Analytic and bootstrap confidence intervals for the common-language effect size estimate
Author(s) / Creator(s)
Li, Johnson Ching-Hong
Tze, Virginia Man Chung
Abstract / Description
Evaluating how an effect-size estimate performs between two continuous variables based on the common-language effect size (CLES) has received increasing attention. While Blomqvist (1950; https://doi.org/10.1214/aoms/1177729754) developed a parametric estimator (q') for the CLES, there has been limited progress in further refining CLES. This study: a) extends Blomqvist’s work by providing a mathematical foundation for Bp (a non-parametric version of CLES) and an analytic approach for estimating its standard error; and b) evaluates the performance of the analytic and bootstrap confidence intervals (CIs) for Bp. The simulation shows that the bootstrap bias-corrected-and-accelerated interval (BCaI) has the best protected Type 1 error rate with a slight compromise in Power, whereas the analytic-t CI has the highest overall Power but with a Type 1 error slightly larger than the nominal value. This study also uses a real-world data-set to demonstrate the applicability of the CLES in measuring the relationship between age and sexual compulsivity.
Keyword(s)
common-language effect size confidence intervals bootstrapping Monte Carlo simulation probability-of-superiorityPersistent Identifier
Date of first publication
2021-03-31
Journal title
Methodology
Volume
17
Issue
1
Page numbers
1–21
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
Li, J. C.-H., & Tze, V. M. C. (2021). Analytic and bootstrap confidence intervals for the common-language effect size estimate. Methodology, 17(1), 1-21. https://doi.org/10.5964/meth.4495
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Meth.v17i1.4495.pdfAdobe PDF - 1.51MBMD5: d01d114b9809a2d680f312c00f6a9a05
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Author(s) / Creator(s)Li, Johnson Ching-Hong
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Author(s) / Creator(s)Tze, Virginia Man Chung
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PsychArchives acquisition timestamp2022-04-14T11:19:23Z
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Made available on2022-04-14T11:19:23Z
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Date of first publication2021-03-31
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Abstract / DescriptionEvaluating how an effect-size estimate performs between two continuous variables based on the common-language effect size (CLES) has received increasing attention. While Blomqvist (1950; https://doi.org/10.1214/aoms/1177729754) developed a parametric estimator (q') for the CLES, there has been limited progress in further refining CLES. This study: a) extends Blomqvist’s work by providing a mathematical foundation for Bp (a non-parametric version of CLES) and an analytic approach for estimating its standard error; and b) evaluates the performance of the analytic and bootstrap confidence intervals (CIs) for Bp. The simulation shows that the bootstrap bias-corrected-and-accelerated interval (BCaI) has the best protected Type 1 error rate with a slight compromise in Power, whereas the analytic-t CI has the highest overall Power but with a Type 1 error slightly larger than the nominal value. This study also uses a real-world data-set to demonstrate the applicability of the CLES in measuring the relationship between age and sexual compulsivity.en_US
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Publication statuspublishedVersion
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Review statuspeerReviewed
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CitationLi, J. C.-H., & Tze, V. M. C. (2021). Analytic and bootstrap confidence intervals for the common-language effect size estimate. Methodology, 17(1), 1-21. https://doi.org/10.5964/meth.4495en_US
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ISSN1614-2241
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/5099
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.5703
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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.4495
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Is related tohttps://doi.org/10.23668/psycharchives.4720
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Is related tohttps://openpsychometrics.org/_rawdata/SCS.zip
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Keyword(s)common-language effect sizeen_US
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Keyword(s)confidence intervalsen_US
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Keyword(s)bootstrappingen_US
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Keyword(s)Monte Carlo simulationen_US
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Keyword(s)probability-of-superiorityen_US
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Dewey Decimal Classification number(s)150
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TitleAnalytic and bootstrap confidence intervals for the common-language effect size estimateen_US
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DRO typearticle
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Issue1
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Journal titleMethodology
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Page numbers1–21
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Volume17
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Visible tag(s)Version of Recorden_US