A Monte Carlo Study Investigating Missing Data, Differential Item Functioning, and Effect Size

Autor/in	Garrett, Phyllis
Titel	A Monte Carlo Study Investigating Missing Data, Differential Item Functioning, and Effect Size
Quelle	(2009), (137 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, Georgia State University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-1096-8901-3
Schlagwörter	Hochschulschrift; Dissertation; Sample Size; Monte Carlo Methods; Test Validity; Effect Size; Test Bias; Error Patterns; Error Correction; Evaluation Methods + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Monte-Carlo-Methode; Testvalidität; Testkritik; Fehlertyp; Korrektur
Abstract	The use of polytomous items in assessments has increased over the years, and as a result, the validity of these assessments has been a concern. Differential item functioning (DIF) and missing data are two factors that may adversely affect assessment validity. Both factors have been studied separately, but DIF and missing data are likely to occur simultaneously in real assessment situations. This study investigated the Type I error and power of several DIF detection methods and methods of handling missing data for polytomous items generated under the partial credit model. The Type I error and power of the Mantel and ordinal logistic regression were compared using within-person mean substitution and multiple imputation when data were missing completely at random. In addition to assessing the Type I error and power of DIF detection methods and methods of handling missing data, this study also assessed the impact of missing data on the effect size measure associated with the Mantel, the standardized mean difference effect size measure, and ordinal logistic regression, the R-squared effect size measure. Results indicated that the performance of the Mantel and ordinal logistic regression depended on the percent of missing data in the data set, the magnitude of DIF, and the sample size ratio. The Type I error for both DIF detection methods varied based on the missing data method used to impute the missing data. Power to detect DIF increased as DIF magnitude increased, but there was a relative decrease in power as the percent of missing data increased. Additional findings indicated that the percent of missing data, DIF magnitude, and sample size ratio also influenced the effect size measures associated with the Mantel and ordinal logistic regression. The effect size values for both DIF detection methods generally increased as DIF magnitude increased, but as the percent of missing data increased, the effect size values decreased. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10