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On the practice of dichotomization of quantitative variables.
Open accessAbstract
The authors examine the practice of dichotomization of quantitative measures, wherein relationships among variables are examined after 1 or more variables have been converted to dichotomous variables by splitting the sample at some point on the scale(s) of measurement. A common form of dichotomization is the median split, where the independent variable is split at the median to form high and low groups, which are then compared with respect to their means on the dependent variable. The consequences of dichotomization for measurement and statistical analyses are illustrated and discussed. The use of dichotomization in practice is described, and justifications that are offered for such usage are examined. The authors present the case that dichotomization is rarely defensible and often will yield misleading results.
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“However, grouping loses information and may reduce statistical power [ 87 ] especially when dichotomization is used [ 82 , 85 , 88 ].”
From Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration (2007) · cites this paper as [85]
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“In another survey, of the psychological literature, dichotomization was justified in only 22 of 110 articles (20%) [ 85 ].”
From Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration (2007) · cites this paper as [85]
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“However, such dichotomisation will result in an unnecessary loss of statistical power and precision if continuous alternatives are available (MacCallum et al., 2002; Royston et al., 2006).”
From Rating norms should be calculated from cumulative link mixed effects models (2022) · cites this paper as MacCallum et al., 2002
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“Artificially dichotomizing continuous measures is rarely conceptually or statistically defensible, and frequently yields misleading results (MacCallum et al., 2002).”
From Do growth mindset interventions impact students’ academic achievement? A systematic review and meta-analysis with recommendations for best practices. (2023) · cites this paper as MacCallum et al., 2002
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Metadata
- Authors
- MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D.
- Affiliations
- The Ohio State University (US)
- Venue
- Psychological Methods
- Published
- 2002
- Volume
- 7
- Issue
- 1
- Pages
- 19-40
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2108096958
- Semantic Scholar
- 67646d3def337c40bbfa2476f0f1ff785ae18242
- Cited by
- 3,538
- References
- 42
- Citations by year
- 2026: 282 · 2025: 170 · 2024: 149 · 2023: 134 · 2022: 183 · 2021: 211 · 2020: 204 · 2019: 179 · 2018: 180 · 2017: 223 · 2016: 208 · 2015: 208 · 2014: 184 · 2013: 187 · 2012: 138
- FWCI
- 55.97
- Type
- journal article
- Language
- en
- Open access
- Licence
- other-oa
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- KU ScholarWorks (The University of Kansas) (submittedVersion, other-oa)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- dichotomization, quantitative variables, statistical analysis, change of variables, measurement error
- MeSH terms
- Humans, Models, Psychological, Psychology
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