National Centre for Research Methods

Critical Praxeological Analysis (CPA) is a new approach which provides a way of conducting critical qualitative research. 

Critical Praxeological Analysis (CPA) synthesises ideas from three key areas: Wittgensteinian philosophy, particularly the method of grammatical investigation; ethnomethodology and conversation analysis, with a focus on praxeological gestalts; and critical research, especially critical phenomenology. This synthesis provides a robust method for critical qualitative research. 

In this two day online advanced course, the authors of the approach, Khadijah Diskin and Phil Hutchinson, will help participants identify and plan a project of study, handle data, and make a start on developing a research article. 

This course will assume an understanding of the basics of CPA and spend minimal time on a brief recap of these (see NCRM's online course Introduction to CPA - 10-11 September 2025  if you feel you need an introductory course first). 

The idea is for participants to make significant progress on work towards a CPA article for publication, either individually or working with other participants on the course as co-authors. The convenors will provide advice on every stage of the process and offer follow-up 1:1 sessions to participants.

The course (22 October) will focus on supporting participants in designing and carrying out a project and writing a paper. Two weeks after the course (29 October) there will be a data session, where those who attended the course can bring their data for the group to analyse. 

By completion of the course:

• Participants will have made a start on a CPA project and in some cases a paper for publication.
• Participants will learn and understand the stages of CPA project design.
• Participants will have hands-on experience of handling and analysing data as a CPA researcher and participating in a data session.
• Participants will have tips on overcoming the hurdles which are commonly faced in a CPA project.
• Participants will have the opportunity to make use of subsequent support sessions, which will support them to the completion and submission of their paper over 8 months following the course.

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The study of multi-species relationships is gaining momentum across the humanities, encouraging new ways of understanding how different forms of life shape worlds and societies. While many scholars explore the theoretical aspects of human and nonhuman interdependence, practical, ethnographic research methodologies in this area remain limited.

This three-day workshop at the University of Manchester in 2025 explores innovative approaches for researching more-than-human agencies and experiences. It invites participants to engage with the possibilities and challenges of this work in an immersive and exploratory manner. Discussions will emphasise ethical, critical, and imaginative ways to study the nonhuman. While the focus is primarily zoological, participants will also consider interactions with plant life and other nonhuman entities.

The workshop provides a unique opportunity to collaboratively shape the future of multi-species research.  

The course covers:  

• Working critically with natural science expertise
• Using sensory ethnography and embodied reflexivity to attune to the more-than-human
• Visual (dis)orientations: using video methods as an attentive and reflexive practice with animals

By the end of the course participants will: 

• Appreciate both the opportunities and the epistemological and ethical challenges of multi-species ethnography
• Possess a set of practical approaches and methods for more-than-human research, particularly, but not exclusively, with animals
• Have gained practical experience of applying these methods in fieldwork settings 

Course format  

This course will be delivered as a three-day full-day (10 am-5:30 pm) workshop that blends classroom discussions with outdoor fieldwork. Mornings focus on theory, whilst afternoons focus on practical research. Participants will explore sensory ethnography, scientific collaboration, and visual research methods in urban green spaces, including riversides, parks, and a city farm. Movement-based activities allow engagement at an individual pace, with optional partner work. Sessions foster documentation, reflection, and idea exchange. The workshop’s approach encourages slow, thoughtful exploration, balancing intellectual engagement with hands-on experimentation. 
 
The course leader

Led by Dr Maisie Tomlinson, with support from Russ Hedley (Nature Talks and Walks), this course offers a unique chance to explore emerging multi-species research methodologies. Attendees are encouraged to contribute and shape the field’s future.   
 
Pre-requisites

This workshop welcomes scholars across disciplines, from students to academics. While open to all, theoretical sessions follow a postgraduate standard. Prior knowledge of qualitative research is advised. Outdoor fieldwork will proceed in all weathers—waterproof clothing recommended. Mobility accommodations are available with advance notice. Field locations, within 45 minutes of the university, will be accessed by public transport and taxi. 
 
Preparatory materials Required:  

Please read at least one of the following:

• Hamilton, L. and Taylor, N. (2017). Chapter 3, Listening for the voices of animals, in Hamilton, L. and Taylor, N. (eds). Ethnography after Humanism. London: Palgrave MacMillan
• Pitt, Hannah. (2015). “On Showing and Being Shown Plants - a Guide to Methods for More-than-Human Geography.” Area, 47(1), pp. 48–55.
• Nimmo, R. (2016). ‘From over the horizon: animal alterity and liminal intimacy beyond the anthropomorphic embrace’, Otherness: Essays and Studies, 5(2), pp. 13-45

Please choose two of the animals below and do a little light research a) their ethology b) the social construction of those animals in the society and culture with which you are most familiar. Further guidance will be sent in advance of the workshop.

• Sheep
• Cows
• Pigs
• Chickens

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email 23/05/25

What will AI mean for the future of social research? Claims about the opportunities and risks abound. Meanwhile, there is evidence that government and funding bodies are becoming increasingly confident that this will mark a step change in the value, scale and replicability of social research. Are they ‘hallucinating’ (or worse) and, if so, with what consequences? Critical social researcher interrogation of these claims, the affordances, constraints and risks of AI is essential.

In this one-day course, Prof Les Carr (Web Science Institute, University of Southampton and Visiting Professor at CenSoF) will support researchers in critical exploration of generative AI (in particular large language models and chatbots) and in considering potential uses in participants’ own research. The session will start with a short introduction to how LLMs work and then we will open up to a hands-on-session where you explore your own research questions with some trial ‘data’. 

This course outlines the use of AI tools for the analysis of text data, which may have been generated through qualitative data collection (e.g. through interviews, focus groups etc) or through quantitative data collection, such as open-ended question(s) in surveys. 

