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Bayesian Network Models for Local Dependence Among Observable Outcome Variables

Author(s):
Almond, Russell G.; Mulder, Joris; Hemat, Lisa; Yan, Duanli
Publication Year:
2006
Report Number:
RR-06-36
Source:
ETS Research Report
Document Type:
Report
Page Count:
32
Subject/Key Words:
Bayesian Methods, Testlets, Mantel-Haenszel Technique, Context

Abstract

This paper explores these 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 to be 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 the other models and should be explored with further research.

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