Statistical control in correlational studies: 10 essential recommendations for organizational researchers |
| |
| Authors: | Thomas E Becker Guclu Atinc James A Breaugh Kevin D Carlson Jeffrey R Edwards Paul E Spector |
| |
| Institution: | 1. College of Business, University of South Florida Sarasota–Manatee, Sarasota, Florida, U.S.A.;2. Department of Management, Texas A&M University–Commerce, Commerce, Texas, U.S.A.;3. College of Business Administration, University of Missouri–St. Louis, St. Louis, Missouri, U.S.A.;4. Pamplin College of Business, Virginia Tech, Blacksburg, Virginia, U.S.A.;5. Kenan‐Flagler Business School, University of North Carolina, Chapel Hill, North Carolina, U.S.A.;6. Department of Psychology, University of South Florida, Tampa, Florida, U.S.A. |
| |
| 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. Copyright © 2015 John Wiley & Sons, Ltd. |
| |
| Keywords: | statistical control research methods correlational studies |
|
|
