Logistic regression and its use in detecting nonuniform differential item functioning in polytomous items

Persistent Link:
http://hdl.handle.net/10150/284324
Title:
Logistic regression and its use in detecting nonuniform differential item functioning in polytomous items
Author:
Wilson, Ann Wells, 1962-
Issue Date:
1993
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
A computer simulation study was conducted to determine the feasibility of using logistic regression procedures to detect nonuniform differential item functioning (DIF) in polytomous items. A simulated test of 25 items was generated, of which the 25th item contained nonuniform DIF. The degree of nonuniform DIF in the 25th item was varied in four ways. Item scores were generated using Muraki's generalized partial credit model and the data were artificially dichotomized in three different ways for the logistic regression procedure. The results indicate that logistic regression is a viable procedure in the detection of most forms of nonuniform DIF; however, it was not sensitive to DIF that is uniform within score categories and nonuniform across score categories. Logistic regression procedures were also quite awkward in the polytomous case, because several regressions must be run per polytomous item and it was difficult to determine an omnibus result in most cases. Some logistic regression procedures, however, may be useful in the post hoc analysis of DIF in polytomous items.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Education, Tests and Measurements.; Statistics.; Education, Educational Psychology.; Psychology, Psychometrics.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Educational Psychology
Degree Grantor:
University of Arizona
Advisor:
Sabers, Darrell L.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleLogistic regression and its use in detecting nonuniform differential item functioning in polytomous itemsen_US
dc.creatorWilson, Ann Wells, 1962-en_US
dc.contributor.authorWilson, Ann Wells, 1962-en_US
dc.date.issued1993en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractA computer simulation study was conducted to determine the feasibility of using logistic regression procedures to detect nonuniform differential item functioning (DIF) in polytomous items. A simulated test of 25 items was generated, of which the 25th item contained nonuniform DIF. The degree of nonuniform DIF in the 25th item was varied in four ways. Item scores were generated using Muraki's generalized partial credit model and the data were artificially dichotomized in three different ways for the logistic regression procedure. The results indicate that logistic regression is a viable procedure in the detection of most forms of nonuniform DIF; however, it was not sensitive to DIF that is uniform within score categories and nonuniform across score categories. Logistic regression procedures were also quite awkward in the polytomous case, because several regressions must be run per polytomous item and it was difficult to determine an omnibus result in most cases. Some logistic regression procedures, however, may be useful in the post hoc analysis of DIF in polytomous items.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEducation, Tests and Measurements.en_US
dc.subjectStatistics.en_US
dc.subjectEducation, Educational Psychology.en_US
dc.subjectPsychology, Psychometrics.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineEducational Psychologyen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSabers, Darrell L.en_US
dc.identifier.proquest9322643en_US
dc.identifier.bibrecord.b26933470en_US
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