National Centre for Research Methods

A three-day online course, delivered over three weeks (sessions in the afternoons), on using AI agents to improve survey research workflows while preserving privacy, reproducibility, review discipline, and institutional data-governance boundaries.
The course adapts agentic coding methods to the survey-research lifecycle: designing survey instruments, analysing survey data, documenting metadata and derived variables, checking outputs, and preparing reproducible or release-ready packages. Participants work hands-on with Cursor, GitHub, and role-specific repository workflows, producing auditable artifacts they can reuse in their own organisations.

The course covers:

• Agentic coding foundations and Cursor workflows for survey research
• GitHub issues, branches, pull requests, review logs, and acceptance criteria
• Project guardrails: rules, AGENTS.md, .cursorignore, .gitignore, and documented data-use boundaries
• Restricted-data decision rules for cloud AI, local models, metadata-only work, synthetic data, and TRE/sandboxed environments
• Spec-driven development for survey workflows
• Applied researcher track: literature/context, safe data exploration, cleaning, descriptives, regression or standard analysis, tables/figures, reproducible write-up support
• Survey practitioner track: measure search, questionnaire specification, respondent materials, piloting, ethics documentation, testing notes, fieldwork monitoring
• Survey data manager track: metadata, derived-variable specifications, longitudinal consistency, cleaning checks, disclosure checks, deposit/user documentation
• Reusable skills and subagents for measure mapping, questionnaire review, metadata checks, derived-variable checks, disclosure risk review, and user-guide review
• Role-specific capstone packaging: reproducible analysis, questionnaire/specification workflow, or data-release documentation
• 30-day adoption planning for post-course implementation

By the end of the course participants will:

• Set up a practical agentic workflow using Cursor, GitHub, and a role-specific repository structure
• Use issues, branches, pull requests, review logs, and acceptance criteria to make survey workflow tasks auditable
• Apply restricted-data decision rules for cloud AI, local models, metadata-only work, synthetic data, and TRE/sandboxed environments
• Configure project guardrails with rules, AGENTS.md, .cursorignore, .gitignore, and documented data-use boundaries
• Turn a survey workflow into a spec-driven plan with a safe input map, role-specific deliverables, and verification checks
• Build or adapt reusable skills and subagents for survey-specific work such as measure mapping, questionnaire review, metadata checks, derived-variable checks, disclosure risk review, and user-guide review
• Package a reviewed output for their role track: reproducible analysis, questionnaire/specification workflow, or data-release documentation

Target Audience

The course is designed for a mixed audience across the survey research lifecycle:
• Applied researchers using existing survey data for empirical social science (economics, sociology, political science, and related disciplines)
• Survey practitioners designing questionnaires, respondent materials, pilot tests, and fieldwork processes
• Survey data managers preparing, documenting, checking, and releasing survey datasets
• Government researchers, academic researchers, and staff at survey agencies and research institutions
• Mixed seniority: junior and senior researchers, analysts, and operational staff

Technical levels range from participants comfortable in R/Stata/Python to participants who mainly work through specifications, documents, and operational workflows. Recommended cohort cap: 20 participants unless additional facilitators support breakouts and troubleshooting.

The course will run over 3 live online sessions on 5, 12 and 19 November (13:00-17:00) and there will be asynchronous assignments between sessions. 

Large language models are now embedded in research workflows across the social sciences, yet most researchers interact with these tools through consumer interfaces that obscure how they work, where data goes, and what decisions are being made on their behalf. This three-workshop series closes that gap. Across three standalone half-day sessions, participants build a working understanding of the AI stack: from how models generate text and where inference happens, through prompt engineering, retrieval-augmented generation, and API-based workflows, to the rapidly maturing ecosystem of agentic platforms, harness engineering, and autonomous research infrastructure. Each workshop combines conceptual exposition with live demonstrations and practical exercises grounded in survey research scenarios. No programming experience is required for Workshop 1; Workshops 2 and 3 assume familiarity with earlier concepts.

