Computerized Classification Testing under the Generalized Graded Unfolding Model

Autor/inn/en	Wang, Wen-Chung; Liu, Chen-Wei
Titel	Computerized Classification Testing under the Generalized Graded Unfolding Model
Quelle	In: Educational and Psychological Measurement, 71 (2011) 1, S.114-128 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164410391575
Schlagwörter	Computer Assisted Testing; Adaptive Testing; Classification; Item Response Theory; Likert Scales; Test Items; Selection; Models; Test Length; Computation; Statistical Analysis; Cutting Scores + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Item-Response-Theorie; Likert-Skala; Test content; Testaufgabe; Auslese; Analogiemodell; Statistische Analyse
Abstract	The generalized graded unfolding model (GGUM) has been recently developed to describe item responses to Likert items (agree-disagree) in attitude measurement. In this study, the authors (a) developed two item selection methods in computerized classification testing under the GGUM, the current estimate/ability confidence interval method and the cut score/sequential probability ratio test method and (b) evaluated their accuracy and efficiency in classification through simulations. The results indicated that both methods were very accurate and efficient. The more points each item had and the fewer the classification categories, the more accurate and efficient the classification would be. However, the latter method may yield a very low accuracy in dichotomous items with a short maximum test length. Thus, if it is to be used to classify examinees with dichotomous items, the maximum text length should be increased. (Contains 3 figures and 2 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10