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Participant Motivation: A Critical Consideration

Published online by Cambridge University Press:  28 July 2015

Alyssa K. McGonagle
Alyssa K. McGonagle*
Affiliation:
Wayne State University
*
Correspondence concerning this article should be addressed to Alyssa K. McGonagle, Department of Psychology, Wayne State University, 5057 Woodward Avenue, 7th Floor, Detroit, MI 48202. E-mail: alyssa.mcgonagle@wayne.edu
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Extract

Landers and Behrend (2015) make a good argument that more consideration should be given to sampling strategies in light of the specific research question prior to data collection and that nonorganizational samples should not be automatically dismissed by journal editors and reviewers. Yet, the authors only briefly mention one particular issue that is also relevant to the validity of our research findings—participant motivation. Researchers should seek to better understand why individuals choose to participate in a study and what may be motivating the levels of effort they put forth in participating. Two critical questions include Are participants who they say they are (e.g., working adults)? And, are participants paying attention to the study instructions and questions and participating with effort? In this response, I expand on issues related to participant motivation and apply them to the sampling strategies discussed by Landers and Behrend (2015). I also provide suggestions for ways researchers may address these issues.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 8 , Issue 2 , June 2015 , pp. 208 - 214
Copyright
Copyright © Society for Industrial and Organizational Psychology 2015 

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References

Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6 (1), 3–5.Google Scholar
DeSimone, J. A., Harms, P. D., & DeSimone, A. J. (2014). Best practice recommendations for data screening. Journal of Organizational Behavior. Advance online publication. doi:10.1002/job.1962Google Scholar
Huang, J. L., Curran, P. G., Keeney, J., Poposki, E. M., & DeShon, R. P. (2012). Detecting and deterring insufficient effort respond to surveys. Journal of Business and Psychology, 27, 99–114. doi:10.1007/s10869-011-9231-8CrossRefGoogle Scholar
Huang, J. L., Liu, M., & Bowling, N. A. (2014). Insufficient effort responding: Examining an insidious confound in survey data. Journal of Applied Psychology. Advance online publication. doi:10.1037/a0038510Google Scholar
Landers, R. N., & Behrend, T. S. (2015). An inconvenient truth: Arbitrary distinctions between organizational, Mechanical Turk, and other convenience samples. Industrial and Organizational Psychology: Perspectives on Science and Practice.Google Scholar
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 65, 539–569.Google Scholar
Shoss, M. (2013, September 16). Re: Collecting longitudinal data [Electronic mailing list message]. Retrieved from RMNet listserv: https://lists.unc.edu/Google Scholar
Williams, L. J., & Anderson, S. E. (1994). An alternative approach to method effects by using latent-variable models: Applications in organizational behavior research. Journal of Applied Psychology, 79 (3), 323–331.Google Scholar