Training

Source: https://www.cmi.manchester.ac.uk/connect/training/ Parent: https://www.cmi.manchester.ac.uk/

We offer training in research methods and quantitative data analysis for both staff and students at The University of Manchester.

We offer introductory, 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 2025

For 2025 we have a series of short courses, developed and delivered by staff who are experts in their fields.

The courses are one day intensive courses which will be introductions to a number of exciting topics.

Our courses include a combination of lectures and/or demonstrations, supported by a substantial practical component, to ensure participants gain hands-on experience in the application of the methods being taught.

To find out more about the courses on offer see below.

To register for a course complete the form to request a place on the course(s) you wish to attend. We will email you within 3 working days to confirm if you have been given a place.

For any questions please email cmi@manchester.ac.uk.

7 May - An Introduction to Quantitative Text Analysis in Social Sciences

An Introduction to Quantitative Text Analysis in Social Sciences

This shout course will be led by Dr Yan Wang

Abstract

This course provides an introduction to the quantitative analysis of text from a social science perspective, with a broad range of applications in economics, sociology, communication, and political science. It adopts an applied approach: while theoretical aspects are addressed, the primary focus is to equip students and researchers with fundamental knowledge and practical skills for analysing textual data using basic machine learning methods.

The course helps participants formulate research questions that can be investigated through text data and understand the basic methodologies required to answer them.

Prerequisites and software requirements:

Required knowledge

• Proficiency in the R software environment
• Familiarity with basic statistical concepts

Recommended background

• Basic understanding of linear algebra
• Basic knowledge of probability theory

Software installation

Participants must install recent versions of the following software before the workshop:

• R
• RStudio

Course structure

Lectures (first half)

• Overview of the field and its applications in social sciences
• Fundamental principles of treating text as data
• Basic analytical strategies and their underlying rationales

Practical sessions (second half)

• Hands-on practice using RStudio
• Textual analysis tasks including:
• Dictionary-based analysis
• Classification methods
• Clustering techniques

Sign up for the course by completing this form.

14 May - Agent-Based Modelling and the Simulation of Complex Social Systems

Agent-Based Modelling and the Simulation of Complex Social Systems

This short course will be led by Professor Philip Leifeld

Abstract

In “Agent-based modelling and the simulation of complex social systems”, participants learn how to rethink social, political, and economic phenomena as complex systems. For example, can racially segregated neighbourhoods exist even if most residents have only very mild preferences for same-race neighbours? How fast do infections spread in a population depending on individual behaviour, such as wearing masks or social distancing? Can we design recommendation algorithms for social media that do not cause filter bubbles and political polarisation? And how do we need to design financial institutions to avoid financial risk contagion?

All of these questions link individual behaviour to emergent, collective, population-level consequences. This short course introduces the basics of agent-based modelling in a condensed way, with sessions on theory, methodology, examples, and a first glimpse into prototyping of models in R and NetLogo with some examples, to prepare participants for self-study or longer courses on agent-based modelling.

Prerequisites

Participants should have at least basic familiarity with statistical concepts. It would be desirable for participants to bring their own laptops with R, RStudio (or similar), and NetLogo installed. But we will only use software in the afternoon session, and participants who want to watch and learn during this session are also welcome.

Sign up for this short course by completing this form.

23 May - Bayesian Modelling and Inference: An Introduction to STAN for the Social Sciences

Bayesian Modelling and Inference: An Introduction to STAN for the Social Sciences

This short course will be led by Dr Diego Perez.

Abstract

This course provides a comprehensive introduction to Bayesian statistics, covering the fundamental concepts, programming with STAN, and practical applications in the social sciences. Participants will gain a solid understanding of Bayesian modelling and inference, as well as the skills to apply these techniques to their own research questions.

Prerequisites and software requirements

Required knowledge

• Working knowledge of the R software environment
• Familiarity with (generalized) linear models

It is recommended that participants have a basic understanding of

• Linear algebra
• Probability theory

Software installation

• R
• RStudio

In the second half of the workshop, we will be using the STAN platform for Bayesian modelling, accessed through its R interface, RStan. Setting up RStan can be somewhat time-consuming, as it requires the installation of a C++ compiler.

Participants are encouraged to have these software packages installed and configured prior to the start of the workshop to ensure a smooth learning experience. Detailed instructions for the software setup will be provided to registered participants.

Sign up for this short course by completing this form.

