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Evaluate measurement invariance across multiple groups: a comparison between the alignment optimization and the random item effects model

Lin, Lida (2020) Evaluate measurement invariance across multiple groups: a comparison between the alignment optimization and the random item effects model. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Participants in achievement tests or psychometric scales can be naturally divided into various sub-groups such as gender, race, social economic status, school district, etc. In order to make meaningful comparison between groups, each item in the test/scale should measure the same underlying construct for participants came from different groups. The increasing implementation of cross-national assessments have raised the question about how to evaluate measurement invariance across a large number of groups. This study compared two relatively new methods—the CFA alignment optimization and the random item effects model—on evaluating measurement invariance. The impact of following factors on the performance of each method were assessed: the proportion of DIF items, type of group mean ability, number of groups, group size, DIF size, and type of DIF. The simulation study demonstrated that both methods performed well in conditions with large number of groups, while they were significantly different from each other. When group size was large and the group mean abilities were equal, both methods would lead to highly accurate parameter estimates, and highly accurate DIF detection rate can be achieved by the alignment method.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Lin, Lidalil85@pitt.edulil85
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee CoChairLane, Suzannesl@pitt.edusl
Committee CoChairYe, Feifeifeifeiye@pitt.edufeifeiye
Committee MemberStone, Clementcas@pitt.educas
Committee MemberYu, Lanlan.yu@pitt.edulan.yu
Date: 17 December 2020
Date Type: Publication
Defense Date: 26 October 2020
Approval Date: 17 December 2020
Submission Date: 3 December 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 150
Institution: University of Pittsburgh
Schools and Programs: School of Education > Psychology in Education
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: measurement invariance; differential item functioning; alignment optimization; random item effects model
Date Deposited: 17 Dec 2020 19:28
Last Modified: 17 Dec 2020 19:28


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