The course covers: 

• Workshop 1: How Large Language Models Work: tokens, training, alignment, data security, inference, open- vs closed-weights models, reproducibility challenges, and the limitations of chatbot interfaces for research.

• Workshop 2: Context Engineering: prompt design and optimisation, retrieval-augmented generation (RAG), API-based workflows and batch processing, memory and tool-calling, MCP servers, and evaluation engineering.

• Workshop 3: Agentic AI and Harness Engineering: the agentic AI ecosystem (IDE-native agents, extended-autonomy platforms, orchestration tools), harness engineering and SDKs, memory and token economics, MCP servers and hooks, oversight, auditability, and research transparency.

By the end of the course participants will:

• Explain how LLMs generate text and assess the implications of model architecture, training, and alignment for research practice
• Distinguish between open-weights and closed-weights models and evaluate their data governance implications
• Apply prompt optimisation techniques and build evaluation pipelines to validate LLM outputs
• Make structured API calls, manage parameters, and use retrieval-augmented generation where appropriate
• Map the agentic AI ecosystem, explain harness engineering, and assess how platforms orchestrate memory, tools, and context
• Design human-in-the-loop safeguards and audit protocols appropriate for agentic research workflows

Pre-requisites

No prior programming experience or specialist software knowledge is required for Workshop 1. Workshops 2 and 3 assume familiarity with concepts from Workshop 1 (or equivalent knowledge of how LLMs work). Workshop 3 benefits from some comfort with reading code, but participants are not required to write any. Setup guidance for API access will be provided before Workshops 2 and 3.

No software installation is required for Workshop 1. For Workshops 2 and 3, participants will benefit from having API access to a commercial LLM provider (e.g. Anthropic, OpenAI); setup guidance will be provided in advance. All demonstrations will be conducted live by the instructor. Participants do not need prior experience with any specific software, though basic familiarity with web browsers and text editors is assumed.

Target Audience

Survey researchers, methodologists, and quantitative social scientists across academia and government who use or are considering using large language models in their research. The series is designed to be accessible to researchers at all career stages, from doctoral students to senior investigators. No programming experience is required for Workshop 1; Workshops 2 and 3 assume familiarity with concepts from Workshop 1, and Workshop 3 benefits from some comfort with reading code.

PLEASE NOTE THESE WORKSHOPS WILL RUN ONLINE ON 23 NOVEMBER, 30 NOVEMBER AND 7 DECEMBER FROM 09:30-13:30

This exciting interactive workshop will develop your knowledge and skills in using creative and participatory research methods. Creative and participatory methods are increasingly being utilised by social researchers to tackle complex research questions, enhance participant inclusivity and to generate wide ranging research impact for a broad range of stakeholders. 

This session begins with an overview of developments in creative and participatory research, highlighting the opportunities and challenges in the context of social policy, research impact and advancing academic knowledge. Across the two days, the course covers how and why we use a variety of creative and participatory methods and how to bring them together in analysis, forming a constellation. The workshop will address ethics, opportunities, benefits and challenges during the research process and how to generate multi-level impact from grassroots to social policy. Participants will be given the opportunity to explore how to incorporate creative and participatory approaches (such as zines and photovoice) in their own research, and how to analyse and disseminate effectively.

Over the course you will:

• Be introduced to key debates in creative and participatory research
• Understand the potential for, and the challenges of, using creative and participatory research methods
• Explore how to ethically engage in creative and participatory research
• Learn from active peer-researchers involved in co-creating research

By the end of the course participants will:

• Develop practical skills in different creative and participatory approaches such as Zines, Photovoice, Co-creation/co-production (including peer research)
• Develop skills in designing, conducting, analysing and disseminating creative and participatory research
• Learn how such methods can be incorporated into the generation of meaningful research impact

Indicative Schedule:

The course will run across two consecutive mornings (10am - 1pm) and equates to one day of training for payment purposes.