The course covers:

• Use of Generative AI Chatbots (e.g. ChatGPT, Co-Pilot etc)

• Large Language Models (LLMs)

• Ethical and Responsible AI Issues

• Applying Chatbots (e.g. ChatGPT, Co-Pilot etc) to qualitative data for analysis (this can contain also text data from quantitative data collection methods)

• Prompt Engineering (i.e. the process of refining instructions and input prompts to guide LLMs, including tips on how to write prompts)

• Critical Evaluation of Research-Supported AI

Learning Outcomes:

By the end of the course participants will:

• Be confident users of Microsoft CoPilot or similar AI platform
• Understand the capabilities and limitations of LLM products
• Have experience in applying a Chatbot with qualitative data / text data
• Be able to critically analyse the costs/benefits/risks/opportunities of deploying AI in their research

Schedule:

This one day course will run between 10:00 - 17:00 with one hour for lunch. It will consist of a plenary morning workshop (teaching and discussion) and a practical individual session in the afternoon where each participant individually experiments with using CoPilot/ChatGPT on some of their research data (or relevant data of interest) with support and feedback from the course tutor.

• Introduction to the course
• Explain how generative AI and LLM models work
• Talk through the motivations for using AI and LLMs
• Research vignettes/case studies
• Discussion
• Hands on introduction to Copilot with simple worked examples.
• Break for lunch
• Open session where trainees individually experiment with their research questions and research data on Copilot, supported by trainers as necessary.
• Report back
• Planning for future work

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This course provides an introduction to coding when working with administrative data. The course will include an overview of best practices in coding, with hands-on sessions using synthetic data and different programming languages: R, Python and SQL. It will also discuss the use of AI in coding.

The course will be delivered in three interactive sessions, with breakout rooms in each session, as well as opportunities to engage with the instructors.

All Participants need to attend the first session on Tuesday 29 July (09:30-11:00), with options on the additional sessions. The price is the same, irrelevant of how many sessions you attend.

Tuesday 29 July 2025

• 09:30 – 10:30 Writing efficient code
• 10:30 – 11:00 Using data ethically and legally

Options:

• Option 1: Tuesday 29th July / 11:15 – 14:45 (with 45 minute lunch break) Programming using SQL & Datacise
• Option 2: Wednesday 30th July / 09:30 – 12:30 Programming using Python & ASHE-Census synthetic data
• Option 3: Wednesday 30th July / 13:30 – 15:00 Use of AI in coding using ASHE-Census synthetic data
• Option 4: Thursday 31st July / 09:30 – 12:30 Programming using R & ASHE-Census synthetic data

Registrants will be contacted following booking to confirm which sessions they wish to attend.

By the end of the course participants will:

• Be familiar with best practices in writing efficient, reproducible code
• Understand best practices in using data to ensure transparency, integrity and compliance
• Have practised programming using SQL & the Datacise platform
• Be familiar with coding standards using Python and practised programming using Python & ASHE-Census synthetic data
• Be familiar with how AI can be used throughout the code development lifecycle
• Be familiar with coding standards using R and practiced programming using R and ASHE-Census synthetic data

This course is open to anyone who uses, or will use administrative data. Some knowledge of data manipulation is required.

Participants will be sent instructions as to how they can access Datacise and synthetic ASHE-Census data. Please note that, in order to download the synthetic ASHE-Census data, you will need to register with the UK Data Service. 

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This five-day workshop introduces participants to the theory and practice of experimental methods in the Social Sciences. It provides an overview of prevalent approaches—specifically lab, field, and survey experiments—offering a solid introduction to experimental methodology and the practical skills needed to design, implement, analyse, and present experiments.

The course is designed for researchers, PhD students, professionals, and members of public institutions, particularly those new to experimental methods or those with experience in one method (survey, field, or lab) who wish to deepen their knowledge of the others. No prior knowledge of experimental design or statistics is required. The course is also suitable for those looking to commission an experiment through a survey company or other service provider.

The workshop aims to equip participants with the skills to design, implement, analyse, and report experiments, as well as to critically evaluate experimental literature.

Additionally, the workshop will provide a brief overview of service providers for marketing experiments (Facebook, Google), access panels and online marketplaces (Lucid, MTurk, Prolific), and survey providers that support experiments (YouGov, Ipsos). Participants will also be introduced to Qualtrics, Stata, R, and Excel as tools for experimental research with practical exercises.

An interdisciplinary team of faculty members from the Centre for Behavioural, Experimental, and Action Research (C-BEAR) will lead the workshop, drawing on examples from Politics, Economics, Business, and Psychology. Each day, two faculty members from different disciplines will co-teach, fostering a dynamic and dialogic learning environment. The workshop will be interactive and hands-on, incorporating group work and practical exercises.

The workshop will also cover responsible research conduct in experimental studies, including research ethics, pre-registration, and debriefing practices for deceptive research designs.

Course Content

Days 1 and 2  will provide the basic knowledge to design, analyse and present experiments such as randomised controlled trials (RCTs), while Days 3, 4, and 5 will focus on survey, field and laboratory experiments.

Participants need to bring their own device that can run basic office suites, and free versions of R and Stata. A tablet with a keyboard might also work.

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The aim of this two-day online training course is to introduce participants to the practice and ethics of ethnographic research.

Through a mix of plenary sessions, group and independent work, participants will learn the basic principles of participant observation and research design, as well as the foundations of ethical ethnographic research.

The course will also examine the ways in which other qualitative and creative methods of data collection may be productively integrated in ethnographic research.

The course covers:

• Research design
• Qualitative methods in ethnographic research
• Access and power
• Research ethics in participant observation

By the end of the course participants will:

• Understand the epistemological foundations of ethnographic research
• Have a solid understanding of ethnographic research in action
• Be able to design and conduct research integrating qualitative and ethnographic research methods
• Be able to conduct ethical ethnographic research

The course is suitable for any professional researchers interested in learning more about using ethnographic methods – whether within or outside academia (private sector, government researchers, etc.).

The course is likewise suitable for postgraduate students in any social science (human geography, sociology, business school, political sciences, area studies, education, etc.) with prior knowledge of any qualitative research methods, but not necessarily of ethnography.

Some prior training in qualitative research methods, broadly defined – regardless of whether that includes ethnographic methods specifically.

