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/ Fri, 23 Oct 2026 03:53:58 +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=15024 Training for PhD and MSc students in the design of social research, quantitative and qualitative analysis.Design-based causal inference in observational and experimental settings by Dr Zach Dickson - LSEThis course will introduce students to the principles and methods of causal inference. Causal inference is the process of drawing conclusions about the causal relationships between variables based on observational data. In this course, we will start with an overview of the potential outcomes framework and will cover topics such as confounding, selection bias, and causal identification. We will also discuss directed acyclic graphs (DAGs), and methods for estimating causal effects in observational settings, including difference-in-differences, instrumental variables, and regression discontinuity designs. We will use real-world examples to illustrate the concepts and methods covered in the course, and students will have the opportunity to apply these methods to their own research projects.By the end of the course, students will have a solid understanding of the principles and methods of causal inference, and will be able to apply these methods to a wide range of research questions. A basic knowledge of programming in a scripting language, such as R or Python, is recommended for this course. methodology.admin@lse.ac.uk (LSE) Mon, 05 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15024 https://www.ncrm.ac.uk/training/show.php?article=14889 This one-day foundation course provides participants with an introduction to the key principles and benefits of qualitative research. This includes the main methods and techniques employed in qualitative studies such as interviews, focus groups and observations. It outlines how to design an effective qualitative research question, how to design research instruments, how to sample and recruit participants, data analysis, and related ethical considerations. It also considers how to assess a qualitative research project’s quality and rigour.Qualitative research seeks to understand people’s lived experiences and to answer questions about meaning and perspective, including from the standpoint of the participants themselves. It is particularly effective in obtaining culturally specific information about the values, views, behaviours, and contexts of particular groups. This foundational course provides an introduction to qualitative research tools and techniques which help us to answer these questions. It will equip participants with the knowledge, skills and confidence required to begin their own qualitative investigations.The course is delivered online via Zoom. It includes a combination of presentations, discussions and practical activities. It is designed to equip participants to confidently implement their own qualitative design in research projects in a range of contexts.Looking to book for four or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Tue, 23 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14889 https://www.ncrm.ac.uk/training/show.php?article=15038 Did you know that data from major social surveys such as the British Social Attitudes survey, the Labour Force Survey and the Health Survey for England can be accessed for research and teaching?These UK surveys provide a rich source of data from representative samples on important social and economic topics including work, family life, health, and social and political attitudes. Our collection includes surveys from the National Centre for Social Research, Office for National Statistics and other producers. In most cases, you can simply download the data once registered with the UK Data Service.This free introductory workshop is intended for anyone who wants to learn about survey data available from the UK Data Service.The session will cover:the UK datasets available from the UK Data Servicekey features of survey data with examples from commonly used surveyshow to access the data and get further helpThe workshop will consist of a 30 minute presentation, followed by a practical demonstration of the facilities to find data and a further 20 minutes for questions.Presenters:Placide Abasabanye, UK Data Service. Placide is a Research Associate at the UK Data Service with a PhD in Economics. He has ten years of research experience using survey and census data, alongside eight years of teaching experience at the Universities of Manchester and Lille (France). His expertise spans microdata and macrodata analysis, large dataset processing, and econometrics. He particularly enjoys helping learners build confidence, develop critical thinking, and gain practical data skills they can apply in their professional work and studies.Alle Bloom, UK Data Service. Alle is a Research Associate with the UK Data Service at the University of Manchester, providing support and training to researchers in accessing, understanding, and using social science data. Her previous projects include developing training resources for undergraduate and postgraduate students, creating interactive learning modules on different data types, and collaborating with international data providers to improve data discovery.Level: IntroductoryExperience/knowledge required: NoneTarget audience: Researchers/anyone interested in large-scale surveysThis event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.This event is part of our UK Data Service introductory training series: Autumn 2026. mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15038 https://www.ncrm.ac.uk/training/show.php?article=14390 Level: Intermediate (I)If you’re looking to improve the way you plan your work and improve efficiency by introducing statistical forecasting, then this course is ideal. By the end of the session you will have a firm grasp of how to summarise and measure trends, as well as how to extrapolate trends into a forecast. You will also have a good understanding of how to perform relevant calculations in Excel. This course is being delivered over two morning sessions, which will run from 9:00am to 1:00pm on both days.This course looks at one of the big questions in businesses -- finding out what is going to happen next. It would be so much easier to plan sales, purchases, production, staff and logistics if we knew the answer to this question! Many businesses know how important it is to forecast for the future, yet many fail to apply the fundamental concepts of statistical