A Comparison of Estimation Techniques for IRT Models with Small Samples

Autor/inn/en	Finch, Holmes; French, Brian F.
Titel	A Comparison of Estimation Techniques for IRT Models with Small Samples
Quelle	In: Applied Measurement in Education, 32 (2019) 2, S.77-96 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2019.1577243
Schlagwörter	Item Response Theory; Accuracy; Test Items; Difficulty Level; Maximum Likelihood Statistics; Models; Error of Measurement; Comparative Analysis; Monte Carlo Methods + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Test content; Testaufgabe; Schwierigkeitsgrad; Analogiemodell; Messfehler; Monte-Carlo-Methode
Abstract	The usefulness of item response theory (IRT) models depends, in large part, on the accuracy of item and person parameter estimates. For the standard 3 parameter logistic model, for example, these parameters include the item parameters of difficulty, discrimination, and pseudo-chance, as well as the person ability parameter. Several factors impact traditional marginal maximum likelihood (ML) estimation of IRT model parameters, including sample size, with smaller samples generally being associated with lower parameter estimation accuracy, and inflated standard errors for the estimates. Given this deleterious impact of small samples on IRT model performance, use of these techniques with low-incidence populations, where it might prove to be particularly useful, estimation becomes difficult, especially with more complex models. Recently, a Pairwise estimation method for Rasch model parameters has been suggested for use with missing data, and may also hold promise for parameter estimation with small samples. This simulation study compared item difficulty parameter estimation accuracy of ML with the Pairwise approach to ascertain the benefits of this latter method. The results support the use of the Pairwise method with small samples, particularly for obtaining item location estimates. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01