Programme

Day 1

Morning session:

• 09:30-09:45  Introduction to the course
• 09:45-10:45  Plenary – The Practice of Ethnography
• 10:45-11:00  Break
• 11:00-12:00  Group work followed by class discussion

Afternoon session:

• 12:45-13:45  Plenary - Qualitative methods in ethnographic practice
• 13:45-14:00  Break
• 14:00-15:15  Practical exercise followed by class discussion

Day 2

Morning session:

• 09:30-10:45  Plenary - Research ethics in ethnography
• 10:45-11:00  Break
• 11:00-12:00  Group work followed by class discussion

Afternoon session:

• 12:45-1:345  Plenary – Writing ethnography
• 13:45-14:00  Break
• 14:00-15:00  Practical exercise, followed by class discussion         
• 15:00-15:15  Conclusions and Evaluations

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This one-day online course provides a comprehensive introduction to the principles and practices of Reflexive Thematic Analysis (RTA). It balances theoretical discussions with hands-on practical exercises to facilitate participants to understand and be able to enact an RTA themselves.

This involves discussion of the underpinnings of RTA, the centrality of reflexivity and quality, and how to document and communicate analytic practice as well as learning how to enact the six phases of RTA – i) familiarisation, ii) coding, iii) generating initial themes, iv) developing and reviewing themes, v) refining, defining and naming themes, and vi) writing-up.  

The aim of this course is to open up thinking about the processes of qualitative analysis and enable participants to enact RTA in flexible, iterative and reflexive ways as appropriate to the needs of their projects. To this end, the course is interactive, combining discussion, demonstration and practical exercises.

Participants will work in small groups and engage in individual reflection during the day, as well as actively contribute to discussions.  
The course does not teach the use of digital tools for accomplishing the phases of RTA, but these are outlined, and sources of further information and learning are provided should participants like to explore the options further.

The course covers:

• Thematic analysis approaches in the context of qualitative research
• Principles and methodological underpinnings of RTA
• Planning an RTA
• Understanding and enacting the phases of RTA via practical exercises
• Codes, Coding, and Themes – what are they and why are they important
• Reflexivity and its centrality to RTA
• Accomplishing and evidencing quality in RTA practice
• Tools available for enacting RTA (pen-and-paper and computer-assisted methods) 

By the end of the course participants will: 

• Understand the characteristics of Reflexive Thematic Analysis (RTA) in comparison to other approaches
• Be able to describe and enact the six phases of RTA (familiarising, coding, generating initial themes, develop and review themes, refine, define and name themes, write-up)
• Be able to engage in and document reflexive practices throughout the analysis process
• Understand the importance of quality and what it looks like in RTA practice  
• Consider the appropriateness of different tools to facilitate RTA  
• Know where to access further resources to develop RTA practice

Course format 

This course will be delivered online on the 30th September 2025, running from 9:30 AM to 4:30 PM. The sessions will consist of discussion, lectures, independent work, group work, and interactive feedback (Q+A). The participants will have three breaks: two short 15-minute pauses (morning and afternoon) and a 45-minute lunch break.  
 
The course leader

The course is Led by Dr Christina Silver, an Associate Professor (Teaching) in the Department of Sociology at the University of Surrey and has designed, coordinated and delivered awareness-raising, capacity-building and training in qualitative methods and the use and implications of digital-tools for qualitative analysis since 1998.  Christina has contributed significantly to Computer Assisted Qualitative Data AnalysiS (CAQDAS) pedagogical development, including co-developing the Five-Level QDA method, and has published widely in the field. She is Director of the CAQDAS Networking Project and Co-Founder and Director of Qualitative Data Analysis Services (QDAS) Ltd.  
 
Pre-requisites

There are no formal pre-requisites, but participants who have a general understanding of the principles of qualitative data analysis will find the course easier to follow. The course discusses software for RTA but does not use it.
 
Preparatory materials required:

Participants are required to read the following article before attending the course, and to summarise in their own words, the key points from their perspective.

• Braun, V., & Clarke, V. (2022). Toward good practice in thematic analysis: Avoiding common problems and be(com)ing a knowing researcher. International Journal of Transgender Health, 24(1), 1–6.  

The article is available open access from either of the two links below.  

• Link 1 
• Link 2

There will also be sample data that participants will need to familiarise with before attending the course, which will be made available to registered participants 2 weeks before the course.

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This two-hour and 30-minute workshop will explore what Gen AI tools exist that are helpful for researchers and how we can use them to optimise research processes.

It is a workshop for Gen AI beginners, not Gen AI experts.

We will explore a range of mainstream Gen AI tools (e.g. ChatGPT, Claude, Co-Pilot) and research-specific ones (e.g. Sc iSpace, Research Rabbit, Elicit) and examine how they work through hands-on tasks.

By the end of the workshop, you will feel confident in what Gen AI can do (and what it can’t!), how to prompt engineer for research tasks, and the ethics of using Gen AI as a researcher.

The course will take place on Teams or Zoom on the 1^st of July from 10:00 to 12:30.

Pre-requisites

This is an entry-level course, no previous knowledge and no prior reading is required.

The session will only use free GenAI tools.

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email 30/04/25

This online workshop aims to give participants a range of practical approaches they can adopt when writing about methodology in the social sciences.

Using a range of exercises throughout, the course focuses on 20 or so writing strategies and thought experiments designed to provide more clarity and power to the often-difficult challenge of writing about methods.

The course also looks at common mistakes and how to avoid them when writing about methods. The focus throughout is on building confidence and increasing our repertoire of writing strategies and skills.

The course covers:

• A range of practical writing strategies for handling methodology
• The challenges of writing a PhD methodology chapter or a methods section in a research paper
• Writing for qualitative and quantitative research approaches
• Understanding different audiences and the needs of different academic markets

By the end of the course participants will:

• Better understand who and what ‘methodology writing’ is for
• Know the differences and similarities between PhD methods chapters, research paper methods sections and methods books
• Understand and reflect on 21 principles (or starting points) of best practice in methodology writing
• Focus writing on audience needs and expectations
• Be aware of common mistakes and misunderstandings and so avoid them
• Reflect on the relationship between methodology writing and other parts of your manuscript
• To develop learning and best practice through exercises and examples

Target Audience:

PhD students, post-docs and junior researchers in the social sciences working on their doctoral theses or supervising doctoral students.

