The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.
Open accessAbstract
Classic parametric statistical significance tests, such as analysis of variance and least squares regression, are widely used by researchers in many disciplines, including psychology. For classic parametric tests to produce accurate results, the assumptions underlying them (e.g., normality and homoscedasticity) must be satisfied. These assumptions are rarely met when analyzing real data. The use of classic parametric methods with violated assumptions can result in the inaccurate computation of p values, effect sizes, and confidence intervals. This may lead to substantive errors in the interpretation of data. Many modern robust statistical methods alleviate the problems inherent in using parametric methods with violated assumptions, yet modern methods are rarely used by researchers. The authors examine why this is the case, arguing that most researchers are unaware of the serious limitations of classic methods and are unfamiliar with modern alternatives. A range of modern robust and rank-based significance tests suitable for analyzing a wide range of designs is introduced. Practical advice on conducting modern analyses using software such as SPSS, SAS, and R is provided. The authors conclude by discussing robust effect size indices.
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“We examined the total, direct, and indirect effects of each predictor variable on outcomes using bias-corrected bootstrapped estimates (Efron & Tibshirani, 1993) based on 10,000 bootstrapped samples, which provides a powerful test of mediation (Fritz & MacKinnon, 2007) and is robust to small departures from normality (Erceg-Hurn & Mirosevich, 2008).”
From Five Facets of Mindfulness and Psychological Health: Evaluating a Psychological Model of the Mechanisms of Mindfulness (2015) · cites this paper as Erceg-Hurn & Mirosevich, 2008
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Noguchi, K., Gel, Y. R., Brunner, E., & Konietschke, F. (2012) · Journal of Statistical Software · doi:10.18637/jss.v050.i12 · Cited by 1,146
Why Psychologists Should by Default Use Welch’s t-test Instead of Student’s t-test
Delacre, M., Lakens, D., & Leys, C. (2017) · International Review of Social Psychology · doi:10.5334/irsp.82 · Cited by 1,037
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Metadata
- Authors
- Erceg-Hurn, D. M., & Mirosevich, V. M.
- Affiliations
- Government of Western Australia (AU)
- ORCID
- Erceg-Hurn, David M.
- Venue
- American Psychologist
- Published
- 2008
- Volume
- 63
- Issue
- 7
- Pages
- 591-601
- ISSN
- 0003-066X (ISSN-L)
- 1935-990X
- OpenAlex
- W2102493402
- Cited by
- 959
- References
- 76
- Citations by year
- 2026: 38 · 2025: 59 · 2024: 55 · 2023: 54 · 2022: 81 · 2021: 54 · 2020: 94 · 2019: 77 · 2018: 61 · 2017: 66 · 2016: 73 · 2015: 59 · 2014: 45 · 2013: 36 · 2012: 41
- FWCI
- 20.78
- Type
- journal article
- Language
- en
- Open access
- Locations
- American Psychologist (publishedVersion)
- PubMed (publishedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- robust statistics, rank tests, nonparametric tests, normality, homoscedasticity, statistical power, significance testing
- MeSH terms
- Data Interpretation, Statistical, Humans, Research, Software, Statistics, Nonparametric
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