forecasting. Those that don’t use statistical forecasting face higher costs and uncertainty when reality diverges from their plans. Those that do use statistical forecasting are able plan for the future much more effectively and efficiently. Learning OutcomesUnderstand the distinction between sober & drunk time series (seriously!)Learn how to use hypothesis testing to confirm that a trend is a genuine trend.Learn how to use moving averages correctly to identify potential turning points.Discover the simplest method of identifying seasonality in your time series and to confirm it with hypothesis test.Uncover the basic principles of statistical process control and how you can use it to confirm deviations from an expected trend.Learn 4 different ways of extrapolating an existing trend to produce forecast. Topics CoveredTime series analysis, forecasting, trend identification, seasonality, moving averages, trend extrapolation, statistical process control, forecasting using extrapolations. Target AudienceAnyone involved in business planning, performance analysis and other similar roles that require analyses of historical trends and extrapolation of those trends to create forecasts. Course PrerequisitesThis course would be suitable for anyone who has completed our two day "Basic Statistics" course.Other participants should ideally have an understanding of the following:Basic statistical concepts including expectation, variance, distribution and correlation.Probability and risk including knowing what false positives and false negatives areKnow how to calculate of confidence intervals for the mean of 1 sample and the difference in means between 2 samples.Know how to calculate the slope and intercept of a simple regression models with 1 variableUse of Microsoft Excel including the use of formulae such as IF, VLOOKUP, OFFSET, etc.Produce and interpret line charts, column charts and scatter plots in Excel. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14390 https://www.ncrm.ac.uk/training/show.php?article=15051 This free online drop-in is for anyone working, or thinking of working, with UK census data from the UK Data Service. If you are looking for help and advice with your project or research, join us on 28 October. The session is targeted at academic and non-academic researchers, analysts, students, and anyone with an interest in UK census data, whether absolute beginners or more seasoned users.Come along to discuss any questions you might have about UK census data with experts from the UK Data Service census team. This is a one-hour session and it will take place on Zoom.We can help with topics such as:what census data is available from the UK Data Servicefinding variables that match your needsunderstanding the documentationkey things to consider when using census dataany other queries you might have about census dataBook your place and we will see you soon.  mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15051 https://www.ncrm.ac.uk/training/show.php?article=15063 This training will discuss key theories, methods and ethics on web scraping. We also have exercises. cq225@cam.ac.uk (University of Cambridge) Sun, 18 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15063 https://www.ncrm.ac.uk/training/show.php?article=15039 Data documentation is essential to make sure that well-organised and well-documented research data can be produced from our research projects. This workshop provides an overview of different types of documentation depending on data type (e.g. primary or secondary, survey or transcripts), as an essential part of implementing good data management in research projects, with a focus on optimising data sharing.In this session we will explore the following:The critical role of data documentation in research integrity and data sharing, for both current and future research projects.How to develop comprehensive documentation for various data types, incorporating practical examples and templates that can be adapted to your projects.An overview of metadata: what it is, its importance in making your data discoverable and understandable, and the standards to follow for effective data sharing.During this free 90-minute online workshop, participants will learn the foundational principles and practical strategies for creating robust data documentation that enhances the accessibility, usability, and longevity of research data. Effective documentation is not a one-size-fits-all process; it varies significantly across different types of data and the workshop is tailored to address these nuances, ensuring researchers can apply best practices relevant to their specific data types.The workshop will conclude with a dedicated Q&A session.Presenter: Maureen Haaker, UK Data Service. Maureen has worked with the UK Data Service for over 12 years supporting researchers and institutions in managing, preserving, and sharing qualitative data ethically and effectively. She runs training sessions on research data management, advises on best practices in data curation and anonymisation, and has worked on special projects, including a UKRI-funded project exploring uses of synthetic data in Trusted Research Environments (TREs). She is co-editor of the IASSIST Qualitative Data Special Interest Group and a member of the DDI Working Group developing metadata standards for qualitative data.Level: IntroductoryExperience/knowledge required: Basic understanding of research methods is recommended but not required.Target audience: Academic and non-academic data professionals such as researchers, academic and professional services staff, data managers, data stewards and others keen on improving their data documentation practices.This event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.This event is part of our UK Data Service introductory training series: Autumn 2026. mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15039 https://www.ncrm.ac.uk/training/show.php?article=14407 Level: Intermediate (I)This tutor-lead virtual course will introduce how the tidyverse and ggplot2 can be used to reproducibly create publication quality charts from R. Learning OutcomesReproducibly import and wrangle data with the tidyverse in preparation for charting with ggplot2. Confidently choose the appropriate geoms for visualising data with ggplot2. Understand how to use factors using the forcats package to control the display (or order) of chart elements. Effectively control the use of colours and themes in ggplot2 charts. Understand how to augment GIS data using sf and the tidyverse to be visualised with ggplot2. Reproducibly export publication quality charts for papers, posters and other printed media.Delegates are expected to have a laptop with the R software installed. Topics CoveredR, Data Visualisation, ggplot2, Data Presentation, Exploratory Data Analysis. Target AudienceThis course is designed for both novice and experienced R users who want to create publication quality printed charts with ggplot2. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14407 https://www.ncrm.ac.uk/training/show.php?article=14797 Course detailsDate: Monday 2nd & Monday the 9th November 2026Time: 09:00 – 13:00Location: This is an online course conducted via ZoomDuration: 8 learning hoursWho this course is forThis course is ideal for people who have an awareness of evidence-based health. It's also useful for people who are in the process of or about to undertake a systematic review. Previous delegates have been:ResearchersHealthcare professionalsAcademic cliniciansEducation and policy commissionersMedical studentsPhD or MSc studentsLearning outcomesAfter completing this course, you will understand:What a systematic review isScoping the research question and writing a protocolLiterature searchingInclusion/exclusion screeningData extraction and critical appraisalData synthesisPlease note this course will NOT cover realist synthesis or qualitative analysis.This course is delivered by Southampton Health Technology Assessments Centre (SHTAC)Course Leaders:Jonathan ShepherdCourse TutorsGeoff FramptonEmma MaundKaren PickettJonathan ShepherdLois Woods  A.Vincent@soton.ac.uk (University of Southampton) Wed, 27 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14797 https://www.ncrm.ac.uk/training/show.php?article=14965 A stage by stage introduction to the methods and processes commonly used to conduct systematic reviews of the effects of health and health care interventions.About this courseThis course is for anyone planning or currently doing a systematic review, or anyone curious about how reviews are done and what makes them systematic. It provides a stage by stage introduction to the methods and processes commonly used to conduct systematic reviews of the effects of health and health care interventions. Structured presentations with interactive practical exercises guide participants through the systematic review process, from initial scoping of the review topic to communicating the completed review’s findings. Many participants complete the course in readiness for conducting their own review, and all participants can continue their learning with information and further resources contained in their free comprehensive electronic course manual.Course detailsMonday 2 and Monday 9 November 202609:00 – 13:008 learning hoursthis is an online course conducted via ZoomWho this course is forThis course is ideal for people who have an awareness of evidence-based health. It’s also useful for people who are in the process of or about to undertake a systematic review. Previous delegates have been:researchershealthcare professionalsacademic clinicianseducation and policy commissioners medical studentsPhD or MSc studentsLearning outcomesAfter completing this course, you will understand:what a systematic review isscoping the research question and writing a protocolliterature searchinginclusion/exclusion screeningdata extraction and critical appraisal data synthesisPlease note this course will not cover realist synthesis or qualitative analysis. A.Vincent@soton.ac.uk (University of Southampton) Tue, 25 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14965 https://www.ncrm.ac.uk/training/show.php?article=14891 Capturing the views and experiences of children and young people directly, rather than by proxy (e.g. through parents or professionals), is increasingly recognised as essential for sound research and policy development. Drawing on the tutors' extensive experience and expertise in this field, this long-running, practical, and highly interactive training provides an opportunity for participants to explore the ethical, methodological and real-world considerations of doing research and evaluation with children and young people, both face-to face and remotely.This online course runs over two half days which helps participants reflect on and consolidate the learning and how to apply it to their own work. We also run it in person, as a one-day course, usually about once per year.This course is very popular, and often run in-house as well. For more information about our in-house courses, contact training@the-sra.org.uk. training@the-sra.org.uk (Social Research Association) Tue, 23 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14891 https://www.ncrm.ac.uk/training/show.php?article=14906 About the eventThis webinar highlights the value of using lifecourse data to support physical health research using relevant measures available in four British cohort studies:1958 National Child Development Study (NCDS)1970 British Cohort Study (BCS70)Next StepsMillennium Cohort Study (MCS).Why attend?Learn about the types of physical health information collected, and examples of what we’ve already learned about physical health from the cohorts to help shape your own research questions.The session will include:an outline of physical health data and measures available in the CLS cohorts, including objective and self-reported anthropometrics, biomarkers, health behaviours and linked administrative dataan explanation of the process for accessing the data and resourcesa showcase of research using physical health measures in the cohortsan overview of research opportunities that the data can offera Q&A with our expert panel.Who should attend?This webinar will be beneficial for students, academics, third sector and policy individuals from across disciplines, and all career stages, who are interested in using physical health measures from the cohorts in their research. No prior experience of the data is required.It will also be of interest to those wishing to conduct cross-cohort analyses. radhika.jhamaria.23@ucl.ac.uk (UCL Centre for Longitudinal Studies) Wed, 22 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14906 https://www.ncrm.ac.uk/training/show.php?article=14983 Power up your thematic analysis by learning how to harness NVivo's tools for rigorous and reflexive tasks throughout your analysisCourse overview and aimsNVivo can be harnessed to undertake qualitative and mixed-methods analysis drawing on a range of different analytic methods. Thematic