NCRM is a Hub-Node network of research groups, each conducting research and training in an area of social science research methods, coordinated by the Hub at the University of Southampton. https://www.ncrm.ac.uk/training/ Tue, 25 Aug 2026 14:33:49 +0100 en-uk https://www.ncrm.ac.uk/incoming/furniture/images/sitewide/NCRM_new_Logo.gif https://www.ncrm.ac.uk/training/ NCRM is a Hub-Node network of research groups, each conducting research and training in an area of social science research methods, coordinated by the Hub at the University of Southampton. https://www.ncrm.ac.uk/training/show.php?article=14814 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 landscapePractical skills for AI-assisted coding Ethics, reliability and security considerationsLearning Outcomes:AI Landscape Recall key milestones in the historical development of artificial intelligenceDescribe 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 CodingExplain 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 InstructionsPlease 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’.ProgrammeAn overview of the AI landscapePractical skills for AI-assisted coding Ethics, reliability and security considerationsThis course is taking place on 3rd September 2026 from 13:00 – 16:30. jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 23 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14814 https://www.ncrm.ac.uk/training/show.php?article=14815 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 KerasMonitor the training progressAdvanced layer typesReal world applicationLearning Outcomes:Introduction Define deep learningDescribe how a neural network is build upExplain the operations performed by a single neuronDescribe what a loss function isRecall the sort of problems for which deep learning is a useful toolList some of the available tools for deep learningRecall the steps of a deep learning workflowTest that you have correctly installed the Keras, Seaborn and scikit-learn librariesUse the deep learning workflow to structure the notebookClassification by a neural network using KerasExplore the dataset using pandas and seabornIdentify the inputs and outputs of a deep neural network.Use one-hot encoding to prepare data for classification in KerasDescribe a fully connected layerImplement a fully connected layer with KerasUse Keras to train a small fully connected network on prepared dataInterpret the loss curve of the training processUse a confusion matrix to measure the trained networks’ performance on a test setMonitor the training processExplain the importance of keeping your test set clean, by validating on the validation set instead of the test setUse the data splits to plot the training processExplain how optimization worksDesign a neural network for a regression taskMeasure the performance of your deep neural networkInterpret the training plots to recognize overfittingUse normalization as preparation step for deep learningImplement basic strategies to prevent overfittingAdvanced layer typesUnderstand why convolutional and pooling layers are useful for image dataImplement a convolutional neural network on an image datasetUse a dropout layer to prevent overfittingBe able to tune the hyperparameters of a Keras modelTransfer learningAdapt a state-of-the-art pre-trained network to your own datasetOutlookUnderstand that what we learned in this course can be applied to real-world problemsUse best practices for organising a deep learning projectIdentify next steps to take after this coursePre-requisites:Learners are expected to have the following knowledge:Basic Python programming skills and familiarity with the Pandas package.Basic knowledge on machine learning, including the following concepts: Data cleaning, train & test split, type of problems (regression, classification), overfitting & underfitting, metrics (accuracy, recall, etc.).Setup InstructionsPlease follow the setup instructions here: https://carpentries-lab.github.io/deep-learning-intro/index.html#software-setup Note that software installation can take some time.  Please set up your python environment at least a day in advance of the workshop. If you encounter problems with the installation procedure, ask your workshop organizers via email for assistance so you are ready to go as soon as the workshop begins.ProgrammeWhat is deep learning?Classification by a neural network using KerasMonitor the training progressAdvanced layer typesReal world applicationThis course is taking place on 15-17 September from 09:00 - 17:00.  jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 23 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14815 https://www.ncrm.ac.uk/training/show.php?article=14768 This 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 activityBy 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 projectThe 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 ReadingRequired:· 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.ProgrammeDay One: 21 September 202610:00 - 10:15 Introductions10:15 - 11:30 Series of short introductory video lectures + 1 case study11:30 - 11:45 Short break11:45 - 12:45 Q&A on the videos and institutional ethnography in general12:45 - 13:00 Explain afternoon task and split everyone into small groups based on research interests 13:00 - 14:00 Lunch break 14:00 - 15:00 Small group discussions divided up by discipline/area of interest; participants collectively discuss how their research projects would translate into Institutional Ethnographies, aided by a research proposal template and guiding questions - each group is facilitated by one of the three organisers 15:00 - 15:15 Short break 15:15 - 16:00 Three groups come back together to highlight key points of discussions and any final questions before explaining what will happen on Day 2 - participants will have to choose a 'text' related to their research to bring to Day 2 to analyse. Day 2: 22 September 2026 10:00 - 11:30 Brief introductions and 2 short case studies with Q&A 11:30 - 11:45 Short break 11:45 - 13:00 Any further questions and introduction to the text analysis methods we will use in the afternoon 13:00 - 14:00 Lunch break 14:00 - 15:00 Small groups work facilitated by three organisers in which participants using text analysis methods on their research-related 'text' (in groups or individually) 15:00 - 15:15 Short break 15:00 - 16:00 Everyone comes back together to discuss their text analysis and ask any final questions about how to do Institutional Ethnography text analysis, the overall approach, and distribution of follow-up resources. Completion of online evaluation survey. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 13 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14768 https://www.ncrm.ac.uk/training/show.php?article=14739 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 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 dataHow to build a corpus using textual data How to engage with existing corpora, such as multi-billion word corpora scraped from the webHow to use corpus methods for bottom-up and top-down researchTechniques for the visualisation of unstructured language dataAn 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 corpusBe able to use corpora to answer a variety of research questionsBe able to build their own corporaConduct comparative corpus analysis (e.g. between texts that differ demographically or diachronically)Programme:9:30: Welcome and introduction to corpus linguistics10:00: Interrogating existing corpora - quantitative analysis12:00: Lunch13:00: Interrogating existing corpora - qualitative analysis15:00: Break15:15: Building your own corpus16:15: The Concept Cruncher: The next generation of text analysis16:45: Final remarksSpeakers:Dr Justyna Robinson is a Director of Concept Analytics Lab at the University of Sussex. She researches meaning in language and is interested in methods of analysing meaning empirically. Her publications focus on ways of researching meaning from historical perspectives (2012), from cognitive angles (2014), using socio-demographic information and other text metadata (2012, 2022), using corpus and statistical methods (2014, 2022). She researches meaning represented by words (2010), concepts and themes (2017, 2023). With the research team at Concept Analytics Lab, she delivered a range of projects investigating current meanings of loneliness, aging, UK trade deals post Brexit, political manifestos, recycling practises, or post-covid behaviour changes. Dr Rhys Sandow is a Senior Research Associate at Concept Analytics Lab, University of Sussex. He specialises in applying corpus methods to answer applied research questions, such as in collaborative work with economists, psychologists, historians, and medical humanities researchers, as well as organisations in the private sector. He also specialises in sociolinguistic variation and change, including its intersection with corpus linguistics, where he has worked as an expert witness in a legal context. He has published c.20 academic articles and book chapters on corpus linguistics and sociolinguistics as well as the co-edited book on Sociolinguistic Approaches to Lexical Variation in English.. p.c.white@southampton.ac.uk (University of Southampton) Fri, 12 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14739 https://www.ncrm.ac.uk/training/show.php?article=14877 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 the 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. This is an intermediate level course that requires some prior knowledge of epidemiological research methods and statistical analysis, and familiarity with R or Stata.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 open to anyone with an interest in ECHILD.Although there is no preparatory reading is required, although participants are encouraged to familiarise themselves with the data resources for researchers available on the ECHILD website.Participants should have a basic understanding of epidemiological research methods and statistical analysis. They should be comfortable in using R or Stata. Experience using administrative data is not required but would be an advantage.ProgrammeDay 1: Introduction Case Studies using each component dataset Developing cohorts in ECHILD Getting started with ECHILD data ECHILD best practice and user communityDay 2: Developing e-cohorts Part 1 Phenotyping to define exposures, outcomes and covariates Developing e-cohorts Part 2 Analysing cohort data Developing e-cohorts Part 3 Leveraging longitudinal administrative data for policy evaluation and population researchThis