Calibration of Polytomous Item Families Using Bayesian Hierarchical Modeling

Autor/inn/en	Johnson, Matthew S.; Sinharay, Sandip
Titel	Calibration of Polytomous Item Families Using Bayesian Hierarchical Modeling
Quelle	In: Applied Psychological Measurement, 29 (2005) 5, S.369-400 (32 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621605276675
Schlagwörter	National Competency Tests; Markov Processes; Bayesian Statistics + Suchen Sie Ihr Suchwort? Markowscher Prozess
Abstract	For complex educational assessments, there is an increasing use of item families, which are groups of related items. Calibration or scoring in an assessment involving item families requires models that can take into account the dependence structure inherent among the items that belong to the same item family. This article extends earlier works in three directions: (a) extends the model to take into account item families with polytomous items and implements a Markov chain Monte Carlo algorithm for the estimation of the model parameters, (b) generalizes family response functions to polytomous item families and defines the family score function as ways to examine each family graphically, and (c) uses Bayes factors to select either the more complicated model or a simple model. All three extensions of the earlier works on item families are demonstrated using two data sets: one simulated and one from the National Assessment of Educational Progress. (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10