analysis is a common approach which has become increasingly popular in recent years – amongst students working on qualitative dissertations and researchers working across academic and applied research sectors (e.g. government, policy, charity, market-research etc.).This course focuses on how the techniques involved in the phases of work in a thematic analysis can be accomplished using the software tools provided by NVivo. Using Braun & Clarke’s (2021) six phases of Reflexive Thematic Analysis as an example framework, we set up a thematic analysis in the software and explorea range of tools that can be used to familiarise with data, code data, generate, develop, review and refine themes, capture analytic reflections throughout the process, and share findings and process. We discuss the appropriateness of tools to enact different analytic tasks, and the benefits of using NVivo for thematic analysis in comparison to manual methods.This course teaches the latest version of NVivo – currently v15. Participants do not need to purchase a license to follow the course, as the trial version is sufficient for the purposes of the course. Learning objectivesBy the end of the course, participants will be able to:Understand the range of NVivo tools than can be harnessed throughout common phases of Reflexive Thematic AnalysisSet up an NVivo project and plan its use for analysisUse NVivo tools for data familiarisation, coding, theme development and refinement, and continual reflectionOrganise qualitative data based on factual characteristics (e.g. participant socio-demographics).Be comfortable with the possibilities for interrogation and mapping in the software to identify and explore thematic patternsSave and back up projects, share findings in different forms TopicsStrategies and tactics in thematic analysis – the importance of methodology and how research objectives drive the use of software toolsApproaches to thematic analysis – similarities and differences in thematic analysis approachesPlanning an analysis – the purpose and use of Analytic Planning Worksheets for planning the tasks involved in the phases of thematic analysisData formatting – transcription protocols that maximise functionality in NVivoSetting up a project – structuring the NVivo workspace in line with your objectivesFamiliarising with data – in-depth annotation and initial high-level explorationsConceptualising data – interpretive and inductive coding compared with automated coding options and their role in thematic analysisIdentifying, developing and refining themes – using NVivo tools to construct, work with and explore themes as you develop themOrganising data – attaching socio-demographics or other meta-data to the units in your analysisInterrogating and visualising data – uncovering relationships and mapping ideas, sharing findings in a variety of ways Who is this course for?This course is designed for anyone interested in using NVivo to undertake thematic analysis of qualitative materials including transcripts from interviews, group discussions, observations etc.No prior knowledge of NVivo is required. Format and documentationThis course is delivered in a live online session that combines discussion, demonstration and hands-on exercises. This is not a 'do this to achieve that' course, but uses more interactive and demonstrative techniques to give you real, tangible practice at using the software for what you need it to do.To deliver as tailored an experience as possible, we contact you on enrolment in order to understand your research goals and analytical strategies. Additionally, our participant numbers are capped to a small group size, allowing us to cover the core topics, as well as specialist needs arising out of individual projects.Participants have the opportunity to discuss their qualitative projects with each other, and with the facilitators.Participants are provided with slide decks, reading lists and resources to further knowledge about the topics covered during the day. Feedback from attendees"The way that Christina taught using thematic analysis model short projects was really interesting. The sample projects were easier to understand. One of the best lecturers that simplified the software. I will be recommending this workshop for all""Really interactive session, completing the steps alongside Christina was very rewarding in showing personal progress but also allowing plenty of opportunities to ask questions.""Christina is so knowledgeable and a great presenter. The small classes allow her to address each participant’s needs. The classes are designed to be interactive so we can use the software right along with her instructions and examples. She spent enough time on each section that I felt I could continue on my own with confidence." FacilitatorChristina Silver, PhD is the director of Qualitative Data Analysis Services and manager of the CAQDAS Networking Project at the University of Surrey, UK. Christina’s interests relate to the relationship between technology and methodology and the effective teaching of qualitative methods and digital tools. She is co-author of Using Software in Qualitative Research: A Step-by-Step Guide (Sage publications, 2007, 2014) and Qualitative Analysis using ATLAS.ti/MAXQDA/NVivo: The Five-Level QDA® Method (Routledge 2018). Christina has trained more than 11,000 researchers around the world in qualitative methods and the use of digital tools for analysis, since 1998, and is a Fellow of the UK Academy of Social Sciences. About QDAS | Qualitative Data Analysis ServicesQDAS provide tailored and flexible training, consultancy, coaching and analysis for qualitative and mixed-methods researchers. We specialise in facilitating high-quality analysis through the powerful use of digital tools. Our website provides information about our work, including our pedagogy - the Five-Level QDA method, which underpins the way we think about, undertake and teach methods and tools. christina@qdas.co.uk (QDAS | Qualitative Data Analysis Services) Thu, 24 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14983 https://www.ncrm.ac.uk/training/show.php?article=15040 Do you want to know more about the best resource for population studies in the UK? Do you want to understand how the UK census works and how to access the data within it?This free 90-minute online workshop will introduce you to the UK Census, the data held by the UK Data Service, and how to use our web interfaces to access the data. The presentation will focus mainly on the latest UK censuses, the 2021 