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Nonparametric item response modeling for identifying differential item functioning in the moderate-to-small-scale testing context

Differential item functioning (DIF) can occur across age, gender, ethnic, and/or
linguistic groups of examinee populations. Therefore, whenever there is more than one
group of examinees involved in a test, a possibility of DIF exists. It is important to detect
items with DIF with accurate and powerful statistical methods. While finding a proper
DIP method is essential, until now most of the available methods have been dominated
by applications to large scale testing contexts. Since the early 1990s, Ramsay has
developed a nonparametric item response methodology and computer software, TestGraf
(Ramsay, 2000). The nonparametric item response theory (IRT) method requires fewer
examinees and items than other item response theory methods and was also designed to
detect DIF. However, nonparametric IRT's Type I error rate for DIF detection had not
been investigated.
The present study investigated the Type I error rate of the nonparametric IRT DIF
detection method, when applied to moderate-to-small-scale testing context wherein there
were 500 or fewer examinees in a group. In addition, the Mantel-Haenszel (MH) DIF
detection method was included.
A three-parameter logistic item response model was used to generate data for the
two population groups. Each population corresponded to a test of 40 items. Item statistics
for the first 34 non-DIF items were randomly chosen from the mathematics test of the
1999 TEVISS (Third International Mathematics and Science Study) for grade eight,
whereas item statistics for the last six studied items were adopted from the DIF items
used in the study of Muniz, Hambleton, and Xing (2001). These six items were the focus
of this study. / Education, Faculty of / Educational and Counselling Psychology, and Special Education (ECPS), Department of / Graduate

Identiferoai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/14971
Date11 1900
CreatorsWitarsa, Petronilla Murlita
Source SetsUniversity of British Columbia
LanguageEnglish
Detected LanguageEnglish
TypeText, Thesis/Dissertation
Format4232786 bytes, application/pdf
RightsFor non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.

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