Day 1

• What do we mean by creative and/or participatory methods?
• The value of creative/participatory research methods
• Planning and setting up creative/participatory research tools.
• FOCUS ON (1): zines as creative/participatory methods
• Ethical considerations specific to creative/participatory research (part 1)

Day 2

• Ethical considerations specific to creative/participatory research (part 2)
• Creative/participatory research with children and young people
• Creative/participatory research with marginalised communities    
• FOCUS ON (2): co-creation – creative and participatory research in action*
• Doing co-analysis and co-dissemination
• Creative/participatory methods for generating meaningful research impact
• Wrapping up the workshop/advice clinic

*The workshop facilitators will be joined on this by two peer researchers they have trained and worked with on recent research projects.

Presenters:

This course will be delivered by Dr Linzi Ladlow, Senior Research Fellow from the University of Lincoln, and Dr Laura Way, Senior Lecturer from the University of Roehampton. They are experienced in engaging with creative and participatory research and facilitating training. They are editors of the book, Insights into Creative and Participatory Research: Key Issues and Innovative Developments (2026) Policy Press. 

Target audience:

This short course is suitable for all qualitative researchers at any career stage, including postgraduate students. Whilst we are not expecting you to already be familiar with creative and participatory methods, familiarity with the purposes of qualitative research, as well as with qualitative methods of data generation and analysis, will be assumed.

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.

Ethnographic methods are increasingly popular with researchers across the social sciences, but the full potential and possible pitfalls of this complex practice are often overlooked in favour of catch-phrase definitions.

This course moves beyond standard understandings of ethnography that depict it as a generic qualitative method founded on ‘participant observation’ to provide learners with a sophisticated, state-of-the-art approach based on cutting-edge academic research.

The course will blend theorical and practical considerations. On the one hand, the course examines the theoretical scaffolding of ethnography, recognising that a thorough understanding of the epistemological foundations of the methods we use is essential to conducting rigorous and ethical research.

On the other, the spirit of the course is inherently practical and pragmatic, as it aims at preparing researchers to design and conduct ethnographic fieldwork, as well as writing it up for academic and non-academic audiences.

The course covers:

• Epistemology, method, and research design: ethnography beyond participant observation
• Preparing for fieldwork: a pragmatic approach to designing research projects
• Ethics and power: access, collaboration, co-production, and the possibility of decolonising research
• Writing ethnography: from the practical to the political

By the end of the course participants will:

• Grasp the practice of ethnographic research beyond participant observation
• Understand the potential of ethnography beyond the traditional ‘study of culture’
• Have a sophisticated understanding of ethnographic research, from the design stage to its execution and writing up, including an overview of sensorial considerations and visual methods
• Be able to appreciate the ethical and power dimensions of ethnographic research
• Understand the ethics and politics of writing, publishing, and representing ethnographically

This advanced course is suitable for any researchers equipped with some prior knowledge/experience using both standard qualitative methods (interviews, focus groups, life histories, etc.) as well as ethnographic methods but is interested in advancing their understanding of ethnographic research to a professional level.

Researchers working within and outside academia (private sector, government, charitable institutions, etc.) are equally welcome to apply. The course is likewise suitable for postgraduate students in any social science (human geography, sociology, business school, political sciences, area studies, education, etc.), particularly if enrolled or intending to enrol in a research degree (e.g., PhD, Masters by Research, Masters in Research Methods).

Please note that this course is also suitable for postgraduate researchers with an UG background in anthropology, as the course if pitched to an advanced level.

Pre-requisites

Experience using ethnographic research methods and qualitative research methods.

Preparatory Reading

Demetriou, O. (2023), ‘Reconsidering the vignette as method. Art, ethnography, and refugee studies’, American Ethnologist, 50(2): 208-222.

Hage, G. (2005), ‘A not so multi-sited ethnography of a not so imagined community’, Anthropological Theory 5, no.4: 463-475.

