Using Past Data to Enhance Small Sample DIF Estimation: A Bayesian Approach

Sandip Sinharay, Neil J. Dorans, Mary C. Grant and Edwin O. Blew

Journal of Educational and Behavioral Statistics, 2009, vol. 34, issue 1, 74-96

Abstract: Test administrators often face the challenge of detecting differential item functioning (DIF) with samples of size smaller than that recommended by experts. A Bayesian approach can incorporate, in the form of a prior distribution, existing information on the inference problem at hand, which yields more stable estimation, especially for small samples. A large volume of past data is available for many operational tests and such data could be used to establish prior distributions for a Bayesian DIF analysis. This article discusses how to perform such an analysis. The suggested approach is found to be more conservative and preferable with respect to several overall criteria than the existing DIF detection methods in a realistic simulation study.

Keywords: empirical Bayes; loss function; Mantel-Haenszel statistic; prior distribution (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/1076998607309021 (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:34:y:2009:i:1:p:74-96

DOI: 10.3102/1076998607309021

Access Statistics for this article

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