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A tutorial on the analysis of experiments using BANOVA.

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This tutorial introduces Bayesian analysis of variance (BANOVA) for psychological research. BANOVA offers a flexible framework for analyzing complex experimental designs and mediation effects using an R package.

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Area of Science:

  • Psychology
  • Statistics
  • Data Analysis

Background:

  • Bayesian methods are gaining traction in psychology for experimental data analysis.
  • Identifying mediating mechanisms in experimental treatments is crucial.

Purpose of the Study:

  • To provide a tutorial on Bayesian analysis of variance (BANOVA).
  • To present a comprehensive and coherent framework for analyzing experimental data in psychology.

Main Methods:

  • The article details a Bayesian approach to the analysis of variance (BANOVA).
  • It covers between, within, and mixed experimental designs with normal and non-normal variables.
  • The framework accommodates unobserved individual differences.

Main Results:

  • An accompanying R package simplifies model specification and analysis.
  • The package calculates planned comparisons, simple effects, floodlight ranges, and mediation analyses.
  • It also computes effect sizes for direct and indirect effects.

Conclusions:

  • BANOVA provides a robust framework for psychological data analysis.
  • The R package facilitates complex analyses, including mediation and moderated mediation.
  • The methodology is illustrated with real-world psychological data sets.