Ingold, T. (2014), ‘That’s enough about ethnography!’, HAU Journal of Ethnographic Theory 4, no 1: 383–395

Stefanelli, A. (forthcoming 2024) ‘Reading ethnography in the classroom: complementary strategies to develop students’ ethnographic imagination.’ Learning and Teaching in the Social Sciences.

The course will run from 09:30 to 15:15 both days.

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.

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

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 explain when and why to use documents as data for research, show you how to gather documentary data, and consider some ways to analyse that data. It will also cover some of the associated ethical issues and include case studies of documents as data in practice.

The course covers: 

• When and why to use documents as data

• How to find personal and official documents, historical documents, mainstream print media documents, virtual documents, and self-published documents

• How to assess the quality and usefulness of documents for research purposes

• Some methods of analysing documentary data

• An introduction to the ethical issues of using documents as data

By the end of the course participants will:

• Know how to find the documents they need for their research
• Know how to assess the quality and usefulness of documents
• Understand how to approach the analysis of documentary data
• Have an action plan for using documents in their own research

This is an intermediate level course assuming a good basic knowledge of research methods. It would suit postgraduate students, early career researchers in academia, practice-based and independent researchers.

Preparatory Reading

Desirable: 

Grant, A. (2022) Doing Your Research Project With Documents: A Step-By-Step Guide to Take You from Start to Finish. Bristol: Policy Press.

Grant, A. and Kara, H. (eds) (2026) Using Documents in Research: When, Where, Why and How. Bristol: Policy Press.

THIS COURSE IS TAUGHT OVER TWO MORNINGS (09:30-12:30) AND EQUATES TO A ONE DAY COURSE 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.

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 session invites participants to think critically about what generative AI means for higher
education and academic research in particular at this moment of rapid change. We’ll look at
how AI is already reshaping academic work and consider what this means for research, and
scholarship. Rather than offering fixed answers, the session creates space to explore the
opportunities and risks together. The focus is on developing a clearer understanding of what
AI means for researchers.

Date: 15 October - afternoon (1.5 hours)

The session is free to attend but participants will still need to register via the Online Store.

In this online one day course (taught over two mornings) you will learn what GIS is, how it works and how you can use it to create maps. We assume no prior knowledge of GIS and you will learn how to get data into the GIS, how to produce maps using your own data and what you can and cannot do with spatial data. You will gain practical experience in data importation, map creation, and analysis techniques, empowering them to enhance their research insights with compelling spatial visualisations.

The course covers:

• What is GIS and spatial data?
• How to classify data for a choropleth map
• How to create a publication ready map
• How to work with different data sources including XY coordinate and postcode data

By the end of the course participants will:

• Be able to set up QGIS and add data
• Understand how to add data with latitude & longitude coordinates
• Know how to classify data for a choropleth map
• Be able to join tabular data to spatial data
• Designing and producing a publication ready map in QGIS
• Understand how to import a range of data types into QGIS

This course is ideal for anyone who wishes to use spatial data in their role. This includes students, academic, government & other public sector researchers who have data with some spatial information (e.g. address, postcode, etc.) which they wish to show on a map. This course is also suitable for those who wish to have an overview of what GIS and spatial data can be used for, and how you can better represent your data with maps. No previous experience of spatial data is required.

THIS COURSE IS RUN OVER TWO MORNINGS (10:00-13:00) AND EQUATES TO ONE TEACHING DAY 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.

In this one day course (online over two mornings) we will explore how to use R to import, manage and process spatial data. We will also cover the process of making choropleth maps, as well as some basic spatial analysis.

Finally, we will cover the use of loops to make multiple maps quickly and easily, one of the major benefits of using a scripting language to make maps, rather than traditional graphic point-and-click interface.