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This one-day online, interactive course will provide a practical introduction to Qualitative Secondary Analysis. The morning session will explore key debates and histories characterising the beginnings of this methodology, including how and why we might use existing qualitative data, how to sample from and across datasets and repositories, the unique ethical considerations of using existing qualitative data, and the potential of Qualitative Secondary Analysis to produce new and relevant research findings. The afternoon session will focus on the practicalities of analysing existing data, exploring the possibilities for these methods for delegates’ own research and the diversity of research designs made possible through data reuse. The course will comprise two lectures and two interactive workshops (see below for further details). The course will be delivered by Kahryn Hughes and Anna Tarrant, both specialists in QSA, and the authors of numerous books and papers based on research innovating in this methodology. 

The course will run from 10am to 4pm. 

In the first presentation of the day, delegates will be introduced to the key debates and challenges characterising the histories of Qualitative Secondary Analysis (QSA). They will  be introduced to early debates interrogating the particularities of reusing qualitative data and the evolution of questions from whether we should reuse qualitative data to how we might do it. 

In the second presentation of the day, delegates will be introduced to methods and ethics of QSA, sources of qualitative data and the practicalities of sampling within and across datasets and repositories

In afternoon workshops, delegates will get more ‘hands on’ with existing data with an opportunity to analyse short excerpts from real world research, and engage in guided reflection on the possibilities and limitations of this methodological approach.  A second practical session will offer delegates the opportunity to consider possible research designs incorporating QSA in their own research.

The course is suitable for doctoral and established researchers who are either new to this methodology or wish to refresh or enhance their research practice. Delegates will receive a course pack, comprising powerpoint slides and data files from the Timescapes Archive.

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Critical Praxeological Analysis (CPA) is a new approach which provides a way of conducting critical qualitative research.

Critical Praxeological Analysis (CPA) synthesises ideas from three key areas: Wittgensteinian philosophy, particularly the method of grammatical investigation; ethnomethodology and conversation analysis, with a focus on praxeological gestalts; and critical research, especially critical phenomenology. This synthesis provides a robust method for critical qualitative research. 

In this online introductory course, the authors of the approach, Khadijah Diskin and Phil Hutchinson, will provide newcomers with a foundation in CPA, by laying out the philosophical background, outlining the stages of project development and analysis, and then ending the day by facilitating a CPA data session on some recent data that might serve as a topic for CPA studies.

This course will assume no prior knowledge of either CPA, Critical Phenomenology or Ethnomethodology and Conversation Analysis and so works as perfect introduction for those who are looking for a method of critical qualitative research.

The course will cover the following:

CPA – Respecifying Critical Research. 

We explain how the purpose of CPA is to provide a procedure for conducting critical qualitative research. Where much critical research is based in theoretical or formal analyses, CPA provides a process for conducting qualitative praxeological analyses which are designed to recover the experiences of members of society.

CPA – The philosophical resources of CPA. 

We introduce the resources that feed into CPA. These are the grammatical investigations of Wittgensteinian philosophy, the praxeological Gestalts of ethnomethodology and CPA’s concept of discordant Gestalts and the extending of the unique adequacy requirement. Each of these will be explained in easy-to-grasp ways with the use of examples.

CPA – Selecting a topic and designing a project. 

We take you through a step-by-step process of selecting a topic, designing, and executing a CPA project.

CPA – Data Session

We end the course by conducting a group data session on some recently acquired data. This will help participants become familiar with how a CPA researcher handles data and what it is to be attuned to aspects of the material as a CPA researcher. The data will be audio or video data gathered in the ‘wild’ and will be from recent events (i.e. 2024).

Learning Outcomes:

• Participants will have a good understanding of the purpose of conducting a CPA, understand the philosophical building blocks of CPA, and have a working understanding of what it is to be a CPA researcher.
• Participants will learn and understand the stages of CPA project design.
• Participants will have hands-on experience of handling and analysing data as a CPA researcher.

Schedule:

10th September: 9am - 4:30pm

11th September: 9am - 2pm

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This is a hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.  This three day introduction aims to cover the basics of Deep Learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what next steps to take to improve the model. 

The course covers:

• What is deep learning?
• Classification by a neural network using Keras
• Monitor the training progress
• Advanced layer types
• Real world application

By the end of the course participants will:

• Define deep learning
• Describe how a neural network is build up
• Explain the operations performed by a single neuron
• Describe what a loss function is
• Recall the sort of problems for which deep learning is a useful tool
• List some of the available tools for deep learning
• Recall the steps of a deep learning workflow
• Test that you have correctly installed the Keras, Seaborn and scikit-learn libraries
• Use the deep learning workflow to structure the notebook
• Explore the dataset using pandas and seaborn
• Identify the inputs and outputs of a deep neural network.
• Use one-hot encoding to prepare data for classification in Keras
• Describe a fully connected layer
• Implement a fully connected layer with Keras
• Use Keras to train a small fully connected network on prepared data
• Interpret the loss curve of the training process
• Use a confusion matrix to measure the trained networks’ performance on a test set
• Explain the importance of keeping your test set clean, by validating on the validation set instead of the test set
• Use the data splits to plot the training process
• Explain how optimization works
• Design a neural network for a regression task
• Measure the performance of your deep neural network
• Interpret the training plots to recognize overfitting
• Use normalization as preparation step for deep learning
• Implement basic strategies to prevent overfitting
• Understand why convolutional and pooling layers are useful for image data
• Implement a convolutional neural network on an image dataset
• Use a dropout layer to prevent overfitting
• Be able to tune the hyperparameters of a Keras model
• Adapt a state-of-the-art pre-trained network to your own dataset
• Understand that what we learned in this course can be applied to real-world problems
• Use best practices for organising a deep learning project
• Identify next steps to take after this course

IMPORTANT: Please note that this course includes computer workshops. Before registering please check that you will be able to access the software noted below. Please bear in mind minimum system requirements to run software and administration restrictions imposed by your institution or employer with may block the installation of software.

Pre-requisites:

Learners are expected to have the following knowledge:

• Basic Python programming skills and familiarity with the Pandas package.
• Basic knowledge on machine learning, including the following concepts: Data cleaning, train & test split, type of problems (regression, classification), overfitting & underfitting, metrics (accuracy, recall, etc.).

