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A guide to working with country-year panel data and Bayesian multilevel models

By Andrew Heiss

Added Sun Dec 12, 2021

What is this?

Excerpt from guide: “Here’s a basic guide to dealing with country-year panel data using Bayesian multilevel modeling!”

  1. Link to guide here:


What is this?

Excerpt from site: Lesson files used in the Advanced Research Methods for Psychologists - Practical Applications in R, taught at Ben-Gurion University on the Negev.

  1. Link to repo here:

An introduction to statistics in R

By Mark Peterson

What is this?

Excerpt from site: A series of tutorials by Mark Peterson for working in R

  1. Link to site here:

Analysis of Factorial Designs for Psychologists

What is this?

Excerpt from site: This Github repo contains all lesson files used in the graduate-level course: Analysis of Factorial Designs foR Psychologists - Practical Applications in R, taught at Ben-Gurion University on the Negev (spring 2019 semester). This course assumes basic competence in R (importing, regression modeling, plotting, etc.), a long the lines of the prerequisite course, Advanced Research Methods foR Psychologists, which can be found here.

  1. Link to repo:

Bayesian Data Analysis course

By Aki Vehtari

What is this?

Excerpt from course site: This is the web page for the Bayesian Data Analysis course at Aalto (CS-E5710) by Aki Vehtari.

  1. Link to course:
  2. Electronic book for course here:

Bayesian statistics with R

By Olivier Gimenez

Added Tuesday Oct 12th, 2021

What is this?

Learning objectives from site: Try and demystify Bayesian statistics, and MCMC methods; Make the difference between Bayesian and Frequentist analyses; Understand the Methods section of a paper that does Bayesian stuff; Run Bayesian analyses with R (in Jags)

  1. Link to site:
  2. Link to repo:
  3. Link to recording: =uvU-TmEt8_M

Check how good your model is using “Performance”package

Added Thursday Dec 31st, 2020

What is this?

Excerpt from site: Utilities for computing measures to assess model quality, which are not directly provided by R’s ‘base’ or ‘stats’ packages. These include e.g. measures like r-squared, intraclass correlation coefficient (Nakagawa, Johnson & Schielzeth (2017) doi:10.1098/rsif.2017.0213), root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models.

  1. Link to review video by Yury Zablotski here:
  2. Link to CRAN vignette here:
  3. Link to repo here:

Comparing Multiple Means in R: Repeated Measures ANOVA in R

Added Sun Sep 13th, 2020

What is this?

Excerpt from site: This chapter describes the different types of repeated measures ANOVA, including: One-way repeated-measures ANOVA, two-way repeated-measures ANOVA, three-way repeated-measures ANOVA.

  1. Link to site here:

Conversion in Psychology-MsC course

What is this?

Excerpt from site: This book contains the quantitative research methods materials for students on the MSc Conversion in Psychological Studies/Science. The students are typically a diverse cohort and range from those with no STEM or programming background to engineering and computing science graduates. Compared to the undergraduate degree, the students are older, and there is a greater incidence of computer anxiety.

The focus for the MSc is to provide a basic but solid competency in core data skills and statistics that can be built on in further study. Students who wish to push themselves beyond the core competencies are encouraged to consult the MSc Data Skills course where they can learn about e.g., simulation and custom functions. To support those students who may have very limited computer literacy, the beginning stages are more supported than in the undergraduate programme e.g., with an increased use of screenshots and explanations for terminology.

  1. Course is here:

Crump Lab Statistics for Undergrads in Psychology Textbook

What is this?

This is a FREE Statistics for Undergrads in Psychology Textbook, on a creative commons license. Source code for everything available in the respective repos.

  1. Web-book is here:
  2. Lab manual is here:
  3. Course website is here:

GAM in R

By Noam Ross

What is this?

Excerpt from site: Generalized Additive Models in R. This short course will teach you how to use these flexible, powerful tools to model data and solve data science problems. GAMs offer offer a middle ground between simple linear models and complex machine-learning techniques, allowing you to model and understand complex systems.

  1. Link to the interactive course here:

Learn Tidymodels

What is this?

Excerpt from site: After you know what you need to get started with tidymodels, you can learn more and go further. Find articles here to help you solve specific problems using the tidymodels framework.

