Common method bias in regression models with linear, quadratic, and interaction effects

Enno Siemsen, Aleda Roth, Pedro Oliveira

Research output: Contribution to journalArticlepeer-review

1331 Scopus citations

Abstract

This research analyzes the effects of common method variance (CMV) on parameter estimates in bivariate linear, multivariate linear, quadratic, and interaction regression models. The authors demonstrate that CMV can either inflate or deflate bivariate linear relationships, depending on the degree of symmetry with which CMV affects the observed measures. With respect to multivariate linear relationships, they show that common method bias generally decreases when additional independent variables suffering from CMV are included in a regression equation. Finally, they demonstrate that quadratic and interaction effects cannot be artifacts of CMV. On the contrary, both quadratic and interaction terms can be severely deflated through CMV, making them more difficult to detect through statistical means.

Original languageEnglish (US)
Pages (from-to)456-476
Number of pages21
JournalOrganizational Research Methods
Volume13
Issue number3
DOIs
StatePublished - Jul 2010

Bibliographical note

Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.

Keywords

  • common method variance
  • interaction effects
  • multivariate regression
  • quadratic effects
  • research methods

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