Analysing Categorical Outcomes and Logistic Regression with R (Oct 2026)

Overview

A practical workshop covering the analysis of categorical outcomes in R, from descriptive presentation through to logistic regression modelling. The workshop begins with the theory and visualisation of categorical variables and inferential tests applied to them, before progressing to univariable and multivariable logistic regression. Participants gain hands-on experience fitting and interpreting logistic regression models and calculating odds ratios. The workshop is relevant to researchers from any discipline working with binary or categorical outcome data.

Who Should Attend

Researchers who want to conduct classification or prediction analysis on datasets with categorical outcomes, including those working with clinical, health or social science data.

Prerequisites

Prior experience with R and the RStudio interface is required. The basics of R will not be covered. Participants should also be familiar with statistical hypothesis testing and the principles of regression analysis. Those new to R should first complete R for Reproducible Scientific Analysis. Those new to hypothesis testing and regression should first complete Statistical Comparisons Using R and Exploring and Predicting Using Linear Regression in R. R and RStudio must be installed before the session. A full-function computer (Windows, Mac or Linux) is required. Chromebooks and tablets are not suitable. A second monitor is strongly recommended. A reliable internet connection suitable for video conferencing is essential.

Learning Objectives

By the end of this workshop, participants will be able to:

• Define categorical variables and distinguish between different types of categorical data.

• Present and summarise categorical data appropriately using tables and visualisations in R.

• Select and apply appropriate inferential statistical tests for categorical variables.

• Understand the principles of logistic regression and when it is the appropriate analytical method.

• Fit and interpret a univariable logistic regression model in R, including odds ratios and confidence intervals.

• Extend the analysis to multivariable logistic regression and interpret results in the context of multiple predictors.

Course Activities

All sessions are hands-on. Participants apply each method directly in R as it is introduced. Activities include:

• Identifying and classifying variable types in a real dataset.

• Creating frequency tables and visualisations to summarise categorical data in R.

• Applying inferential tests for categorical variables, including chi-square and Fisher's exact test.

• Fitting a univariable logistic regression model in R and extracting odds ratios with confidence intervals.

• Interpreting model outputs and assessing model fit.

• Extending the model to include multiple predictors and evaluating the contribution of each.

• Guided exercises consolidating skills across all three parts of the workshop.

How can I contact the organiser with any questions?

You can email training@qcif.edu.au

What's the cancellation policy?

Cancellations are accepted up to three working days before the start of the workshop. If you do not join on the day without having cancelled beforehand, you may be blocked from attending any future QCIF training workshops.