is an in-person course and will take place at Friend's House (Elizabeth Fry Suite) on 28-29 September from 09:30-17:00. jmh6@soton.ac.uk (NCRM, University of Southampton) Mon, 06 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14877 https://www.ncrm.ac.uk/training/show.php?article=14929 Course Overview:Navigating Qualitative AI: Ethical, Methodological and Practical ConsiderationsThe 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 ResearchModule 2: Harnessing Generative-AI tools for qualitative analysisModule 3. Integrating Generative-AI tools into qualitative analysis processesChoose 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 onlyPathway 2 – Module 1 and 2Pathway 3 – Module 1 and 3Module 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 - 30 September 2026 (13:30-16:45)development of AI tools in qualitative research spacesethical considerations and how to navigate themmethodological consequences when using AI for qualitative analysisCritically engaging with the Qualitative AI space - 1 October 2026 (13:30-16:45)potential uses of AI throughout the qualitative workflowgenres of AI tools for qualitative data analysiswhen 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 fieldUnderstand the range of ethical issues involved in using AI for Qualitative Research and how to navigate use in appropriate waysPossess critical thinking skills around the role of AI in different Qualitative Research contextsThis 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. This page is for Module 1: Mindsets for considering the use of AI for Qualitative Research. If you wish to register for Modules 2 or 3, please do so here:Module 2 - Harnessing Generative-AI tools for qualitative analysis - 12 and 13 OctModule 3 - Integrating Generative-AI tools into qualitative analysis processes - 16 and 18 Nov jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 05 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14929 https://www.ncrm.ac.uk/training/show.php?article=14935 This 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. 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 onlyPathway 2 – Module 1 and 2Pathway 3 – Module 1 and 3 YOU CAN FIND OUT MORE INFORMATION ON EACH MODULE, INCLUDING HOW TO REGISTER HERE:Module 1: Mindsets for considering the use of AI for Qualitative Research Module 2: Harnessing Generative-AI tools for qualitative analysisModule 3. Integrating Generative-AI tools into qualitative analysis processesEach module is taught over two afternoons and equates to one teaching day for payment purposes. jmh6@soton.ac.uk (NCRM, University of Southampton) Mon, 10 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14935 https://www.ncrm.ac.uk/training/show.php?article=14764 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 EthnographyOrdinary Language InterviewParticipant ObservationDigital EthnographyTheory building with qualitative dataBy 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 projectsCritically assess the methodological and ethical considerations involved in designing and conducting ethnographic research.Analyse fieldwork data to generate theoretical insightsTarget AudiencePostgraduate 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.Preparatory ReadingBorges Martins da Silva, Mariana, 2025. "Notes from the Classroom: Lessons and Best Practices for Teaching Digital Ethnography", Qualitative and Multi-Method Research.Schatz, Edward. 2009. “Ethnography Immersion and the Study of Politics.” In Political Ethnography: What Immersion Contributes to the Study of Power. University of Chicago Press.Hammersley, G., M. Hammersley, and P. Atkinson. 1995. Ethnography: Principles in Practice. Research Methods, Sociological Theory, Ethnography. Routledge. (Chapter 1)Jerolmack, Colin, and Shamus Khan. 2014. ‘Talk Is Cheap: Ethnography and the Attitudinal Fallacy’. Sociological Methods & Research 43 (2): 178–209.Schaffer, F.C. 2014. Elucidating Social Science Concepts: An Interpretivist Guide. Routledge Series on Interpretive Methods. Routledge. (Chapter 1, 2)Schaffer, Frederic Charles. 2006. ‘Ordinary Language Interviewing’. In Interpretation and Method: Empirical Research Methods and the Interpretive Turn, edited by Dvora Yanow and Peregrine Schwartz-Shea, 150–60. Armonk, London: M.E. Sharpe.Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up. Chicago Guides to Writing, Editing, and Publishing. Chicago ; London: The University of Chicago Press. (chapter 4 and 5)Walt, Kathleen M., and Billie R. DeWalt. 2011. Participant Observation: A Guide for Fieldworkers. Rowman Altamira. (chapter 2-5)Emerson, R.M., R.I. Fretz, and L.L. Shaw. 2011. Writing Ethnographic Fieldnotes, Second Edition. Chicago Guides to Writing, Editing, and Publishing. University of Chicago Press. (Chapters 1-3)Fujii, Lee Ann. 2012. “Research Ethics 101: Dilemmas and Responsibilities.” PS: Political Science & Politics 45 (4): 717–23. https://doi.org/10.1017/S1049096512000819.Fu, Diana. 2017. “Disguised Collective Action in China.” Comparative Political Studies 50 (4): 499–527. (Please also read the methodological appendix)Borges Martins da Silva, Mariana. 2023. “Weapons of Clients: Why Do Voters Support Bad       Patrons? Ethnographic Evidence from Rural Brazil.” Latin American Politics and Society 65 (1): 22–46.Timmermans, Stefan, and Iddo Tavory. 