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A Tale of Three Perspectives

Examining Post Hoc Statistical Techniques for Detection and Correction of Common Method Variance

Louisiana State University, hricha4{at}lsu.edu

Marcia J. Simmering

Louisiana Tech University

Michael C. Sturman

Cornell University

Many researchers who use same-source data face concerns about common method variance (CMV). Although post hoc statistical detection and correction techniques for CMV have been proposed, there is a lack of empirical evidence regarding their efficacy. Because of disagreement among scholars regarding the likelihood and nature of CMV in self-report data, the current study evaluates three post hoc strategies and the strategy of doing nothing within three sets of assumptions about CMV: that CMV does not exist, that CMV exists and has equal effects across constructs, and that CMV exists and has unequal effects across constructs. The implications of using each strategy within each of the three assumptions are examined empirically using 691,200 simulated data sets varying factors such as the amount of true variance and the amount and nature of CMV modeled. Based on analyses of these data, potential benefits and likely risks of using the different techniques are detailed.

Key Words: common method variance • post hoc statistical detection and correction • self-report data • data simulation

This version was published on October 1, 2009

Organizational Research Methods, Vol. 12, No. 4, 762-800 (2009)
DOI: 10.1177/1094428109332834


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