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, 13 Oct 2026 00:23:39 +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=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) Tue, 01 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14767 https://www.ncrm.ac.uk/training/show.php?article=14971 Longitudinal research frequently seeks to understand not only whether people or other units change over time, but also whether they differ in their starting points, rates of change, and the factors associated with these differences. Growth modelling provides a family of statistical approaches for addressing such questions.This masterclass introduces longitudinal growth modelling with a particular focus on Structural Equation Modelling (SEM). The first part of the session will introduce the concepts underlying growth models, contrast multilevel and SEM approaches, and consider the distinctive possibilities offered by latent growth models. The second part will provide a worked demonstration in R, using lavaan to specify, estimate, interpret and extend a basic latent growth model.The session is intended for researchers who already have some familiarity with quantitative analysis. Previous exposure to regression and/or SEM will be helpful, but specialist knowledge of longitudinal modelling is not assumed.The session is free to attend and will take place online via Microsoft Teams. J.E.Hall@Soton.ac.uk (University of Southampton) Wed, 09 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14971 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=14966 The proliferation of digital data and devices has enabled social scientists to collect, store, manage, manipulate, and interpret data in multifarious ways. Researchers frequently need to access online data sources, such as social media platforms, and present their analysis on the web through interactive dashboards and visualisations. Traditionally, developing these digital systems has required technical programming skills that take years to master. This hands-on course introduces an emerging alternative: “vibe coding”. Vibe coding is the practice of generating code by prompting an AI system through natural language. Rather than writing code line-by-line, participants will learn how to interact with Large Language Models (LLMs) to generate, test, and debug code. Designed specifically for social science researchers with zero technical or programming background, this course focuses on transferable skills rather than tying participants to a single platform. While a specific AI coding platform will be used for practical exercises, the course primarily introduces foundational concepts and adaptable, ‘future-proof’ strategies needed to confidently explore and integrate new generative AI tools as the technological landscape rapidly evolves. Participants will learn how to map their existing research methodologies into manageable computational workflows. Crucially, the course explores not just how to use these technologies, but when tasks can be justifiably and safely delegated to AI, and when they should not be. Through interactive live demonstrations and flexible hands-on workshops, participants will practice using AI to build custom research tools from scratch. Beyond practical application, the course places a strong emphasis on the ethics and data security concerns of using LLMs for generating code. Participants will learn how to set up AI-assisted coding environments and critically evaluate the methodological and ethical implications of embedding AI-generated code into their research processes. No prior coding experience is required.PLEASE NOTE THIS COURSE IS BEING TAUGHT OVER TWO MORNINGS AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES. Engage@liverpool.ac.uk (University of Liverpool) Wed, 26 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14966 https://www.ncrm.ac.uk/training/show.php?article=14967 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 practiceDistinguish between open-weights and closed-weights models and evaluate their data governance implicationsApply prompt optimisation techniques and build evaluation pipelines to validate LLM outputsMake structured API calls, manage parameters, and use retrieval-augmented generation where appropriateMap the agentic AI ecosystem, explain harness engineering, and assess how platforms orchestrate memory, tools, and contextDesign human-in-the-loop safeguards and audit protocols appropriate for agentic research workflowsPre-requisitesNo 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 AudienceSurvey 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. Although the recommendation is to attend all three workshops, you are welcome to join them individually. PLEASE NOTE THESE WORKSHOPS WILL RUN ONLINE ON 23 NOVEMBER, 30 NOVEMBER AND 7 DECEMBER 2026 FROM 09:30-13:30. jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 03 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14967 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=14989 To be held Online, via Zoom SummaryThis three-day course provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN, R, or Stata. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or hierarchical). These models can be viewed as an extension of conventional linear and logistic regression models to account for and learn from the clustering in the data. Such models are appropriate when, for example, analysing exam scores of students nested within schools, or health outcomes of patients nested within hospitals. Special interest lies in disentangling social processes operating at different levels of analysis by decomposing the within- from the between-cluster effects of covariates (explanatory or predictor variables). Longitudinal data are also clustered, with repeated measurements on individuals or multiple panel waves per survey respondent. Throughout the course we emphasize how to interpret multilevel models and the types of research question they can be used to explore. Full course information and excerpts can be viewed here: https://www.bristol.ac.uk/cmm/learning/introworkshop.html  Testimonials“The course was excellent - far exceeded expectations. The course has given me the confidence to use MLM, something I very much lacked before. I feel I understand the theory behind MLM, why each stage is so important, and the various interpretations. Without this course I would be lost. I cannot thank you all enough.”