Behaviormetrika
Online ISSN : 1349-6964
Print ISSN : 0385-7417
ISSN-L : 0385-7417
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PARAMETER RECOVERY STUDIES WITH A DIAGNOSTIC BAYESIAN NETWORK MODEL
Russell AlmondDuanli YanLisa Hemat
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2007 Volume 35 Issue 2 Pages 159-185

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Abstract

This paper describes a Bayesian network model for a candidate assessment design that had four proficiency variables and 48 tasks with 3-12 observable outcome variables per task and scale anchors to identify the location of the subscales. The domain experts' view of the relationship among proficiencies and tasks established a complex prior distribution over 585 parameters. Markov Chain Monte Carlo (MCMC) estimation recovered the parameters of data simulated from the expert model. The sample size and the strength of the prior had only a modest effect on parameter recovery, but did affect the standard error of estimated parameters. Finally, an identifiability issue involving relabeling of proficiency states and permutations of the matrixes is addressed in the context of this study.

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© 2007 The Behaviormetric Society
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