Controlling for Common Method Variance in PLS Analysis: The Measured Latent Marker Variable Approach

  • Wynne W. Chin
  • Jason B. Thatcher
  • Ryan T. Wright
  • Doug Steel
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 56)

Abstract

Common method variance (CMV) continues to be an important issue for social scientists. To date, methodologists have yet to agree upon a best practice for detecting and controlling for CMV. In a recent paper, the unmeasured latent marker variable approach, a frequently employed technique, was shown to be incapable of detecting or controlling CMV in PLS analyses. Unfortunately, this was the only method to date suggested for handling CMV in PLS models. To fill this gap, we introduce a measured latent marker variable (MLMV) approach and demonstrate how it is able to both detect and correct for CMV when using Partial Least Squares.

Key words

Common method variance (CMV) Unmeasured latent marker variable Measured latent marker variable (MLMV) 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Wynne W. Chin
    • 1
  • Jason B. Thatcher
    • 2
  • Ryan T. Wright
    • 3
  • Doug Steel
    • 4
  1. 1.Department of Decision and Information Systems, C.T. Bauer College of BusinessUniversity of HoustonHoustonUSA
  2. 2.College of Business and Behavioral ScienceClemson UniversityClemsonUSA
  3. 3.School of ManagementUniversity of Massachusetts AmherstAmherstUSA
  4. 4.School of BusinessUniversity of Houston-Clear LakeHoustonUSA

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