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Estimating Conditional Group Mean Difference: A Comparison of Four Approaches.

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Title: Estimating Conditional Group Mean Difference: A Comparison of Four Approaches.
Authors: Liu, Lu1 (AUTHOR) ll18g@fsu.edu, Yang, Yanyun1 (AUTHOR)
Source: Journal of Experimental Education. 2026, Vol. 94 Issue 3, p658-680. 23p.
Subject Terms: *Factor analysis, Structural equation modeling, Measurement errors, Errors-in-variables models, Estimation theory
Abstract: The present study evaluated four alternative approaches to structural regression modeling in estimating conditional group mean differences under conditions with small samples, measurement errors, and measurement model misspecification. Two simulation studies were conducted: one considered unidimensional items and the other involved multidimensional items conformed to a bifactor model. Factor loadings, conditional group mean difference, and sample size were manipulated. The single-indicator approach using coefficient alpha yielded unbiased estimates when items were unidimensional or primarily measured a general factor. The traditional ANCOVA produced unbiased estimates when composite score reliability was high. ANCOVA using weighted composites or factor scores and the single-indicator approach using coefficient omega or omega hierarchical were not recommended as they were prone to inadmissible solutions in small samples. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Experimental Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Estimating Conditional Group Mean Difference: A Comparison of Four Approaches.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Lu%22">Liu, Lu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ll18g@fsu.edu</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yanyun%22">Yang, Yanyun</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Experimental+Education%22">Journal of Experimental Education</searchLink>. 2026, Vol. 94 Issue 3, p658-680. 23p.
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  Data: *<searchLink fieldCode="DE" term="%22Factor+analysis%22">Factor analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+errors%22">Measurement errors</searchLink><br /><searchLink fieldCode="DE" term="%22Errors-in-variables+models%22">Errors-in-variables models</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink>
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  Label: Abstract
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  Data: The present study evaluated four alternative approaches to structural regression modeling in estimating conditional group mean differences under conditions with small samples, measurement errors, and measurement model misspecification. Two simulation studies were conducted: one considered unidimensional items and the other involved multidimensional items conformed to a bifactor model. Factor loadings, conditional group mean difference, and sample size were manipulated. The single-indicator approach using coefficient alpha yielded unbiased estimates when items were unidimensional or primarily measured a general factor. The traditional ANCOVA produced unbiased estimates when composite score reliability was high. ANCOVA using weighted composites or factor scores and the single-indicator approach using coefficient omega or omega hierarchical were not recommended as they were prone to inadmissible solutions in small samples. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Experimental Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/00220973.2025.2522863
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 23
        StartPage: 658
    Subjects:
      – SubjectFull: Factor analysis
        Type: general
      – SubjectFull: Structural equation modeling
        Type: general
      – SubjectFull: Measurement errors
        Type: general
      – SubjectFull: Errors-in-variables models
        Type: general
      – SubjectFull: Estimation theory
        Type: general
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      – TitleFull: Estimating Conditional Group Mean Difference: A Comparison of Four Approaches.
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            NameFull: Liu, Lu
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            NameFull: Yang, Yanyun
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            – D: 01
              M: 07
              Text: 2026
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              Y: 2026
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