Differential Item Functioning Effect Size from the Multigroup Confirmatory Factor Analysis for a Meta-Analysis: A Simulation Study

Autor/inn/en	Park, Sung Eun; Ahn, Soyeon; Zopluoglu, Cengiz
Titel	Differential Item Functioning Effect Size from the Multigroup Confirmatory Factor Analysis for a Meta-Analysis: A Simulation Study
Quelle	In: Educational and Psychological Measurement, 81 (2021) 1, S.182-199 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Park, Sung Eun) ORCID (Zopluoglu, Cengiz)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164420925885
Schlagwörter	Item Analysis; Effect Size; Difficulty Level; Monte Carlo Methods; Sample Size; Correlation; Error of Measurement; Factor Analysis; Test Items; Evaluation Methods; Statistical Bias; Item Response Theory + Suchen Sie Ihr Suchwort? Itemanalyse; Schwierigkeitsgrad; Monte-Carlo-Methode; Korrelation; Messfehler; Faktorenanalyse; Test content; Testaufgabe; Item-Response-Theorie
Abstract	This study presents a new approach to synthesizing differential item functioning (DIF) effect size: First, using correlation matrices from each study, we perform a multigroup confirmatory factor analysis (MGCFA) that examines measurement invariance of a test item between two subgroups (i.e., focal and reference groups). Then we synthesize, across the studies, the differences in the estimated factor loadings between the two subgroups, resulting in a meta-analytic summary of the MGCFA effect sizes (MGCFA-ES). The performance of this new approach was examined using a Monte Carlo simulation, where we created 108 conditions by four factors: (1) three levels of item difficulty, (2) four magnitudes of DIF, (3) three levels of sample size, and (4) three types of correlation matrix (tetrachoric, adjusted Pearson, and Pearson). Results indicate that when MGCFA is fitted to tetrachoric correlation matrices, the meta-analytic summary of the MGCFA-ES performed best in terms of bias and mean square error values, 95% confidence interval coverages, empirical standard errors, Type I error rates, and statistical power; and reasonably well with adjusted Pearson correlation matrices. In addition, when tetrachoric correlation matrices are used, a meta-analytic summary of the MGCFA-ES performed well, particularly, under the condition that a high difficulty item with a large DIF was administered to a large sample size. Our result offers an option for synthesizing the magnitude of DIF on a flagged item across studies in practice. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01