Using Data-Dependent Priors to Mitigate Small Sample Bias in Latent Growth Models: A Discussion and Illustration Using M"plus"

Autor/in	McNeish, Daniel M.
Titel	Using Data-Dependent Priors to Mitigate Small Sample Bias in Latent Growth Models: A Discussion and Illustration Using M"plus"
Quelle	In: Journal of Educational and Behavioral Statistics, 41 (2016) 1, S.27-56 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998615621299
Schlagwörter	Models; Statistical Analysis; Hierarchical Linear Modeling; Sample Size; Computer Software; Statistical Bias; Bayesian Statistics; Comparative Analysis; Differences; Markov Processes; Monte Carlo Methods; Maximum Likelihood Statistics; Computation + Suchen Sie Ihr Suchwort? Analogiemodell; Statistische Analyse; Unterscheiden; Markowscher Prozess; Monte-Carlo-Methode
Abstract	Mixed-effects models (MEMs) and latent growth models (LGMs) are often considered interchangeable save the discipline-specific nomenclature. Software implementations of these models, however, are not interchangeable, particularly with small sample sizes. Restricted maximum likelihood estimation that mitigates small sample bias in MEMs has not been widely developed for LGMs, and fully Bayesian methods, while not dependent on asymptotics, can encounter issues because the choice for the factor covariance matrix prior distribution has substantial influence with small samples. This tutorial discusses differences between LGMs and MEMs and demonstrates how data-dependent priors, an established class of methods that blend frequentist and Bayesian paradigms, can be implemented within M"plus" 7.1 to abate the small sample bias that is prevalent with LGM software while keeping additional programming to the bare minimum. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01