Handling Missing Data in Growth Mixture Models

Autor/inn/en	Lee, Daniel Y.; Harring, Jeffrey R.
Titel	Handling Missing Data in Growth Mixture Models
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 3, S.320-348 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986221149140
Schlagwörter	Monte Carlo Methods; Research Problems; Statistical Inference; Bayesian Statistics; Maximum Likelihood Statistics; Algorithms; Item Response Theory; Statistical Bias; Growth Models + Suchen Sie Ihr Suchwort? Monte-Carlo-Methode; Forschungskritik; Inferential statistics; Schließende Statistik; Algorithm; Algorithmus; Item-Response-Theorie
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01