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/ Wed, 11 Mar 2026 12:47:10 +0000 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=14761 Module 1 – Responsible AI and EthicsDate: 12th March 2026 - 13:00 - 15:00Trainer: Dr Naomi TyrrellPurposeThis session introduces participants to the ethical, legal, and social dimensions of using Artificial Intelligence (AI) and Large Language Models (LLMs) in environmental work. It focuses on building awareness of fairness, transparency, accountability, and public trust in AI driven decision-making. Through open discussion and case-based reflection, participants will explore how to apply responsible AI principles within the Environment Agency’s data and policy context.Learning ObjectivesBy the end of this module, participants will be able to:Identify common ethical risks in AI and LLM use (bias, privacy, opacity, misuse).Understand fairness, transparency, and explainability as key principles of responsible AI.Recognise the importance of data governance and accountability frameworks.Apply basic fairness and explainability checks when using AI tools.Reflect on how ethical AI practice aligns with public trust and regulatory standards. Module 2 – AI and Large Language Models (LLMs) for Environmental DataDate: 17th March 2026 - 10:00 - 12:00Trainer: Prof Leslie CarrPurposeThis module provides a clear and accessible overview of Artificial Intelligence (AI) and Large Language Models (LLMs) and how they can support the Environment Agency’s analytical and policy work. Participants will learn what AI and LLMs are, how they differ from traditional analytical tools, what kinds of data they can use, and how they are already transforming environmental science worldwide.Learning ObjectivesBy the end of this module, participants will be able to:Explain what AI, Machine Learning, and LLMs are and how they relate to one another.Distinguish between traditional analytical methods and AI-based approaches.Identify data types (numerical, spatial, image, and text) suitable for AI and LLM applications.Recognise use-cases of AI and LLMs in environmental monitoring, modelling, and communication.Understand the idea of the “black box” and the importance of explainability and ethics.Reflect on where LLMs could help within their own EA projects (e.g., coding, summarisation, or automation). Module 3 – NLP for Survey and Text DataDate: 18th March 2026 - 10:00 - 12:00Trainer: Prof Leslie CarrPurposeThis module introduces participants to Natural Language Processing (NLP) and demonstrates how it can be applied to environmental survey and text data. It focuses on using Large Language Models (LLMs) to automate tasks such as coding, summarisation, sentiment detection, and theme extraction. The session shows how these tools can support faster, more consistent qualitative analysis within the Environment Agency’s work.Learning ObjectivesBy the end of this module, participants will be able to:Understand the fundamentals of NLP and its relevance to environmental data.Apply NLP techniques to survey and textual datasets.Use LLMs to automate coding, summarisation, and classification.Evaluate NLP model performance using appropriate metrics.Communicate insights from text data clearly to both technical and non-technical audiences.  p.c.white@southampton.ac.uk (University of Southampton) Wed, 11 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14761 https://www.ncrm.ac.uk/training/show.php?article=14736 Friday 13 March 202613:00–14:30 (UK time)Online (MS Teams):  Link to MeetingAbstract:Dynamic Structural Equation Modelling (DSEM) is a statistical technique for analysing intensive longitudinal data. This masterclass begins with a pedagogical introduction to key concepts in time-series modelling, including lagged effects, autoregressive processes, stationarity, and the modelling of within-person dynamics. It then demonstrate how DSEM can be specified within a multilevel framework, allowing individual differences in within-person processes to be modelled and interpreted.Using data from a study of university students’ positive and negative emotions measured over a two-week period, the session then illustrates the specification of autoregressive and cross-lagged models in Mplus, alongside the visualisation and interpretation of results in R. The session concludes with a discussion of practical considerations and next steps for researchers working with intensive longitudinal data.Materials for R and Mplus will be made available to participants.Short Bio:Lars-Erik Malmberg is Professor of Quantitative Methods in Education, at the Department of Education, University of Oxford, UK. He has more than 100 publications. He was Editor-in-Chief of the Journal of Learning and Instruction 2018-21. His current research interests are on intraindividual approaches to learning processes, and modelling of intensive longitudinal data. He has published on effects of education, child care and parenting on developmental and educational outcomes, and teacher development. He applies advanced quantitative models to the investigation of substantive research questions in education. Links between educational phenomena and physiology were explored in the Emerging Field Group “The potential of biophysiology for understanding learning and teaching experiences”. p.c.white@southampton.ac.uk (University of Southampton) Tue, 17 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14736 https://www.ncrm.ac.uk/training/show.php?article=14328 A one day introduction to machine learning using Scikit Learn in Python.  