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 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.

https://github.com/rich-ramsey/reproducible_workflow

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.

https://github.com/rich-ramsey/Bayesian_workflow

A PsychoPy Tutorial

This tutorial is intended as a one-stop guide to understanding and using PsychoPy/Pavlovia. Andrew Wildman was the primary author.

https://github.com/rich-ramsey/psychopy_tutorial

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.

https://github.com/rich-ramsey/small_n_sims

Analysing pupilomoetry data using hgams

This project tries to provide an example of how you might model pupilometry data using hierarchical generalised additive models (hgams).

https://github.com/rich-ramsey/pupil_example

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