2012. ‘Theory Construction in Qualitative Research: From Grounded Theory to Abductive Analysis’. Sociological Theory 30 (3): 167–86There 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.Programme2 October – 10AM-12PMIntroduction to Ethnography and Ordinary Language Interview9 October - 10AM-12PMParticipant Observation16 October 10AM-12PMWriting Fieldnotes; Digital Ethnography23 October - 10AM-12PMConstructing Theory with Ethnographic Data   jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 11 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14764 https://www.ncrm.ac.uk/training/show.php?article=14897 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 GitCreating a repositoryTracking changesExploring historyRemote repositoriesBranchingIgnoring things in version controlBy the end of the course participants will:Understand the benefits of an automated version control systemUnderstand the basics of how automated version control systems workConfigure Git and GitHub on their computerCreate a repository from a templateClone and use a Git repositoryGo through the modify-add-commit cycle for one or more filesDescribe where changes are stored at each stage in the modify-add-commit cycleCompare files with previous versions of themselvesRestore old versions of filesUnderstand git push and git pullEncounter and resolve a conflictUnderstand why you would use a branchMerge together two modified version of a fileUse a gitignore file to ignore specific files and explain why this is usefulThis 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.Setup InstructionsGitHubWe’ll be using the website GitHub (https://github.com/) to host, back up, and distribute our code. You’ll need to create an account there. As your GitHub username will appear in the URLs of your projects there, it’s best to use a short, clear version of your name if you can.Go to https://github.com and follow the “Sign up” link at the top-right of the window. Follow the instructions to create an account. Verify your email address with GitHub. Configure multifactor authentication (if necessary)GitHub DesktopVisit the download page for GitHub Desktop at https://desktop.github.com/download/ Click the relevant button to download GitHub Desktop for your operating system. In your computer’s Downloads folder, double-click the GitHub Desktop setup file and follow the on-screen prompts to complete installation.ProgrammeWhat is version control?Setting up GitCreating a repositoryTracking changesExploring historyRemote repositoriesBranchingIgnoring things in version controlThis course will run on 7th October 2026 from 13:00 – 16:30. jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 15 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14897 https://www.ncrm.ac.uk/training/show.php?article=14932 Course OverviewNavigating Qualitative AI: Ethical, Methodological and Practical ConsiderationsThe 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 ResearchModule 2: Harnessing Generative-AI tools for qualitative analysisModule 3. Integrating Generative-AI tools into qualitative analysis processesChoose 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 onlyPathway 2 – Module 1 and 2Pathway 3 – Module 1 and 3Module 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 - 12 October 2026 (13:30-16:45)Overview of the capabilities of general-purpose Generative-AI toolsHands-on exercises following process of Query-Based Analysis (Morgan 2025)Chatbots – e.g. ChatGPT, Claude, Gemini etc.AI Research Assistant platforms – e.g. Notebook LMGenerative-AI driven analysis with bespoke online Apps - 13 October 2026 (13:30-16:45)Overview of the capabilities of bespoke online Apps for qualitative analysisHands-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 workflowUnderstand the methodological consequences of using AI tools (potentials and risks)Identify skills in harnessing AI tools in ways that enable human-centred approachesThis 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 and equates to one teaching day for payment purposes. This page is for Module 2: Harnessing Generative-AI tools for qualitative analysis.If you wish to register for Modules 1 or 3 please do so here:Module 1 - Mindsets for considering the use of AI for Qualitative Research - 30 Sept and 1 OctModule 1 - Mindsets for considering the use of AI for Qualitative Research - 2 and 4 NovModule 3 - Integrating Generative-AI tools into qualitative analysis processes - 16 and 18 Nov jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 05 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14932 https://www.ncrm.ac.uk/training/show.php?article=14767 This online session invites participants to think critically about what generative AI means for highereducation and academic research in particular at this moment of rapid change. We’ll look athow AI is already reshaping academic work and consider what this means for research, andscholarship. Rather than offering fixed answers, the session creates space to explore theopportunities and risks together. The focus is on developing a clearer understanding of whatAI means for researchers.Date: 15 October - afternoon (1.5 hours)PresenterDr Mark Carrigan FRSA FHEA is a Senior Lecturer in Education at the University of Manchester, where he