The course covers:

• Using R to import, manage and process spatial data
• Design and creation of choropleth maps
• Basic spatial analysis
• Working with loops in R to create multiple maps

 By the end of the course participants will:

• Use R to read in CSV data & spatial data
• Know how to plot spatial data using R
• Join spatial data to attribute data
• Customize colour and classification methods
• Understand how to use loops to make multiple maps
• Know how to reproject spatial data
• Be able to perform point in polygon operations
• Know how to write shapefiles

This course is ideal for anyone who wishes to use spatial data in their role. This includes government & other public sector researchers who have data with some spatial information (e.g. address, postcode, etc.) which they wish to show on a map.

This course is also suitable for those who wish to have an overview of what spatial data can be used for. Although no previous experience of spatial data is required it would be beneficial (eg Google Maps).

This course will be taught over two mornings (10:00 – 13:00, including a mid morning break) and equates to one teaching day 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.

Course Overview:

Navigating Qualitative AI: Ethical, Methodological and Practical Considerations

The Navigating Qualitative AI course provides an introduction into developments, debates and practicalities in the role and use of Generative AI in Qualitative Research. The course is split into three modules combining synchronous and asynchronous learning to provide a comprehensive introduction to the principles, practices and ethics of using AI tools to facilitate the analysis of qualitative materials. 

Module 1: Mindsets for considering the use of AI for Qualitative Research

Module 2: Harnessing Generative-AI tools for qualitative analysis

Module 3. Integrating Generative-AI tools into qualitative analysis processes

Choose from three pathways through the course according to your interests and needs (note, to take Module 2 or 3 it is a pre-requisite to have first completed Module 1):

• Pathway 1 – Module 1 only
• Pathway 2 – Module 1 and 2
• Pathway 3 – Module 1 and 3

Module 1. Mindsets for considering the use of AI for Qualitative Research 

(Synchronous Delivery = lectures, illustration, and discussion)

This module provides essential context to considering the potential use of AI in qualitative projects, focusing on the current landscape of AI tools and their impact on qualitative research practice. Practical, ethical and methodological implications are discussed in relation to the contexts in which research and evaluation happen. 

The course covers:

The landscape of Qualitative AI - 26 January 2027 (13:30-16:45)

• development of AI tools in qualitative research spaces
• ethical considerations and how to navigate them
• methodological consequences when using AI for qualitative analysis

Critically engaging with the Qualitative AI space - 27 January 2027 (13:30-16:45)

• potential uses of AI throughout the qualitative workflow
• genres of AI tools for qualitative data analysis
• when the use of AI is - and is not - methodologically appropriate 

By the end of the course participants will:

• Understand historical contexts of current developments in AI within in the Qualitative Research field
• Understand the range of ethical issues involved in using AI for Qualitative Research and how to navigate use in appropriate ways
• Possess critical thinking skills around the role of AI in different Qualitative Research contexts

This course is aimed at students and researchers interested in qualitative analysis, within any sector or discipline.

The course takes places over two afternoons and equates to one teaching day for payment purposes.

Course Overview

Navigating Qualitative AI: Ethical, Methodological and Practical Considerations

The Navigating Qualitative AI course provides an introduction into developments, debates and practicalities in the role and use of Generative AI in Qualitative Research. The course is split into three modules combining synchronous and asynchronous learning to provide a comprehensive introduction to the principles, practices and ethics of using AI tools to facilitate the analysis of qualitative materials. 

Module 1: Mindsets for considering the use of AI for Qualitative Research

Module 2: Harnessing Generative-AI tools for qualitative analysis

Module 3. Integrating Generative-AI tools into qualitative analysis processes

Choose from three pathways through the course according to your interests and needs (note, to take Module 2 or 3 it is a pre-requisite to have first completed Module 1):

• Pathway 1 – Module 1 only
• Pathway 2 – Module 1 and 2
• Pathway 3 – Module 1 and 3

Module 2. Harnessing Generative-AI tools for qualitative analysis

(Synchronous Delivery = hands-on guided exercises using selected tools, and discussion)

This module comprises demonstrations and hands-on exercises using selected AI tools for Qualitative Data Analysis, and discussion about their potential for research and evaluation. Using sample data, we consider a series of analytic tasks and methodological implications of use in different contexts, with different types of data and at different stages in the analytic process. The tools-focus in this module is on general-purpose Generative-AI tools and new online Apps designed for GenAI-driven qualitative analysis. 

