PLS‐SEM: A method demonstration in the R statistical environment
In line with calls to stimulate methodological diversity and support evidence‐based human resource development (HRD) through quantitative competencies, we present a methods demonstration leveraging open‐source tools and lesser‐known quantitative research methods to support the HRD research community...
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Veröffentlicht in: | Human resource development quarterly 2023-11, Vol.35 (4), p.501-529 |
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creator | Legate, Amanda E. Ringle, Christian M. Hair, Joseph F. |
description | In line with calls to stimulate methodological diversity and support evidence‐based human resource development (HRD) through quantitative competencies, we present a methods demonstration leveraging open‐source tools and lesser‐known quantitative research methods to support the HRD research community and applied HRD in the workplace. In this paper, we provide an informative introduction to partial least squares structural equation modeling (PLS‐SEM). We discuss PLS‐SEM application trends in the field of HRD, present key characteristics of the method, and demonstrate up‐to‐date metrics and evaluation guidelines using an illustrative model. Our PLS‐SEM demonstration and explanations can serve as a valuable resource for practitioners concerned with substantiating results for organizational stakeholders and support researchers in methodological decision‐making while avoiding common pitfalls associated with less familiar methods. Our step‐by‐step demonstration is conducted in open‐source software and accompanied by explicitly coded operations so that readers can easily replicate the illustrative analyses presented. |
doi_str_mv | 10.1002/hrdq.21517 |
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Our step‐by‐step demonstration is conducted in open‐source software and accompanied by explicitly coded operations so that readers can easily replicate the illustrative analyses presented.</description><subject>causal‐predictive modeling</subject><subject>HRD</subject><subject>human resource development</subject><subject>Human resource management</subject><subject>mediation analysis</subject><subject>Open source software</subject><subject>partial least squares structural equation modeling</subject><subject>PLS‐SEM</subject><subject>R statistical software</subject><subject>SEMinR</subject><subject>Structural equation modeling</subject><issn>1044-8004</issn><issn>1532-1096</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kE1OwzAQRi0EEqWw4QSW2CGlzMSOHbMrpVCkIqCFtZUfR03VJK3tgrrjCJyRk5AS1qxmNHrzfdIj5BxhgADh1cLmm0GIEcoD0sOIhQGCEoftDpwHMQA_JifOLQGAARc9cvM8nX9_fs3Hj9d0SCvjF01Oc1M1tfM28WVT07KmfmHojDrfHpwvs2RFTf1e2qauTO1PyVGRrJw5-5t98nY3fh1NgunT_cNoOA0yBigDGSWpzEUuUjTA0xANYygjZYxUuVIy43kaKxGlSsSsyEQUxxFHxWOepEVRhKxPLrrctW02W-O8XjZbW7eVmiEXXABi3FKXHZXZxjlrCr22ZZXYnUbQe0d670j_Omph7OCPcmV2_5B6Mrt96X5-ACdZaJc</recordid><startdate>20231128</startdate><enddate>20231128</enddate><creator>Legate, Amanda E.</creator><creator>Ringle, Christian M.</creator><creator>Hair, Joseph F.</creator><general>Wiley Periodicals, Inc</general><general>Wiley Periodicals Inc</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-7763-7630</orcidid><orcidid>https://orcid.org/0000-0002-7027-8804</orcidid></search><sort><creationdate>20231128</creationdate><title>PLS‐SEM: A method demonstration in the R statistical environment</title><author>Legate, Amanda E. ; Ringle, Christian M. ; Hair, Joseph F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3017-75ab7d6d6b1e04b21e331759ee79d997c4db8965b9683fc65885419484abfff23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>causal‐predictive modeling</topic><topic>HRD</topic><topic>human resource development</topic><topic>Human resource management</topic><topic>mediation analysis</topic><topic>Open source software</topic><topic>partial least squares structural equation modeling</topic><topic>PLS‐SEM</topic><topic>R statistical software</topic><topic>SEMinR</topic><topic>Structural equation modeling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Legate, Amanda E.</creatorcontrib><creatorcontrib>Ringle, Christian M.</creatorcontrib><creatorcontrib>Hair, Joseph F.</creatorcontrib><collection>CrossRef</collection><jtitle>Human resource development quarterly</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Legate, Amanda E.</au><au>Ringle, Christian M.</au><au>Hair, Joseph F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PLS‐SEM: A method demonstration in the R statistical environment</atitle><jtitle>Human resource development quarterly</jtitle><date>2023-11-28</date><risdate>2023</risdate><volume>35</volume><issue>4</issue><spage>501</spage><epage>529</epage><pages>501-529</pages><issn>1044-8004</issn><eissn>1532-1096</eissn><abstract>In line with calls to stimulate methodological diversity and support evidence‐based human resource development (HRD) through quantitative competencies, we present a methods demonstration leveraging open‐source tools and lesser‐known quantitative research methods to support the HRD research community and applied HRD in the workplace. 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subjects | causal‐predictive modeling HRD human resource development Human resource management mediation analysis Open source software partial least squares structural equation modeling PLS‐SEM R statistical software SEMinR Structural equation modeling |
title | PLS‐SEM: A method demonstration in the R statistical environment |
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