Model Diagnostics for Bayesian Networks

Sandip Sinharay

Journal of Educational and Behavioral Statistics, 2006, vol. 31, issue 1, 1-33

Abstract: Bayesian networks are frequently used in educational assessments primarily for learning about students’ knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A number of aspects of model fit, those of usual interest to practitioners, are assessed using various diagnostic tools. This article suggests a direct data display for assessing overall fit, suggests several diagnostics for assessing item fit, suggests a graphical approach to examine if the model can explain the association among the items, and suggests a version of the Mantel–Haenszel statistic for assessing differential item functioning. Limited simulation studies and a real data application demonstrate the effectiveness of the suggested model diagnostics.

Keywords: discrepancy measure; Mantel–Haenszel statistic; p-values; posterior predictive model checking (search for similar items in EconPapers)
Date: 2006
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/10769986031001001 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:31:y:2006:i:1:p:1-33

DOI: 10.3102/10769986031001001

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().