Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models

Autor/inn/en	Liu, Yang; Wang, Xiaojing
Titel	Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models
Quelle	In: Journal of Educational and Behavioral Statistics, 45 (2020) 3, S.274-296 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998619887913
Schlagwörter	Bayesian Statistics; Nonparametric Statistics; Regression (Statistics); Item Response Theory; Markov Processes; Monte Carlo Methods; Educational Testing; Longitudinal Studies + Suchen Sie Ihr Suchwort? Regression; Regressionsanalyse; Item-Response-Theorie; Markowscher Prozess; Monte-Carlo-Methode; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung
Abstract	Parametric methods, such as autoregressive models or latent growth modeling, are usually inflexible to model the dependence and nonlinear effects among the changes of latent traits whenever the time gap is irregular and the recorded time points are individually varying. Often in practice, the growth trend of latent traits is subject to certain monotone and smooth conditions. To incorporate such conditions and to alleviate the strong parametric assumption on regressing latent trajectories, a flexible nonparametric prior has been introduced to model the dynamic changes of latent traits for item response theory models over the study period. Suitable Bayesian computation schemes are developed for such analysis of the longitudinal and dichotomous item responses. Simulation studies and a real data example from educational testing have been used to illustrate our proposed methods. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01