Learners will be introduced to several machine learning techniques including regression, clustering, dimensionality reduction, and neural networks.  The course also includes a brief overview of the ethics and implications of machine learning.The course covers: Introduction to machine learningRegressionIntroducing Scikit LearnClustering with Scikit LearnDimensionality reductionNeural networksEthics and implications of machine learning By the end of the course participants will:Gain an overview of what machine learning is and the techniques available.Understand how machine learning and artificial intelligence differ.Be aware of some caveats when using Machine Learning.Apply linear regression with Scikit-Learn to create a model.Measure the error between a regression model and input data.Analyse and assess the accuracy of a linear model using Scikit-Learn’s metrics library.Understand how more complex models can be built with non-linear equations.Apply polynomial modelling to non-linear data using Scikit-Learn.Use two different supervised methods to classify data.Learn about the concept of hyper-parameters.Learn to validate and cross-validate modelsUnderstand the difference between supervised and unsupervised learningIdentify clusters in data using k-means clustering.Understand the limitations of k-means when clusters overlap.Use spectral clustering to overcome the limitations of k-means.Recall that most data is inherently multidimensional.Understand that reducing the number of dimensions can simplify modelling and allow classifications to be performed.Apply Principle Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce the dimensions of data.Evaluate the relative peformance of PCA and t-SNE in reducing data dimensionality.Understand the basic architecture of a perceptron.Be able to create a perceptron to encode a simple function.Understand that layers of perceptrons allow non-linear separable problems to be solved.Train a multi-layer perceptron using Scikit-Learn.Evaluate the accuracy of a multi-layer perceptron using real input data.Understand that cross validation allows the entire data set to be used in the training process.Consider the ethical implications of machine learning, in general, and in research. 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.Pre-requisites:A basic understanding of Python. You will need to know how to write a for loop, if statement, use functions, libraries and perform basic arithmetic. The short course Digital Research Skills for Social Scientists - online (3rd-6th March 2026) covers sufficient background.Setup Instructions:You will need a terminal, Python 3.8+, and the ability to create Python virtual environments.To install Python, follow the Beginner’s Guide or head straight to the download page.You will need the MatPlotLib, Pandas, Numpy and OpenCV packages.Create a new directory for the workshop, then launch a terminal in it:mkdir workshop-mlcd workshop-mlCreating a new Virtual EnvironmentWe’ll install the prerequisites in a virtual environment, to prevent them from cluttering up your Python environment and causing conflicts. To create a new virtual environment (“venv”) called “intro_ml” for the project, open the terminal (Max/Linux), Git Bash (Windows) or Anacomda Prompt (Windows), and type one of the below OS-specific options:python3 -m venv intro_ml # mac/linuxpython -m venv intro_ml # windowsIf you’re on Linux and this doesn’t work, you may need to install venv first. Try running sudo apt-get install python3-venv first, then python3 -m venv intro_mlActivate environmentTo activate the environment, run the following OS-specific commands in Terminal (Mac/Linux) or Git Bash (Windows) or Anaconda Prompt (Windows):Windows + Git Bash: source intro_ml/Scripts/activateWindows + Anaconda Prompt: intro_ml/Scripts/activateMac/Linux: source intro_ml/bin/activateInstall the prerequisitespip install numpy pandas matplotlib opencv-python scikit-learn  p.c.white@southampton.ac.uk (University of Southampton) Tue, 28 Oct 2025 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14328 https://www.ncrm.ac.uk/training/show.php?article=14371 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 DesignGetting 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 (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 surveysDetermining 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 Reading (desirable):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) Fri, 20 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14371 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:17.04.2026  - Data cleaning and visualization of longitudinal data24.04.2026 - Cross-lagged models (covering also an introduction to Structural Equation Modelling and auto-regressive models)01.05.2026 - Multilevel model of change (covering also an introduction to multilevel modelling)08.05.2026 -  Latent Growth Modelling22.05.2026  - 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) Wed, 28 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14670 https://www.ncrm.ac.uk/training/show.php?article=14372 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) Wed, 28 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14372 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) Mon, 16 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14733 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=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=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