. Harring, 2023. "Handling Missing Data in Growth Mixture Models," Journal of Educational and Behavioral Statistics, , vol. 48(3), pages 320-348, June.
    • Handle: RePEc:sae:jedbes:v:48:y:2023:i:3:p:320-348
    DOI: 10.3102/10769986221149140
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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