Using Log-Linear Smoothing to Improve Small-Sample DIF Estimation

Autor/inn/en	Puhan, Gautam; Moses, Timothy P.; Yu, Lei; Dorans, Neil J.
Titel	Using Log-Linear Smoothing to Improve Small-Sample DIF Estimation
Quelle	In: Journal of Educational Measurement, 46 (2009) 1, S.59-83 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/j.1745-3984.2009.01069.x
Schlagwörter	Test Items; Reference Groups; Testing Programs; Raw Scores; African Americans; Test Bias; Sample Size; Whites; Focus Groups; Simulation + Suchen Sie Ihr Suchwort? Test content; Testaufgabe; Reference group; Bezugsgruppe; Afroamerikaner; Testkritik; White; Weißer; Simulation program; Simulationsprogramm
Abstract	This study examined the extent to which log-linear smoothing could improve the accuracy of differential item functioning (DIF) estimates in small samples of examinees. Examinee responses from a certification test were analyzed using White examinees in the reference group and African American examinees in the focal group. Using a simulation approach, separate DIF estimates for seven small-sample-size conditions were obtained using unsmoothed (U) and smoothed (S) score distributions. These small sample U and S DIF estimates were compared to a criterion (i.e., DIF estimates obtained using the unsmoothed total data) to assess their degree of variability (random error) and accuracy (bias). Results indicate that for most studied items smoothing the raw score distributions reduced random error and bias of the DIF estimates, especially in the small-sample-size conditions. Implications of these results for operational testing programs are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10