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/ Thu, 30 Apr 2026 09:56:54 +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=14733 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) Wed, 08 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14733 https://www.ncrm.ac.uk/training/show.php?article=14820 DESCRIPTIONTuesday 5 May 202615:00–16:30 (UK time)Online (MS Teams): Link to MeetingABSTRACTThis session focuses on a central question: when data arise from complex sampling designs, can machine learning methods support rigorous inference in the same way as traditional statistical models? We begin by contrasting the fundamental objectives of the two approaches: while traditional statistical models emphasise parameter estimation and uncertainty quantification, machine learning methods tend to prioritise predictive performance.The session then considers what this distinction implies in the context of complex survey data. In particular, it focuses on key design features such as sampling weights, clustering structures, and plausible values, which are essential for valid inference but are often not systematically addressed within machine learning frameworks. Using empirical analysis based on TIMSS 2023 data, the session illustrates how different methodological approaches handle these features, and how these choices shape our understanding of how students’ learning behaviours influence learning outcomes.SHORT BIO:Dr Yin Wang is a Lecturer in Research Methods and AI Skills in the Department of Social Statistics and Demography at the University of Southampton, and a member of the National Centre for Research Methods (NCRM). Her current work within the UKRI-funded programmes Using Artificial Intelligence Methods in Education Data and New Approaches to Digital Skills Development examines the integration of AI-based and traditional statistical methods to improve the validity, transparency, and policy relevance of quantitative research using international large-scale assessment (ILSA) data.  jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 28 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14820 https://www.ncrm.ac.uk/training/show.php?article=14767 This two-part in-person training course combines critical reflection with hands-on practice to help researchers navigate generative AI thoughtfully and responsibly. The first session explores what AI means for higher education and research at this moment of rapid change, examining both opportunities and risks. The second session is a practical workshop where participants bring their own work and AI tools to explore ethical and professional use, developing personal principles for responsible AI integration into research practice. Participants must bring their own device with access to a generative AI chatbot they already have an account with and have previously used (such as ChatGPT, Claude, Gemini, or Copilot).The course covers: The current landscape of generative AI in higher education and academic researchHow AI is reshaping academic work, including writing, analysis, and collaborationOpportunities and risks of AI adoption in research contextsEthical considerations around integrity, authorship, and responsibilityPractical exploration using participants' own research materials and AI toolsScenario-based discussions on responsible AI usePeer exchange on emerging practices and challengesDeveloping personal guiding principles for AI use in researchBy the end of the course participants will:Articulate a clearer understanding of what generative AI means for researchers and scholarshipCritically evaluate the opportunities and risks of AI in their own research contextReflect on how language models are entering their research processesIdentify key ethical considerations around integrity, authorship, and responsibility when using AIExperiment critically with AI tools using their own research materialsBegin developing their own guiding principles for responsible AI useShare and learn from peers' emerging practices and approachesScheduleWednesday 13th May 2026, 10:00 - 16:00LocationRoom 1.69, Humanities Bridgeford Street Building, The University of Manchester, M15 6ADPre-requisitesSome prior experience using a generative AI chatbotAn active account with a generative AI tool of your choice A paper they have published (open access or pre-print version)A work-in-progress paper or chapterAccess to their preferred AI chatbotPresenterDr 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.  p.c.white@southampton.ac.uk (University of Southampton) Tue, 14 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14767 https://www.ncrm.ac.uk/training/show.php?article=14377 In this online course, run over two mornings, we will show you how to prepare and conduct spatial analysis on a variety of spatial data in R, including a range of spatial overlays and data processing techniques. We will also cover how to use GeoDa to perform exploratory spatial data analysis, including making use of linked displays and measures of spatial autocorrelation and clustering.The course covers: Understanding and being able to interpret Spatial Autocorrelation measure Moran's IUnderstanding Local Indicators of Spatial Association statisticPerform Spatial Decision Making in RPerform Point in Polygon analysis using different approachesBe aware of the advantages and disadvantages of using point based or polygon based dataUsing buffers as a part of spatial decision makingBy the end of the course participants will:Be aware of some spatial statistics concepts and be able to apply them to their own data using GeoDaBe able to perform spatial decision makingUnderstand the limitations and benefits of working with data in this wayThis course is aimed as PhD students, post-docs and lecturers who have some existing knowledge of using R as a GIS and want to develop their knowledge of spatial stats and spatial decision making in R. It is also appropriate for those in public sector and industry who wish to gain similar skills. Some prior knowledge of both R and GIS is required. Those with little or no experience should complete the Introductory course Introduction to Spatial Data and using R as a GIS - Online (28-29 April 2026). The Introductory course will provide the necessary knowledge required for the Advanced course. Contact Dr. Nick Bearman if you need clarification about whether your existing knowledge is sufficient for this course.  jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 28 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14377 https://www.ncrm.ac.uk/training/show.php?article=14671 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 research By the end of the course participants will:Develop practical skills in different creative and participatory approaches such as Zines, Photovoice, Co-creation/co-production (including peer research)Develop skills in designing, conducting, analysing and disseminating creative and participatory researchLearn how such methods can be incorporated into the generation of meaningful research impact Indicative Schedule:The course will run across two consecutive mornings (10am - 1pm) and equates to one day of training for payment purposes. Day 1-    What do we mean by creative and/or participatory methods? -    The value of creative/participatory research methods -    Planning and setting up creative/participatory research tools. -    FOCUS ON (1): zines as creative/participatory methods-    Ethical considerations specific to creative/participatory research (part 1) Day 2-    Ethical considerations specific to creative/participatory research (part 2)-    Creative/participatory research with children and young people-    Creative/participatory research with marginalised communities    -    FOCUS ON (2): co-creation – creative and participatory research in action* -    Doing co-analysis and co-dissemination -    Creative/participatory methods for generating meaningful research impact -    Wrapping up the workshop/advice clinic*The workshop facilitators will be joined on this by two peer researchers they have trained and worked with on recent research projects. Presenters:This course will be delivered by Dr Linzi Ladlow, Senior Research Fellow from the University of Lincoln, and Dr Laura Way, Senior Lecturer from the University of Roehampton. They are experienced in engaging with creative and participatory research and facilitating training. They are editors of the book, Insights into Creative and Participatory Research: Key Issues and Innovative Developments (2026) Policy Press.  Target audience:This short course is suitable for all qualitative researchers at any career stage, including postgraduate students. Whilst we are not expecting you to already be familiar with creative and participatory methods, familiarity with the purposes of qualitative research, as well as with qualitative methods of data generation and analysis, will be assumed. p.c.white@southampton.ac.uk (University of Southampton) Wed, 28 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14671 https://www.ncrm.ac.uk/training/show.php?article=14431 This online workshop aims to give participants a range of practical approaches they can adopt when writing about methodology in the social sciences, with a particular focus on writing a PhD methodology chapter. Using a range of exercises throughout, the course focuses on 20 or so writing strategies and thought experiments designed to provide more clarity and power to the often-difficult challenge of writing about methods. The course also looks at common mistakes and how to avoid them when writing about methods. The focus throughout is on building confidence and increasing our repertoire of writing strategies and skills.The course covers:A range of practical writing strategies for handling methodologyThe challenges of writing a PhD methodology chapter or a methods section in a research paperWriting for qualitative and quantitative research approachesUnderstanding different audiences and the needs of different academic marketsBy the end of the course participants will:Better understand who and what ‘methodology writing’ is forKnow the differences and similarities between PhD methods chapters, research paper methods sections and methods booksUnderstand and reflect on 20+ principles (or starting points) of best practice in methodology writingFocus writing on audience needs and expectationsBe aware of common mistakes and misunderstandings and so avoid themReflect on the relationship between methodology writing and other parts of your manuscriptTo develop learning and best practice through exercises and examplesThis course is aimed at PhD students, post-docs and junior researchers in the social sciences working on their doctoral theses or supervising doctoral students.Programme:09:25 - Log in to zoom09:30 - Seminar11:00 - Tea/Coffee Break12:30 - Close of Seminar13:30-15:30 - Time devoted to workbook exercises offline15:30-16:30 - Q&A and exercise feedback with Patrick jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 28 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14431 https://www.ncrm.ac.uk/training/show.php?article=14796 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. No programming experience is required for Workshop 1; Workshops 2 and 3 assume familiarity with concepts from Workshop 1, and Workshop 3 benefits from some comfort with reading code.PLEASE NOTE THESE WORKSHOPS WILL RUN ONLINE ON 8 JUNE, 22 JUNE and 6 JULY FROM 09:30-13:30 jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 14 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14796 https://www.ncrm.ac.uk/training/show.php?article=14788 PAR aims to create a space for researcher and participants to co-produce knowledge and where relevant, action for change. PAR is considered as a research paradigm in itself, that embodies a particular set of concepts under which researchers operate (Minkler and Wallerstein 2008). These include respect for diversity, community strengths, reflection of cultural identities, power-sharing, and co-learning (Minkler 2000).During this two day online course we will explore these principles, the cyclical approach to PAR and what this means in practice. Participants will be given the opportunity to learn terminology, understand participation in community engaged research, explore how power and positionality can change health outcomes in PAR, and learn about a variety of participatory methods and how they have been applied in different contexts, globally and within the UK. Participants will also be provided with the space to explore challenges they are facing in designing or implementing community engaged collaborative research within a discussion clinic forum.   Programme of ActivitiesDay 1 - The history of PAR and underpinning orientationPlanning and setting up a PAR projectSkills required for a PAR studyEthical considerations specific to a PAR studyParticipatory research with children and young peoplePhotovoice methodologyIndependent activityGroup discussion  Day  2 - Doing co-analysisParticipatory research methods (examples of other visual methods, social mapping, seasonal calendars and other non-visual methods but still participatory such as narratives and others that have been used)Participation and inclusion Dissemination and writing for PAR projects – different approaches, narratives/thematic analysis, thesis, publications, policy briefs, blogs and othersGroup discussion on pre-workshop task Advice clinicAfternoon independent learning and practical exercises Practical activities: Day 1: Photovoice activity and reflectionsDay 2: Individual PAR project outline and feedback  Engage@liverpool.ac.uk (University of Liverpool) Thu, 26 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14788 https://www.ncrm.ac.uk/training/show.php?article=14670 Longitudinal data is essential in a number of research fields as it enables analysts to concurrently understand aggregate and individual level change in time, the occurrence of events and improves our understanding of causality in the social sciences. In this course you will learn both how to clean longitudinal data as well as the main statistical models used to analyse it. The course will cover three fundamental frameworks for analysing longitudinal data: multilevel modelling, structural equation modelling and event history analysis. The course is organized as a mixture of lectures and hands on practicals using real world data. During the course there will also be opportunities to discuss also how to apply these models in your own research.Objectives:To gain competence in the concepts, designs and terms of longitudinal research;To be able to apply a range of different methods for longitudinal data analysis;To have a general understanding of how each method represents different kinds of longitudinal processes;To be able to choose a design, a plausible model and an appropriate method of analysis for a range of research questions.The course consists of five sessions (all Fridays) spread over six weeks (note there is no session on 15th May).Topics covered by day:22.06.2026  - Data cleaning and visualization of longitudinal data23.06.2026 - Cross-lagged models (covering also an introduction to Structural Equation Modelling and auto-regressive models)24.06.2026 - Multilevel model of change (covering also an introduction to multilevel modelling)25.06.2026 -  Latent Growth Modelling26.06.2026  - Survival models (also known as event history analysis)Teaching will take place online (using Zoom) between 09:00 to 16:00 UK time. There will be 1 hour lunch break from 12:00 to 13:00.IMPORTANT: Please note that this course includes computer workshops. Before registering please check that you will be able to access the software noted below. Please bear in mind minimum system requirements to run software and administration restrictions imposed by your institution or employer with may block the installation of software.PrerequisitesGood knowledge of regression modellingBasic knowledge of R or good programming experience with a different statistical softwareRecommended readingCernat, A. (in press). Longitudinal Data Analysis using R. LeanPub.Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data (First edition). O’Reilly. (also available free online)Singer, J., & Willett, J. (2003). Applied longitudinal data analysis: modeling change and event occurrence. Oxford University Press.Newsom, J. T. (2015). Longitudinal Structural Equation Modeling: A Comprehensive Introduction. Routledge. p.c.white@southampton.ac.uk (University of Southampton) Mon, 13 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14670 https://www.ncrm.ac.uk/training/show.php?article=14735 This five-day summer school introduces experimental methods in the Social Sciences, covering lab, field, and survey experiments. Participants will gain a solid foundation in experimental methodology and practical skills for designing, implementing, analysing, and presenting experiments. The interdisciplinary team of the Centre for Behavioural Experimental Action and Research (C-BEAR) will lead the five-day course, using examples from Politics, Economics, Business, and Psychology. Days 1 and 2 cover the basics of designing, analysing, and