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A one day introduction to machine learning using Scikit Learn in Python.  Learners will be introduced to several machine learning techniques including regression, clustering, dimensionality reduction, and neural networks.  The course also includes a brief overview of the ethics and implications of machine learning.

The course covers:

• Introduction to machine learning
• Regression
• Introducing Scikit Learn
• Clustering with Scikit Learn
• Dimensionality reduction
• Neural networks
• Ethics and implications of machine learning 

By the end of the course participants will:

• Gain an overview of what machine learning is and the techniques available.
• Understand how machine learning and artificial intelligence differ.
• Be aware of some caveats when using Machine Learning.
• Apply linear regression with Scikit-Learn to create a model.
• Measure the error between a regression model and input data.
• Analyse and assess the accuracy of a linear model using Scikit-Learn’s metrics library.
• Understand how more complex models can be built with non-linear equations.
• Apply polynomial modelling to non-linear data using Scikit-Learn.
• Use two different supervised methods to classify data.
• Learn about the concept of hyper-parameters.
• Learn to validate and cross-validate models
• Understand the difference between supervised and unsupervised learning
• Identify clusters in data using k-means clustering.
• Understand the limitations of k-means when clusters overlap.
• Use spectral clustering to overcome the limitations of k-means.
• Recall that most data is inherently multidimensional.
• Understand that reducing the number of dimensions can simplify modelling and allow classifications to be performed.
• Apply Principle Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce the dimensions of data.
• Evaluate the relative peformance of PCA and t-SNE in reducing data dimensionality.
• Understand the basic architecture of a perceptron.
• Be able to create a perceptron to encode a simple function.
• Understand that layers of perceptrons allow non-linear separable problems to be solved.
• Train a multi-layer perceptron using Scikit-Learn.
• Evaluate the accuracy of a multi-layer perceptron using real input data.
• Understand that cross validation allows the entire data set to be used in the training process.
• Consider the ethical implications of machine learning, in general, and in research.

IMPORTANT: Please note that this course includes computer workshops. Before registering please check that you will be able to access the software noted below. Please bear in mind minimum system requirements to run software and administration restrictions imposed by your institution or employer with may block the installation of software.

Pre-requisites:

A basic understanding of Python. You will need to know how to write a for loop, if statement, use functions, libraries and perform basic arithmetic. The ‘Introduction to Software Development’ (20th-23rd October 2025  ) covers sufficient background.

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This course provides an introduction to National Pupil Database (NPD), an administrative data resource covering the education system in England.

The course covers:

• The population coverage of NPD
• The component modules of NPD and how they link together
• How to create a longitudinal picture of pupils’ lives in schools
• Key data cleaning routines
• Accessing NPD

By the end of the course participants will:

•  Be familiar with the structure of NPD
•  Understand the strengths and limitations of the data available
•  Know where to go to find more detailed information
•  Know how to apply for access

This course is suitable for anyone intending to undertake quantitative research on the school system in England. No prior knowledge of the NPD or statistical code is required to access the course.

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To prevent obesity or smoking initiation among teenagers, who should be targeted in an intervention? How can we contain the spread of an infectious disease under limited resources? Who should be vaccinated first in order to be most effective during vaccination shortages? How can we dismantle a terrorist organization, a drug distribution network or disrupt the communication flow of a criminal gang?

Social network analysis offers the theoretical framework and the appropriate methodology to answer questions like these by focusing on the relationships between and among social entities. Unlike transitional research methods, we shift the object of study from the individual as the unit of analysis, to the social relations that connect these individuals. A network is therefore a structure composed of units and the relationships that connect them. Network analysis is about the position of these units, the overall structure and how these affect the flow of information.

The focus of this two day online course is not so much on how to express these concepts formally through mathematics, but rather on how to use appropriate software to acquire measurements for these concepts in the data and use them rigorously in empirical hypothesis testing. The majority of the course will focus on descriptive methods of network analysis, but we will also discuss network-specific models and inferential methods for network analysis.

The course covers:

• Foundations social networks data: relational structures and data collection;
• Manipulation of network data (matrix algebra and graph theory);
• Node-level measurements;
• Graph-level measurements;
• Network visualization.

Learning outcomes:

• Navigate the key areas of research in social networks;
• Acquire knowledge of data collection and suitable data structures for analysing social networks;
• Develop an understanding of social phenomena through the lenses of the social networks theory;
• Learn how to operate software package for the analysis of social network data;
• Learn how to use and interpret graph-theoretic and matrix algebra concepts with real-world data;
• Acquire the ability and comprehension to independently read scientific literature using social network analysis methodology;
• Learn the fundamentals of social network analysis to acquire the necessary proficiency to explore more advanced topics autonomously.

Basic knowledge of Excel and data matrices will be required.

This course will run from 9.00am - 5.30pm each day.

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This two day introductory short course to software development with Python is spread across four consecutive mornings and covers the following:

Automating Tasks with the Unix Shell - 20th October  (half day - morning)

Learn the basic concepts of using a text-based interface with a computer. Use the shell to run basic productivity commands and then chain these commands to perform more complicated actions. Use loops to automate time-consuming tasks such as processing or moving large numbers of individual data files. Use the powerful search features to find text in files.

Version Control with Git - 21st October  (half day - morning)

Track changes to files made by yourself to be able to more effectively recover from mistakes. Track changes made by a team to be able to work more effectively in collaboration.

Building Programs with Python - 22nd and 23rd October (2 x half days - mornings)

Learn the basics of the Python programming language. Learn how to document and add comments to Python code to make it sustainable and user-friendly. Use Python libraries to access the massive amount of tools available from the Python community. Learn the basics of making graphs using MatPlotLib.

This one-day intermediate-level course introduces the concepts and techniques of moderation and mediation analysis using R, with a focus on social science applications. Participants will learn how to test for interaction effects (moderation) using regression models, conduct mediation analysis using structural equation modelling and combine both approaches to explore mediated moderation. Emphasis is placed on interpretation, visualization, and reproducible R workflows using real-world data examples.

