A New Perspective on Method Variance: A Measure-Centric Approach

Paul E. Spector, Christopher C. Rosen, Hettie A. Richardson, Larry Williams, Russell E. Johnson

Research output: Contribution to journalArticle

Abstract

A widespread methodological concern in the organizational literature is the possibility that observed results are due to the influence of common-method variance or mono-method bias. This concern is based on a conception of method variance as being produced by the nature of the method itself, and therefore, variables assessed with the same method would share common-method variance that inflates observed correlations. In this paper, we argue for a more complex view of method variance that consists of multiple sources that affect each measured variable in a potentially unique way. Shared sources among measures (common-method variance) act to inflate correlations, whereas unshared sources (uncommon-method variance) act to attenuate correlations. Two empirical examples, one from a simulation study and the other from a single-source survey, are presented to illustrate the complex action of multiple sources of method variance. A five-step approach is suggested whereby a theory of the measure
Original languageEnglish
Pages (from-to)0149206316687295
JournalDefault journal
DOIs
StatePublished - Jan 2017

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    Spector, P. E., Rosen, C. C., Richardson, H. A., Williams, L., & Johnson, R. E. (2017). A New Perspective on Method Variance: A Measure-Centric Approach. Default journal, 0149206316687295. https://doi.org/10.1177/0149206316687295