Categorical Data Analysis in R

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Categorical Data Analysis in R
Published 6/2026
Created by Stat Hacks
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 21 Lectures ( 4h 52m ) | Size: 2.1 GB


Dealing with Counts, Frequencies & Percentages

What you'll learn
Select and apply the correct statistical technique when analysing counts, frequencies, percentages, and categorical outcomes
Perform Chi-Square Tests, Tests of Proportions, Logistic Regression, Multinomial Logistic Regression, and Ordinal Logistic Regression using R
Determine when to use Chi-Square Tests, Tests of Proportions, Logistic Regression, Multinomial Logistic Regression, or Ordinal Logistic Regression for real-worl
Analyse categorical datasets commonly encountered in data analyst, business analyst, healthcare, psychology, and social science roles

Requirements
Understanding of Traditional Statistical Methods (t-tests, ANOVA, Regression)

Description
Traditional statistical methods such as t-tests, ANOVA, and linear regression are designed primarily for continuous numerical outcomes. However, many real-world datasets consist of categories, counts, frequencies, and percentages rather than continuous measurements.

This course provides a practical introduction to categorical data analysis using R. You will start with contingency tables and the Chi-Square test to examine relationships between categorical variables. You will then learn how to measure the strength of these relationships using effect size measures such as Phi Correlation and Cramer's V, and how to compare proportions across groups. The course then progresses to regression methods for categorical outcomes, including binary logistic regression, probit regression, multinomial logistic regression, and ordinal logistic regression. You will also learn how to interpret model outputs and communicate results clearly for real-world applications.

By the end of this course, you will be able to analyse and model

Chi-Square & categorical association (contingency tables, relationships between variables)

Tests of proportions for comparing groups

Effect size measures (Phi Correlation, Cramer's V)

Logistic regression for binary, multinomial, and probit outcomes

Ordinal regression for ordered categories (e.g. Likert scales)

Interpreting and reporting categorical data analysis results in R

Working with real-world categorical data: counts, frequencies, percentages, binary outcomes, multi-category outcomes, and ordered outcomes

This course is designed for data analysts, researchers, and students working with survey, customer, behavioural, or demographic data who want to confidently apply the correct statistical methods for categorical data in R. It is especially useful for those familiar with basic statistical techniques who want to move beyond t-tests and linear regression.

Who this course is for
Data analysts, researchers, and students who want to confidently analyse categorical data and make data-driven decisions using R
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