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Evaluating hypotheses with dominance analysis

Published online by Cambridge University Press:  14 December 2021

Rick A. Laguerre Open the ORCID record for Rick A. Laguerre [Opens in a new window]
Rick A. Laguerre*
Affiliation:
Department of Psychological Sciences, University of Connecticut
*
Corresponding author. Email: [email protected]
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Abstract

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Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 14 , Issue 4 , December 2021 , pp. 514 - 517
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Society for Industrial and Organizational Psychology

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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), 129–148. 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), 157–180.10.3102/10769986031002157CrossRefGoogle 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), 593–602.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), 542–551. 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), 350–378. 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), 328–331.CrossRefGoogle Scholar
Kleine, A.-K., Rudolph, C. W., & Zacher, H. (2019). Thriving at work: A meta-analysis. Journal of Organizational Behavior, 40(9–10), 973–999. 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), 258–282.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), 461–477.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