Training
We offer training in research methods and quantitative data analysis for both staff and students at The University of Manchester.
We offer intermediate and advanced courses covering all aspects of the research process, including research design, data collection, and data analysis.
Additionally, we provide in-house courses and workshops tailored for local authorities and other organisations.
Short Courses 2026
Our short courses for 2026 are now finished. We will be running another set of courses in 2027. To register your interest and be the first to find out more please complete this form: CMI Short Courses - registration of interest
This short course will be led by Tanja Kecojevic.
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Date: Wednesday, 8 July |
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Session 1 |
9.30am – 12pm |
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Break |
12 – 1pm |
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Session 2 |
1 – 4pm |
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Room: Ellen Wilkinson_ A.3.6 |
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Abstract
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.
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.
Cancellation by the University
The University of Manchester reserves the right to cancel a short course ten (10) days before if there are not sufficient delegates registered on the short course or any associated event at its sole discretion.
In the event of such a cancellation the University will refund the value of the booking or any ticket sold upon proof of purchase.
The University expressly excludes any liability for any direct or indirect losses or damages howsoever arising as a result of such cancellation and will not, for example, be responsible for any travel or accommodation costs incurred.
In the event of cancellation, the University will use reasonable endeavours to publicise the cancellation and details will be posted on the website associated with the short course.
Attendees are responsible for checking this information prior to the event.
Wherever possible, the University will endeavour to contact delegates by email in the event of cancellation.
Cancellation by you
We will refund the value of the booking or any ticket sold upon proof of purchase when cancellation takes place at least three weeks before the short course.
When cancelled less than three weeks but no more than seven (7) working days before the short course we will refund a percentage not less than 50% of the charge.
No refunds will be given for cancellation less than seven (7) working days before the short course date or for non-attendance without notification, unless a refund is made entirely at the discretion of the University.
Notice of cancellation must be sent to cmi@manchester.ac.uk.