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Robust statistical methods in R using the WRS2 package.

Patrick Mair1, Rand Wilcox2

  • 1Harvard University, Cambridge, MA, 02138, USA. mair@fas.harvard.edu.

Behavior Research Methods
|June 2, 2019
PubMed
Summary
This summary is machine-generated.

This paper introduces the R package WRS2 for robust statistical methods, offering practical tools for applied researchers in social and behavioral sciences. It provides tutorials and R code for robust analyses, enhancing data interpretation and reliability.

Keywords:
RRobust ANCOVARobust ANOVARobust mediationRobust statistics

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Area of Science:

  • Statistics
  • Social Sciences
  • Behavioral Sciences

Background:

  • Traditional statistical methods can be sensitive to outliers and violations of assumptions.
  • Robust statistical methods offer more reliable analyses when data deviates from normality or contains extreme values.

Purpose of the Study:

  • Introduce the R package WRS2 for implementing robust statistical methods.
  • Provide a non-technical, tutorial-style guide for applied researchers.
  • Demonstrate applications in social and behavioral sciences using R.

Main Methods:

  • Introduction to robust location, dispersion, and correlation measures.
  • Implementation of robust variants of t-tests, ANOVA (including between-within designs and quantile ANOVA).
  • Introduction to robust ANCOVA and robust mediation models.

Main Results:

  • The WRS2 package provides accessible tools for robust statistical analyses.
  • The paper illustrates practical applications with R code examples.
  • Supplementary materials offer reproducible R code for all analyses.

Conclusions:

  • WRS2 package facilitates the application of robust statistics in research.
  • The tutorial style and focus on social/behavioral sciences make robust methods more accessible.
  • The package enhances the reliability and validity of statistical findings in applied research.