co-leads the Digital Education Manchester group and serves as an AI Fellow at the Institute for Teaching and Learning. His work centers on three interconnected commitments: developing ontological and epistemological frameworks for understanding Large Language Models (LLMs) beyond current inadequate conceptualisations; examining higher education as a critical site where the social and cultural dynamics of LLMs unfold through practical challenges; and advancing Margaret Archer’s morphogenetic approach as a route to addressing these urgent questions.He is the author of Platform and Agency: Becoming Who We Are (Routledge, 2025), which develops a framework for understanding personal transformation in the digital age. His recent work includes Generative AI for Academics (Sage, 2024) and Social Media for Academics (Sage, 2nd edition), alongside eight other books. He co-edited Building the Post-Pandemic University (Edward Elgar, 2023), examining how universities are transforming in response to technological and social disruption.  jmh6@soton.ac.uk (NCRM, University of Southampton) Mon, 13 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14767 https://www.ncrm.ac.uk/training/show.php?article=14931 Course OverviewNavigating Qualitative AI: Ethical, Methodological and Practical ConsiderationsThe 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 ResearchModule 2: Harnessing Generative-AI tools for qualitative analysisModule 3. Integrating Generative-AI tools into qualitative analysis processesChoose 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 onlyPathway 2 – Module 1 and 2Pathway 3 – Module 1 and 3Module 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 - 2 November 2026 (13:30-16:45)development of AI tools in qualitative research spacesethical considerations and how to navigate themmethodological consequences when using AI for qualitative analysisCritically engaging with the Qualitative AI space - 4 November 2026 (13:30-16:45)potential uses of AI throughout the qualitative workflowgenres of AI tools for qualitative data analysiswhen 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 fieldUnderstand the range of ethical issues involved in using AI for Qualitative Research and how to navigate use in appropriate waysPossess critical thinking skills around the role of AI in different Qualitative Research contextsThis course is aimed at students and researchers interested in qualitative analysis, within any sector or discipline.The course takes places over two afternoons (2 and 4 November) and equates to one teaching day for payment purposes. This page is for Module 1: Mindsets for considering the use of AI for Qualitative Research. If you wish to register for Modules 2 or 3, please do so here:Module 2 - Harnessing Generative-AI tools for qualitative analysis - 12 and 13 OctModule 3 - Integrating Generative-AI tools into qualitative analysis processes - 16 and 18 Nov jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 05 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14931 https://www.ncrm.ac.uk/training/show.php?article=14958 A three-day online course, delivered over three weeks, 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 implementationBy 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 documentationTarget AudienceThe 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 staffTechnical 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.Pre-requisitesNo specialist prior knowledge of agentic coding or AI tools is required. Participants should:• Be comfortable using a computer, web browser, and video conferencing• Have interest in survey research workflows (design, analysis, or data management)• Be willing to create GitHub and Cursor accounts before Day 1• Complete the pre-course setup checklist or attend the setup clinic• Choose one role track before or during prework: applied researcher, survey practitioner, or survey data manager• Bring a non-sensitive workflow description, public dataset, synthetic example, questionnaire specification, or metadata extractSoftware prerequisites:• Cursor and GitHub accounts (required)• Python 3.10+ recommended for examples; not required for participants using R/Stata/SPSS patterns only• Participants must avoid bringing real restricted microdata unless explicit permission exists for the exact tool workflowPreparatory readingRequired before Day 1:• Create GitHub and Cursor accounts• Complete the course setup guide and prework checklist (course repository)• Choose one role track: applied researcher, survey practitioner, or survey data manager• Select a non-sensitive workflow or use the provided synthetic survey example• Read the restricted-data safety guidanceDesirable:• Attend the pre-course setup clinic if you have limited admin rights or limited Git/GitHub experience• Review basic GitHub concepts: repositories, issues, branches, and pull requests• Install minimum viable toolset: Cursor, browser access to GitHub, ability to download/unzip files• Optional recommended setup: Git, Python 3.10+, Node.js 18+, and any role-specific tools (R, Stata, SPSS)Course materialsParticipants will receive course slides and access to course repository materials.Participants should have/bring:• A computer with internet access and ability to join live video sessions and share screen• GitHub and Cursor accounts (set up before Day 1)• A non-sensitive workflow description, public dataset, synthetic example, questionnaire specification, or metadata extract for their capstone work• Headphones/microphone recommended for breakout-room workParticipants should NOT bring real restricted microdata unless explicit permission exists for the exact tool workflow.