The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
The Presence of Something or the Absence of Nothing: Increasing Theoretical Precision in Management Research
Abstract
In management research, theory testing confronts a paradox described by Meehl in which designing studies with greater methodological rigor puts theories at less risk of falsification. This paradox exists because most management theories make predictions that are merely directional, such as stating that two variables will be positively or negatively related. As methodological rigor increases, the probability that an estimated effect will differ from zero likewise increases, and the likelihood of finding support for a directional prediction boils down to a coin toss. This paradox can be resolved by developing theories with greater precision, such that their propositions predict something more meaningful than deviations from zero. This article evaluates the precision of theories in management research, offers guidelines for making theories more precise, and discusses ways to overcome barriers to the pursuit of theoretical precision.
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Citations
Cites
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Significance Tests Die Hard: The Amazing Persistence of a Probabilistic Misconception
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Background
Cited by
At what sample size do correlations stabilize?
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Bayesian estimation supersedes the t-test
Kruschke, J. K. (2013) · Journal of Experimental Psychology: General · doi:10.1037/a0029146 · Cited by 1,530
The Too-Much-of-a-Good-Thing Effect in Management
Pierce, J. R., & Aguinis, H. (2013) · Journal of Management · doi:10.1177/0149206311410060 · Cited by 1,085
Correlational effect size benchmarks
Bosco, F. A., Aguinis, H., Singh, K., Field, J. G., & Pierce, C. A. (2015) · Journal of Applied Psychology · doi:10.1037/a0038047 · Cited by 768
Evaluating and Developing Theories in the Information Systems Discipline
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Metadata
- Authors
- Edwards, J. R., & Berry, J. W.
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 2010-09-07
- Volume
- 13
- Issue
- 4
- Pages
- 668-689
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W2142274884
- Cited by
- 226
- References
- 11
- FWCI
- 12.64
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
