Fitting Residual Error Structures for Growth Models in SAS PROC MCMC

Autor/in	McNeish, Daniel
Titel	Fitting Residual Error Structures for Growth Models in SAS PROC MCMC
Quelle	In: Educational and Psychological Measurement, 77 (2017) 4, S.587-612 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164416652441
Schlagwörter	Models; Bayesian Statistics; Statistical Analysis; Computer Software; Error of Measurement; Markov Processes; Monte Carlo Methods + Suchen Sie Ihr Suchwort? Analogiemodell; Statistische Analyse; Messfehler; Markowscher Prozess; Monte-Carlo-Methode
Abstract	In behavioral sciences broadly, estimating growth models with Bayesian methods is becoming increasingly common, especially to combat small samples common with longitudinal data. Although Mplus is becoming an increasingly common program for applied research employing Bayesian methods, the limited selection of prior distributions for the elements of covariance structures makes more general software more advantages under certain conditions. However, as a disadvantage of general software's software flexibility, few preprogrammed commands exist for specifying covariance structures. For instance, PROC MIXED has a few dozen such preprogrammed options, but when researchers divert to a Bayesian framework, software offer no such guidance and requires researchers to manually program these different structures, which is no small task. As such the literature has noted that empirical papers tend to simplify their covariance matrices to circumvent this difficulty, which is not desirable because such a simplification will likely lead to biased estimates of variance components and standard errors. To facilitate wider implementation of Bayesian growth models that properly model covariance structures, this article overviews how to generally program a growth model in SAS PROC MCMC and then demonstrates how to program common residual error structures. Full annotated SAS code and an applied example are provided. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01