Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-22T22:51:19.612Z Has data issue: false hasContentIssue false

Evaluating hypotheses with dominance analysis

Published online by Cambridge University Press:  14 December 2021

Rick A. Laguerre*
Affiliation:
Department of Psychological Sciences, University of Connecticut
*
Corresponding author. Email: [email protected]

Abstract

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Commentaries
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

There are no known conflicts of interest to disclose.

References

Azen, R., & Budescu, D. V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8(2), 129148. https://doi.org/10.1037/1082-989X.8.2.129 CrossRefGoogle ScholarPubMed
Azen, R., & Budescu, D. V. (2006). Comparing predictors in multivariate regression models: An extension of dominance analysis. Journal of Educational and Behavioral Statistics, 31(2), 157180.10.3102/10769986031002157CrossRefGoogle Scholar
Braun, M. T., Converse, P. D., & Oswald, F. L. (2019). The accuracy of dominance analysis as a metric to assess relative importance: The joint impact of sampling error variance and measurement unreliability. Journal of Applied Psychology, 104(4), 593602.CrossRefGoogle ScholarPubMed
Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3), 542551. https://doi.org/10.1037/0033-2909.114.3.542 CrossRefGoogle Scholar
Cortina, J. M., Green, J. P., Keeler, K. R., & Vandenberg, R. J. (2017). Degrees of freedom in SEM: Are we testing the models that we claim to test? Organizational Research Methods, 20(3), 350378. https://doi.org/10.1177/1094428116676345 CrossRefGoogle Scholar
Gelman, A., & Stern, H. (2006). The difference between “significant” and “not significant” is not itself statistically significant. American Statistician, 60(4), 328331.CrossRefGoogle Scholar
Kleine, A.-K., Rudolph, C. W., & Zacher, H. (2019). Thriving at work: A meta-analysis. Journal of Organizational Behavior, 40(9–10), 973999. https://doi.org/10.1002/job.2375 CrossRefGoogle Scholar
LeBreton, J. M., Ployhart, R. E., & Ladd, R. T. (2004). A Monte Carlo comparison of relative importance methodologies. Organizational Research Methods, 7(3), 258282.CrossRefGoogle Scholar
Murphy, K. R. (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), 461477.Google Scholar
Navarrete, C. B., & Soares, F. C. (2020). dominanceanalysis: Dominance analysis (2.0.0) [Computer software]. https://CRAN.R-project.org/package=dominanceanalysis Google Scholar