Censuses for England and Wales and Northern Ireland and the 2022 Census for Scotland, and their specific features. We will also introduce previous censuses and other sources for population studies.The session will consist of a:30-minute presentation on UK Censuses and other population studies40-minute interface demonstration and hands-on practical activity20-minute presentation on the support available from the UK Data Service, followed by questions.Presenters:Placide Abasabanye, UK Data Service. Placide is a Research Associate at the UK Data Service with a PhD in Economics. He has ten years of research experience using survey and census data, alongside eight years of teaching experience at the Universities of Manchester and Lille (France). Bridging his work as a researcher and educator, he leads the UK Data Service Census training programme. His expertise spans microdata and aggregate data analysis, large dataset processing, and econometrics. He particularly enjoys helping learners build confidence, develop critical thinking, and gain practical data skills they can apply in their professional work and studies.Olajuwon Yakub, UK Data Service. Olajuwon is a Data and Platform Engineer at the UK Data Service, working across data and platform development with a focus on census, population and international data. He specialises in automating data-processing workflows and building reliable data pipelines that handle large and complex datasets. His work focuses on improving efficiency, data quality and platform processes to ensure data are accurate, reliable and fit for purpose for users.Eve Little, UK Data Service. Eve is a Research Impact and Engagement Manager at the UK Data Service. She works across evaluation, communications and research engagement, with a particular focus on translating complex research into engaging, accessible stories that resonate with a range of audiences. She supports early career researchers as they develop their skills and confidence in working with data for impactful social science research.Level: IntroductoryExperience/knowledge required: Some familiarity with spreadsheets may be useful but is not essentialTarget audience: Anyone interested in learning more about the UK population and using census data for research or studyThis event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.This event is part of our UK Data Service introductory training series: Autumn 2026. mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15040 https://www.ncrm.ac.uk/training/show.php?article=15047 Feeling overwhelmed by the command line? Confused by file pathways? Want to navigate the world of coding with confidence? Join the Code Anxiety Club!Viewers can follow along as we work through common beginner topics while coding live for a quick half hour. No prior experience, installed software or setup required. Viewers can interact via the YouTube chat (you must have a YouTube account to comment) and we will try our best to answer your questions and comments.There is no need to book a place, please follow the livestream link to join the session.Introduction to the Command LineContent:What is the command line and why should you care? Basic commands to get startedCommand line interfaces: Windows, Mac, and Linux comparedTo join this session, please follow the link to our livestream - 3 November 2026 mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15047 https://www.ncrm.ac.uk/training/show.php?article=14914 A well designed theory-based evaluation (TBE) helps unpick the often unclear expectations and assumptions which underlie new policy, programmes and initiatives. It can go well beyond measuring what comes out of an intervention to better understand what works (and what doesn’t), and why, and what may be holding it back from working better. Although not a new approach, its use has been held back by confusion about the different approaches to TBE, and a lack of practitioner knowledge about how to apply these promising methods. The course will provide a practical introduction to TBE principles, setting out different ‘layered’ options and approaches and practical examples of how it can be used in often complex social and community development contexts. This online course provides a more flexible opportunity for an introduction to TBE. It shares much of the same content as the ‘face to face’ SRA course and is led by the same tutor.Looking to book for six or more people from your organisation? Contact us to find our more about our in-house training: training@the-sra.org.uk training@the-sra.org.uk (Social Research Association) Wed, 29 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14914 https://www.ncrm.ac.uk/training/show.php?article=14958 A three-day online course, delivered over three weeks, on using AI agents to improve survey research workflows while preserving privacy, reproducibility, review discipline, and institutional data-governance boundaries.The course adapts agentic coding methods to the survey-research lifecycle: designing survey instruments, analysing survey data, documenting metadata and derived variables, checking outputs, and preparing reproducible or release-ready packages. Participants work hands-on with Cursor, GitHub, and role-specific repository workflows, producing auditable artifacts they can reuse in their own organisations.The course covers:• Agentic coding foundations and Cursor workflows for survey research• GitHub issues, branches, pull requests, review logs, and acceptance criteria• Project guardrails: rules, AGENTS.md, .cursorignore, .gitignore, and documented data-use boundaries• Restricted-data decision rules for cloud AI, local models, metadata-only work, synthetic data, and TRE/sandboxed environments• Spec-driven development for survey workflows• Applied researcher track: literature/context, safe data exploration, cleaning, descriptives, regression or standard analysis, tables/figures, reproducible write-up support• Survey practitioner track: measure search, questionnaire specification, respondent materials, piloting, ethics documentation, testing notes, fieldwork monitoring• Survey data manager track: metadata, derived-variable specifications, longitudinal consistency, cleaning checks, disclosure checks, deposit/user documentation• Reusable skills and subagents for measure mapping, questionnaire review, metadata checks, derived-variable checks, disclosure risk review, and user-guide review• Role-specific capstone packaging: reproducible analysis, questionnaire/specification workflow, or data-release