This page will collect links and resources related to open-source tutorials in psychology. It’s obviously nowhere near a complete list, but aims to highlight some of the tutorials that inspire our group. We will continue to update it as we hear about more great tutorials.
Curation sites are where authors or groups can repost content written by others. For example, creating a collection of tutorials or linking to tutorials to reach wider audiences.
Framework for Open and Reproducible Research Training (FORRT)
FORRT aim to improve teaching and mentoring practices in Higher Education. They include pages which organise tutorials into topics.
Open Scholarship Knowledge Base
OSKB curates a collection of resources that support open scholarship for researchers and educators. Resources include training materials for early career researchers and tutorials for applying statistical techniques for reproducible science.
Rushworth, A. Posts you might have missed!
Daily reposts of tutorials written for R and Python. They take tutorials other users have written and write short summaries of what they contain.
UK Reproducibility Network. Reproducibility Resources
The UKRN’s Resources page curates action plans and primers to guide research organisations and researchers in implementing best practices for open and reproducible science.
McIntyre, K. P. (2021). Open Stats Lab
This site curates lab activities for statistical methods at the undergraduate level. Each activity focuses on a published paper with its data and code.
Tutorial papers are where authors provide a step-by-step guide on the process and decision making involved in a task. Tutorials that are accompanied by code and data examples must find effective ways to make those resources open, findable, and accessible. We have collated these resources to demonstrate tutorials aimed at beginner, intermediate, and advanced users.
Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., van Langen, J., & Kievit, R. A. (2021). Raincloud plots: a multi-platform tool for robust data visualization [version 2]. Wellcome Open Resources, 4:63. https://doi.org/10.12688/wellcomeopenres.15191.2
The authors introduce tools in R, Python, and Matlab that allow users to visualise data with raincloud plots.
Bartlett, J. E., & Charles, S. J. (2021, December 10). Power to the People: A Beginner’s Tutorial to Power Analysis using jamovi. https://doi.org/10.31234/osf.io/bh8m9
The authors specifically target beginners to power analysis by explaining core concepts and elements of decision making, before walking readers through examples of power analysis.
Marsman, M., & Wagenmakers, E.-J. (2017). Bayesian benefits with JASP. European Journal of Developmental Psychology, 14(5), 545–555. https://doi.org/10.1080/17405629.2016.1259614
The authors explain the rationale behind using Bayesian statistics, before demonstrating different analyses in the free software JASP. Readers can follow along to the analyses by downloading an annotated file on the OSF.
Kathawalla, U.-K., Silverstein, P., & Syed, M. (2021). Easing Into Open Science: A Guide for Graduate Students and Their Advisors. Collabra: Psychology, 7(18684). https://doi.org/10.1525/collabra.18684
The authors explain how to incorporate different open science practices into your research pipeline. For each entry, they explain what, why, how, and outline any potential worries.
Nordmann, E., McAleer, P., Toivo, W., Paterson, H., & DeBruine, L. M. (2021, June 21). Data visualisation using R, for researchers who don’t use R. https://doi.org/10.31234/osf.io/4huvw
Targeting researchers who have little to no prior experience in R, this tutorial introduces basic R setup, data wrangling, and data visualisation.
Brown, V. A. (2021). An Introduction to Linear Mixed-Effects Modeling in R. Advances in Methods and Practices in Psychological Science, 4(1), 2515245920960351. https://doi.org/10.1177/2515245920960351
Brown provides an intermediate introduction to linear mixed-effect models in R as the topic is relatively complex, but the focus is on how to analyse data in R rather than the mathematical background. The author provides further reading for those who want to delve into the mathematics.
DeBruine, L. M., & Barr, D. J. (2021). Understanding mixed-effects models through data simulation. Advances in Methods and Practices in Psychological Science, 4(1), 251524592096511. https://doi.org/10.1177/2515245920965119
The authors explain how to simulate data with random-effects structure, how to analyse data using linear mixed-effects regression (with the lme4 R package), and how to interpret the output.
Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence Testing for Psychological Research: A Tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10.1177/2515245918770963
The article focuses on reporting and interpretating equivalence testing applied to psychological research. The authors then provide supplementary material on an OSF page to reproduce their analyses in R.
Bürkner, P-C., & Vuorre, M. (2019). Ordinal Regression Models in Psychology: A Tutorial. Advances in Methods and Practices in Psychological Science, 2(1), 77–101. https://doi.org/10.1177/2515245918823199
The authors provide an intermediate to advanced tutorial based on outlining the mathematical concepts behind different applications of ordinal regression. They provide a walkthrough using example data but highlight how you can apply these techniques to your own data.
