# Contrast analysis for competing hypotheses: A tutorial using the R package cofad

## TL;DR

This paper introduces contrast analysis as a useful alternative to traditional methods in psychology for testing hypotheses between different groups or conditions.

## Contribution

The paper introduces the use of competing-contrast analysis and demonstrates its implementation in R using the cofad package.

## Key findings

- Contrast analysis is a valuable alternative to traditional ANOVA for directional hypothesis testing.
- Competing-contrast analysis can be conducted using the R package cofad.
- This method is flexible, powerful, and hypothesis-driven for psychological research.

## Abstract

Researchers in psychology traditionally use analysis of variance to examine differences between multiple groups or conditions. A less well-known, but valuable alternative is contrast analysis — a simple statistical method for testing directional, theoretically motivated hypotheses that are defined prior to data collection. In this article, we review the core concepts of contrast analysis for testing hypotheses in between-subjects and within-subjects designs. We also outline and demonstrate the largely unknown possibility of directly testing two competing contrasts against each other. In the tutorial part of the article, we show how such competing-contrast analyses can be conducted in the free, open-source software R using the package cofad. Because competing-contrast analysis is a straightforward, flexible, highly powered, and hypothesis-driven approach, it is a valuable tool to extend the understanding of cognitive and behavioral processes in psychological research.