Bayesian Analysis of Multidimensional Item Response Theory Models: A Discussion and Illustration of Three Response Style Models

Autor/inn/en	Leventhal, Brian C.; Stone, Clement A.
Titel	Bayesian Analysis of Multidimensional Item Response Theory Models: A Discussion and Illustration of Three Response Style Models
Quelle	In: Measurement: Interdisciplinary Research and Perspectives, 16 (2018) 2, S.114-128 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1536-6367
DOI	10.1080/15366367.2018.1437306
Schlagwörter	Bayesian Statistics; Item Response Theory; Models; Psychometrics; Computer Software; Markov Processes; Monte Carlo Methods; Goodness of Fit; Correlation + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Analogiemodell; Psychometry; Psychometrie; Markowscher Prozess; Monte-Carlo-Methode; Korrelation
Abstract	Interest in Bayesian analysis of item response theory (IRT) models has grown tremendously due to the appeal of the paradigm among psychometricians, advantages of these methods when analyzing complex models, and availability of general-purpose software. Possible models include models which reflect multidimensionality due to designed test structure, construct-irrelevant variance, and mixed item formats. Using Markov Chain Monte Carlo methods, models can be estimated and evaluated for model-fit. In addition to discussing Bayesian analysis, analyses of three IRT models designed to account for extreme response style are illustrated: IRTree, multidimensional nominal response model (MNRM), and modified generalized partial credit model (MPCM). While results indicated there may be little impact of model choice on substantive trait estimates for the data set studied herein, model-fit results favored the MNRM and MPCM over the IRTree Model. (As Provided).
Anmerkungen	Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01