The course covers:

Using general-purpose Generative-AI tools - 3 February 2027 (13:30-16:45)

• Overview of the capabilities of general-purpose Generative-AI tools
• Hands-on exercises following process of Query-Based Analysis (Morgan 2025)
  • Chatbots – e.g. ChatGPT, Claude, Gemini etc.
  • AI Research Assistant platforms – e.g. Notebook LM

Generative-AI driven analysis with bespoke online Apps - 4 February 2027 (13:30-16:45)

• Overview of the capabilities of bespoke online Apps for qualitative analysis
• Hands-on exercises using Reveal platform (analysis of transcripts)
• Hands-on exercises using Blix platform (analysis of open-ended survey responses)

By the end of the course participants will:

• Understand the potential uses of AI tools at different stages of the qualitative analysis workflow
• Understand the methodological consequences of using AI tools (potentials and risks)
• Identify skills in harnessing AI tools in ways that enable human-centred approaches

This course is aimed at students and researchers interested in qualitative analysis, within any sector or discipline. Participants must have attended Module 1 in order to attend this module.

The course takes place over two afternoons (3 and 4 February) and equates to one teaching day for payment purposes. 

Course Overview

Navigating Qualitative AI: Ethical, Methodological and Practical Considerations

The Navigating Qualitative AI course provides an introduction into developments, debates and practicalities in the role and use of Generative AI in Qualitative Research. The course is split into three modules combining synchronous and asynchronous learning to provide a comprehensive introduction to the principles, practices and ethics of using AI tools to facilitate the analysis of qualitative materials. 

Module 1: Mindsets for considering the use of AI for Qualitative Research

Module 2: Harnessing Generative-AI tools for qualitative analysis

Module 3. Integrating Generative-AI tools into qualitative analysis processes

Choose from three pathways through the course according to your interests and needs (note, to take Module 2 or 3 it is a pre-requisite to have first completed Module 1):

• Pathway 1 – Module 1 only
• Pathway 2 – Module 1 and 2
• Pathway 3 – Module 1 and 3

Module 3. Integrating Generative-AI tools into qualitative analysis processes

(Synchronous Delivery = hands-on guided exercises using selected tools, and discussion)

This module comprises demonstrations and hands-on exercises focused on integrating the use of GenAI capabilities into existing qualitative analysis workflows, using selected qualitative analysis programs. The sessions combine demonstrations, hands-on exercises, and discussion about how integrating GenAI may impact practice. Using sample data, we consider a series of analytic tasks and methodological implications of use in different contexts, with different types of data and at different stages in the analytic process. The tools-focus in this module is on existing qualitative software programs, using NVivo and MAXQDA as core examples. 

The course covers:

Principles of integrating AI tools into qualitative analysis processes - 22 February 2027 (13:30-16:45)

• Overview of the capabilities of established qualitative software programs (AI and non-AI tools)
• Ensuring analytic methods drive the use of AI tools 
• Hands-on exercises using NVivo

Exploring MAXQDA’s AI tools for qualitative analysis - 23 February 2027 (13:30-16:45)

• Overview of MAXQDA’s tools 
• Hands-on exercises integrating AI tools into a qualitative analysis process (using literature, transcripts and open-ended survey responses),

By the end of the course participants will:

• Understand opportunities for the use of AI tools at different stages of the qualitative analysis workflow
• Understand the methodological consequences of using AI tools (potentials and risks)
• Identify skills in harnessing AI tools in ways that enable human-centred approaches

This course is aimed at students and researchers interested in qualitative analysis, within any sector or discipline. Participants must have attended Module 1 in order to attend this module.

The course takes place over two afternoons (22 and 23 February) and equates to one teaching day for payment purposes. 

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.