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. This course equates to two teaching days for payment purposes. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 21 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14958 https://www.ncrm.ac.uk/training/show.php?article=14933 Course OverviewNavigating Qualitative AI: Ethical, Methodological and Practical ConsiderationsThe 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 ResearchModule 2: Harnessing Generative-AI tools for qualitative analysisModule 3. Integrating Generative-AI tools into qualitative analysis processesChoose 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 onlyPathway 2 – Module 1 and 2Pathway 3 – Module 1 and 3Module 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 - 16 November 2026 (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 NVivoExploring MAXQDA’s AI tools for qualitative analysis - 18 November 2026 (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 workflowUnderstand the methodological consequences of using AI tools (potentials and risks)Identify skills in harnessing AI tools in ways that enable human-centred approachesThis 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 (16 and 18 November) and equates to one teaching day for payment purposes. This page is for Module 3: Integrating Generative-AI tools into qualitative analysis processesIf you wish to register for Modules 1 or 2, please do so here:Module 1 - Mindsets for considering the use of AI for Qualitative Research - 30 Sept and 1 OctModule 1 - Mindsets for considering the use of AL for Qualitative Research - 2 and 4 NovModule 2 - Harnessing Generative-AI tools for qualitative analysis - 12 and 13 Oct jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 05 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14933 https://www.ncrm.ac.uk/training/show.php?article=14899 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 peopleThe world from participants’ perspectivesHow to work with people in an inclusive and accessible wayThis online training workshop will be structured as follows:  IntroductionsOrigins 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 course will take place on Wednesday 9 December from 10:00 - 16:00, with 1 hour for lunch from 12:30 - 13:30.  jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 09 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14899 https://www.ncrm.ac.uk/training/show.php?article=14911 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 designQualitative methods in ethnographic researchAccess and powerResearch ethics in participant observationBy the end of the course participants will:Understand the epistemological foundations of ethnographic researchHave a solid understanding of ethnographic research in actionBe able to design and conduct research integrating qualitative and ethnographic research methodsBe able to conduct ethical ethnographic researchThe 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.Day 1Morning 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 discussionAfternoon 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 discussionDay 2Morning session:•           09:30-10:45     Plenary - Research ethics in ethnography•           10:45-11:00      Break•           11:00-12:00      Group work followed by class discussionAfternoon 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 jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 30 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14911 https://www.ncrm.ac.uk/training/show.php?article=14947 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 researchUnderstand the potential for, and the challenges of, using creative and participatory research methodsExplore how to ethically engage in creative and participatory researchLearn from active peer-researchers involved in co-creating researchBy 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 researchLearn how such methods can be incorporated into the generation of meaningful research impactIndicative 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. jmh6@soton.ac.uk (NCRM, University of Southampton) Mon, 17 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14947 https://www.ncrm.ac.uk/training/show.php?article=14912 This online 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.The course covers: When and why to use documents as dataHow to find personal and official documents, historical documents, mainstream print media documents, virtual documents, and self-published documentsHow to assess the quality and usefulness of documents for research purposesSome methods of analysing documentary dataBy the end of the course participants will:Know how to find the documents they need for their researchKnow how to assess the quality and usefulness of documentsUnderstand how to approach the analysis of documentary dataHave an action plan for using documents in their own researchThis 