Detecting Differential Item Functioning Using Posterior Predictive Model Checking: A Comparison of Discrepancy Statistics

Autor/inn/en	Joo, Seang-Hwane; Lee, Philseok
Titel	Detecting Differential Item Functioning Using Posterior Predictive Model Checking: A Comparison of Discrepancy Statistics
Quelle	In: Journal of Educational Measurement, 59 (2022) 4, S.442-469 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Joo, Seang-Hwane) ORCID (Lee, Philseok)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12316
Schlagwörter	Test Items; Bayesian Statistics; Monte Carlo Methods; Prediction; Models; Evaluation Methods; Error Patterns + Suchen Sie Ihr Suchwort? Test content; Testaufgabe; Monte-Carlo-Methode; Vorhersage; Analogiemodell; Fehlertyp
Abstract	Abstract This study proposes a new Bayesian differential item functioning (DIF) detection method using posterior predictive model checking (PPMC). Item fit measures including infit, outfit, observed score distribution (OSD), and Q1 were considered as discrepancy statistics for the PPMC DIF methods. The performance of the PPMC DIF method was evaluated via a Monte Carlo simulation manipulating sample size, DIF size, DIF type, DIF percentage, and subpopulation trait distribution. Parametric DIF methods, such as Lord's chi-square and Raju's area approaches, were also included in the simulation design in order to compare the performance of the proposed PPMC DIF methods to those previously existing. Based on Type I error and power analysis, we found that PPMC DIF methods showed better-controlled Type I error rates than the existing methods and comparable power to detect uniform DIF. The implications and recommendations for applied researchers are discussed. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01