A General Bayesian Multidimensional Item Response Theory Model for Small and Large Samples

Autor/inn/en	Fujimoto, Ken A.; Neugebauer, Sabina R.
Titel	A General Bayesian Multidimensional Item Response Theory Model for Small and Large Samples
Quelle	In: Educational and Psychological Measurement, 80 (2020) 4, S.665-694 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Fujimoto, Ken A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164419891205
Schlagwörter	Bayesian Statistics; Item Response Theory; Sample Size; Factor Structure; Statistical Inference; Simulation + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Faktorenstruktur; Inferential statistics; Schließende Statistik; Simulation program; Simulationsprogramm
Abstract	Although item response theory (IRT) models such as the bifactor, two-tier, and between-item-dimensionality IRT models have been devised to confirm complex dimensional structures in educational and psychological data, they can be challenging to use in practice. The reason is that these models are multidimensional IRT (MIRT) models and thus are highly parameterized, making them only suitable for data provided by large samples. Unfortunately, many educational and psychological studies are conducted on a small scale, leaving the researchers without the necessary MIRT models to confirm the hypothesized structures in their data. To address the lack of modeling options for these researchers, we present a general Bayesian MIRT model based on adaptive informative priors. Simulations demonstrated that our MIRT model could be used to confirm a two-tier structure (with two general and six specific dimensions), a bifactor structure (with one general and six specific dimensions), and a between-item six-dimensional structure in rating scale data representing sample sizes as small as 100. Although our goal was to provide a general MIRT model suitable for smaller samples, the simulations further revealed that our model was applicable to larger samples. We also analyzed real data from 121 individuals to illustrate that the findings of our simulations are relevant to real situations. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01