Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-19T21:58:56.145Z Has data issue: false hasContentIssue false
  • English
  • Français

Challenges and pitfalls of Cinderella-izing descriptive statistics in Table 1

Published online by Cambridge University Press:  14 December 2021

In-Sue Oh Open the ORCID record for In-Sue Oh [Opens in a new window]
In-Sue Oh*
Affiliation:
Fox School of Business, Department of Human Resource Management, Temple University
*
Corresponding author: Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 14 , Issue 4 , December 2021 , pp. 521 - 523
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Society for Industrial and Organizational Psychology

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

I would like to thank Frank Schmidt for his insightful comments and his endless support. He will be sorely missed, and forever remembered.

References

Bobko, P. (1995). Editorial. Journal of Applied Psychology, 80(1), 3–5.10.1037/h0092450CrossRefGoogle Scholar
Eden, D. (2002). Replication, meta-analysis, scientific progress, and AMJ’s publication policy. Academy of Management Journal, 45(5), 841–846.10.5465/amj.2002.7718946CrossRefGoogle Scholar
Gonzalez-Mulé, E., Kim, M. M., & Ryu, J. W. (2020). Ameta-analytic test of multiplicative and additive models of job demands, resources, and stress. Journal of Applied Psychology. Advance online publication.Google Scholar
Loftus, G. R. (1996). Psychology will be a much better science when we change the way we analyze data. Current Directions in Psychological Science, 8, 161–171.10.1111/1467-8721.ep11512376CrossRefGoogle Scholar
Mackey, J. D., McAllister, C. P., Maher, L. P., & Wang, G. (2019). Leaders and followers behaving badly: Ameta-analytic examination of curvilinear relationships between destructive leadership and followers’ workplace behaviors. Personnel Psychology, 72(1), 3–47.10.1111/peps.12286CrossRefGoogle Scholar
Murphy, K. (2021). In praise of Table 1: The importance of making better use of descriptive statistics. Industrial and Organizational Psychology: Perspectives on Science and Practice, 14(4), 461–477.Google Scholar
Schmidt, F. L. (2010). Detecting and correcting the lies that data tell. Perspectives on Psychological Science, 5(3), 233–242.10.1177/1745691610369339CrossRefGoogle ScholarPubMed
Schmitt, N. (1989). Editorial. Journal of Applied Psychology, 74(6), 843–845.10.1037/h0092216CrossRefGoogle Scholar
Van Iddekinge, C. H., Aguinis, H., Mackey, J. D., & DeOrtentiis, P. S. (2018). A meta-analysis of the interactive, additive, and relative effects of cognitive ability and motivation on performance. Journal of Management, 44(1), 249–279.10.1177/0149206317702220CrossRefGoogle Scholar