  1. Link to site here:

Learning Stats with Andy Wills and others:

  1. Group differences
  2. Evidence: What’s a P value?, t-tests by Andy Wills & Chris Berry
  3. More on T-tests
  4. More on Bayes Factors by Andy Wills & Chris Berry
  5. Inter-rater reliability by Michaela Gummerum & Andy Wills 💯
  6. More on Cohen’s Kappa 💯
  7. Within-subject differences by Andy Wills and Clare Walsh
  8. Factorial differences by Andy Wills and Clare Walsh
  9. Factorial differences, part 2 by Andy Wills and Clare Walsh
  10. Traditional ANOVA by Andy Wills
  11. Better tables by Paul Sharpe, Andy Wills
  12. Analysing scales by Paul Sharpe, Andy Wills, Sophie Homer
  13. Traditional non-parametric tests by Paul Sharpe

Linear Mixed Effects Models - R

By Gamlen-Greene

Added by Tue Oct 12, 2021

What is this?

Excerpt from twitter post: an accessible guide to Linear Mixed Effects Models in R using the lmer package (with background stats intro & coding examples).

  1. Link to repo here:

Open Source Research Methods for the Social Sciences

By Ben Rottman

Added Fri May 5th, 2023

What is this?

Excerpt from course: This website has all the materials I use for teaching research methods within the Psychology major at Pitt. I’m making these materials public, and calling this project Open Source Research Methods for the Social Sciences (osRMss). I hope that you find the content useful. I look forward to getting feedback and suggestions and to continually improve osRMss over time.

  1. Link to course here:

Pckages for Exploratory Data Analysis💯

What is this?

This is a list of all the packages mentioned in the “The Landscape of R Packages for Automated Exploratory Data Analysis” article by Mateusz Staniak & Przemysław Biecek. Check the paper out!

  1. The arsenal package (Heinzen et al., 2019):
  2. The autoEDA package (Horn, 2018a):
  3. The DataExplorer (Cui, 2019):
  4. The dataMaid (Petersen and Ekstrom, 2018):
  5. The dlookr (Ryu, 2019) package:
  6. The ExPanDaR package (Gassen, 2018):
  7. The explore package (Krasser, 2019):
  8. The exploreR package (Coates, 2016):
  9. The package funModeling (Casas, 2019):
  10. The inspectdf package (Rushworth, 2019):
  11. The RtutoR package (Nair, 2018a):
  12. The SmartEDA package (Ubrangala et al., 2018):
  13. The summarytools package (Comtois, 2019):
  14. The package visdat (Tierney, 2017):
  15. The xray (Seibelt, 2017) package:
  16. The package tableone (Yoshida and Bohn., 2018):
  17. The describe function from describer package (Hendricks, 2015):
  18. The skimr (Quinn et al., 2019) package:
  19. The prettyR (Lemon and Grosjean, 2018) package:
  20. The package Hmisc (Harrell Jr et al., 2019) Describe function:

PSYCH 252: Statistical Methods at Stanford University

What is this?

Excerpt from site: This course offers an introduction to advanced topics in statistics with the focus of understanding data in the behavioral and social sciences. It is a practical course in which learning statistical concepts and building models in R go hand in hand.

  1. Link to e-course here:

PsyTeachR University of Glasgow

What is this?

Excerpt from site: The psyTeachR team at the University of Glasgow School of Psychology and Institute of Neuroscience and Psychology has successfully made the transition to teaching reproducible research using R across all undergraduate and postgraduate levels. Our curriculum now emphasizes essential ‘data science’ graduate skills that have been overlooked in traditional approaches to teaching, including programming skills, data visualisation, data wrangling and reproducible reports. Students learn about probability and inference through data simulation as well as by working with real datasets.

This website contains our open materials for teaching reproducible research.

Courses books:

  1. Link to site here:
  2. Link to Level 1 Data Skills here:
  3. Link to Level 2 Research Methods and Statistics Practical Skills here:
  4. Link to Level 3 Learning Statistical Models Through Simulation in R here:
  5. Link to MSc Conversion in Psychological Studies/Science here:
  6. Link to Data Skills for Reproducible Science here:

R course

By Page Piccinini

What is this?

Excerpt from course: The goal of this course is to give you the skills to do the statistics that are in current published papers, and make pretty figures to show off your results. While we will go over the mathematical concepts behind the statistics, this is NOT meant to be a classical statistics class. We will focus more on making the connection between the mathematical equation and the R code, and what types of variables fit into each slot of the equation.

  1. Link to course here:


By Eric Brewe

What is this?

Excerpt from site: This workshop was designed to help get you started on using R to analyze social network data.

  1. Link to three workshops:
  2. Link to Workshop 1:
  3. Link to Workshop 2:
  4. Link to Workshop 3:

Reproducible Statistics for Psychologists with R-Lab Tutorials

By Mattew J. C. Crump 2020

What is this?

Excerpt from site: This is a series of labs/tutorials currently under development (2020-2021) for a two-semester graduate-level statistics sequence in Psychology @ Brooklyn College of CUNY. The goal of these tutorials is to 1) develop a deeper conceptual understanding of the principles of statistical analysis and inference; and 2) develop practical skills for data-analysis, such as using the increasingly popular statistical software environment R to code reproducible analyses.

