Common methods variance detection in business research
The issue of common method variance (CMV) has become almost legendary among today's business researchers. In this manuscript, a literature review shows many business researchers take steps to assess potential problems with CMV, or common method bias (CMB), but almost no one reports problematic...
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Veröffentlicht in: | Journal of business research 2016-08, Vol.69 (8), p.3192-3198 |
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description | The issue of common method variance (CMV) has become almost legendary among today's business researchers. In this manuscript, a literature review shows many business researchers take steps to assess potential problems with CMV, or common method bias (CMB), but almost no one reports problematic findings. One widely-criticized procedure assessing CMV levels involves a one-factor test that examines how much common variance might exist in a single dimension. This paper presents a data simulation demonstrating that a relatively high level of CMV must be present to bias true relationships among substantive variables at typically reported reliability levels. The simulation data overall suggests that at levels of CMV typical of multiple item measures with typical reliabilities reporting typical effect sizes, CMV does not represent a grave threat to the validity of research findings. |
doi_str_mv | 10.1016/j.jbusres.2015.12.008 |
format | Article |
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In this manuscript, a literature review shows many business researchers take steps to assess potential problems with CMV, or common method bias (CMB), but almost no one reports problematic findings. One widely-criticized procedure assessing CMV levels involves a one-factor test that examines how much common variance might exist in a single dimension. This paper presents a data simulation demonstrating that a relatively high level of CMV must be present to bias true relationships among substantive variables at typically reported reliability levels. 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In this manuscript, a literature review shows many business researchers take steps to assess potential problems with CMV, or common method bias (CMB), but almost no one reports problematic findings. One widely-criticized procedure assessing CMV levels involves a one-factor test that examines how much common variance might exist in a single dimension. This paper presents a data simulation demonstrating that a relatively high level of CMV must be present to bias true relationships among substantive variables at typically reported reliability levels. The simulation data overall suggests that at levels of CMV typical of multiple item measures with typical reliabilities reporting typical effect sizes, CMV does not represent a grave threat to the validity of research findings.</description><subject>CMB</subject><subject>CMV</subject><subject>Error</subject><subject>Harman's one-factor test</subject><subject>Measurement</subject><subject>Scale models</subject><subject>Studies</subject><subject>Surveys</subject><subject>Variances</subject><issn>0148-2963</issn><issn>1873-7978</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNqFUE1LxDAQDaLguvoThILn1pkkbdKTyOIXLHjRc8i2EzbFNmvSXfDfG9m9e5qBeR_zHmO3CBUCNvdDNWz2KVKqOGBdIa8A9BlboFaiVK3S52wBKHXJ20ZcsquUBgDgGbRgzSqMY5iKkeZt6FNxsNHbqaOip5m62eeTn4os7ydKqcgmZGO3vWYXzn4lujnNJft8fvpYvZbr95e31eO67KSQc9mjReWcAhQAee8dSi60tk3fcum6_AJoElCrxmogtwEiqTZNjQRArRNLdnfU3cXwvac0myHs45QtDapWCK7rWmZUfUR1MaRchDO76EcbfwyC-avIDOZUkfmryCA32TrzHo48yhEOnqJJnaecvvcxhzd98P8o_ALnLHFX</recordid><startdate>20160801</startdate><enddate>20160801</enddate><creator>Fuller, Christie M.</creator><creator>Simmering, Marcia J.</creator><creator>Atinc, Guclu</creator><creator>Atinc, Yasemin</creator><creator>Babin, Barry J.</creator><general>Elsevier Inc</general><general>Elsevier Sequoia S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>20160801</creationdate><title>Common methods variance detection in business research</title><author>Fuller, Christie M. ; Simmering, Marcia J. ; Atinc, Guclu ; Atinc, Yasemin ; Babin, Barry J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c434t-d1a17ff7013001a1df142388a6d924fc00808e30576a80efb0ee47b651e00e9f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>CMB</topic><topic>CMV</topic><topic>Error</topic><topic>Harman's one-factor test</topic><topic>Measurement</topic><topic>Scale models</topic><topic>Studies</topic><topic>Surveys</topic><topic>Variances</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fuller, Christie M.</creatorcontrib><creatorcontrib>Simmering, Marcia J.</creatorcontrib><creatorcontrib>Atinc, Guclu</creatorcontrib><creatorcontrib>Atinc, Yasemin</creatorcontrib><creatorcontrib>Babin, Barry J.</creatorcontrib><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Journal of business research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fuller, Christie M.</au><au>Simmering, Marcia J.</au><au>Atinc, Guclu</au><au>Atinc, Yasemin</au><au>Babin, Barry J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Common methods variance detection in business research</atitle><jtitle>Journal of business research</jtitle><date>2016-08-01</date><risdate>2016</risdate><volume>69</volume><issue>8</issue><spage>3192</spage><epage>3198</epage><pages>3192-3198</pages><issn>0148-2963</issn><eissn>1873-7978</eissn><abstract>The issue of common method variance (CMV) has become almost legendary among today's business researchers. 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subjects | CMB CMV Error Harman's one-factor test Measurement Scale models Studies Surveys Variances |
title | Common methods variance detection in business research |
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