“This was a beautifully constructed course. It was clear throughout that careful thought had been given to providing a balance between lecture content, time for questions and discussion, and practical sessions. Both George and Bill delivered fantastic lectures - explanations were clear and thorough (including critiques of each approach) and content built up in complexity over time with plenty of worked examples of different kinds. The course was superb - can't rate it highly enough.”“I thought it was a really good double act between George and Bill - they are both hugely knowledgeable so having one person focused on the slides and the other manning the chat was a good approach as it meant the teaching didn't get derailed by people's questions.”Topics Overview of multilevel modellingVariance-components modelsRandom-intercept models with covariatesBetween- and within-effects of level-1 covariatesRandom-coefficient modelsGrowth-curve modelsThree-level modelsReview of single-level logistic regressionTwo-level logistic regression FormatThe course will consist of a 2:1 mix of lectures and hands-on practical sessions applying the taught methods to real datasets. The lectures are software independent and are delivered live via Zoom, but recordings of the lectures will be made available shortly afterwards for twelve weeks following the course if participants are unable to attend at the scheduled time. The instructors alternate the lecturing. Participants can ask questions via Zoom’s text-based chat facility and these will be monitored and answered by the instructor not presenting or relayed to the instructor presenting to answer live.Each lecture is immediately followed by a self-directed practical, offered in participants’ choice of MLwiN, R, or Stata, giving participants the chance to replicate the presented analyses and to consolidate their knowledge. At the end of each practical session the instructors demo the different software, each in a different breakout room.ApplicationsIf you would like to attend the workshop, please complete and submit the online booking form (see below). Please note the closing date for applications is 22nd November 2026.Applications will be processed on a rolling basis, once a week, until the application deadline. A link to the University of Bristol’s online shop will be provided and your place on the course will be confirmed upon successful payment.If you have any queries, please email info-cmm@bristol.ac.uk. info-cmm@bristol.ac.uk (Centre for Multilevel Modelling, University of Bristol) Fri, 09 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14989 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=14991 This online course on questionnaire design, explores question wording issues and the questionnaire-as-a-whole with a focus on web surveys and mobile-friendly web surveys. The course is full of practical advice. It also provides tips for anyone moving from interviewer-administered surveys to web surveys. Mirroring in-person training, this course will be interactive. There will also be 6 small group workshops to facilitate putting the course concepts into practice.Questionnaire DesignThe popularity of web surveysGetting started with a new questionnaireTrade-offs – short and simple versus clearFour cognitive stages a respondent goes through in answering a survey questionSolutions to ambiguous term, understanding recall error and reducing question sensitivityQuestion wording guidelines - This about the do's and don'ts of writing survey questions for any context.Workshop 1 - Critiquing a survey questionSome additional issues with factual questionsHighlights from mini appendix: Demographic questions are always the most difficult to writeWorkshop 2 - Writing a survey questionMini appendix on actual versus usual behaviourHighlights from mini appendix on some additional issues with subjective questionsKnow the deeper issues with open and closed questionsProblematic question formats to be aware of or avoid (eg. agree / disagree)Mini appendix on other problematic formats (satisfaction, tick all that apply, ranking and hypothetical questions)Web surveysDon't rely on web survey software templatesWorkshop 3 - Critiquing web survey software templatesModes of quantitative data collectionModes of quantitative data collection: Mixing modesModes of quantitative data collection: Overall mode differences (the obvious ones)Mini Appendix on mode effects due to satisficingWorkshop 4 - Interpreting data from a mixed mode experimentBack to web surveysDesigning the web survey itselfDay 2 appendix - 8 question testing methods for web surveysThe special things that web surveys can do, but should we?Visual versus not visualMini appendix on tips for paper self-completionWorkshop 5 - Visual problemsWeb surveys for mobile phones - earlier evidence, current thinkingHighlights from mini appendix on data collection differences: What should you do?Mini appendix on "push to web"Back to questionnaire designExamples of question revisions based on testing resultsWorkshop 6 - Revising survey questionsHighlights from mini appendix on extra tasks on mobile phones By the end of the course participants will:Have greater questionnaire design skills in general and the ability to critique existing web survey software templatesHave the ability to create effective web survey questionnaires as well as mobile-friendly onesHave better knowledge about questionnaire-related mode differences and effectsThis course is for anyone interested in questionnaire design for web and mobile web surveys. Ideally participants need some familiarity with surveys and questionnaire design.Preparatory ReadingDesirable for participants to