28 May - Introduction to Social Network Analysis

Introduction to Social Network Analysis

This short course will be led by Dr Nikita Basov

Abstract

This one-day course provides a from-scratch introduction into analysing structures of relations, such as friendships, collaborations, exchanges, or alliances between social actors, such as individuals, groups, or organisations.

The course familiarises the participants with the basic concepts of social network analysis, network data, and introduces key measures and visualisation techniques offered by the UCINET point-and-click software. The acquired knowledge and skills are expected to enable exploring network data, such as finding key actors or evaluating the overall network structure and visually representing the findings in an accessible form – as well as to further navigate the vast universe of network analysis.

The course features hands-on exercises with real network data in order to start with applying network analysis to participants’ own research questions.

Prerequisites and software requirements

No quantitative analysis skills are necessary.

To ensure a smooth learning experience, participants are required to bring their laptops with installed UCINET, available from the University Software centre or  to be downloaded (the latter provides a limited-time-freeware version). This is Windows software, Mac users would need a Windows emulator.

Sign up for this short course by completing this form.

4 June - An Introduction to Multilevel Structural Equation Modelling in R

An Introduction to Multilevel Structural Equation Modelling in R

This short course will be led by Dr Nick Shryane

Abstract

This course provides an introduction to the application of multilevel mediation and confirmatory factor analysis models for continuous predictors using the lavaan library in R. The focus is on application of the models to answer social research questions.

Prerequisites and software requirements

Required knowledge

• Familiarity with R and the RStudio environment
• Familiarity with linear regression models
• Familiarity with Confirmatory Factor Analysis models
• Familiarity with the need for, and consequences of, centring predictors in linear models

Software installation

This workshop requires the installation of recent versions of the following software:

• R (including tidyverse and lavaan libraries)
• RStudio

Participants are encouraged to have these software packages installed and configured prior to the start of the workshop to ensure a smooth learning experience.

Sign up for this short course by completing this form.

11 June: Semantic Network Analysis

Semantic Network Analysis

This short course will be led by Nikita Basov

Abstract

Semantic network induction allows for computational mapping of discoursive and cultural landscapes using a variety of verbal expressions, would these be originally written texts or transcripts of oral speech. This method relies on the idea that meaning of a word is shaped by its relations with other words in a particular social and temporal context (semantic/pragmatic interface).

Semantic network mapping allows for capturing such relations, representing them as a semantic network in order to explore the corresponding discoursive/cultural landscape and identify the particular elements of this network to inspect closer.

Mixed-method semantic network analysis offered by this workshop includes:

1. manually enhanced computational word collocation approach rooted in corpus linguistics to produce semantic networks of associations between words;
2. basic network analysis—to locate key nodes and links in these networks; and
3. interpretive analysis of texts—to understand these key elements in their textual contexts.

Apart from capturing and understanding what is expressed directly, this approach enables revealing the latent contextual surroundings that mould the meaning of focal elements.

After completing the workshop, participants will be able to use point-and-click software tools Automap and ORA to pre-process texts for semantic mapping and produce semantic networks, visualise the networks, and use them to inspect discoursive/cultural landscapes represented in texts, using basic network analysis techniques.

Prerequisites and software requirements

• No quantitative analysis skills are necessary.
• To ensure a smooth learning experience, participants are required to bring their laptops with two installed and pre-configured freeware tools: Automap and ORA Lite. This is a Windows software tool, Mac users would need a Windows emulator.
• Participants can bring a text of their own choice that they would like to analyse (e.g., a research paper, a newspaper article, a blog entry, a webpage, a narrative interview transcript), limiting size from 1,000 to 3,000 words for training purposes.

Recommended reading

• Basov, N., W. de Nooy, and A. Nenko. 2021. Local Meaning Structures: A Mixed-method Socio-semantic Network Analysis. American Journal of Cultural Sociology, 9.
• Basov, N., 2020. The Ambivalence of Cultural Homophily: Field Positions, Semantic Similarities, and Social Network Ties in Creative Collectives. Poetics, 78.
• Deichmann, D., C. Moser, J. M. Birkholz, A. Nerghes, P. Groenewegen, and S. hui Wang. 2020. Ideas with Impact: How Connectivity Shapes Idea Diffusion. Research Policy, 49.
• Lee, M., and J. L. Martin. 2015. Coding, Counting and Cultural Cartography. American Journal of Cultural Sociology 3:1–33.

Sign up for this short course by completing this form.

Further information

For details of the training offered at the CMI, and for information on how to make a booking for a specific course, please refer to our Events page.

Terms and conditions for short courses

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.