The course covers: 

• A brief refresher on linear regression in R
• Moderation analysis using interaction terms in regression
• Probing and visualizing interaction effects
• Mediation analysis using path models
• Estimating indirect effects  
• Mediated moderation  
• Applied examples in social sciences
• Practical coding sessions using real datasets in R

By the end of the course participants will:

• Understand the conceptual foundations of moderation and mediation
• Conduct moderation analysis using interaction terms in R
• Interpret and visualize interaction effects  
• Specify and estimate mediation models using path analysis
• Test indirect effects and interpret mediation results
• Use SEM to explore differences in mediation across groups
• Apply techniques to real-world social science datasets

Course format  

This course will be delivered online on the 10th October 2025 from 9:00 am to 4:00 pm with lunch break from 12:00 pm to 1:00 pm. The day will be divided into two parts (morning and afternoon). The first part will cover moderation, and the second part will cover mediation. Each part will be divided in a lecture, followed by a hands on practical and then going through the solution as a group.
 
The course leader 

The course is Led by Dr Alexandru Cernat, a Professor at the University of Manchester, specialising in collecting and analysing longitudinal data. Over the past decade, he has published over 50 papers and book chapters using advanced statistical models to investigate how people and societies change. His main focus in on data quality and how to estimate it using latent variable modelling. 
Dr Alexandru Cernat is also the founder of longitudinalanalysis.com, a platform developed to help researchers and analysts learn how to collect, clean, and analyse longitudinal data.
 
Pre-requisites 
The course includes hands-on computer workshops. Participants will use the R programming language. 
All software is free and open source. Participants should have R and RStudio installed prior to the course (ideally the latest versions).  

Participants should be familiar with:

• Basic R usage (e.g., using lm(), loading data, basic plots)
• Multiple regression and interpreting coefficients

Recommended reading:  

• MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation Analysis. Annual Review of Psychology, 58(1), 593–614. LINK
• Spiller, S. A., Fitzsimons, G. J., LynchJR., J. G., & Mcclelland, G. H. (2013). Spotlights, Floodlights, and the Magic Number Zero: Simple Effects Tests in Moderated Regression. Journal of Marketing Research, 50(2), 277–288. LINK 

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This one-day online workshop will introduce participants to mixed methods research and help them structure qualitative and quantitative methods' design, analysis, and integration within a single study.

The course covers: 

• Common mixed methods designs will be presented and explored, followed by analysis, approaches to integrating data and discussion of the application of the sessional learning to your own developing study design.
• Content will be presented in sessions which include lecture, interactivity and peer discussion.
• Embedded within the workshop are applied learning sessions, where participants apply their learning to their own planned or in-progress mixed methods work (where applicable).
• Time to work independently and in groups on delegates’ own Mixed Methods work will be facilitated by the workshop lead.
• Participants will come away from the workshop with a clearer understanding of the purposes and potential of mixed methods research, as well as commonly used mixed methods designs and analysis procedures, focusing on how these approaches can aid the design of participants' own work.

By the end of the course participants will: 

• Participants will come away from the workshop with a clearer understanding of the purposes and potential of mixed methods research, as well as commonly used mixed methods designs and analysis procedures.
• Participants will be able to consider, plan and design a mixed methods study using the knowledge and skills learned at the workshop.  

Course format  
This full-day course will be delivered online on the 22nd of September 2025 from 9:00 am to 5:00 pm.
 
The course leader 
The course is led by Dr Rebecca Johnson, an Associate Professor of Health Services Research Education at the University of Warwick. Rebecca has been conducting research and teaching in the health sciences since 2008. She undertook her PhD from 2010 – 2013 studying the implementation and effectiveness of mental wellbeing interventions delivered in community settings. She is a Senior Fellow of the Higher Education Academy. 

Rebecca has been teaching and researching since 2009. She has led on Mixed Methods education delivery since 2015 and has delivered numerous mixed methods workshops in the UK and Ireland. Rebecca is a current member of the NCRM research methods pedagogy network and remains an active researcher as well as educator using mixed methods.
 
Pre-requisites

• The course is recommended for those who have prior knowledge of quantitative and qualitative methods. It is ideal for delegates who plan to begin or are currently working on a mixed methods project.
• A personal laptop may be useful for participants on this course and will be essential for the course. An internet connection is essential, as well as access to Teams or Zoom.

Preparatory reading: 

• A paper will be sent out in advance of the course for participants to read

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This course is designed to familiarize students and researchers with various facets of qualitative research, particularly focusing on challenging fieldwork environments involving complex and intimate inquiries, expansive research scopes and diverse participant types. We will draw on our personal experience of undertaking ethnographic work and collecting semi-structured interviews with adults and children, presenting examples from the field to illustrate key challenges. The course will particularly benefit researchers engaging in qualitative research with vulnerable communities for short-term periods and in international contexts.

This course will discuss:

• Making sense of the field: As researchers working on sensitive issues and with families living in precarious conditions, how can one effectively understand and document the field (space, participants, communities and surroundings)? How can we make decisions about community engagement in research while recognizing that our participants are part of existing networks and communities? How can our research ensure that voices are heard without causing harm or disruption to people’s daily lives and social structures? How does our definition of the boundaries of a ‘community’ influence how we define and include community/peer researchers? Further, do we concentrate on noticeable elements that define the field for us, or should we pay attention to aspects that may not be prominent to us but hold significance for the participants? What leads us to make these decisions? Likewise, in informal discussions when new subthemes of our primary research objective emerge, what strategies can we employ to capture the evolving field effectively. 

• Working with ‘vulnerable’ participants: How can we define vulnerability in a way that respects participants’ right to participate and be heard but also attends to situated realities? How do you interact with participants who are traditionally seen as vulnerable, considering both the environment they live in and the potential vulnerability their involvement in the research might entail?

  • Research with children

  • Researching daily wage labourers in factory settings

  • Navigating the complexities of posing tough questions in qualitative research

• Locating researcher and participant vulnerabilities in qualitative research: While participants may be structurally vulnerable and situated in precarious circumstances, it is likely that both researchers and participants will encounter additional vulnerabilities during the research process. How should these challenges be managed and navigated as the research progresses?

