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Loglinear Model as a DIF Detection Method for Dichotomous and Polytomous Items and Its Comparison with Other Observed Score Matching DIF Methods

Title: Loglinear Model as a DIF Detection Method for Dichotomous and Polytomous Items and Its Comparison with Other Observed Score Matching DIF Methods.
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Name(s): Yesiltas, Gonca, author
Paek, Insu, professor directing dissertation
Huffer, Fred W. (Fred William), university representative
Becker, Betsy Jane, committee member
Almond, Russell G., committee member
Florida State University, degree granting institution
College of Education, degree granting college
Department of Educational Psychology and Learning Systems, degree granting department
Type of Resource: text
Genre: Text
Issuance: monographic
Date Issued: 2016
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (136 pages)
Language(s): English
Abstract/Description: DIF detection methods identify the difference between the performances of subgroups when the subgroups are matched by examinees' ability level or a proxy variable, such as total test score (Holland & Wainer, 1993). Log-linear Models (LLM) method is one of the DIF detection methods. This method was first introduced by Mellenbergh (1982) to investigate the relationship among item responses, subgroups, and categorized total test score in terms of DIF detection. This study examined the performance of LLM as a DIF detection method for dichotomous items and polytomous items. LLM method was compared with Mantel-Haenszsel (MH) and logistic regression (LR) methods to detect uniform DIF and with LR to detect non-uniform DIF in dichotomous item response data. MH was not included in non-uniform DIF detection, because, the previous studies indicated that it is not able to detect non-uniform DIF (Narayanon & Swaminathan, 1996; Uttaro & Milsap, 1994). In addition, LLM was compared with Mantel, generalized Mantel-Haenszsel (GMH), ordinal logistic regression (OLR), logistic discriminate function analysis (LDFA) methods in polytomous item response data. For this purpose, both simulation study and empirical study were conducted under various sample sizes, ability mean differences (impact) and item parameters. Since the previous studies did not investigate the effect of ability mean differences on DIF detection with LLM, this study also focused on the effect of ability mean differences between subgroups. This study found that MH was better to detect uniform DIF when LR and LLM indicated equally well performance on uniform and non-uniform DIF detection. In Addition, GMH and LLM performed better than Mantel, OLR, and LDFA for the polytomous item response data.
Identifier: FSU_2016SP_Yesiltas_fsu_0071E_13119 (IID)
Submitted Note: A Dissertation submitted to the Department of Educational Psychology and Learning Systems in partial fulfillment of the Doctor of Philosophy.
Degree Awarded: Spring Semester 2016.
Date of Defense: April 14, 2016.
Bibliography Note: Includes bibliographical references.
Advisory Committee: Insu Paek, Professor Directing Dissertation; Fred Huffer, University Representative; Betsy Jane Becker, Committee Member; Russell Almond, Committee Member.
Subject(s): Educational psychology
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_2016SP_Yesiltas_fsu_0071E_13119
Owner Institution: FSU

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Yesiltas, G. (2016). Loglinear Model as a DIF Detection Method for Dichotomous and Polytomous Items and Its Comparison with Other Observed Score Matching DIF Methods. Retrieved from http://purl.flvc.org/fsu/fd/FSU_2016SP_Yesiltas_fsu_0071E_13119