National Centre for Research Methods

A practical introduction to using AI to support coding in research. This course will help researchers understand how to use AI to help them write code effectively and responsibly. This course is designed for researchers with little to no experience coding. The course provides clear, hands-on guidance for using AI to write, debug, and understand code, while addressing key ethical, security, and reliability considerations in research contexts.

The course covers: 

• An overview of the AI landscape

• Practical skills for AI-assisted coding 

• Ethics, reliability and security considerations

Learning Outcomes:

AI Landscape 

• Recall key milestones in the historical development of artificial intelligence
• Describe where ChatGPT and similar large language models fit within the broader AI landscape.
• Explain, at a conceptual level, what generative AI and ChatGPT are.
• Summarize the primary functions and intended use cases of common AI coding assistants.

AI-Assisted Coding

• Explain why delegating full software development to AI without understanding the solution introduces technical, ethical, and reliability risks.
• Describe appropriate roles for AI tools as assistants rather than autonomous developers.
• Use ChatGPT as a reference tool to locate, summarize, and clarify technical information more precisely than traditional search methods.
• Apply AI tools to explain unfamiliar code to support learning.
• Use AI-generated suggestions to debug code and resolve errors.
• Generate boilerplate code using AI assistance.
• Use AI tools to draft technical documentation.
• Analyse when AI assistance enhances productivity versus when it may obscure understanding or introduce errors. 

Ethics, Reliability and Security Considerations 

• Describe common sources of bias, inaccuracy, and unreliability in AI-generated outputs.
• Explain data privacy, confidentiality, and security risks associated with using AI tools in coding and research contexts.
• Summarize intellectual property, authorship, and citation considerations related to AI-generated code and text.
• Analyse the potential long-term consequences of researchers relying on AI tools without developing foundational coding skills.
• Assess the appropriateness of AI tool usage in specific research or coding scenarios.
• Develop personal or team-level guidelines for responsible and ethical AI use in coding and data analysis workflows.

This course is aimed at Researchers with little to no programming experience who are interested in using AI to help them write code for their research. 

Setup Instructions

Please follow the instructions on this web page to download the data and install the required software before attending the workshop: https://southampton-rsg-training.github.io/coding-with-ai/index.html 

Note: If using a University of Southampton machine follow the instructions under the tab labelled ‘University of Southampton Computers’.  If using a personal machine or a machine from another university, please follow the instructions under the tab labelled ‘Personal Computers’.

Programme

• An overview of the AI landscape

• Practical skills for AI-assisted coding 

• Ethics, reliability and security considerations

This course is taking place on 3^rd September 2026 from 13:00 – 16:30.

In this one-day online training workshop you will be introduced to four qualitative research methods to better understand diverse lives - Photo Go-Alongs, Collage, Life History Interviews and Participant Packs.

When researching social groups, researchers may focus on categories such as age, gender, sexuality and so on. These categories can turn catch-all terms into catch-all agendas. Treating groups of people with one shared characteristic as homogenous risks a cookie-cutter approach which overlooks diverse lives and needs. Given the complexity of what it means to be a person, a one-size fits all approach to engagement cannot suffice.

The methods introduced in this training workshop are beneficial in exploring diverse lives and can be used when researching with any group. 

The session is aimed at PhD students and academics of all career stages across the UK who want to better understand: 

• The specific place-based needs of people 
• The everyday practices of people
• The world from participants’ perspectives
• How to work with people in an inclusive and accessible way

This online training workshop will be structured as follows:  

• Introductions
• Origins and Approach 
• Methods deep dive: 
  • Photo Go-Alongs
  • Participant packs
  • Collage 
  • Life Histories 
  • Workshops 
• Learnings and close 

By the end of the course participants will:

• Be able to think critically about how creative, participatory methods might be incorporated into their research and/ or teaching. 
• Have broadened their understanding of research methods from tools of data collection to techniques for capacity building.
• Have workshopped four qualitative methods for creatively engaging with people (Photo Go-Alongs, Collage, Life Histories and Participant packs).

This online training workshop will take place over the course of one day between 10:00 and 16:00, with 1 hour for lunch between 12:30 and 13:30.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

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.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

This online workshop will introduce Institutional Ethnography (IE), an interdisciplinary feminist approach to social research that focuses on how texts and language organise our everyday lives. IE is not just a methodology, but an entire approach to research with a specific ontology of how the social world works and the organising role of texts and language. In IE, the researcher ‘takes sides’ using a specific version of standpoint to explore how institutions work in practice rooted in peoples’ experiences. This often involves researching as, with, or alongside marginalised groups and making visible how institutions exclude or make invisible certain groups of people and experiences.