presenting different types of experimental designs, while Days 3, 4, and 5 will provide in-depth knowledge and insights on survey, field, and laboratory experiments. The hands-on activities throughout the week ensure that participants not only understand the theoretical aspects of experimental methods but also acquire the practical skills necessary to apply these methods in their own research.Coffee and light refreshments will be served every day during dedicated breaks in the morning and afternoon sessions. Lunch and planned dinners are self-catered.Software requirementsStudents should bring their own laptop and install R, the free version of Stata, and Excel (or an open alternative).Day 1: Foundations in experimental methods Instructors: Dr. Jana Sadeh, Dr. Vanessa Cheng-Matsuno, Prof. Tereza CapelosDay 1 provides a balanced mix of theory, storytelling, discussion, and hands-on practice to engage participants and build a strong foundation in experimental methods. The learning outcomes for the program encompass both theoretical and practical aspects. Participants will delve into the rich history and foundational definitions of experiments, exploring the diverse types that have shaped research across disciplines. They will gain insights into the advantages and disadvantages of using experiments compared to other research designs, providing a comprehensive understanding of when and why to employ experimental methods. On the practical side, participants will have the opportunity to implement a simple experiment themselves. This hands-on experience will guide them through designing the experiment, then analysing and presenting the results.Morning Session: 9:30 AM – 12:30 PM (refreshments break 11:00am)Lunch Break: 12:30 PM - 2:00 PM (buy/bring your own)Afternoon Session: 2:00 PM - 5:00 PM (refreshments break 3:30pm) Highlights include:• Welcome and Introduction• Engaging stories that illustrate the value of experiments• Group work and discussion on the fundamental aspects of experimental design• Introduction to different types of designs• Research questions suitable for experimentation• Practical session on designing a simple experiment• Welcome GROUP DINNER (self-catering)Day 2: Key concepts and essential experimental techniques Instructors: Dr. Paolo Spada and Prof. Konstantinos KatsikopoulosDay 2 delves into both the theoretical underpinnings and practical applications of experimental methods in social sciences. The day is structured to enhance participants' understanding of key concepts and provide hands-on experience with essential research techniques. We will review the Potential Outcome Model, a fundamental framework for causal inference in experiments, and discuss the ethical principles that govern experimental research, ensuring that participants understand the importance of conducting studies responsibly and ethically. The practical sessions on Day 2 are designed to reinforce the theoretical concepts through hands-on activities. We will design experiments, learn how to calculate average treatment effects and determine statistical power, review pre-registration examples, and engage in replication exercises.Morning Session: 9:30 AM – 12:30 PM (refreshments break 11:00am)Lunch Break: 12:30 PM - 2:00 PM (buy/bring your own)Afternoon Session: 2:00 PM - 5:00 PM (refreshments break 3:30pm) Highlights include:• Understand the Potential Outcome Model• Design experiments• Calculate average treatment effects and statistical power• Learn about pre-registration with examples• Discuss ethical principles and guidelines• Analyse data and report experimental results with graphs and plotsDay 3: Survey Experiments Instructors: Professor Robert JohnsDay 3 focuses on survey experiments, offering participants a blend of theoretical knowledge and practical skills. The day is designed to deepen their understanding of survey-based experimental methods and provide hands-on experience with designing and analysingsurvey experiment data. We will introduce survey experiments and how they differ from other experimental methods, and we will highlight key studies that have shaped the field. In the practical sessions, participants will focus on designing and analysing conjoint experiments and presenting the results clearly and effectively. By the end of Day 3, participants will be equipped with the theoretical understanding and practical skills needed to design, implement, and analyse survey experiments, with a particular focus on conjoint analysis.Morning Session: 9:30 AM – 12:30 PM (refreshments break 11:00am)Lunch Break: 12:30 PM - 2:00 PM (buy/bring your own)Afternoon Session: 2:00 PM - 5:00 PM (refreshments break 3:30pm) Highlights include:• Understand the significance and applications of survey experiments in social sciences.• Overview of classic survey experiments• Design a conjoint experiment• Replicate the analysis of a conjoint experiment• Interpret the findings, draw conclusions, present resultsDay 4: Field Experiments Instructors: Dr. Monica Beeder and Dr. Paolo SpadaDay 4 is dedicated to field experiments, providing participants with a comprehensive understanding of this essential research method. The day's agenda combines theoretical insights with practical exercises to ensure participants can effectively design, manage, and analyse field experiments. The introduction to field experiments is followed by an overview of the strengths and challenges of field experiments, a discussion of classic studies that have made significant contributions to the field, offering appreciation for the methodological rigour and practical implications of field experiments. The practical sessions focus on the design and analysis of field experiments, and the review of the logistical considerations involved