read:https://web.stanford.edu/dept/communication/faculty/krosnick/docs/2009/2009_handbook_krosnick.pdfhttps://www.gesis.org/fileadmin/admin/Dateikatalog/pdf/guidelines/mixed_device_mobile_web_surveys_beuthner_2021.pdfPLEASE NOTE THIS COURSE IS TAUGHT OVER THREE DAYS (10:00-15:00), AND EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 29 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14991 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 - 26 January 2027 (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 - 27 January 2027 (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 (26 and 27 January) 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 - 3 and 4 FebModule 3 - Integrating Generative-AI tools into qualitative analysis processes - 22 and 23 Feb jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 25 Sep 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 These courses provide an introduction into developments, debates and practicalities in the role and use of Generative AI in Qualitative Research. The courses are delivered as 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) Thu, 01 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14935 https://www.ncrm.ac.uk/training/show.php?article=15022 Why do our friends often know one another, even though we are only a few connections away from people in very different social circles? Why does information spread easily through some networks, while changes in behaviour require repeated encouragement? Who should be targeted in an intervention, and how does the answer depend on what we want to achieve?Social network analysis offers a theoretical framework and a methodology to answer questions like these by focusing on the relationships between and among social entities. A network is a structure composed of units and the relationships that connect them. The position of these units and the overall structure help us understand how social ties form, how information and behaviour spread, and how individuals gain access to resources and opportunities.The focus of the course is on the connection between the mathematical foundations of networks and the social phenomena we want to understand. We will introduce concepts from matrix algebra and graph theory across both days, and use empirical examples and practical exercises to examine what these concepts tell us about social life. We will also discuss how network data are collected and what evidence is needed to distinguish between different explanations for the patterns we observe.The course covers:Mathematical foundations of social networks: matrix algebra and graph theory;Network structure, cohesion and models of network formation;Centrality, betweenness and brokerage;Contagion, diffusion and network interventions;Network data, sampling and research designLearning outcomes:Develop an understanding of social phenomena through social network theory;Learn how to use and interpret graph-theoretic and matrix algebra concepts with real-world data;Understand how local patterns of connection contribute to the overall structure of a network;Examine how network position and structure affect social processes;Acquire knowledge of network data collection and the implications of sampling and missing data;Assess competing explanations for observed network patterns and identify the evidence needed to investigate them.The course is aimed researchers and analysts interested in using social network analysis to understand social phenomena, including students, academics, government researchers and researchers in third sector organisations. The course is relevant to work in sociology, survey research, public health and related disciplines.Pre-requisitesBasic familiarity with data matrices and introductory statistical concepts. No previous training in social network analysis is required. Mathematical concepts will be introduced as they are used throughout the course. Practical exercises and demonstrations will accompany the theoretical sessions. Software requirements will be circulated before the course.THIS COURSE IS BEING DELIVERED OVER TWO DAYS. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 02 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15022 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 - 3 February 2027 (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 - 4 February 2027 (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 (3 and 4 February) 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 - 26 and 27 JanModule 1 - Mindsets for considering the use of AI for Qualitative Research - 11 and 12 FebModule 3 - Integrating Generative-AI tools into qualitative analysis processes - 22 and 23 Feb jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 25 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14932 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=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 - 11 February 2027 (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 - 12 February 2027 (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 (11 and 12 February) 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 - 3 and 4 FebruaryModule 3 - Integrating Generative-AI tools into qualitative analysis processes - 22 and 23 February jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 25 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14931 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 - 22 February 2027 (13:30-16:45)Overview of the capabilities of established qualitative software programs (AI and non-AI tools)Ensuring analytic methods drive the use of AI tools Hands-on exercises using NVivoExploring MAXQDA’s AI tools for qualitative analysis - 23 February 2027 (13:30-16:45)Overview of MAXQDA’s tools Hands-on exercises integrating AI tools into a qualitative analysis process (using literature, transcripts and open-ended survey responses),By the end of the course participants will:Understand opportunities for the use of AI tools at different stages of the qualitative analysis 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 (22 