  • Managing unanticipated challenges during fieldwork

  • Understanding and iteratively addressing multi-layered power dynamics in working with community/peer researchers

• Ethics as an ongoing process in qualitative research: How does one navigate ethical dilemmas in the field while collecting data and later representing participants’ and their experiences in academic writing? How does one continue to maintain ethical rigour throughout the research and beyond the application process?

  • Navigating positionality constraints in short-term field research

  • Adopting inclusive research practices

  • Praxis-oriented reflexive research

  • Adopting more collaborative methods, including working with community/peer researchers

By the end of the course participants will:

• Understand key challenges and ethical considerations in qualitative research
• Be able to articulate their own positionality and why it might matter during fieldwork
• Have a nuanced view of how to define a ‘vulnerable’ group and understand the methodological and ethical challenges while working with such groups
• Understand what a praxis-oriented, reflexive approach entails
• Understand how local contexts might shape participants’ understandings
• Be able to identify non-disruptive community engagement strategies
• Identify benefits and challenges of working with community/peer researchers
• Be able to identify some of the unique considerations involved in international research
• Acquire essential insights into the challenges and experiences of working with children

This course is aimed at students, researchers and academics in the social sciences with little or no training in qualitative methods.

The course will run from 11:00-16:00 and equates to one teaching day for payment purposes.

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This course aims to teach participants how to conduct qualitative field research, particularly participant observation and ordinary language interviewing. The course provides an understanding of the distinctiveness of ethnographic fieldwork compared to other data collection methods. By the end of the course, students should be able to understand how to conduct ethnography rigorously and the skills needed to produce high-quality ethnographic research. Students will be able to practice data collection methods associated with ethnography, such as participant observation, field notes, and ordinary language interviews. Finally, the course will discuss how to use fieldwork data to produce new and general theoretical insights.

The course covers:

• Introduction to Fieldwork
• Ordinary Language Interview 
• Participant Observation
• Theory building with qualitative data 

By the end of the course participants will:

• Explain the distinctive features of ethnographic fieldwork, particularly how participant observation and ordinary language interviewing differ from other qualitative research methods.
• Apply core ethnographic methods such as participant observation, field notes, and interviews in their own research projects
• Critically assess the methodological and ethical considerations involved in designing and conducting ethnographic research.
• Analyse fieldwork data to generate theoretical insights

Target Audience

• Postgraduate students (Master’s and PhD) in political science, sociology, anthropology, international relations, cultural studies, linguistics, arts, geography, archaeology, anthropology, and development studies, and related fields who are interested in incorporating ethnographic methods into their research;
• Early-career researchers and practitioners studying political or social dynamics who wish to strengthen their qualitative fieldwork skills—especially in participant observation and interviewing;
• Students planning or currently conducting fieldwork, particularly those working on topics like political parties, social movements, state institutions, or the everyday practices of politics.

There are no prerequisites. The course is designed to be accessible to those new to ethnographic research, though some familiarity with qualitative methods may enhance your experience.

PLEASE NOTE THIS COURSE IS BEING HELD AT THE UNIVERSITY OF SOUTHAMPTON AND IS 2.5 DAYS LONG FOR PAYMENT PURPOSES.

Programme

Day 1

AM Session
Introduction to Fieldwork
Ordinary Language Interview

PM Session
Practice of Ordinary Language Interview

Day 2

AM Session
Lessons from Interview Practice 
Introduction to Participant Observation

PM Session
Practice of Participant Observation

Day 3

AM Session
Lessons from Participant Observation 
Constructing Theory with Ethnographic Data 

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Human-computer interaction (HCI) is an ever-more pervasive phenomenon. Many societies are at the point where avoiding interaction with digital technologies is hugely challenging. In this way HCI – both as a phenomenon and as a field of research – has the potential for widespread relevance well beyond its initial disciplinary origins (which stem largely from university computer science and psychology departments).

Simultaneously, approaches from the human sciences (and arts and humanities) have pushed well into HCI’s mainstream. One strand of this having significant formative impact in HCI is, broadly, what we might gloss as ‘sociological interactionism’ or pragmatics (although ‘pragmatics’ is a less used term in HCI); that is, research approaches that foreground ‘interaction’ with / around digital technologies, infrastructures and services, and simultaneously formulate this as constitutively interactional in nature.

This course will explore one key version of this trend: video-based studies of social interaction in which digital technologies play a role, whether in so-called ‘naturalistic’ settings or as part of more experimental rollouts of technology. The course will focus on approaches grounded strongly in traditions of ethnomethodology and conversation analysis. As part of this, the course will contextualise the use of video to study social life with technology, both in terms of human-computer interaction as everyday, routine phenomena, and with respect to HCI as a field (and its connections with both technical and sociotechnical fields of research).

A corresponding practical element will complement these discussions. By looking at various existing examples coupled with participants having a go at their own analyses, the course will provide pointers for what is involved in doing video based EMCA studies of technology in use, including what kind of outcomes they might produce.

The course covers:

• Introduction to human-computer interactions (HCI) and collaborative computing as phenomena
• Understanding and situating HCI as a ‘discipline’: Methods, approaches, disciplinarity
• Social turns and the ‘missing what’: introduction to and critical review of traditions of ethnomethodology and conversation analysis in / of HCI
• Video as aid: Why study human-computer interactional phenomena with video?

Half of the course will be dedicated to practical hands-on work:

• Discussion of existing cases studies (e.g., prior work)
• Analysing some examples of data via individual work (experimenting with forms of transcription) and a joint data session (presenting and discussing findings amongst the group)

Schedule: The course is run across two consecutive mornings with individual and group assignments inbetween and equates to one teaching day for payment purposes.

