Simultaneously Modeling Differential Testlet Functioning and Differential Item Functioning: Addressing Variance Heterogeneity with a Multigroup One-Parameter Testlet Model

Autor/inn/en	Luo, Yong; Liang, Xinya
Titel	Simultaneously Modeling Differential Testlet Functioning and Differential Item Functioning: Addressing Variance Heterogeneity with a Multigroup One-Parameter Testlet Model
Quelle	In: Measurement: Interdisciplinary Research and Perspectives, 17 (2019) 2, S.93-105 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1536-6367
DOI	10.1080/15366367.2018.1533783
Schlagwörter	Test Items; Item Response Theory; Test Bias; Models; Markov Processes; Monte Carlo Methods; Testing Problems + Suchen Sie Ihr Suchwort? Test content; Testaufgabe; Item-Response-Theorie; Testkritik; Analogiemodell; Markowscher Prozess; Monte-Carlo-Methode
Abstract	Current methods that simultaneously model differential testlet functioning (DTLF) and differential item functioning (DIF) constrain the variances of latent ability and testlet effects to be equal between the focal and the reference groups. Such a constraint can be stringent and unrealistic with real data. In this study, we propose a multigroup one-parameter testlet model (M1PLTM) that relaxes this equal variance assumption and also allows simultaneous modeling of DTLF and DIF. M1PLTM is a more generalized version of the extant models. The results of a simulation study show that parameters of M1PLTM could be accurately recovered via a Markov chain Monte Carlo (MCMC) method; when data were generated based on M1PLTM, falsely assuming equal variances of latent ability and testlet effects resulted in inferior parameter recovery. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01