Unravelling the Nuances of Data With Quantile Regression

A Comprehensive Tutorial

Bibliographic Data

ID21337006
AuthorsRenaud Mabire‐yon (0000-0003-1972-5741, UMR1296 ‘Radiations: Defense, Health, Environment’, INSERM University of Lyon 2 Bron France, corresponding author)
Year2025
Volume60
Issue2
Pagese70006-e70006
Publication date2025-04-01
Open AccessYes
TypeARTICLE
VenueInternational Journal of Psychology (JOURNAL)
Journal identifiersISSN: 0020-7594 • E-ISSN: 1464-066X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ijop.70006
PMID39956533
OpenAlexW4407607887
LanguageEN
Citations received2
References cited31

In applied psychology, traditional statistical methods often provide only a broad overview, potentially overlooking nuanced variable relationships. This article presents a comprehensive tutorial on quantile regression (QR), a statistical modelling technique ideally suited for psychological data analysis. Unlike conventional regression, QR examines relationships across different quantiles of the data distribution, revealing complex dynamics and offering robustness to non‐normality and heteroscedasticity. We demonstrate its utility through a practical example, analysing the relationship between age and life satisfaction, supported by annotated R code. The tutorial emphasises grounding QR in a sound theoretical framework and introduces the quantile loss approach as an alternative to p value interpretation. By providing both theoretical understanding and practical tools, this tutorial aims to empower researchers to improve the depth and reproducibility of their findings in psychological research

Econometrics · Heteroscedasticity · Machine learning · Normality · Quantile · Quantile regression · Regression · Regression analysis · Robustness (evolution) · Statistics · Computer Science · Decision-Making and Behavioral Economics · Mathematics · Mental Health Research Topics · Psychological Well-being and Life Satisfaction · Psychology · Social Psychology

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