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LDR | 02272nam 2200469 4500 | ||
001 | 0100803584▲ | ||
005 | 20240329143004▲ | ||
006 | m o d ▲ | ||
007 | cr#unu||||||||▲ | ||
008 | 240116s2023 us |||||||||||||||c||eng d▲ | ||
020 | ▼a9798380595322▲ | ||
035 | ▼a(MiAaPQ)AAI30788288▲ | ||
035 | ▼a(MiAaPQ)OhioLINKosu1689514451649832▲ | ||
040 | ▼aMiAaPQ▼cMiAaPQ▲ | ||
082 | 0 | ▼a151▲ | |
100 | 1 | ▼aZhu, Jingdan.▲ | |
245 | 1 | 0 | ▼aInvestigating Measurement Invariance for Many Groups▼h[electronic resource]▲ |
260 | ▼a[S.l.]: ▼bThe Ohio State University. ▼c2023▲ | ||
260 | 1 | ▼aAnn Arbor : ▼bProQuest Dissertations & Theses, ▼c2023▲ | |
300 | ▼a1 online resource(141 p.)▲ | ||
500 | ▼aSource: Dissertations Abstracts International, Volume: 85-04, Section: A.▲ | ||
500 | ▼aAdvisor: De Boeck, Paul.▲ | ||
502 | 1 | ▼aThesis (Ph.D.)--The Ohio State University, 2023.▲ | |
506 | ▼aThis item must not be sold to any third party vendors.▲ | ||
506 | ▼aThis item must not be added to any third party search indexes.▲ | ||
520 | ▼aThis dissertation concerns methods to investigate measurement invariance (MI) violation in a confirmatory factor analytic framework when the number of groups is large. The focus of the dissertation is on the implementation of the proposed methods and application with real datasets. Four methods, (1) multilevel confirmatory factor analysis, (2) k-means clustering, (3) hierarchical clustering, (4) MI-tree models are proposed to explore scalar invariance violation among groups. One simulated dataset was used to demonstrate the four methods and two real datasets were used as application.▲ | ||
590 | ▼aSchool code: 0168.▲ | ||
650 | 4 | ▼aQuantitative psychology.▲ | |
653 | ▼aMeasurement invariance▲ | ||
653 | ▼aConfirmatory factor analysis▲ | ||
653 | ▼aReal datasets▲ | ||
690 | ▼a0632▲ | ||
690 | ▼a0703▲ | ||
710 | 2 | 0 | ▼aThe Ohio State University.▼bPsychology.▲ |
773 | 0 | ▼tDissertations Abstracts International▼g85-04A.▲ | |
773 | ▼tDissertation Abstract International▲ | ||
790 | ▼a0168▲ | ||
791 | ▼aPh.D.▲ | ||
792 | ▼a2023▲ | ||
793 | ▼aEnglish▲ | ||
856 | 4 | 0 | ▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16935768▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.▲ |
Investigating Measurement Invariance for Many Groups[electronic resource]
Document Type
국외eBook
Title
Investigating Measurement Invariance for Many Groups [electronic resource]
Author
Corporate Name
Publication
[S.l.] : The Ohio State University. 2023 Ann Arbor : ProQuest Dissertations & Theses , 2023
Physical Description
1 online resource(141 p.)
General Note
Source: Dissertations Abstracts International, Volume: 85-04, Section: A.
Advisor: De Boeck, Paul.
Advisor: De Boeck, Paul.
Dissertation Note
Thesis (Ph.D.)--The Ohio State University, 2023.
Summary Note
This dissertation concerns methods to investigate measurement invariance (MI) violation in a confirmatory factor analytic framework when the number of groups is large. The focus of the dissertation is on the implementation of the proposed methods and application with real datasets. Four methods, (1) multilevel confirmatory factor analysis, (2) k-means clustering, (3) hierarchical clustering, (4) MI-tree models are proposed to explore scalar invariance violation among groups. One simulated dataset was used to demonstrate the four methods and two real datasets were used as application.
ISBN
9798380595322
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