CATSIB: A Modified SIBTEST Procedure To Detect Differential Item Functioning in Computerized Adaptive Tests. Law School Admission Council Computerized Testing Report. LSAC Research Report Series.

Autor/inn/en	Nandakumar, Ratna; Roussos, Louis
Institution	Law School Admission Council, Princeton, NJ.
Titel	CATSIB: A Modified SIBTEST Procedure To Detect Differential Item Functioning in Computerized Adaptive Tests. Law School Admission Council Computerized Testing Report. LSAC Research Report Series.
Quelle	(2001), (18 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Ability; Adaptive Testing; Computer Assisted Testing; Identification; Item Banks; Item Bias; Item Response Theory; Regression (Statistics); Simulation + Suchen Sie Ihr Suchwort? Fähigkeit; Fertigkeit; Identifikation; Identifizierung; Item-Response-Theorie; Regression; Regressionsanalyse; Simulation program; Simulationsprogramm
Abstract	Computerized adaptive tests (CATs) pose major obstacles to the traditional assessment of differential item functioning (DIF). This paper proposes a modification of the SIBTEST DIF procedure for CATs, called CATSIB. CATSIB matches test takers on estimated ability based on unidimensional item response theory. To control for impact-induced Type I error inflation, the SIBTEST regression correction is shown to have an easily implemented and theoretically justified counterpart in the CAT setting. A simulation study was conducted to evaluate the performance of CATSIB. Simulated test takers were adaptively administered 25 simulated operational items from a pool of 1,000 and were linearly administered 16 simulated pretest items that were evaluated for DIF. The pretest items were designed to represent varying levels of discrimination, difficulty, and amounts of DIF. Sample size varied from 250 to 500 in each group. Simulated levels of impact ranged from 0 to 1 standard deviations difference in mean ability levels. Results show that CATSIB with the regression correction displays impact-induced Type I error inflation. In terms of power, even with as few as 250 test takers in each group, CATSIB had detection rates of 64% or greater for large values of DIF. When sample size was increased to 500 in each group, these power rates increased to more than 90%. CATSIB displayed nearly unbiased estimation under nearly all the simulated conditions. (Contains 7 tables, 1 figure, and 11 references.) (Author/SLD)
Erfasst von	ERIC (Education Resources Information Center), Washington, DC