Estimating a Noncompensatory IRT Model Using Metropolis within Gibbs Sampling

Autor/in	Babcock, Ben
Titel	Estimating a Noncompensatory IRT Model Using Metropolis within Gibbs Sampling
Quelle	In: Applied Psychological Measurement, 35 (2011) 4, S.317-329 (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621610392366
Schlagwörter	Item Response Theory; Sampling; Computation; Statistical Analysis; Models; Test Items; Bayesian Statistics; Monte Carlo Methods; Markov Processes + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Statistische Analyse; Analogiemodell; Test content; Testaufgabe; Monte-Carlo-Methode; Markowscher Prozess
Abstract	Relatively little research has been conducted with the noncompensatory class of multidimensional item response theory (MIRT) models. A Monte Carlo simulation study was conducted exploring the estimation of a two-parameter noncompensatory item response theory (IRT) model. The estimation method used was a Metropolis-Hastings within Gibbs algorithm that accepted or rejected new parameters in a bivariate fashion. Results showed that acceptable estimation of the noncompensatory model required a sample size of 4,000 people, six unidimensional items per dimension, and latent traits that are not highly correlated. Although the data requirements to estimate this model are a bit daunting, future advances in methodology could make this model valuable for modeling multidimensional data where the latent traits are not expected to be highly correlated. (Contains 5 tables and 2 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10