documentation• 30-day adoption planning for post-course implementationBy the end of the course participants will:• Set up a practical agentic workflow using Cursor, GitHub, and a role-specific repository structure• Use issues, branches, pull requests, review logs, and acceptance criteria to make survey workflow tasks auditable• Apply restricted-data decision rules for cloud AI, local models, metadata-only work, synthetic data, and TRE/sandboxed environments• Configure project guardrails with rules, AGENTS.md, .cursorignore, .gitignore, and documented data-use boundaries• Turn a survey workflow into a spec-driven plan with a safe input map, role-specific deliverables, and verification checks• Build or adapt reusable skills and subagents for survey-specific work such as measure mapping, questionnaire review, metadata checks, derived-variable checks, disclosure risk review, and user-guide review• Package a reviewed output for their role track: reproducible analysis, questionnaire/specification workflow, or data-release documentationTarget AudienceThe course is designed for a mixed audience across the survey research lifecycle:• Applied researchers using existing survey data for empirical social science (economics, sociology, political science, and related disciplines)• Survey practitioners designing questionnaires, respondent materials, pilot tests, and fieldwork processes• Survey data managers preparing, documenting, checking, and releasing survey datasets• Government researchers, academic researchers, and staff at survey agencies and research institutions• Mixed seniority: junior and senior researchers, analysts, and operational staffTechnical levels range from participants comfortable in R/Stata/Python to participants who mainly work through specifications, documents, and operational workflows. Recommended cohort cap: 20 participants unless additional facilitators support breakouts and troubleshooting.Pre-requisitesNo specialist prior knowledge of agentic coding or AI tools is required. Participants should:• Be comfortable using a computer, web browser, and video conferencing• Have interest in survey research workflows (design, analysis, or data management)• Be willing to create GitHub and Cursor accounts before Day 1• Complete the pre-course setup checklist or attend the setup clinic• Choose one role track before or during prework: applied researcher, survey practitioner, or survey data manager• Bring a non-sensitive workflow description, public dataset, synthetic example, questionnaire specification, or metadata extractSoftware prerequisites:• Cursor and GitHub accounts (required)• Python 3.10+ recommended for examples; not required for participants using R/Stata/SPSS patterns only• Participants must avoid bringing real restricted microdata unless explicit permission exists for the exact tool workflowPreparatory readingRequired before Day 1:• Create GitHub and Cursor accounts• Complete the course setup guide and prework checklist (course repository)• Choose one role track: applied researcher, survey practitioner, or survey data manager• Select a non-sensitive workflow or use the provided synthetic survey example• Read the restricted-data safety guidanceDesirable:• Attend the pre-course setup clinic if you have limited admin rights or limited Git/GitHub experience• Review basic GitHub concepts: repositories, issues, branches, and pull requests• Install minimum viable toolset: Cursor, browser access to GitHub, ability to download/unzip files• Optional recommended setup: Git, Python 3.10+, Node.js 18+, and any role-specific tools (R, Stata, SPSS)Course materialsParticipants will receive course slides and access to course repository materials.Participants should have/bring:• A computer with internet access and ability to join live video sessions and share screen• GitHub and Cursor accounts (set up before Day 1)• A non-sensitive workflow description, public dataset, synthetic example, questionnaire specification, or metadata extract for their capstone work• Headphones/microphone recommended for breakout-room workParticipants should NOT bring real restricted microdata unless explicit permission exists for the exact tool workflow.The course will run over 3 live online sessions on 5, 12 and 19 November (13:00-17:00) and there will be asynchronous assignments between sessions. This course equates to two teaching days for payment purposes. jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 21 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14958 https://www.ncrm.ac.uk/training/show.php?article=15041 Did you know that longitudinal data from major social studies such as the British Birth Cohort Studies, Understanding Society and the English Longitudinal Study of Ageing can be accessed for research?The longitudinal studies are extremely rich data sources for high impact and policy relevant research. The British Birth Cohort Studies (e.g. the National Child Development Study 1958 and The Millennium Cohort Study) follow the same group of children born in a specific year throughout their lives, tracking changes and unravelling the reasons behind them. Understanding Society is an innovative and world-leading study about live and its changes in the UK in the 21st century. The real world impact of these studies continues to be substantial. Additionally, the Covid-19 pandemic has created a defining moment for longitudinal research.Longitudinal studies provide a unique opportunity to track both short and long term effects of the pandemic, alongside invaluable pre-pandemic measures of health and behaviour. Many longitudinal studies have added Covid-19 specific datasets and we will explore what some of these cover.Most of the studies can be downloaded after a short registration. Subsamples of some of these datasets are also available for teaching purposes.This free 90-minute online workshop will consist of a 30-minute presentation, followed by 20 minutes for questions, and an optional 40-minute hands-on practical. The presentation and hands-on are separate parts of the workshop and attending the hands-on is optional.The workshop will include:An overview of the longitudinal studies available via the UK Data Service.A tour on how to navigate our new website to search for longitudinal studies.Quiz and participant interaction.Updates and findings from Covid-19 datasets.The key features of longitudinal surveys and the policy relevance (impact) of the information they contain.How to access the data, undertake exploratory online analysis and get further help.Presenters:Beate