Dima, A. L. (2018). Scale validation in applied health research: Tutorial for a 6-step R-based psychometrics protocol. Health Psychology and Behavioral Medicine, 6(1), 136–161. https://doi.org/10.1080/21642850.2018.1472602
The author provides a tutorial of how to use R for scale validation. Readers can download an example data set and script to follow the article. The tutorial is listed as advanced as readers would either need prior understanding of scale validation concepts, or engage in further reading after following the tutorial.
Books are the traditional sources of tutorials where there are longer explanations organised into chapters. Authors provide step-by-step instructions on how to perform different analyses and caution where there can be problems. We have collated these resources to demonstrate tutorials aimed at beginner, intermediate, and advanced users.
Navarro, D. (2018, December 31). Learning Statistics with R
This book is the curriculum of an introductory statistics course using R. The book covers descriptive statistics, graphing, probability theory, null hypothesis testing, and Bayesian statistics.
McAleer, P. (2022, January 16). A Handy Workbook for Research Methods & Statistics The book guides the reader through hand calculations of basic statistical tests. Worked examples and self-tests are provided to promote understanding.
McAleer, P., Kuepper-Tetzel, C., & Paterson, H. (2022, January 21). PsyTeachR. Year 2 Research Methods and Statistics Practical Skills
This book is aimed at second year undergraduates, building on prior data wrangling and visualisation skills. The content covers Null Hypothesis Significance Testing and basic statistical tests.
Nordmann, E. & Cleland-Woods, H. (2021, December 10). PsyTeachR. Level 1 Data Skills
This book is aimed at first year undergraduates to introduce them to R programming. The book covers basic skills such as loading data, data wrangling, and data visualisation.
Nordmann, E. & McAleer, P. (2022, January 7). PsyTeachR. Fundamentals of Quantitative Analysis
This book supports an MSc conversion course, where students with a prior degree complete an accelerated psychology programme. Students learn about content covered in years 1-3 in an undergraduate course, from basic programming in R to multiple regression.
Bartlett, J.E. (2021, July 6). Introduction to Power Analysis: A Guide to G*Power, jamovi, and Superpower
The author provides a practical, step-by-step guide to calculating power and effect size using G*Power, jamovi, and the R Superpower package.
Barr, D. (2021, November 10). PsyTeachR. Level 3: Statistical Models
This book is aimed at third year undergraduates who are now specialising in psychology. The content builds on levels 1 and 2 to cover statistical modelling (e.g., mixed effects models and ordinal regression) and data simulation.
BBC Visualisation cookbook Written for data journalists, this book walks through how to create data visualisations that balance aesthetics, clarity, and information.
DeBruine, L. & Barr, D. (2021, October 15). PsyTeachR. Data Skills for Reproducible Research
This book supports an MSc psychology course, where students have a previous psychology degree but they are learning about more advanced research methods. The material expects a basic understanding of statistical concepts, but no prior knowledge of R programming to focus on reproducible workflows.
Klein, O., Hardwicke, T. E., Aust, F., Breuer, J., Danielsson, H., Hofelich Mohr, A., IJzerman, H., Nilsonne, G., Vanpaemel, W., & Frank, M. C. (2018). A Practical Guide for Transparency in Psychological Science. Collabra: Psychology, 4(1), 20. https://doi.org/10.1525/collabra.158
This is a guide to transparency practices in various stages of research. Topics covered include preregistration, preprint, and open-source access to scripts and data.
Mineault, P. & The Good Research Code Handbook Community. (2021). The Good Research Code Handbook (DOI) The authors provide a practical guide on how to apply programming principles to organise workflows that support the researcher’s productivity and benefit reproducibility for the broader community.
A massive open online course (MOOC) or other online courses can be a high-impact way to share your tutorials and curricula with global and diverse communities of researchers and learners. Tutorials in online courses typically combine written and visual information.
Video tutorials are a good way to share a curriculum in multimedia formats and let anyone learn at their own pace.
Buchanan, E. Statistics of Doom This Youtube channel features statistics tutorials in R, Python, JASP, and SPSS.
Christiansen, P. Statistics Guides with Dr Paul Christiansen
This YouTube channel features tutorials on using R and SPSS for different analysis techniques.
