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Article
Statistical Control in Correlational Studies: 10 Essential Recommendations for Organizational Researchers
Journal of Organizational Behavior
  • Thomas E. Becker, University of South Florida Sarasota–Manatee
  • Guclu Atinc, Texas A&M University–Commerce
  • James A. Breaugh, University of Missouri–St. Louis
  • Kevin D. Carlson, Virginia Tech
  • Jeffrey R. Edwards, University of North Carolina
  • Paul E. Spector, University of South Florida
Document Type
Article
Publication Date
2-1-2016
Keywords
  • statistical control,
  • research methods,
  • correlational studies
Digital Object Identifier (DOI)
https://doi.org/10.1002/job.2053
Disciplines
Abstract

Statistical control is widely used in correlational studies with the intent of providing more accurate estimates of relationships among variables, more conservative tests of hypotheses, or ruling out alternative explanations for empirical findings. However, the use of control variables can produce uninterpretable parameter estimates, erroneous inferences, irreplicable results, and other barriers to scientific progress. As a result, methodologists have provided a great deal of advice regarding the use of statistical control, to the point that researchers might have difficulties sifting through and prioritizing the available suggestions. We integrate and condense this literature into a set of 10 essential recommendations that are generally applicable and which, if followed, would substantially enhance the quality of published organizational research. We provide explanations, qualifications, and examples following each recommendation.

Citation / Publisher Attribution

Journal of Organizational Behavior, v. 37, issue 2, p. 157-167

Citation Information
Thomas E. Becker, Guclu Atinc, James A. Breaugh, Kevin D. Carlson, et al.. "Statistical Control in Correlational Studies: 10 Essential Recommendations for Organizational Researchers" Journal of Organizational Behavior Vol. 37 Iss. 2 (2016) p. 157 - 167
Available at: http://works.bepress.com/thomas-becker/35/