National Centre for Research Methods

In this online course, run over two mornings, we will show you how to prepare and conduct spatial analysis on a variety of spatial data in R, including a range of spatial overlays and data processing techniques. We will also cover how to use GeoDa to perform exploratory spatial data analysis, including making use of linked displays and measures of spatial autocorrelation and clustering.

The course covers: 

• Understanding and being able to interpret Spatial Autocorrelation measure Moran's I
• Understanding Local Indicators of Spatial Association statistic
• Perform Spatial Decision Making in R
• Perform Point in Polygon analysis using different approaches
• Be aware of the advantages and disadvantages of using point based or polygon based data
• Using buffers as a part of spatial decision making

By the end of the course participants will:

• Be aware of some spatial statistics concepts and be able to apply them to their own data using GeoDa
• Be able to perform spatial decision making 
• Understand the limitations and benefits of working with data in this way

This course is aimed as PhD students, post-docs and lecturers who have some existing knowledge of using R as a GIS and want to develop their knowledge of spatial stats and spatial decision making in R. Some prior knowledge of both R and GIS is required. It is also appropriate for those in public sector and industry who wish to gain similar skills. 

Students will be using R, RStudio and GeoDa. 

Students need to have completed my Introduction to Spatial Data and Using R as a GIS (https://www.ncrm.ac.uk/training/show.php?article=13142) course, or have equivalent experience.

This includes:

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

For more information, please look at the link.

Students will need R (v > 4.0), and the sf, tmap, dplyr libraries. They will also need RStudio (v > 2023.01 or greater)

No prior knowledge of GeoDa is needed. It can be downloaded following the instructions at https://nickbearman.github.io/installing-software/geoda. Version 1.20 or greater is required. 

THIS COURSE WILL RUN OVER TWO MORNINGS (10AM TO 1PM) AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES.

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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 8 JUNE, 22 JUNE and 6 JULY 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.

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This five-day summer school introduces experimental methods in the Social Sciences, covering lab, field, and survey experiments. Participants will gain a solid foundation in experimental methodology and practical skills for designing, implementing, analysing, and presenting experiments. The interdisciplinary team of the Centre for Behavioural Experimental Action and Research (C-BEAR) will lead the five-day course, using examples from Politics, Economics, Business, and Psychology. Days 1 and 2 cover the basics of designing, analysing, and presenting different types of experimental designs, while Days 3, 4, and 5 will provide in-depth knowledge and insights on survey, field, and laboratory experiments. The hands-on activities throughout the week ensure that participants not only understand the theoretical aspects of experimental methods but also acquire the practical skills necessary to apply these methods in their own research.

The target audience of the course are professionals, members of public institutions and researchers that are approaching experimental methods for the first time and are interested to implement an experiment for the first time or to commission an experiment to a survey company or other service provider.

The course does not require any previous knowledge of experimental design or statistics and is open to anybody with basic high school knowledge of mathematics.  The level (junior, senior, etc.) of the course is open. The first two days will provide the students the mathematical and statistical tools to engage effectively with the rest of the course.

Participants need to bring their own device that can run basic office suites, and free versions of R and Stata.  

PLEASE NOTE REFRESHMENTS WILL BE PROVIDED BUT PARTICIPANTS WILL NEED TO BRING/BUY THEIR OWN LUNCH.

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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.

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.
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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.
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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.

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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 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

Longitudinal data is essential in a number of research fields as it enables analysts to concurrently understand aggregate and individual level change in time, the occurrence of events and improves our understanding of causality in the social sciences. 

In this course you will learn both how to clean longitudinal data as well as the main statistical models used to analyse it. The course will cover three fundamental frameworks for analysing longitudinal data: multilevel modelling, structural equation modelling and event history analysis. 

The course is organized as a mixture of lectures and hands on practicals using real world data. During the course there will also be opportunities to discuss also how to apply these models in your own research.

Objectives:

• To gain competence in the concepts, designs and terms of longitudinal research;

• To be able to apply a range of different methods for longitudinal data analysis;

• To have a general understanding of how each method represents different kinds of longitudinal processes;

• To be able to choose a design, a plausible model and an appropriate method of analysis for a range of research questions.

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

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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.

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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.
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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 13th May 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.