Lichtwardt, UK Data Service.Melissa Ogwayo, UK Data Service. Melissa is a User Support and Training Officer at the UK Data Service, based at the UK Data Archive at the University of Essex. She holds an MSc in Data Science and supports researchers using the UK Data Service SecureLab, with expertise in data and software import requests. Melissa enjoys delivering practical training that helps researchers navigate secure research environments and has a particular interest in synthetic data, statistical programming and responsible data access.Level: IntroductoryExperience/knowledge required: NoneTarget audience: Researchers/anyone interested in longitudinal dataThis event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.This event is part of our UK Data Service introductory training series: Autumn 2026. mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15041 https://www.ncrm.ac.uk/training/show.php?article=14692 Want to learn more about mixed methods research? Need support or advice with your mixed methods research? Our online course could help you!The ‘Introduction to Mixed Methods Research’ course from (Methodical) will be delivered by experienced mixed methods researchers Dr Sarah Jasim, a senior research fellow at University College London and London School of Economics and Dr Ruth Plackett, a senior research fellow at King’s College London.The course will cover:Key principles and procedures in mixed methods researchWhat is mixed methods research and why do we use it?How to plan a mixed methods research project.Understanding models of sequence and priority used in mixed methods research.How to analyse data and combine results.There will be opportunity to discuss your own research questions, methods, and desired outcomes.Relevant for PhD students, post-docs and researchers across disciplines and industries.The course will also be available on Monday Mar 2nd or Monday October 19th, 2025, 10-4pm online.   ruth.l.plackett@kcl.ac.uk (KCL) Thu, 08 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14692 https://www.ncrm.ac.uk/training/show.php?article=14915 Artificial intelligence and large language models (LLMs) are becoming part of everyday social research, being used in everything from literature reviews to survey question development, analysis and reporting. This course will equip participants with the tools to understand the current capabilities of these models and how to use them effectively in the quantitative research workflow.The course starts by introducing key concepts such as AI, machine learning, and Large Language Models (LLMs) in non-technical language. It then discusses the stages of quantitative research and how LLMs can be used effectively at each stage. Participants will learn how to choose between different types of tools, such as chatbots, in-editor assistants, and coding agents, and why instructions, context, tools, and human review shape what LLM systems can do.The course is a mix of lectures covering key concepts in non-technical terms, workflows, and examples, as well as hands-on practicals where researchers can apply key concepts to real-world problems to better understand the strengths and limitations of these tools.Looking to book for four or more people from your organisation? Please let us know before booking by emailing: training@the-sra.org.uk training@the-sra.org.uk (Social Research Association) Wed, 29 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14915 https://www.ncrm.ac.uk/training/show.php?article=14916 Research design always has to spell out how to gather data in a way that helps researcher(s) tackle the main aims and questions of a project. When those aims and questions are already fixed, as they usually are when responding to a tender, the focus shifts to creating a design that is realistic, convincing, and gives the researcher(s) what they need to deliver the project.This course will give participants a live experience of qualitative research design. We will work collectively and in small groups using a sample tender to scope out the multiple ways in which it would be possible to approach the research. We will draw on established and novel research methods and different combinations of both, to show the variety of options available to participants when they undertake qualitative research. We will consider the relationships between research questions, data and analysis; and the key issues of sampling, access, engagement, timing and ethics.By the end of the course, participants will have gained new knowledge and experience of qualitative research design. They will have a good understanding of relevant practical and ethical issues, and be in a position to propose robust designs that meet the needs of relevant calls for tender.Looking to book for four or more people from your organisation? Please let us know before booking by emailing: training@the-sra.org.uk training@the-sra.org.uk (Social Research Association) Wed, 29 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14916 https://www.ncrm.ac.uk/training/show.php?article=14966 The proliferation of digital data and devices has enabled social scientists to collect, store, manage, manipulate, and interpret data in multifarious ways. Researchers frequently need to access online data sources, such as social media platforms, and present their analysis on the web through interactive dashboards and visualisations. Traditionally, developing these digital systems has required technical programming skills that take years to master. This hands-on course introduces an emerging alternative: “vibe coding”. Vibe coding is the practice of generating code by prompting an AI system through natural language. Rather than writing code line-by-line, participants will learn how to interact with Large Language Models (LLMs) to generate, test, and debug code. Designed specifically for social science researchers with zero technical or programming background, this course focuses on transferable skills rather than tying participants to a single platform. While a specific AI coding platform will be used for practical exercises, the course primarily introduces foundational concepts and adaptable, ‘future-proof’ strategies needed to confidently explore and integrate new generative AI tools as the technological landscape rapidly evolves. Participants will learn how to map their existing research methodologies into manageable computational workflows. Crucially, the course explores not just how to use these technologies, but when tasks can be justifiably and safely