MORNING 1

9:30    Welcome, 20 second intros around the room (10 mins)

9:40    Teaching session 1 (1hr – short break included) – laying the ground work

Presentations on:

1) Brief intro to human-computer interactions (scoping HCI as phenomena and                   discipline);

2) Primer on EMCA and HCI research and its position within HCI broadly

10:40   Break (20 mins)

11:00   Teaching session 2 (1hr – short break included) – thinking about analysis

Presentations on continued intro to EMCA, and intro to using video to study human-computer interactions – note video segment will include interactive components as preparatory for exercise

12:00   Break (10 mins)

12:20   Briefing (20 mins) on the practical activity / task (video analysis)

12:40    End     

Afternoon session (own time):

Practical activity taking place in own time in groups and / or individually (TBD)

MORNING 2

9:30     Welcome back / recap (5 mins)

9:35     Teaching session 3 (1.5hrs inc. 15 min break) – analysing data

Interactive data session / feedback on video analysis task from groups / individuals

11:00   Break (10 mins)

11:10   Teaching session 4 (max 1hr – short break included if necessary) – wrap up

Open: depending upon outcomes of session 3, could be further group analysis of data, opportunity for discussion and deeper questions from the course, or more structured discussion around EMCA and technology / further studies, future of HCI discourses, etc.

12:10   End

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The measurement of health in surveys involves collecting data from individuals about their health status, health-related behaviours, and experiences, often as part of multi-purpose surveys. These surveys may include both subjective self-reports (e.g., self-assessed health measures) and objectively measured health data (e.g., physical health assessments, blood-based biomarkers, or DNA data).

Survey measurement of health plays a vital role in advancing social science research on health. It is essential for the analysis of health inequalities, for informing policy decisions, and evaluating the effect of interventions. By capturing individual health behaviours alongside the social, economic, and environmental contexts influencing health outcomes, surveys offer a nuanced understanding of the complex interplay between society and health.

However, measurement error in health data can significantly affect and compromise the quality of social science research in health. Importantly, such errors are not confined to self-reported data. While self-reports are often susceptible to certain types of inaccuracies, other sources of error can arise, including those associated with nurse-administered health assessments or blood-based biomarker data.

Survey mode – e.g. face-to-face interview, telephone interview, online questionnaire - is also well known to affect the distribution of the survey variables so is a special case of measurement error.  Mode effects are hypothesised to be driven by well-known sources of survey bias such as social desirability, positivity and satisficing and the presence (or not) of an interviewer. Mode effects can be defined in the same way as causal or treatment effects and estimated from mixed-mode surveys, and estimated using the same methods.

We provide an overview of methods for understanding measurement error and mode effects. We will also provide practical sessions and illustrative examples demonstrating the impact of measurement error and mode effects in the social and health sciences.

The course covers: 

• Collection of health data in multi-purpose social science surveys

• Measurement errors in health data, including both self-reports and objective measures

• Implications of measurement errors in health data for existing social science research in health

• The nature of mode effects and the connection with classical measurement error

• The definition and identification of different kinds of mode effect from mixed-mode surveys and a case study based on Wave 8 of Understanding Society.

• Practical sessions with illustrative examples of measurement error and mode effects

By the end of the course participants will be able:

• Understand the basis on the process of survey measurement of health, including the collection of both self-reported and nurse-collected health data.
• Explain the role of survey measurement in advancing social science research, particularly in understanding health inequalities, guiding policy, and tracking interventions.
• Recognize the impact of measurement error in health data, including errors in self-reported, nurse-administered, and biomarker data.
• To understand the potential impact of survey mode on survey data, learn how to estimate different kinds of mode effects from mixed-mode surveys, and how to do so robustly using instrumental variable estimation.
• Apply practical knowledge of how measurement error in survey health data can affect the accuracy and interpretation of social science research.

This course is aimed at Post-graduate researchers and analysts, including (but not limited to): Academics, Government Researchers, Third sector organisations, (Health) Consultancy analysts and Survey methodologists. Participants will need a basic knowledge of STATA.

Programme TBC

Day 1

9:00-11:00 Measurement error in self-reported health measures regularly available in large-scale multipurpose datasets

11:00-11:15 (Virtual) coffee break (Q&A session)

11:15-12:45 Beyond self-reported health measures – characterizing and quantifying measurement errors in administrative health data and nurse-collected bio-measures.

12:45-13:45 Lunch

13:45-14:45 Assessing the potential implications for the existing research in economics and social science that rely on health data

14:45-15:45 Practical s

This online course will introduce participants to visual and embodied methodologies (VEM) and how they can be used in social science research. VEM can increase understanding about the social world, enhance participation and collaboration through the arts-based or creative research process, and add meaning and value to the analysis and communication of research data. By learning about VEM participants will learn how these methods can be used to develop original research approaches, work with vulnerable and minoritised groups, and address key social science research challenges.

This modular course consists of one core ‘Introduction to VEM’ module followed by a selection of optional modules on specific VEM methods. All participants undertake the core introductory module and can then select optional modules to customise the training according to the VEM methods of interest.

The Introduction to VEM module (13th October 2025 09:00 - 11:00) covers:

• What are visual and embodied methodologies?
• How can VEM be used to understand and address social science research challenges?
• Differentiating between different forms of creative research collaborations and interventions.
• Key ethical issues when using VEM.
• How to manage research collaborations with artists and participants.
• Creative data analysis.

The ‘pick and mix’ two-hour sessions on particular VEM methods include:

13th October 2025

• Photovoice - 12:00 - 14:00
• Body Mapping - 15:00 - 17:00

14th October 2025

• Digital Storytelling - 09:00 - 11:00
• Poetry - 12:00 - 14:00
• Policy - 15:00 - 17:00

15th October 2025

• Performance and participatory theatre - 09:00 - 11:00
• Zine-making and collage - 12:00 - 14:00
• Textiles, Banners and Sewing - 15:00 - 17:00

The course is charged as one day for the core module plus up to two modules from the ‘pick and mix’ options above, two days for the core module plus up to five modules and three days for the core module plus up to eight modules. Please book the required number of days and you will be contacted to choose which modules you wish to attend.  Numbers will be limited to 12 participants on each module.

Learning outcomes

• By the end of the course participants will:
• Understand what VEM methods are and how they can be applied to social science research
• Assess the risks and opportunities of using VEM
• Be able to design an ethical VEM research methodology
• Have the tools to manage researcher-participant relations
• Understand and think critically about specific VEM methods, their applications, processes, advantages and limitations

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