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How to correctly analyze reaction time (RT) data

2020-05-18 · Methods · Dominique Makowski

This is a very, very important topic given the widespread usage of reaction times in psychology. Most of the time, we analyze it as a regular variable, using traditional models such as linear models, ANOVAs etc. The problem is that these models assume that the RT is normally distributed, which is false.

This leads us to adjustements like outliers removal or log-transformation, distorting the data because of our non-appropriate models.

The good news is, it's very easy to use better models, that account for the non-normal distribution of RT. And these alternatives are beautifully presented by Jonas K. Lindeløv in the guide below:

👉 Reaction time distributions: an interactive overview 👈

It is a must-read for all psychologists. Do check it out!

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Exploring the neuropsychology of reality and its distortions.

The Reality Bending Lab, led by Dominique Makowski at the University of Sussex in Brighton, UK, conducts world-leading research on reality perception, illusions, fake news, AI-beliefs, deception and its links with emotions, cognitive control and the Self. Using recording signals from the body (ECG, EDA…) and the brain (EEG), we analyse data using advanced modelling (Bayesian statistics, chaos theory, computational models) and develop open-source tools to improve neuropsychological science.

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