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Detection of latent differential item functioning (DIF) using mixture 2PL IRT model with covariate

Zhang, Ya (2017) Detection of latent differential item functioning (DIF) using mixture 2PL IRT model with covariate. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Mixture IRT models have been shown to improve the identification of latent group structure and facilitate the estimation of model parameters when covariates are incorporated or the Bayesian estimation method is employed. However, the efficiency of mixture IRT models in DIF analysis has not been systematically studied due to the challenges of identifying DIF with a relatively complex model. The present dissertation aims to explore the effect of covariate and estimation method on the detection of latent DIF under the mixture IRT framework. A Monte Carlo simulation study was performed by manipulating the magnitude of DIF, type of DIF, proportion of DIF items, group impact, and relationship between the covariate and the latent group membership. The generated response data were analyzed using the mixture 2PL IRT model by manipulating the inclusion of covariate and the estimation method. The estimation results were evaluated in terms of the recovery of the latent group structure, recovery of the model parameters, and detection of DIF. The goal is to provide insights and suggestions on the use of mixture IRT models in the analysis of DIF.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zhang, Yayaz55@pitt.eduyaz55
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorLane, Suzannesl@pitt.edusl
Thesis AdvisorStone, Clementcas@pitt.educas
Committee MemberYe, Feifeifeifeiye@pitt.edufeifeiye
Committee MemberTherhost, Laurenlat15@pitt.edulat15
Date: 29 September 2017
Date Type: Publication
Defense Date: 16 August 2017
Approval Date: 29 September 2017
Submission Date: 28 September 2017
Access Restriction: 3 year -- Restrict access to University of Pittsburgh for a period of 3 years.
Number of Pages: 155
Institution: University of Pittsburgh
Schools and Programs: School of Education > Administrative and Policy Studies
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Mixture IRT, DIF, Bayesian estimation, Monte Carlo simulation
Date Deposited: 29 Sep 2017 12:53
Last Modified: 29 Sep 2017 12:53
URI: http://d-scholarship.pitt.edu/id/eprint/33233

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