The overall aim of the workshop is to provide attendees with a comprehensive overview of institutional ethnography as an approach and the opportunity to translate their own research ideas and projects into an IE research proposal and do a small piece of text-focused analysis. This hands-on workshop is suitable for students, academics, and anyone else interested in feminist methodologies, text and discourse analysis, and institutional or organisational ethnographies. No prior training in, or knowledge of, IE is required.

The course covers:

· An overview of Institutional Ethnography and the work of feminist sociologist, Dorothy Smith, who developed Institutional Ethnography

· Case studies of Institutional Ethnography research projects to show how it works in practice in different disciplines

· How to translate your research into an Institutional Ethnography project using a research proposal framework

· Practical explanation of how to do text and discourse analysis within Institutional Ethnography through a short text analysis activity

By the end of the course participants will:

· understand of the origin and development of Institutional Ethnography

· know how to use Institutional Ethnography to analyse texts, processes, and discourses

· have an outline of how their research ideas could become an Institutional Ethnography project

The course is aimed at Academics, students, any other qualitative researchers, including policymakers, organisers, and activists interested in analysing organisational processes.
Participants must have at least some experience in qualitative research methods, but no experience of Institutional Ethnography is required.

Preparatory Reading

Required:

· 1 hour lecture by Dorothy Smith summarising Institutional Ethnography -

https://www.youtube.com/watch?v=1RI2KEy9NDw 

· Murray, Ó.M., 2020. Text, Process, Discourse: Doing feminist text analysis in institutional ethnography, Available at: https://doi.org/10.1080/13645579.2020.1839162  

Desirable: · 

Earles, J., & Crawley, S. L. 2020. Institutional ethnography. In P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug, & R. A. Williams (Eds.), Foundation: SAGE research methods. Retrieved July 17, 2020, from: http://dx.doi.org/10.4135/9781526421036759274  · Smith, D.E. & Griffith, A.I., 2022. Simply Institutional Ethnography: Creating a Sociology for People. Toronto: University of Toronto Press.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

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 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

Learning Outcomes:

Introduction 

• 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

Classification by a neural network using Keras

• 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

Monitor the training process

• 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

Advanced layer types

• 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

Transfer learning

• Adapt a state-of-the-art pre-trained network to your own dataset

Outlook

• 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

This short course is designed to give participants a practical introduction to ECHILD (Educational and Child Health Insights from Linked Data). ECHILD is a collection of linked, longitudinal administrative datasets covering health, education and children’s social care. More information about the ECHILD, and resources for researchers and the public can be found on the ECHILD website. 

The course is aimed at both analysts intending to use ECHILD and researchers who want to understand more about how the data can be used for policy relevant research. This course includes a mixture of lectures and practical sessions that will enable participants to put theory into practice.   

Day 1 will provide information on the strengths and limitations of the different component datasets of ECHILD, through case studies of the National Pupil Database, Hospital Episode Statistics, Maternity Services Data, Mental Health Services Data, and the Community Services Dataset. Interactive lectures / tutorials will teach participants how to design a research study to answer a specific research question in ECHILD, focusing on the power and complexity of working with linked datasets. We will also discuss how to extract ECHILD data from SQL tables on the ONS Secure Research Service platform and provide an overview of access arrangements. 

Day 2 will focus on a series of practical sessions (in Stata and R) allowing participants to progress through an exemplar research study using ECHILD, covering phenotyping, developing cohorts, and analysing ECHILD cohort data. 

The current course builds on our previous in person and online training courses with the inclusion of some updated information reflecting newer developments of ECHILD. ECHILD users who have previously attended this training course and have an interest in learning about emerging ECHILD research and newly available data are encouraged to attend the next ECHILD User Day, which is available for anyone with an interest in ECHILD.

The course covers: 

• Overview of the component datasets of ECHILD, with reference to case studies 
• Accessing teh data on the ONS SRS
• Strengths and limitations of ECHILD 
• Development of electronic cohorts in ECHILD
• Phenotyping in ECHILD
• Analysing ECHILD data

By the end of the course participants will: 

• Understand what data are available in ECHILD 
• Know how to access ECHILD data 
• Understand the strengths and limitations of ECHILD 
• Know how to developing electronic cohorts in ECHILD 

The course is aimed at academic or government analysts and researchers who would like to know more about ECHILD and how ECHILD could be used in their own research, or who would like to know how it is currently being used to generate policy-relevant research. 

Participants should bring their own laptop and should have access to Stata or R for the practical sessions. 

PLEASE NOTE THIS COURSE IS TAKING PLACE IN PERSON AT FRIEND'S HOUSE, LONDON - Elizabeth Fry Suite, 173-177 Euston Road, London

The problem: Your team collected thousands of words of data. You try a traditional thematic analysis of the text. Soon, colour coding, close reading, writing ad hoc reflections about the text become too onerous a task. You doubt the validity of your observations. You wish there was another way to streamline the process, that would extract key themes in data in a faster and empirically-valid way.

