Parameter Estimation in Rasch Models for Examinee-Selected Items

Autor/inn/en	Liu, Chen-Wei; Wang, Wen-Chung
Titel	Parameter Estimation in Rasch Models for Examinee-Selected Items
Quelle	In: Journal of Educational Measurement, 54 (2017) 4, S.518-549 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
Schlagwörter	Item Response Theory; Models; Maximum Likelihood Statistics; Data Analysis; Simulation; Test Items; Test Format; Computation; Evaluation Research + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Analogiemodell; Auswertung; Simulation program; Simulationsprogramm; Test content; Testaufgabe; Testentwicklung; Evaluationsforschung
Abstract	The examinee-selected-item (ESI) design, in which examinees are required to respond to a fixed number of items in a given set of items (e.g., choose one item to respond from a pair of items), always yields incomplete data (i.e., only the selected items are answered and the others have missing data) that are likely nonignorable. Therefore, using standard item response theory models, which assume ignorable missing data, can yield biased parameter estimates so that examinees taking different sets of items to answer cannot be compared. To solve this fundamental problem, in this study the researchers utilized the specific objectivity of Rasch models by adopting the conditional maximum likelihood estimation (CMLE) and pairwise estimation (PE) methods to analyze ESI data, and conducted a series of simulations to demonstrate the advantages of the CMLE and PE methods over traditional estimation methods in recovering item parameters in ESI data. An empirical data set obtained from an experiment on the ESI design was analyzed to illustrate the implications and applications of the proposed approach to ESI data. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01