Robust Bayesian Approaches in Growth Curve Modeling: Using Student's "t" Distributions versus a Semiparametric Method

Autor/inn/en	Tong, Xin; Zhang, Zhiyong
Titel	Robust Bayesian Approaches in Growth Curve Modeling: Using Student's "t" Distributions versus a Semiparametric Method
Quelle	(2020), (45 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Robustness (Statistics); Bayesian Statistics; Models; Error of Measurement; Statistical Distributions; Monte Carlo Methods; Mathematics Skills; Achievement Tests; Mathematics Tests; Scores; National Surveys; Longitudinal Studies; Peabody Individual Achievement Test; National Longitudinal Survey of Youth + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit; Analogiemodell; Messfehler; Wahrscheinlichkeitsverteilung; Monte-Carlo-Methode; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung
Abstract	Despite broad applications of growth curve models, few studies have dealt with a practical issue -- nonnormality of data. Previous studies have used Student's "t" distributions to remedy the nonnormal problems. In this study, robust distributional growth curve models are proposed from a semiparametric Bayesian perspective, in which intraindividual measurement errors follow unknown random distributions with Dirichlet process mixture priors. Based on Monte Carlo simulations, we evaluate the performance of the robust semiparametric Bayesian method and compare it to the robust method using Student's "t" distributions as well as the traditional normal-based method. We conclude that the semiparametric Bayesian method is more robust against nonnormal data. An example about the development of mathematical abilities is provided to illustrate the application of robust growth curve modeling, using school children's Peabody Individual Achievement Test mathematical test scores from the National Longitudinal Survey of Youth 1997 Cohort. [This paper was published in "Structural Equation Modeling" v27 n4 p544-560 2020.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01