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First published online March 11, 2009

A Tale of Three Perspectives: Examining Post Hoc Statistical Techniques for Detection and Correction of Common Method Variance

Abstract

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.

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1.
1. Techniques reviewed by Podsakoff et al. (2003) but not addressed here are Harman’s single factor test (which is subsumed in the unmeasured latent method construct [ULMC] approach), partial correlation, and multitrait-multimethod (MTMM). We do not consider partial correlation (Williams & Anderson, 1994) because it controls for common method variance (CMV) using a variable with a specific conceptual meaning (e.g., social desirability). Although CMV effects can be simulated across data, simulated variables have no inherent conceptual meaning. We do not consider the MTMM approach because it requires use of multiple methods—making it as much a procedural as statistical means of dealing with CMV. As we address statistical corrections, we deemed the MTMM technique as beyond our focus.
2.
2. Note that we cannot hypothesize the performance of the correlational marker, confirmatory factor analysis (CFA) marker, and ULMC strategies relative to no correction in the nonideal condition, as the effects of over-correcting versus not correcting will be a function of the extent to which the marker has a true relationship with the study’s focal variables compared with the degree of CMV contamination. Likewise, we cannot hypothesize the performance of the correlational and ULMC techniques relative to one another because this performance will depend on whether the measurement error present in data is greater than the spurious additional variance captured by the ULMC strategy.
3.
3. Corrected correlations were not used in the analyses of the CFA marker and the ULMC approaches unless the statistical tests indicated CMV and bias were present. Researchers applying these approaches would use model comparison to test for bias or CMV before drawing conclusions based on the corrected correlation. If model comparison did not indicate bias or CMV, the corrected correlation would not be used. In these instances, we constructed confidence intervals around the uncorrected correlation and also used it to calculate error— thereby making these tests very conservative. The logic behind this approach is that it allowed us to compare the accuracy of conclusions about correlations based on the choices researchers are likely to take given the results of CMV and/or bias detection from the same technique, that is, as opposed to the accuracy of the techniques only in the subset of data for which CMV or bias is found.
4.
4. We thank an anonymous reviewer for suggesting this apt description.

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Published In

Article first published online: March 11, 2009
Issue published: October 2009

Keywords

  1. common method variance
  2. post hoc statistical detection and correction
  3. self-report data
  4. data simulation

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Authors

Affiliations

Hettie A. Richardson
Marcia J. Simmering
Louisiana Tech University
Michael C. Sturman

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