The Cathie Marsh Institute are proud to introduce their 2026 short courses.

 All participants will receive state of the art teaching and a lunch voucher as part of the course.

An Introduction to Quantitative Text Analysis in Social Sciences 

Taught by: Tatjana Kecojevic

Location: Ellen Wilkinson Building A3.6

Description, Learning Objectives, and Prerequisites  

Many research questions in the social sciences focus not only on average outcomes but also on how relationships differ across the distribution of an outcome. For example, the effects of education on wages, socio-economic status on health, or housing characteristics on prices may vary substantially between lower and higher parts of the outcome distribution. Standard regression methods such as Ordinary Least Squares (OLS) focus on conditional means and may therefore obscure important forms of heterogeneity.

This short course introduces quantile regression, a statistical method that allows researchers to estimate and interpret relationships across different points of an outcome distribution. The course will combine conceptual explanations with practical implementation using R. Participants will learn when quantile regression is appropriate, how it differs from standard regression approaches, and how to interpret distributional effects in applied research. 

 The course will be divided into two parts. The morning session will introduce the conceptual foundations of quantile regression, including the motivation for modelling conditional quantiles, interpretation of coefficients across quantiles, and comparisons with standard regression models. The afternoon session will consist of guided hands-on exercises in R, where participants will estimate quantile regression models using real-world data, compare results to OLS models, and visualise how effects vary across the outcome distribution. 

Learning Objectives  

By the end of the course participants will be able to:  

• Understand the difference between modelling conditional means and conditional quantiles. 

 • Identify research questions where quantile regression is appropriate. 

 • Estimate quantile regression models using R.  

• Interpret coefficients across different quantiles of the outcome distribution.  

• Visualise and interpret heterogeneous effects across the outcome distribution.  

Prerequisites  

Participants should have:  

• Basic familiarity with regression analysis, such as OLS.  

• Some experience using R for statistical analysis.  

• Rand RStudio installed on their laptop prior to the workshop. No prior experience with quantile regression is required.  using R with real-world datasets.  

Course Description

Additional Information  

The course is designed for researchers and postgraduate students in the social sciences who are interested in modelling heterogeneous or distributional effects in their data. Quantile regression is widely used in fields such as economics, sociology, health research, and education, particularly in studies of inequality and outcomes that exhibit substantial variation across their distribution. All teaching materials, example datasets, and R scripts will be provided to participants in advance to facilitate learning and reproducibility. The course is designed to be reusable and could be delivered in subsequent years with updated datasets or applied examples.