Adjusting group intercept and slope bias in predictive equations
Author(s) / Creator(s)
Austin, Bruce W.
French, Brian F.
Abstract / Description
Methods to assess measurement invariance in constructs have received much attention, as invariance is critical for accurate group comparisons. Less attention has been given to the identification and correction of the sources of non-invariance in predictive equations. This work developed correction factors for structural intercept and slope bias in common regression equations to address calls in the literature to revive test bias research. We demonstrated the correction factors in regression analyses within the context of a large international dataset containing 68 countries and regions (groups). A mathematics achievement score was predicted by a math self-efficacy score, which exhibited a lack of invariance across groups. The proposed correction factors significantly corrected structural intercept and slope bias across groups. The impact of the correction factors was greatest for groups with the largest amount of bias. Implications for both practice and methodological extensions are discussed.
Keyword(s)
invariance noninvariance predictive bias test bias intercept bias slope bias multigroupPersistent Identifier
Date of first publication
2020-09-30
Journal title
Methodology
Volume
16
Issue
3
Page numbers
241–257
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
Austin, B. W., & French, B. F. (2020). Adjusting group intercept and slope bias in predictive equations. Methodology, 16(3), 241-257. https://doi.org/10.5964/meth.4001
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meth.v16i3.4001.pdfAdobe PDF - 399.83KBMD5: 376e21a3420b9f80b9bd19d6c463bc36
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There are no other versions of this object.
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Author(s) / Creator(s)Austin, Bruce W.
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Author(s) / Creator(s)French, Brian F.
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PsychArchives acquisition timestamp2022-04-14T11:24:43Z
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Made available on2022-04-14T11:24:43Z
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Date of first publication2020-09-30
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Abstract / DescriptionMethods to assess measurement invariance in constructs have received much attention, as invariance is critical for accurate group comparisons. Less attention has been given to the identification and correction of the sources of non-invariance in predictive equations. This work developed correction factors for structural intercept and slope bias in common regression equations to address calls in the literature to revive test bias research. We demonstrated the correction factors in regression analyses within the context of a large international dataset containing 68 countries and regions (groups). A mathematics achievement score was predicted by a math self-efficacy score, which exhibited a lack of invariance across groups. The proposed correction factors significantly corrected structural intercept and slope bias across groups. The impact of the correction factors was greatest for groups with the largest amount of bias. Implications for both practice and methodological extensions are discussed.en_US
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Publication statuspublishedVersion
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Review statuspeerReviewed
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CitationAustin, B. W., & French, B. F. (2020). Adjusting group intercept and slope bias in predictive equations. Methodology, 16(3), 241-257. https://doi.org/10.5964/meth.4001en_US
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ISSN1614-2241
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/5695
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.6299
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Language of contenteng
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PublisherPsychOpen GOLD
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Is version ofhttps://doi.org/10.5964/meth.4001
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Is related tohttps://doi.org/10.23668/psycharchives.3465
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Keyword(s)invarianceen_US
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Keyword(s)noninvarianceen_US
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Keyword(s)predictive biasen_US
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Keyword(s)test biasen_US
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Keyword(s)intercept biasen_US
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Keyword(s)slope biasen_US
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Keyword(s)multigroupen_US
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Dewey Decimal Classification number(s)150
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TitleAdjusting group intercept and slope bias in predictive equationsen_US
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DRO typearticle
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Issue3
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Journal titleMethodology
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Page numbers241–257
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Volume16
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Visible tag(s)Version of Recorden_US