IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v48y2023i3p320-348.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/10769986221149140
    Download Restriction: no
    File URL: https://libkey.io/10.3102/10769986221149140?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Donald B. Rubin, 2003. "Nested multiple imputation of NMES via partially incompatible MCMC," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 3-18, February.
    2. Casey Codd & Robert Cudeck, 2014. "Nonlinear Random-Effects Mixture Models for Repeated Measures," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 60-83, January.
    3. Eric F. Lock & Nidhi Kohli & Maitreyee Bose, 2018. "Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 733-750, September.
    4. Ofer Harel, 2009. "The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(10), pages 1109-1118.
    5. William Meredith & John Tisak, 1990. "Latent curve analysis," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 107-122, March.
    6. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    7. Daniel McNeish & Jeffrey R. Harring, 2017. "The Effect of Model Misspecification on Growth Mixture Model Class Enumeration," Journal of Classification, Springer;The Classification Society, vol. 34(2), pages 223-248, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Guerra & Francesca Bassi & José G. Dias, 2020. "A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 361-381, January.
    2. Pietro Lovaglio & Mario Mezzanzanica, 2013. "Classification of longitudinal career paths," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 989-1008, February.
    3. Jost Reinecke & Daniel Seddig, 2011. "Growth mixture models in longitudinal research," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 415-434, December.
    4. Casey Codd & Robert Cudeck, 2014. "Nonlinear Random-Effects Mixture Models for Repeated Measures," Psychometrika, Springer;The Psychometric Society, vol. 79(1), pages 60-83, January.
    5. Pennoni, Fulvia & Romeo, Isabella, 2016. "Latent Markov and growth mixture models for ordinal individual responses with covariates: a comparison," MPRA Paper 72939, University Library of Munich, Germany.
    6. Lu, Zhenqiu (Laura) & Zhang, Zhiyong, 2014. "Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 220-240.
    7. repec:jss:jstsof:45:i03 is not listed on IDEAS
    8. Daniel M. McNeish, 2016. "Using Data-Dependent Priors to Mitigate Small Sample Bias in Latent Growth Models," Journal of Educational and Behavioral Statistics, , vol. 41(1), pages 27-56, February.
    9. Johan Oud & Manuel Voelkle, 2014. "Do missing values exist? Incomplete data handling in cross-national longitudinal studies by means of continuous time modeling," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3271-3288, November.
    10. Yih-Ing Hser & Haikang Shen & Chih-Ping Chou & Stephen C. Messer & M. Douglas Anglin, 2001. "Analytic Approaches for Assessing Long-Term Treatment Effects," Evaluation Review, , vol. 25(2), pages 233-262, April.
    11. Stephen Toit & Robert Cudeck, 2009. "Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable," Psychometrika, Springer;The Psychometric Society, vol. 74(1), pages 65-82, March.
    12. Jin Liu & Robert A. Perera & Le Kang & Roy T. Sabo & Robert M. Kirkpatrick, 2022. "Obtaining Interpretable Parameters From Reparameterized Longitudinal Models: Transformation Matrices Between Growth Factors in Two Parameter Spaces," Journal of Educational and Behavioral Statistics, , vol. 47(2), pages 167-201, April.
    13. Roe, R.A., 2005. "Studying time in organizational behavior," Research Memorandum 046, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    14. Michael Prendergast & David Huang & Yih-Ing Hser, 2008. "Patterns of Crime and Drug Use Trajectories in Relation to Treatment Initiation and 5-Year Outcomes," Evaluation Review, , vol. 32(1), pages 59-82, February.
    15. Silvia Bacci & Francesco Bartolucci & Giulia Bettin & Claudia Pigini, 2017. "A mixture growth model for migrants' remittances: An application to the German Socio-Economic Panel," Mo.Fi.R. Working Papers 145, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    16. Patrick Sturgis & Louise Sullivan, 2008. "Exploring social mobility with latent trajectory groups," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 65-88, January.
    17. Ellen L. Hamaker, 2005. "Conditions for the Equivalence of the Autoregressive Latent Trajectory Model and a Latent Growth Curve Model With Autoregressive Disturbances," Sociological Methods & Research, , vol. 33(3), pages 404-416, February.
    18. Getachew A. Dagne, 2016. "A growth mixture Tobit model: application to AIDS studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1174-1185, July.
    19. Bacci, Silvia & Bartolucci, Francesco & Pigini, Claudia & Signorelli, Marcello, 2014. "A finite mixture latent trajectory model for hirings and separations in the labor market," MPRA Paper 59730, University Library of Munich, Germany.
    20. Laura Castro-Schilo & Barbara L. Fredrickson & Dan Mungas, 2019. "Association of Positive Affect with Cognitive Health and Decline for Elder Mexican Americans," Journal of Happiness Studies, Springer, vol. 20(8), pages 2385-2400, December.
    21. Proust-Lima, Cécile & Joly, Pierre & Dartigues, Jean-François & Jacqmin-Gadda, Hélène, 2009. "Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1142-1154, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jedbes:v:48:y:2023:i:3:p:320-348. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.