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.Please note the course will run from 09:30 - 12:30 on the 9th and 10th March and equates to one full teaching day for payment purposes. jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 30 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14912 https://www.ncrm.ac.uk/training/show.php?article=14913 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, debiasingPotential ethical pitfalls at each stage of the research process, from question setting to aftercareHow to think and act ethically throughout researchBy the end of the course participants will:Recognise the importance of context for ethical decision-makingUnderstand why they need to think and act ethically throughout research workBe clearer about potential ethical pitfalls at different stages of the research processKnow 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.THIS COURSE IS TAUGHT OVER TWO MORNINGS AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES.Programme:Day One09:30    Welcome and introductions09:40    Research ethics in context: presentation10:00    Discussion, Q&A10.15    When do we need research ethics?10:20    Video and discussion10:40    Trauma-informed research: presentation10:50    Debiasing techniques: presentation11.00    Discussion, Q&A11.10    Break time11.25    Ethical research design: discussion11.35    Ethical context-setting: discussion         11:45    Ethical data gathering: discussion11:55    Ethical data analysis: discussion12:05    Video and discussion12:20    Q&A12:30    CloseDay Two09:30    Welcome, questions arising from Day 109:40    Ethical research reporting: discussion09:50    Ethical research presenting: discussion10.00    Ethical research dissemination: discussion10.10    Ethical aftercare: discussion10.20    Researcher wellbeing10.30    Unethical research today – presentation10.45    Video and discussion11.10    Break time11.25    Real-life ethical dilemmas from research #111:40    Real-life ethical dilemmas from research #211:55    Real-life ethical dilemmas from research #312.10    Discussion, Q&A, evaluation12.30    Close jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 30 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14913 https://www.ncrm.ac.uk/training/show.php?article=14921 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 mapHow to create a publication ready mapHow to work with different data sources including XY coordinate and postcode dataBy the end of the course participants will:Be able to set up QGIS and add dataUnderstand how to add data with latitude & longitude coordinatesKnow how to classify data for a choropleth mapBe able to join tabular data to spatial dataDesigning and producing a publication ready map in QGISUnderstand how to import a range of data types into QGISThis 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. jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 30 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14921 https://www.ncrm.ac.uk/training/show.php?article=14922 In this one day online course (taught over 2 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 dataDesign and creation of choropleth mapsBasic spatial analysisWorking with loops in R to create multiple mapsBy the end of the course participants will:Use R to read in CSV data & spatial dataKnow how to plot spatial data using RJoin spatial data to attribute dataCustomize colour and classification methodsUnderstand how to use loops to make multiple mapsKnow how to reproject spatial dataBe able to perform point in polygon operationsKnow 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. No previous experience of spatial data is required.No previous experience of coding is required, although participants would benefit from some experience of using spatial data (e.g. Google Maps).THIS COURSE IS BEING TAUGHT OVER TWO MORNINGS AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 30 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14922 https://www.ncrm.ac.uk/training/show.php?article=14934 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 observationPreparing for fieldwork: a pragmatic approach to designing research projectsEthics and power: access, collaboration, co-production, and the possibility of decolonising researchWriting ethnography: from the practical to the politicalBy the end of the course participants will:Grasp the practice of ethnographic research beyond participant observationUnderstand 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 methodsBe able to appreciate the ethical and power dimensions of ethnographic researchUnderstand the ethics and politics of writing, publishing, and representing ethnographicallyThis 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-requisitesExperience using ethnographic research methods and qualitative research methods.Preparatory ReadingDemetriou, 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: 383–395Stefanelli, A. (forthcoming 2024) ‘Reading ethnography in the classroom: complementary strategies to develop students’ ethnographic imagination.’ Learning and Teaching in the Social Sciences.This online course will run from 09:30 to 15:15 both days. jmh6@soton.ac.uk (NCRM, University of Southampton) Mon, 17 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14934