An Information Matrix Test for the Collapsing of Categories under the Partial Credit Model

Autor/inn/en	Harel, Daphna; Steele, Russell J.
Titel	An Information Matrix Test for the Collapsing of Categories under the Partial Credit Model
Quelle	In: Journal of Educational and Behavioral Statistics, 43 (2018) 6, S.721-750 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Harel, Daphna)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998618787478
Schlagwörter	Matrices; Models; Item Response Theory; Research Methodology; Classification; Data Analysis; Goodness of Fit; Simulation + Suchen Sie Ihr Suchwort? Matrizenrechnung; Analogiemodell; Item-Response-Theorie; Research method; Forschungsmethode; Classification system; Klassifikation; Klassifikationssystem; Auswertung; Simulation program; Simulationsprogramm
Abstract	Collapsing categories is a commonly used data reduction technique; however, to date there do not exist principled methods to determine whether collapsing categories is appropriate in practice. With ordinal responses under the partial credit model, when collapsing categories, the true model for the collapsed data is no longer a partial credit model, and therefore refitting a partial credit model may result in model misspecification. This article details the implementation and performance of an information matrix test (IMT) to assess the implications of collapsing categories for a given data set under the partial credit model and compares its performance to the application of a nominal response model (NRM) and the S - X[superscript 2] goodness-of-fit statistic. The IMT and NRM-based test are able to correctly determine the true number of categories for an item, given reasonable power through this goodness-of-fit test. We conclude by applying the test to a well-studied data set from the literature. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01