Bayesian Network Models for Local Dependence among Observable Outcome Variables

Autor/inn/en	Almond, Russell G.; Mulder, Joris; Hemat, Lisa A.; Yan, Duanli
Titel	Bayesian Network Models for Local Dependence among Observable Outcome Variables
Quelle	In: Journal of Educational and Behavioral Statistics, 34 (2009) 4, S.491-521 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998609332751
Schlagwörter	Bayesian Statistics; Models; Observation; Experiments; Simulation; Test Construction; Achievement Tests + Suchen Sie Ihr Suchwort? Analogiemodell; Beobachtung; Erprobung; Simulation program; Simulationsprogramm; Testaufbau; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen
Abstract	Bayesian network models offer a large degree of flexibility for modeling dependence among observables (item outcome variables) from the same task, which may be dependent. This article explores four design patterns for modeling locally dependent observations: (a) no context--ignores dependence among observables; (b) compensatory context--introduces a latent variable, context, to model task-specific knowledge and use a compensatory model to combine this with the relevant proficiencies; (c) inhibitor context--introduces a latent variable, context, to model task-specific knowledge and use an inhibitor (threshold) model to combine this with the relevant proficiencies; (d) compensatory cascading--models each observable as dependent on the previous one in sequence. This article explores the four design patterns through experiments with simulated and real data. When the proficiency variable is categorical, a simple Mantel-Haenszel procedure can test for local dependence. Although local dependence can cause problems in the calibration, if the models based on these design patterns are successfully calibrated to data, all the design patterns appear to provide very similar inferences about the students. Based on these experiments, the simpler no context design pattern appears more stable than the compensatory context model, while not significantly affecting the classification accuracy of the assessment. The cascading design pattern seems to pick up on dependencies missed by other models and should be explored with further research. (Contains 1 note, 3 tables, and 9 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10