Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.

Erceg-Hurn, David M.; Mirosevich, Vikki M.

doi:10.1037/0003-066x.63.7.591

Cited by 965Advanced Statistical Methods and ModelsStatistical Methods and Bayesian InferenceProbability and Statistical Research

Modern robust statistical methods: An easy way to maximize the accuracy and power of your research.

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Erceg-Hurn, David M., Mirosevich, Vikki M.·2008·American Psychologist

DOI 10.1037/0003-066x.63.7.591

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Abstract

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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Bias-corrected bootstrapped estimates with 10,000 samples were used; this approach gives a powerful mediation test and tolerates small normality departures.
“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).”
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Authors: Erceg-Hurn, D. M., & Mirosevich, V. M.
Affiliations: Government of Western Australia (AU)
ORCID: Erceg-Hurn, David M.
Venue: American Psychologist
Publisher: American Psychological Association (APA)
Published: 2008
Volume: 63
Issue: 7
Pages: 591-601
ISSN: 0003-066X (ISSN-L)
1935-990X
DOI: 10.1037/0003-066x.63.7.591
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: green
research-repository.uwa.edu.au
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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