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Selected Works of Christie M. Fuller
Article
Common Methods Variance Detection in Business Research
Journal of Business Research (2016)
  • Christie M. Fuller, Louisiana Tech University
  • Marcia J. Simmering, Louisiana Tech University
  • Guclu Atinc, Texas A&M University-Commerce
  • Yasemin Atinc, Texas A&M University-Commerce
  • Barry J. Babin, Louisiana Tech University
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Abstract
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.
Keywords
  • CMV,
  • CMB,
  • measurement,
  • error,
  • surveys,
  • Harman's one-factor test
Disciplines
Publication Date
August, 2016
DOI
10.1016/j.jbusres.2015.12.008
Citation Information
Christie M. Fuller, Marcia J. Simmering, Guclu Atinc, Yasemin Atinc, et al.. "Common Methods Variance Detection in Business Research" Journal of Business Research Vol. 69 Iss. 8 (2016) p. 3192 - 3198 ISSN: 01482963
Available at: http://works.bepress.com/christie-fuller/1/