Ethical research is better quality research. This course is designed to raise your awareness of why and how you need to think and act ethically in practice throughout your research work. The current system of ethical review by committee can lead to the misleading sense of having ‘done ethics’. This course shows you how to conduct research which is truly ethical. It also provides the opportunity for discussion of your own ethical dilemmas, if you wish.

The course covers:

• Research ethics in context – ethical breaches past and present, ethics activism, trauma-informed research, debiasing, ethics of technology
• Potential ethical pitfalls at each stage of the research process, from question setting to aftercare
• How to think and act ethically throughout research

By the end of the course participants will:

• Recognise the importance of context for ethical decision-making
• Understand why they need to think and act ethically throughout research work
• Be clearer about potential ethical pitfalls at different stages of the research process
• Know how to approach ethical thought and action at any point in their research 

This course is aimed at Doctoral students, early career researchers (any discipline), practice-based/applied researchers and possibly government researchers and independent researchers.

Note: If you are currently facing an ethical dilemma in your research work, or have faced one in the past that you would like to review, you can bring it for discussion on the second day. If you would like to do this anonymously you can email it to the course leader (via NCRM) and she will present it for you.

THIS COURSE IS TAUGHT OVER TWO MORNINGS AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES.

Programme:

Day One

09:30    Welcome and introductions

09:40    Research ethics in context: presentation

10:00    Discussion, Q&A

10.15    When do we need research ethics?

10:20    Video and discussion

10:40    Trauma-informed research: presentation

10:50    Debiasing techniques: presentation

11.00    Discussion, Q&A

11.10    Break time

11.25    Ethical research design: discussion

11.35    Ethical context-setting: discussion         

11:45    Ethical data gathering: discussion

11:55    Ethical data analysis: discussion

12:05    Video and discussion

12:20    Q&A

12:30    Close

Day Two

09:30    Welcome, questions arising from Day 1

09:40    Ethical research reporting: discussion

09:50    Ethical research presenting: discussion

10.00    Ethical research dissemination: discussion

10.10    Ethical aftercare: discussion

10.20    Researcher wellbeing

10.30    Unethical research today – presentation

10.45    Video and discussion

11.10    Break time

11.25    Real-life ethical dilemmas from research #1

11:40    Real-life ethical dilemmas from research #2

11:55    Real-life ethical dilemmas from research #3

12.10    Discussion, Q&A, evaluation

12.30    Close

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 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.

The proliferation of digital data and devices has enabled social scientists to collect, store, manage, manipulate, and interpret data in multifarious ways. Researchers frequently need to access online data sources, such as social media platforms, and present their analysis on the web through interactive dashboards and visualisations. Traditionally, developing these digital systems has required technical programming skills that take years to master. This hands-on course introduces an emerging alternative: “vibe coding”. 

Vibe coding is the practice of generating code by prompting an AI system through natural language. Rather than writing code line-by-line, participants will learn how to interact with Large Language Models (LLMs) to generate, test, and debug code. Designed specifically for social science researchers with zero technical or programming background, this course focuses on transferable skills rather than tying participants to a single platform. While a specific AI coding platform will be used for practical exercises, the course primarily introduces foundational concepts and adaptable, ‘future-proof’ strategies needed to confidently explore and integrate new generative AI tools as the technological landscape rapidly evolves. 

Participants will learn how to map their existing research methodologies into manageable computational workflows. Crucially, the course explores not just how to use these technologies, but when tasks can be justifiably and safely delegated to AI, and when they should not be. Through interactive live demonstrations and flexible hands-on workshops, participants will practice using AI to build custom research tools from scratch. 

Beyond practical application, the course places a strong emphasis on the ethics and data security concerns of using LLMs for generating code. Participants will learn how to set up AI-assisted coding environments and critically evaluate the methodological and ethical implications of embedding AI-generated code into their research processes. No prior coding experience is required.

PLEASE NOTE THIS COURSE IS BEING TAUGHT OVER TWO MORNINGS AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES.