Some of the below projects are tutorials in the sense that they include step-by-step instructions, whereas others just represent coding projects with comments.
The topics covered are all connected to open science, meta-science and reproducibility and available via my GitHub account.
They are free to download and use in the hope that they can be somewhat useful to others.
This project tries to outline a reproducible scientific workflow using R and R Studio that starts with raw data and ends with the finished manuscript.
This project takes aspects of the Bayesian workflow paper by Gelman and colleagues and applies them to an example scenario in experimental psychology.
This tutorial is intended as a one-stop guide to understanding and using PsychoPy/Pavlovia. Andrew Wildman was the primary author.
This project simulates multi-level data for a range of factorial designs.
https://github.com/rich-ramsey/sim_demo
And this one does the same for “small-N” designs, where trial counts per participant are particularly important.
This project aims to setup and test a preprocessing pipeline in R for pupil size timeseries data, which uses the pupillometry package.
This project tries to provide an example of how you might model pupilometry data using hierarchical generalised additive models (hgams).
This project provides a tutorial on how to perform Bayesian and Frequentist Event History Analyses for time-to-event psychological data, such as the speed and accuracy of responses.
https://github.com/sven-panis/Tutorial_Event_History_Analysis
pre-print: https://osf.io/preprints/psyarxiv/57bh6