Quantitative methods and statistics with examples in R, Jamovi and JASP

Author

Daniel Hammarström

Published

September 2, 2025

Introduction

Welcome to the course notes related to the course Quantitative Methods and Statistics (IDR4000), held at the University of Inland Norway.

Prerequisites

To fully benefit from the course I recommend that students bring a basic understanding of statistical concepts to the course, such as knowledge about descriptive data summarises, usage of inferential statistics and regression models. We aim to cover these concepts in the course but also hope to get further. If you are not sure your basic understanding is up to date you may want to review introductory material, such as Statistisk dataanalyse med jamovi.

In the course we will use R to do analyses, create figures and tables, and write reports. If you are unfamiliar with R, that is OK, the course hopefully provides a gentle introduction. Using R, together with additional data science tools, means that you will encounter many infuriating problems. Such problems might arise from a missing comma or a misspelled word making your computer program produce gibberish. Solving such problems requires patience, systematic thinking and a positive attitude! The skill set I hope you will develop during the course includes knowledge about how to identify problems, where to find help to address problems and how to learn from problems. An introduction to using R can be found here (A R Crash Course)

If you feel that R is not for you, that’s ok, the course will also provide examples of how to do analyses in two other open source software packages, jamovi and JASP. These software packages are user friendly and have a graphical user interface. They are also based on R, so you can use them to learn R if you want to.

Data sets

This course uses a collection of data sets, some of them are stored in the exscidata package. If you are using R you can install it from its GitHub repository. Start up your version of RStudio and paste the following code into a script. You can execute the whole script at once, or line by line.

# Check if remotes is not installed, if TRUE, install remotes
if (!"remotes" %in% installed.packages()) install.packages("remotes")

# Check if exscidata is not installed, if TRUE, install exscidata from github
if (!"exscidata" %in% installed.packages()) remotes::install_github("dhammarstrom/exscidata")

# Load exscidata
library(exscidata)

When you have installed exscidata you will have access to several data sets. For example, the data sets strengthvolume contain data from a resistance training intervention. We can have a look at the help files for the data set by writing ?strengthvolume.

If you aim to use the data sets in a Jamovi or JASP session you may access the in the table below.