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Relationships Between Shared Group Properties: Theory, Measurement, Estimation, and Adjustment.

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Title: Relationships Between Shared Group Properties: Theory, Measurement, Estimation, and Adjustment.
Authors: Maltarich, Mark A. (AUTHOR), Bliese, Paul D. (AUTHOR), Carpenter, Nichelle C. (AUTHOR)
Source: Personnel Psychology. Summer2026, Vol. 79 Issue 2, p135-160. 26p.
Subjects: Monte Carlo method, Multilevel models, Effect sizes (Statistics), Parameters (Statistics), Evidence synthesis, Statistical correlation
Abstract: Teams researchers, meta‐analysts, and others often study relationships between group‐level constructs measured by aggregating individual‐level variables, but it is well‐known that group‐mean correlations are influenced by individual‐level relationships. We review multilevel issues and current practices in estimating and adjusting group‐mean associations. We give special attention to comparisons across levels of analysis, and to adjustments for estimating population parameters. A Monte Carlo simulation study indicates that the two most commonly used adjustments in meta‐analysis are ineffective in estimating population‐based effect sizes. They frequently overestimate true relationships, sometimes severely. Indeed, simulations show that common adjustments move estimates away from population values in many situations. We suggest an alternative approach to adjustment that produces more accurate and conservative estimates. We apply our approach to examples in the literature and call into question estimates and interpretations in a large body of meta‐analyses based on aggregated group‐mean variables. [ABSTRACT FROM AUTHOR]
Copyright of Personnel Psychology is the property of Wiley-Blackwell 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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Relationships Between Shared Group Properties: Theory, Measurement, Estimation, and Adjustment.
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  Data: <searchLink fieldCode="AR" term="%22Maltarich%2C+Mark+A%2E%22">Maltarich, Mark A.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bliese%2C+Paul+D%2E%22">Bliese, Paul D.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Carpenter%2C+Nichelle+C%2E%22">Carpenter, Nichelle C.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Personnel+Psychology%22">Personnel Psychology</searchLink>. Summer2026, Vol. 79 Issue 2, p135-160. 26p.
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  Data: <searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Multilevel+models%22">Multilevel models</searchLink><br /><searchLink fieldCode="DE" term="%22Effect+sizes+%28Statistics%29%22">Effect sizes (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Parameters+%28Statistics%29%22">Parameters (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Evidence+synthesis%22">Evidence synthesis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Teams researchers, meta‐analysts, and others often study relationships between group‐level constructs measured by aggregating individual‐level variables, but it is well‐known that group‐mean correlations are influenced by individual‐level relationships. We review multilevel issues and current practices in estimating and adjusting group‐mean associations. We give special attention to comparisons across levels of analysis, and to adjustments for estimating population parameters. A Monte Carlo simulation study indicates that the two most commonly used adjustments in meta‐analysis are ineffective in estimating population‐based effect sizes. They frequently overestimate true relationships, sometimes severely. Indeed, simulations show that common adjustments move estimates away from population values in many situations. We suggest an alternative approach to adjustment that produces more accurate and conservative estimates. We apply our approach to examples in the literature and call into question estimates and interpretations in a large body of meta‐analyses based on aggregated group‐mean variables. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Personnel Psychology is the property of Wiley-Blackwell 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=194014958
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        Text: English
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      – SubjectFull: Multilevel models
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      – SubjectFull: Effect sizes (Statistics)
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      – SubjectFull: Parameters (Statistics)
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      – SubjectFull: Evidence synthesis
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      – SubjectFull: Statistical correlation
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              Text: Summer2026
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