Solution: Join us for a session in which we showcase empirical methods for the extraction and analysis of meaning, concepts, and themes in texts. The session will provide training in corpus linguistics and mixed-method tools that enable the analysis of texts in an empirical, bottom-up fashion. Through a range of case-studies, you will be guided to extract meaning and other thematic patterns from texts to gain insight into thoughts and behaviours of authors of those texts. We will share best practises on the thematic analysis of various data types, such as diaries, interview transcripts, data scraped from the web, and outputs of both new and traditional media. We also demonstrate ways of building the results of such analyses into answering research questions, developing business strategy, or a public policy.

This session will be run by researchers from the University of Sussex’s Concept Analytics Lab (https://conceptanalytics.org.uk/) using texts from Mass Observation Archive https://massobs.org.uk/ to showcase approaches to thematic analysis. We will demonstrate solutions developed for a variety of problems and text types coming from our work with medical sciences, psychology, economics, and the energy industry. We will also show how linguistic patterns within or between texts (e.g. those that differ demographically or diachronically) can be explored, particularly through the use of new visualisation techniques. The workshop will conclude with a showcase of next-generation textual analysis tools that have been developed at Concept Analytics Lab.

This will be a practical session, enabling attendees to develop hands-on experience with using corpus analysis tools. The course will consist of six hours of training over the course of one day [9.30am - 5pm] and will be delivered online. 

The course covers: 

• How to extract meaning from large textual data
• How to build a corpus using textual data 
• How to engage with existing corpora, such as multi-billion word corpora scraped from the web
• How to use corpus methods for bottom-up and top-down research
• Techniques for the visualisation of unstructured language data
• An introduction to discourse analysis and its application to corpora (corpus-assisted discourse analysis)

By the end of the course participants will:

• Know how to engage a suite of mixed-method corpus linguistic tools to extract meaning from a corpus
• Be able to use corpora to answer a variety of research questions
• Be able to build their own corpora
• Conduct comparative corpus analysis (e.g. between texts that differ demographically or diachronically)

Programme:

9:30: Welcome and introduction to corpus linguistics

10:00: Interrogating existing corpora - quantitative analysis

12:00: Lunch

13:00: Interrogating existing corpora - qualitative analysis

15:00: Break

15:15: Building your own corpus

16:15: The Concept Cruncher: The next generation of text analysis

16:45: Final remarks

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

PAR aims to create a space for researcher and participants to co-produce knowledge and where relevant, action for change. PAR is considered as a research paradigm in itself, that embodies a particular set of concepts under which researchers operate (Minkler and Wallerstein 2008). These include respect for diversity, community strengths, reflection of cultural identities, power-sharing, and co-learning (Minkler 2000). In this session we will explore these principles, the cyclical approach to PAR and what this means in practice. Participants will be given the opportunity to learn terminology, understand participation in community engaged research, explore how power and positionality can change health outcomes in PAR, and learn about a variety of participatory methods and how they have been applied in different contexts, globally and within the UK. Participants will also be provided with the space to explore challenges they are facing in designing or implementing community engaged collaborative research within a discussion clinic forum.  

Programme of Activities:

The course will take place between 9:30am and 3.30/4.00pm on both days.

Mornings: online teaching and discussion with example videos and guests

Day 1: The history of PAR and underpinning orientation.

• Planning and setting up a PAR project
• Skills required for a PAR study
• Ethical considerations specific to a PAR study
• Participatory research with children and young people
• Photovoice methodology
• Independent activity
• Group discussion

Day 2 : Doing co-analysis

• Participatory research methods (examples of other visual methods, social mapping, seasonal calendars and other non-visual methods but still participatory such as narratives and others that have been used)
• Participation and inclusion
• Dissemination and writing for PAR projects – different approaches, narratives/thematic analysis, thesis, publications, policy briefs, blogs and others
• Group discussion on pre-workshop task
• Advice clinic

Afternoons: independent learning and practical exercises

• Day 1: Photovoice activity and reflections
• Day 2: Individual PAR project outline and feedback

Preparatory reading and videos will be shared beforehand.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

This online course, taught over four mornings, 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 Ethnography 

• Ordinary Language Interview 

• Participant Observation

• Digital Ethnography 

• 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, digital ethnography, 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 EQUATES TO 1.5 DAYS FOR PAYMENT PURPOSES.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

This course will offer an in-depth exploration of decolonising academia by asking: What are the possibilities when it comes to decolonial research? 

