Tutorials
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.
A Workflow for Experiment Planning and Data Analysis in Education Research
This book documents a data analysis workflow that might be useful for people in the Learning and Instruction Lab at ETH Zürich. It might also be useful for researchers in education research more generally, as well as anyone else who analyses behavioural data from human participants.
The book follows the natural sequence of a study from planning to inference, split into two parts.
Part 1: Before data collection: how to use pilot data or simulations to justify a sample size (Planning), and what and how to preregister (Preregistration).
Part 2: After data collection: how to process and visualise data (Data processing), fit a multilevel Bayesian regression model (Modelling), and generate and communicate inferences (Inference).
You can read the book here: https://rich-ramsey.github.io/li-lab-workflow/
A reproducible scientific workflow in R and R Studio
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.
The latest version using Quarto and the quarto-preprint extension:
https://github.com/rich-ramsey/reproducible_workflow_quarto
An older version using R markdown and the papaja() package:
A Bayesian workflow
This project takes aspects of the Bayesian workflow paper by Gelman and colleagues and applies them to an example scenario in experimental psychology.
A PsychoPy Tutorial
This tutorial is intended as a one-stop guide to understanding and using PsychoPy/Pavlovia. Andrew Wildman was the primary author.
Data simulation to determine sample sizes
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.
Analysing pupilometry data
preprocessing pupil data
This project aims to setup and test a preprocessing pipeline in R for pupil size timeseries data, which uses the pupillometry package.
modelling pupil data with hgams
This project tries to provide an example of how you might model pupilometry data using hierarchical generalised additive models (hgams).
Analysing time-to-event psychological data (e.g., RTs) using event history analysis
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
Using ETH’s High Performance Computing cluster (Euler) to fit Bayesian regression models
This project summarises our lab’s current workflow using the ETH Zurich high performance computing cluster - Euler - to fit Bayesian regression models.
Sven Panis wrote this tutorial.