will offer a real-world perspective on managing field experiments. We will replicate the analysis and presentation of a simple field experiment, calculate treatment effects, test hypotheses, and communicate findings through visual aids.Morning Session: 9:30 AM – 12:30 PM (refreshments break 11:00am)Lunch Break: 12:30 PM - 2:00 PM (buy/bring your own)Afternoon Session: 2:00 PM - 5:00 PM (refreshments break 3:30pm) Highlights include:• Understand the significance and characteristics of field experiments• Overview of classic field experiments• Project management of field experiments• Hands-on analysis of data from a field experiment• Perform statistical analyses to interpret results and test hypotheses• Presentation of field experiment results using visual aidsDay 5: Laboratory Experiments & Online Incentivized Experiments Instructors: Dr. João V. FerreiraDay 5 marks the culmination of the C-BEAR summer school with a session on laboratory and online incentivized experiments. We will introduce these experiments and explore their unique features, with a focus on ways to incentivize the truthful revelation of preferences and beliefs and the design of paradigmatic games used by experimental economists. The practical sessions will focus on the tools and techniques necessary to design your own lab or online incentivized experiment, with an opportunity to design a simple experiment in groups. We will also replicate the analysis and presentation of a simple lab experiment. Using real data, we will calculate treatment effects, conduct hypothesis tests, and learn how to present results clearly and concisely. The day is designed to equip participants with the necessary knowledge and skills to leverage a range of tools used in laboratory and online incentivized experiments in their own research endeavours.Morning Session: 9:30 AM – 12:30 PM (refreshments break 11:00am)Lunch Break: 12:30 PM - 2:00 PM (buy/bring your own)Afternoon Session: 2:00 PM - 5:00 PM (refreshments break 3:30pm) Highlights include:• Understand the significance and features of laboratory and online incentivized experiments• Overview of classic lab experiments and paradigmatic games used by experimental economists• Learn about methods and tools to incentivize the truthful revelation of preferences and beliefs• Participate in paradigmatic games• Design your own simple experiment• Replicate the analysis of a lab experiment• Perform statistical analyses to interpret results and test hypotheses• Presentation of lab experimental results• Farewell GROUP DINNER (self-catering)The target audience of the course are professionals, members of public institutions and researchers that are approaching experimental methods for the first time and are interested to implement an experiment for the first time or to commission an experiment to a survey company or other service provider.The course does not require any previous knowledge of experimental design or statistics and is open to anybody with basic high school knowledge of mathematics.  The level (junior, senior, etc.) of the course is open. The first two days will provide the students the mathematical and statistical tools to engage effectively with the rest of the course.The workshop is taught by a team of faculty members from Politics, Economics, Psychology and Business, and it is targeted to people with interests in any discipline in the social sciences. Participants need to bring their own device that can run basic office suites, and free versions of R and Stata.  PLEASE NOTE REFRESHMENTS WILL BE PROVIDED BUT PARTICIPANTS WILL NEED TO BRING/BUY THEIR OWN LUNCH. jmh6@soton.ac.uk (NCRM, University of Southampton ) Wed, 18 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14735 https://www.ncrm.ac.uk/training/show.php?article=14787 Introduction to Multilevel Modelling Using MLwiN, R, or Stata30th June – 2nd July 2026, Online via Zoom-------------------------------------------------------------------------------------------------------Deadline for applications: 17th May 2026Full course information and excerpts can be viewed here: https://www.bristol.ac.uk/cmm/learning/introworkshop.html  Go to booking form >>-------------------------------------------------------------------------------------------------------InstructorsProfessor George Leckie and Professor William Browne 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. 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.” TopicsOverview 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. FeesFor UK-registered MSc and PhD students - £180For UK university academics, UK public sector staff, and staff at UK registered charity organisations - £360For all other participants - £660Please note, in order to be eligible for the reduced pricing brackets please submit your application using your UK academic/organisational email address. 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 17th May 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.Go to booking form >> info-cmm@bristol.ac.uk (Centre for Multilevel Modelling, University of Bristol) Tue, 24 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14787 https://www.ncrm.ac.uk/training/show.php?article=14813 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 15th July 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=14813 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 academic articles and book chapters on corpus linguistics and sociolinguistics and has a forthcoming co-edited book on Sociolinguistic Approaches to Lexical Variation in English to be published by Routledge. p.c.white@southampton.ac.uk (University of Southampton) Thu, 19 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14739 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