The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Statistical difficulties of detecting interactions and moderator effects.
Abstract
Although interaction effects are frequently found in experimental studies, field researchers report considerable difficulty in finding theorized moderator effects. Previous discussions of this discrepancy have considered responsible factors including differences in measurement error and use of nonlinear scales. In this article we demonstrate that the differential efficiency of experimental and field tests of interactions is also attributable to the differential residual variances of such interactions once the component main effects have been partialed out. We derive an expression for this residual variance in terms of the joint distribution of the component variables and explore how properties of the distribution affect the efficiency of tests of moderator effects. We show that tests of interactions in field studies will often have less than 20% of the efficiency of optimal experimental tests, and we discuss implications for the design of field studies.
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“First, this study involves a relatively small sample size, which limits power to detect significant moderation effects (McClelland & Judd, 1993).”
From Who Benefits Most from a Brief Mindfulness Intervention to Reduce Anxiety During Stereotactic Breast Biopsy: the Moderating Effect of Trait Mindfulness, Spiritual Well-being, and Distress Tolerance (2021) · cites this paper as McClelland & Judd, 1993
In the citing paper
“This consistent pattern of findings is noteworthy given the acknowledged difficulty in detecting moderation within field settings (McClelland and Judd, 1993).”
From Narcissistic Leaders and Their Victims: Followers Low on Self-Esteem and Low on Core Self-Evaluations Suffer Most (2018) · cites this paper as McClelland and Judd, 1993
In the citing paper
“An additional advantage of high-risk designs is that the greater variability in vulnerability constructs they often afford yields greater statistical power to detect the presence of moderating effects by reducing standard errors without compromising parameter estimates (Carter & Garber, 2011; McClelland & Judd, 1993).”
From A Latent Structure Analysis of Cognitive Vulnerability to Depression in Adolescence (2019) · cites this paper as McClelland & Judd, 1993
Citations
Cites
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Self-citationData Analysis: A Model-Comparison Approach
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Liu, R. T., McArthur, B. A., Burke, T. A., Hamilton, J. L., Mac Giollabhui, N., Stange, J. P., Hamlat, E. J., Abramson, L. Y., & Alloy, L. B. (2019) · Behavior Therapy · doi:10.1016/j.beth.2018.11.004 · Cited by 11
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Metadata
- Authors
- McClelland, G. H., & Judd, C. M.
- Affiliations
- University of Colorado Boulder (US)
- Venue
- Psychological Bulletin
- Published
- 1993
- Volume
- 114
- Issue
- 2
- Pages
- 376-390
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W2029079171
- Cited by
- 3,270
- References
- 36
- Citations by year
- 2026: 144 · 2025: 82 · 2024: 61 · 2023: 65 · 2022: 92 · 2021: 103 · 2020: 97 · 2019: 101 · 2018: 127 · 2017: 122 · 2016: 157 · 2015: 144 · 2014: 150 · 2013: 162 · 2012: 162
- FWCI
- 29.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Bulletin (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Mental Health (MH-45049, R01MH045049)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- interaction effects, moderator effects, statistical power, residual variance, fieldwork, experimental design, measurement error, non-linear scales, joint probability distribution
- MeSH terms
- Data Interpretation, Statistical, Humans, Methods, Psychological Tests, Psychometrics, Research Design, Models, Statistical, Bias
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