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Common Method Variance

Introduction

Common Method Variance In Organizational Research, Special issue of Organizational Research Methods, Edited by Michael Brannick and Paul Spector; Deadline 10 Jul 2009

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CALL FOR PAPERS
Organizational Research Methods

SPECIAL FEATURE TOPIC ISSUE ON
Common Method Variance In Organizational Research

Common method variance (CMV), also known as mono-method bias, is often mentioned in lists of criticisms by reviewers of submitted manuscripts, particularly when those manuscripts report results from self-report surveys. The idea of CMV is that the method itself serves as a source of variation among observed scores, and that two or more variables assessed with the same method will share variance due to method rather than construct of interest. CMV thus would serve as a methodological artifact that might render observed relationships among variables to be partially or even entirely spurious. The presumption, therefore, is that theoretical inferences from the observed relationships are suspect. Despite the widespread beliefs about CMV among journal reviewers and researchers, and the publication of some papers on the topic in the researcher literature, there remains little consensus about its existence and if present, its true effects.

The primary goal of the issue is to raise the level of discourse so that reviewers’ comments are more precise and thoughtful and researchers’ research designs address likely biases instead of merely trying to explain away CMV. We are seeking manuscripts that deal with CMV from a variety of perspectives including:

  1. The precise conceptual and/or mathematical definitions of CMV.
  2. Whether and under what conditions does CMV as defined truly exist and how does one know that to be the case,
    1. What are the design issues?
    2. What are the measurement issues?
    3. What are the analysis issues?
    4. What are the interpretation issues?
  3. Assuming an existence, how and under what conditions it might or might not affect measurement and observed relationships among variables — that is, how much CMV must be present and under what conditions to absolutely render inferences meaningless or questionable.
  4. Assuming an impact,
    1. What strategies may be undertaken both before and/or after data collection to deal with it?
    2. What evidence exists to prove the effectiveness of such strategies?
  5. Are there differential impacts given the type of hypothesis testing analysis (i.e., traditional regression using means to operationalize constructs vs. an SEM approach with constructs operationalized through measurement models).
  6. While the above are empirical in nature, we also encourage attempts to develop a better theoretical explication of CMV including its antecedents, consequents, and mechanisms of operation.

The list above is not exhaustive. Collectively, therefore, we invite empirical and conceptual papers, and when empirical using either real or simulated data or both. Again, the goal here is to raise the level of discourse about CMV and what it represents by providing the readership with a strong sense of the nature and severity of the underlying issues, the conditions that make it more or less severe, what strategies may be undertaken to deal with the issues prior to and after data collection, and how to best design studies that allow for reasonably conclusive tests of hypotheses.. Doing so will provide an informed basis from which to judge whether CMV is present and how bad it is, and perhaps put a halt to the blanket use of the CMV excuse by researchers and readers/reviewers as an automatic reaction to manuscripts using self-report measures.

All papers will undergo the standard double-blind Organizational Research Methods review process and must meet the standards of the Organizational Research Methods, Editorial Policy Statement (see ). All articles published in this feature topic must make strong contributions to improving our understanding of CMV.

The guest editors for this special issue are Michael Brannick (mbrannic@luna.cas.usf.edu) and Paul Spector (spector@shell.cas.usf.edu) at the University of South Florida.

Please submit manuscripts to the special feature through the Organizational Research Methods manuscript central portal (). Be sure to indicate in the cover letter that you are submitting the paper for the feature topic on common method variance. To be considered, manuscripts must be submitted on or before midnight (Eastern Standard Time) July 10, 2009.