  1. Link to website here:

Running Multiple Linear Regression Models in for-Loop

By Joachim Schork

Added Sun June 6th, 2021

What is this?

Excerpt from site: In this article, I’ll show how to estimate multiple regression models in a for-loop in the R programming language.

  1. Link to article here:

Statistical Analysis and Visualizations Using R

By Okan Bulut

Added Thu April 15th, 2021

What is this?

Excerpt from site: This full-day course is intended to provide participants with a hands-on training in exploring, visualizing, and analyzing data using the R programming language.1 To control R, participants will use RStudio, which is a free, user-friendly program with a console, syntax-highlighting editor that supports direct code execution, and a variety of robust tools for plotting.

  1. Link to course here:

Statistical Thinking for the 21st Century 💯

By Russell Poldrack

What is this?

Excerpt from site: The goal of this book is to the tell the story of statistics as it is used today by researchers around the world. It’s a different story than the one told in most introductory statistics books, which focus on teaching how to use a set of tools to acheive very specific goals. This book focuses on understanding the basic ideas of statistical thinking — a systematic way of thinking about how we describe the world and make decisions and predictions, all in the context of the inherent uncertainty that exists in the real world. It also brings to bear current methods that have only become feasible in light of the amazing increases in computational power that have happened in the last few decades. Analyses that would have taken years in the 1950’s can now be completed in a few seconds on a standard laptop computer, and this power unleashes the ability to use computer simulation to ask questions in new and powerful ways.

  1. Link to site here:
  2. Link to core text here:
  3. Link to R repo here:
  4. Link to Python repo here (under work now):

STAT 545 Data wrangling, exploration, and analysis with R 💯

By Jenny Bryan

What is this?

Excerpt from site: This site is about everything that comes up during data analysis except for statistical modelling and inference. This might strike you as strange, given R’s statistical roots. First, let me assure you we believe that modelling and inference are important. But the world already offers a lot of great resources for doing statistics with R.

  1. Link to the site:

Statistics of DOOM Youtube Channel

What is this?

Excerpt from site: Support Statistics of DOOM! This page and the YouTube channel to help people learn statistics by including step-by-step instructions for SPSS, R, Excel, and other programs. Demonstrations are provided including power, data screening, analysis, write up tips, effect sizes, and graphs. Help guides and course materials are also provided!

  1. Link to Youtube Channel here:

Teaching Methods with R

What is this?

Excerpt from site: Research Methods in R is a set of guides on how to use R as your central research methods tool. The target audience is psychology undergraduate students. Research Methods in R is Creative Commons, so you are free to reuse these materials and adapt them as you wish, as long as you attribute them to their authors, and as long as your modifications have a Creative Commons licence. They come with absolutely no warranty of any kind.

  1. Link to site here:

Teacup Giraffes_Intro to Statistics🦒

By Hasse Walum & Desirée De Leon

What is this?

The site’s purpose is to introduce you to statistics with R. Very concise and clear 😄 !

  1. Link to the “Introduction to the Normal Distribution” module here:
  2. Link to the “Measures of centrality: Mean, Median, & Mode” module here:
  3. Link to the “The Spread of the Data: Variance * Standard Deviation” module here:
  4. Link to the “A tale of two variables: Covariance & Correlation” module here:
  5. Link to the “Introduction to Inference: Standard Error” module here:

Test your model!! Performance package

What is this?

Test if your model is a good model! The primary goal of the performance package is to provide utilities for computing indices of model quality and goodness of fit. This includes measures like r-squared (R2), root mean squared error (RMSE) or intraclass correlation coefficient (ICC), but also functions to check (mixed) models for overdispersion, zero-inflation, convergence or singularity.

  1. Link to information:

Power Analysis

This section has all resources I encounter to run power analysis regardless of them using R or not.


Excerpt from site: powerLATE implements the generalized power analysis for the local average treatment effect (LATE), proposed by Bansak (2020).

Power analysis is in the context of estimating the LATE (also known as the complier average causal effect, or CACE), with calculations based on a test of the null hypothesis that the LATE equals 0 with a two-sided alternative. The method uses standardized effect sizes to place a conservative bound on the power under minimal assumptions. powerLATE allows users to recover power, sample size requirements, or minimum detectable effect sizes. It also allows users to work with absolute effects rather than effect sizes, to specify an additional assumption to narrow the bounds, and to incorporate covariate adjustment.

Simulation for Power Analysis

By Nick Huntington-Klein

Excerpt from site: In this document we’ll talk about power analysis in general and how it’s done, and then we’ll go into how to perform a power analysis using simulation in R, making use of tools from the tidyverse.