and 23 February) 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 - 26 and 27 JanuaryModule 1 - Mindsets for considering the use of AL for Qualitative Research - 11 and 12 FebruaryModule 2 - Harnessing Generative-AI tools for qualitative analysis - 3 and 4 February jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 25 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14933 https://www.ncrm.ac.uk/training/show.php?article=15020 Qualitative Diary Methods (QDMs) are increasingly recognised as a valuable and important method in social science research, due to concern across disciplines with an overreliance on cross-sectional research, a lack of focus on temporality, and the need to capture evolving processes and the daily dynamics of phenomena. QDMs offer a range of innovative approaches and tools for social science researchers that enable us to capture and subsequently begin to understand, how phenomena are experienced in the moment, as well as how they evolve over time. However, they remain a methodological blindspot in much postgraduate research training.This workshop will provide researchers with a new range of methods to add to their methodological toolkit, as well as support and guidance in managing some of the challenges associated with these methods, including insights into qualitative diary (longitudinal) analysis approaches.  The course covers: Intro to Qualitative Diary Methods (QDMs) When and why to use different qualitative diary methodsAddressing challenges associated with QDMsEthical considerations when using QDMsDesigning pen and paper diariesDesigning audio and video diariesDesigning photo-elicitation diariesDesigning qualitative ‘App’ diariesAnalysing data from QDMs: Moving beyond thematic analysisBy the end of the course participants will:Have a comprehensive understanding of the benefits and challenges of QDMs, and how to manage these challengesBe able to evaluate when different QDMs should be used and whyBe able to design appropriate QDM research projects to address particular research questionsHave a critical awareness of particular ethical issues associated with the use of QDMsHave a comprehensive understanding of analytical approaches suited to data gathered using QDMsPlease note that this is a full day event running from 10:30am to 4:30pm Engage@liverpool.ac.uk (University of Liverpool) Wed, 07 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15020 https://www.ncrm.ac.uk/training/show.php?article=14912 This course will explain when and why to use documents as data for research, show you how to gather documentary data, and consider some ways to analyse that data. It will also cover some of the associated ethical issues and include case studies of documents as data in practice.The course covers: When and why to use documents as 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 dataAn introduction to the ethical issues of using documents as 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.Preparatory ReadingDesirable: Grant, A. (2022) Doing Your Research Project With Documents: A Step-By-Step Guide to Take You from Start to Finish. Bristol: Policy Press.Grant, A. and Kara, H. (eds) (2026) Using Documents in Research: When, Where, Why and How. Bristol: Policy Press.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) Wed, 26 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14912 https://www.ncrm.ac.uk/training/show.php?article=15026 Within current contexts of heightened political polarisation, evolving digital environments and growing scrutiny of research on contentious issues, social science researchers are increasingly required to navigate complex safeguarding considerations. Exposure to distressing and sometimes hateful content can be emotionally taxing and can impact on researchers’ mental health. Researchers who work on sensitive and potentially ‘controversial’ subjects have to contend with the risks of doxxing and online/offline harassment, especially when choosing to become professionally visible in/on (social) media. Risks are significantly heightened for minoritised and marginalised researchers, including female(-presenting), gender non-conforming, neurodiverse, disabled, LGBTQ+ and racialised researchers.The aim of this course is to support participants in understanding and addressing the ethical, safeguarding, emotional and psychological dimensions of their work, as well as the challenges that come with safely navigating professional visibility – paying particular attention to how risks intersect with identity and career stage. The course will further provide participants with practical guidance how to protect themselves both online and offline, how to create and establish support networks and communities, and to engage constructively with their institutions, including ethical review processes. We will further discuss how researchers can look after their wellbeing, set boundaries and engage in self-care whilst doing high risk and sensitive research.Please note that this course will be facilitated on Microsoft Teams and will run over two consecutive mornings with the afternoon of Day 1 being used for an individual task to feed back to the group on Day 2. PLEASE NOTE THIS COURSE EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES. Engage@liverpool.ac.uk (University of Liverpool) Fri, 09 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15026 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, debiasing, ethics of technologyPotential 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.Note: If you are currently facing an ethical dilemma in your research work, or have faced one in the past that you would like to review, you can bring it for discussion on the second day. If you would like to do this anonymously you can email it to the course leader (via NCRM) and she will present it for you.THIS COURSE IS TAUGHT OVER TWO MORNINGS AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES.Programme:Day 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) Wed, 26 Aug 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