Using Stan for Item Response Theory Models

Autor/inn/en	Ames, Allison J.; Au, Chi Hang
Titel	Using Stan for Item Response Theory Models
Quelle	In: Measurement: Interdisciplinary Research and Perspectives, 16 (2018) 2, S.129-134 (6 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1536-6367
DOI	10.1080/15366367.2018.1437304
Schlagwörter	Item Response Theory; Computer Software Evaluation; Programming Languages; Bayesian Statistics; Monte Carlo Methods; Efficiency; Usability; Computer Interfaces + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Softwareanalyse; Monte-Carlo-Methode; Effectiveness; Effektivität; Wirkungsgrad
Abstract	Stan is a flexible probabilistic programming language providing full Bayesian inference through Hamiltonian Monte Carlo algorithms. The benefits of Hamiltonian Monte Carlo include improved efficiency and faster inference, when compared to other MCMC software implementations. Users can interface with Stan through a variety of computing environments, including R, Python, MATLAB, Stata, and Mathematica. Programs written in Stan are portable across these interfaces, encouraging collaboration and transparency. These benefits, and others, offer several advantages for measurement practitioners; this review uses a simple example of Stan for a two-parameter logistic IRT model to illustrate the utility of Stan and its relevant features. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01