[We’re pleased to welcome Marcia Simmering of Louisiana Tech University. Dr. Simmering recently published an article in Organizational Research Methods with Christie M. Fuller, Hettie A. Richardson, Yasemin Ocal, and Guclu M. Atinc entitled “Marker Variable Choice, Reporting, and Interpretation in the Detection of Common Method Variance: A Review and Demonstration.”]
- What inspired you to be interested in this topic?
After the publication of my earlier piece on common method variance (Richardson, Simmering, Sturman, 2009 in ORM), where we found that marker variables could be potentially useful in detecting method variance, I kept getting questions from other researchers about what marker variables they should use in their own studies. I didn’t always have an answer, because the appropriateness of a marker variable depends on the study variables. So, I worked with a team of co-authors from different business disciplines on the current paper to find good marker variables in a variety of studies. As we all read articles using marker variables, we found so much variation in how they were used, and we learned that many had not been chosen or implemented properly. So, my coauthors and I decided to give an overview of how these techniques have been used (and misused). We took it a step further and tried to find out what these marker variables are really measuring and whether they’re measuring something different from presumed causes of common method variance (CMV), like social desirability and affectivity.
- Were there findings that were surprising to you?
Yes! I would say that most of what we found in both studies surprised us. In Study 1 (the review of marker variable use), I didn’t expect so many authors to choose marker variables that really couldn’t properly capture CMV. And, I was surprised at how little journal space was given to tests of CMV. In Study 2, we didn’t know what we would find about what marker variables might detect in comparison to presumed causes of CMV, but we were still surprised to find that one added measure (either marker or presumed cause) is likely not enough to reasonably detect CMV and that multiple marker and CMV-cause variables in one study give much more information.
- How do you see this study influencing future research and/or practice?
We hope that other researchers can find this article helpful in choosing appropriate marker variables and analyzing them in a way that can reasonably detect CMV. This is easier said than done, because a good marker variable is often chosen before data collection, and perhaps this article can influence more authors to do that. But, we hope, too, that reviewers gain some knowledge about how these techniques can be used to detect CMV. And, our ultimate goal is that this work can get us a little bit closer to understanding the large, complex, and still ambiguous phenomenon of CMV in social science research.
You can read “Marker Variable Choice, Reporting, and Interpretation in the Detection of Common Method Variance: A Review and Demonstration” from Organizational Research Methods for free for the next two weeks by clicking here. Want to know about all the latest research like this from Organizational Research Methods? Click here to sign up for e-alerts!
Marcia J. Simmering is the Francis R. Mangham Endowed professor of Management and assistant dean of Undergraduate Programs in the College of Business at Louisiana Tech University. Her current research focuses on the methods topics of common method variance and control variables. Additionally, she has published research on feedback, compensation, and training.
Christie M. Fuller is Thomas O’Kelly-Mitchener associate professor of Computer Information Systems at Louisiana Tech University. Her research in deception and decision support systems has been published in Decision Support Systems, Expert Systems with Applications, IEEE Transactions on Professional Communication, along with other journals and conference proceedings.
Hettie A. Richardson is an associate professor and Chair of the Department of Management, Entrepreneurship, and Leadership in the Neeley School of Business at Texas Christian University. Her methodological research interests focus on common method variance and other measurement-related issues. She also studies employee involvement, empowerment, and strategic human resource management.
Yasemin Ocal is an assistant professor of Marketing at Texas A&M University-Commerce. Her research focuses on response rate and response bias in marketing research and has appeared in journals such as Journal of Leadership and Organizational Studies and numerous international conferences, including organization of a survey response rate issues session in World Marketing Congress of the Academy of Marketing Science.
Guclu M. Atinc is an assistant professor of Management at Texas A&M University-Commerce. His current research addresses board composition, top management teams and ownership structures of young entrepreneurial firms, and research methods. Dr. Atinc’s research has appeared in journals such as Organizational Research Methods, Journal of Managerial Issues, and Journal of Leadership and Organizational Studies.