This one-day course will be split into four sections: 

1. Examining the decolonial critique of academia. This will involve exploring some of the main themes found within decolonial scholarship and activism with a specific focus on the decolonial critique of social science research. 
2. Exploring some of the key predecessors of decolonial research which have had a significant impact on current thinking about decolonial research. More specifically, consideration will be given to the relationship between feminist research, Indigenous research, and decolonial research. 
3. Considering various strategies which have been suggested to decolonise research. This will involve discussing some of the practical strategies that may be used when trying to produce decolonial research. 
4. Offering a critique of decolonial research. This will involve recognising the limitations of decolonial research which make it an imperfect approach to conducting research. 

The course covers: 

• Theoretical and practical insights into what decolonial research is and how it can be conducted
• Robust exposition of decolonial theory and its critique of contemporary academia
• Examination of the significant influence that feminist research and Indigenous research has had on decolonial research
• Consideration of various strategies which have been adopted in the pursuit of decolonial research.
• Exploration of the limitations of decolonial research

By the end of the course participants will:

• Be well-versed in the decolonial critique of academia, particularly in relation to social science research
• Be able to identify the core principles and strategies which constitute decolonial research
• Be prepared to offer a lucid critique of decolonial research

This course is aimed at researchers and academics with a keen interest in the topic. Researchers from all disciplines, expertise, career stages, and backgrounds are welcome to attend.

This is an in-person course, taking place at University of Bradford, Room WB19, Richmond Building on the 14^th July from 10:00 to 17:00. Please note lunch will not be provided.

Payment using the Online Store can only be completed via Visa and Mastercard Credit/Debit Card or PayPal.  AMEX is not accepted.
If you have not previously created an account for the Online Store, you will need to create an account to make a booking.

This two-part in-person training course combines critical reflection with hands-on practice to help researchers navigate generative AI thoughtfully and responsibly. The first session explores what AI means for higher education and research at this moment of rapid change, examining both opportunities and risks. The second session is a practical workshop where participants bring their own work and AI tools to explore ethical and professional use, developing personal principles for responsible AI integration into research practice. Participants must bring their own device with access to a generative AI chatbot they already have an account with and have previously used (such as ChatGPT, Claude, Gemini, or Copilot).

The course covers: 

• The current landscape of generative AI in higher education and academic research

• How AI is reshaping academic work, including writing, analysis, and collaboration
• Opportunities and risks of AI adoption in research contexts
• Ethical considerations around integrity, authorship, and responsibility
• Practical exploration using participants' own research materials and AI tools
• Scenario-based discussions on responsible AI use
• Peer exchange on emerging practices and challenges
• Developing personal guiding principles for AI use in research

By the end of the course participants will:

• Articulate a clearer understanding of what generative AI means for researchers and scholarship
• Critically evaluate the opportunities and risks of AI in their own research context
• Reflect on how language models are entering their research processes
• Identify key ethical considerations around integrity, authorship, and responsibility when using AI
• Experiment critically with AI tools using their own research materials
• Begin developing their own guiding principles for responsible AI use
• Share and learn from peers' emerging practices and approaches

Schedule

Wednesday 14th October 2026, 10:00 - 16:00

Location

Room 1.69, Humanities Bridgeford Street Building, The University of Manchester, M15 6AD

Pre-requisites

• Some prior experience using a generative AI chatbot
• An active account with a generative AI tool of your choice 
• A paper they have published (open access or pre-print version)
• A work-in-progress paper or chapter
• Access to their preferred AI chatbot

This course introduces researchers to version control using Git and GitHub through an accessible graphical interface, requiring no prior experience with Git or the command line. Participants will learn the core concepts of version control and work through the full Git workflow - from setting up Git and creating repositories, to tracking files, working with remote repositories, and managing branches. By the end of the course, researchers will be able to manage their project files using Git and collaborate with others through GitHub.

The course covers: 

• What is version control?

• Setting up Git

• Creating a repository

• Tracking changes

• Exploring history

• Remote repositories

• Branching

• Ignoring things in version control

By the end of the course participants will:

• Understand the benefits of an automated version control system
• Understand the basics of how automated version control systems work
• Configure Git and GitHub on their computer
• Create a repository from a template
• Clone and use a Git repository
• Go through the modify-add-commit cycle for one or more files
• Describe where changes are stored at each stage in the modify-add-commit cycle
• Compare files with previous versions of themselves
• Restore old versions of files
• Understand git push and git pull
• Encounter and resolve a conflict
• Understand why you would use a branch
• Merge together two modified version of a file
• Use a gitignore file to ignore specific files and explain why this is useful

This course is aimed at academic researchers at all career stages, across all disciplines. No prior experience with Git, GitHub, or the command line is required. This course is relevant to any researchers who want to adopt better practices for tracking and organising their work.