Cross-classified models in I/O psychology.
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| Title: | Cross-classified models in I/O psychology. |
|---|---|
| Authors: | Claus, Anna M.1 (AUTHOR) anna.claus@psych.rwth-aachen.de, Arend, Matthias G.1 (AUTHOR), Burk, Christian L.1 (AUTHOR), Kiefer, Christoph1 (AUTHOR), Wiese, Bettina S.1 (AUTHOR) |
| Source: | Journal of Vocational Behavior. Aug2020, Vol. 120, pN.PAG-N.PAG. 1p. |
| Subject Terms: | Research teams, Data structures, Psychology |
| Abstract: | Cross-classified models accommodate data structures that have more than one cluster variable, which are not nested in each other but overlap. They simultaneously consider all clustering variables. This allows one to study effects on several levels at once. Cross-classified data structures are common in various field of applied research (e.g., research on teams, career paths, interventions). The present article demonstrates modeling options and specifications for cross-classified data to be used within these different research strands. For more specific demonstration purposes, we use a data set on rater variance in assessment centers. Commonly, raters observe only a subset of participants and hence ratings are both nested in participants and raters, but participants and raters are not nested in each other. Using cross-classified models allows studying sources of rater variance (e.g., professional expertise) and interactions between cluster level variables, for example, interactions between participants' and raters' personality and sociodemographic characteristics (e.g., gender). From a practical research point of view and to ease application, we deliver a step-by-step overview of modeling procedures and power analysis including software code. • Cross-classified models as a powerful approach to hierarchical data with overlapping clusters • Numerous examples for applications of cross-classified models in I/O research • Empirical demonstration with cross-classified data from an assessment center • Step-by-step overview of modeling procedures and power analysis including software code [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Vocational Behavior is the property of Academic Press Inc. 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: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 144341376 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Cross-classified models in I/O psychology. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Claus%2C+Anna+M%2E%22">Claus, Anna M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> anna.claus@psych.rwth-aachen.de</i><br /><searchLink fieldCode="AR" term="%22Arend%2C+Matthias+G%2E%22">Arend, Matthias G.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Burk%2C+Christian+L%2E%22">Burk, Christian L.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kiefer%2C+Christoph%22">Kiefer, Christoph</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wiese%2C+Bettina+S%2E%22">Wiese, Bettina S.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Vocational+Behavior%22">Journal of Vocational Behavior</searchLink>. Aug2020, Vol. 120, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Research+teams%22">Research teams</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Psychology%22">Psychology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Cross-classified models accommodate data structures that have more than one cluster variable, which are not nested in each other but overlap. They simultaneously consider all clustering variables. This allows one to study effects on several levels at once. Cross-classified data structures are common in various field of applied research (e.g., research on teams, career paths, interventions). The present article demonstrates modeling options and specifications for cross-classified data to be used within these different research strands. For more specific demonstration purposes, we use a data set on rater variance in assessment centers. Commonly, raters observe only a subset of participants and hence ratings are both nested in participants and raters, but participants and raters are not nested in each other. Using cross-classified models allows studying sources of rater variance (e.g., professional expertise) and interactions between cluster level variables, for example, interactions between participants' and raters' personality and sociodemographic characteristics (e.g., gender). From a practical research point of view and to ease application, we deliver a step-by-step overview of modeling procedures and power analysis including software code. • Cross-classified models as a powerful approach to hierarchical data with overlapping clusters • Numerous examples for applications of cross-classified models in I/O research • Empirical demonstration with cross-classified data from an assessment center • Step-by-step overview of modeling procedures and power analysis including software code [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Vocational Behavior is the property of Academic Press Inc. 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: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jvb.2020.103447 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Research teams Type: general – SubjectFull: Data structures Type: general – SubjectFull: Psychology Type: general Titles: – TitleFull: Cross-classified models in I/O psychology. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Claus, Anna M. – PersonEntity: Name: NameFull: Arend, Matthias G. – PersonEntity: Name: NameFull: Burk, Christian L. – PersonEntity: Name: NameFull: Kiefer, Christoph – PersonEntity: Name: NameFull: Wiese, Bettina S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 00018791 Numbering: – Type: volume Value: 120 Titles: – TitleFull: Journal of Vocational Behavior Type: main |
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