One important contribution can be translating a tutorial - often written in English - into your first language. This widens access to tutorials as not everyone is fluent in English. Other elements of translation are taking the materials in one tutorial and rewriting it for another programming language or piece of software.
jamovi Resources by the community
The jamovi team collate a list of resources such as open textbooks and guides, and their translations written by other contributors. This means you can access the quick start guide in languages like Norwegian and Greek.
Kurz, S. (2021, May 6). Doing Bayesian Data Analysis in brms and the tidyverse
Kurz took the popular Bayesian statistics textbook by Kruschke (2015) and translated his code. Instead of relying on JAGS and Stan, he translated the analyses to use the packages from R tidyverse and brms, making the material more accessible to those who prefer these packages.
Kurz, S. (2019). Statistical Rethinking with brms, ggplot2, and the tidyverse
Kurz builds upon the popular Bayesian textbook, McElreath (2020), and translated the code into R brms, ggplot and tidyverse.
Journal clubs and coding clubs provide an informal setting to combine learning with a supportive community for early career researchers. Clubs can facilitate access to best practices in statistics and scientific methods in a more accessible format.
Coding Club
Collection of coding, data science and statistics tutorials with examples in R, Python, JavaScript and Python.
Data Colada. https://datacolada.org/
Seminar series and blog on working with data.
Hack your data beautiful
Series of short workshops aimed at new users for R on data wrangling, visualisation, and analysis.
Blogs can be a flexible, dynamic space for publishing a short tutorial for various projects. There is a networking component to blogs, where authors can receive immediate feedback from their audience. What starts as an informal tutorial blog post can often develop into a software package or a publishable paper.
Bartlett, J. E. (2021, October 6). Learning To Read Scientific Journal Articles
The author explains how to use the QALMRI (Question, Alternatives, Logic, Method, Results, and Inferences) method to read journal articles, and provide self-checking exercises.
Buchanan, E. Dr. Erin Buchanan
The author shares tutorials on using R packages and techniques to solve problems and build projects, such as building a CV with R Markdown or using R packages for web scraping. The blog also curates resources, including workshop materials and videos.
Choe, J. June Choe Tutorials about improving explanatory data viz in academic research and other topics related to R and stats.
DeBruine, L. Blog Posts
This blog publishes tutorials about using R in various projects, with a focus on explaining the rationale behind each step to help learners build their skills.
Fried, E. Psych Networks
The author and guests provide R tutorials and informative posts mostly on visualisation and modeling in psychology.
Kruschke, J. K. Doing Bayesian Data Analysis
This blog features intermediate-to-advanced tutorials on Bayesian statistics and modeling techniques, using R.
Lakens, D. The 20% Statistician
The author writes detailed blog posts on how to correctly apply and interpret statistics. Discussions and tutorials shared in the blog posts are often produced into published papers.
Magnusson, K. R Psychologist
This blog showcases interactive web apps that visualise statistical concepts and techniques. Magnusson also writes about common issues in statistical modeling and suggests solutions.
Quintana, D. dsquintana.blog Tutorials on ways to evaluate and improve scientific practices, featuring excellent interactive visuals.
Rousselet, G. Basic Statistics
This blog features tutorials on intermediate-to-advanced statistical techniques, illustrated with graphs and code examples.
Roye, D. R visualization of spatial, geographical, and climate data
The author as a climate scientist writes tutorials on R visualization of geographical data.
Scherer, C. Data Visualization in R, Computational Ecology
The blog focuses on advanced data visualisation that combines aesthetics and statistical knowledge, working with large datasets.
Stefan, A. & Schönbrodt, F. (2018, November 14). Gazing into the Abyss of P-Hacking: HARKing vs. Optional Stopping
As a warning to scientists on what not to do, this blog post explores the topic of p-hacking by using code to simulate how it could be done.
Schönbrodt, F. (2017, June 1). Correcting bias in meta-analyses: What not to do (meta-showdown Part 1)
This blog post highlights common pitfalls in meta-analyses and makes recommendations on how to avoid and correct biases.
Sleegers, W. (2021, 23 September). Simulation-based power analyses
In this post, Sleegers explains how to perform a simulation-based power analysis using R. The author explains the logic behind simulation-based power analysis and covers different designs.
Wood, K. (2018, January 29). CORVIDS: A provably-complete data reconstruction tool
This blog post introduces a data reconstruction tool that can reconstruct the distribution of Likert-scale data from its summary statistics.
Wood, K. (2017, October 9). Pre-screen MTurk workers with custom qualifications
This blog post targets researchers using MTurk and guides them on pre-screening MTurk workers by qualifications (using Python boto3).