delegated to AI, and when they should not be. Through interactive live demonstrations and flexible hands-on workshops, participants will practice using AI to build custom research tools from scratch. Beyond practical application, the course places a strong emphasis on the ethics and data security concerns of using LLMs for generating code. Participants will learn how to set up AI-assisted coding environments and critically evaluate the methodological and ethical implications of embedding AI-generated code into their research processes. No prior coding experience is required.PLEASE NOTE THIS COURSE IS BEING TAUGHT OVER TWO MORNINGS AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES. Engage@liverpool.ac.uk (University of Liverpool) Wed, 26 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14966 https://www.ncrm.ac.uk/training/show.php?article=15028 Training for PhD and MSc students in the design of social research, quantitative and qualitative analysis.Measuring policy positions of political agents by Michael J Laver (Emeritus)Measuring the position and salience of agents' preferences on key issues is a core empirical project for social science. Methods used to do this include: surveys of experts, political elites and voters; scaling of legislative roll-calls; expert, crowd-sourced or automated analyses of text. Large language models (LLMs) can now do this much more quickly and cheaply.Differences between methods highlight important conceptual and methodological questions about what we mean by, and how we measure, issue position and salience. These include the separability of estimated positions and saliences on different issue dimensions. We focus on a range of different approaches to measuring the position and salience of agents' preferences, but particularly on the systematic and replicable use of LLMs.  methodology.admin@lse.ac.uk (LSE) Mon, 12 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15028 https://www.ncrm.ac.uk/training/show.php?article=14404 Level: Intermediate (I)This course will be delivering over 4 afternoon sessions, running from 1:00pm to 5:00pm each day.This course covers the fundamentals of machine learning and the methodology for applying these to real-world analytics problems. The course outlines the stages involved in a machine learning analysis, and walks through how to perform them using the R programming language and the tidymodels suite of packages. Participants will be provided with exercises to complete through the course in order to gain hands-on experience in using the methods presented.The individual stages of: problem formulation, data preparation, feature engineering, model selection and model refinement will be walked through in detail giving participants a solid process to follow for any machine-learning analysis. This includes methods for evaluating machine-learning models in terms of a performance metric as well as assessing bias and variance.  Learning OutcomesFollowing this course the attendees will:Be familiar with the overall process of how to apply machine-learning methods in an analysis projectUnderstand the differences and similarities between statistical modelling and machine-learning theoriesHave gained hands-on experience in working with the tidymodels suite of packages in RGain an intuitive understanding of how several specific machine-learning methods solve the problems of prediction and classification Topics CoveredIntroduction to machine-learning: parsnip package; basic train and testStages of machine-learning: problem formulation; data preparation; feature engineering; model selectionHighlighted Models: Decision trees and random forests; K-nearest neighbours, linear regression and logistic regression. Target AudienceMachine Learning can be applied to data in a whole range of fields from Finance to Pharmaceutical, Retail to Marketing, Sports to Travel and many, many more! This course is aimed at anyone interested in applying machine learning methods to their data in order to: gain deeper insight, make better decisions or build data products Assumed KnowledgeThis course assumes participants are comfortable with the basic syntax and data structures in the R languageFor this online course, participants are not required to have R installed on their own laptops. A virtual environment, which can be accessed through a web browser, will be used to run R and view course materials. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14404 https://www.ncrm.ac.uk/training/show.php?article=14876 Overview of 1-day courseAre you working in Learning Analytics or Student Analytics?Ever been asked for the margin of error on a percentage?Or if the difference between your percentage and a benchmark is statistically significant?Or if the change in student retention rate from last year is statistically significant?This one-day course provides participants with hands-on experience of analysing their own type of records for data-driven planning and confidently interpreting numerical results for reports to policy makers and committees, or for returns to the Higher Education Statistics Agency.Presentations, demonstrations and hands-on computer practicals will make use of the free statistical software R; see https://www.r-project.org/.  Note the course does not focus on teaching R, but on the use of supplied R functions to perform statistical analyses of interest.  Formulae are kept to a minimum; instead, we concentrate on results, their interpretation and reporting in plain language.​PresentersSandro Leidi and James GallagherCost£282 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 17:30 (GMT).  The delivery platform is Zoom, which may be freely accessed.  Questions may be asked verbally or using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Administrators in educational establishments working in Policy, Planning and Strategy units; Data and Insight units; Business Intelligence units; those involved in learning analytics or extracting actionable insights from student records and in reporting to policy makers or committees; those involved in producing returns to the HESA.  Anyone in these positions will benefit greatly from this course.No previous experience of the R software is required.How You Will BenefitBy the end of the course you will be familiar with basic statistical methods for making sound assessments, and extracting actionable insights.  What Do We Cover?Estimation: standard errors and confidence intervals for percentagesComparing a percentage to a benchmark: one-sample z-testComparing two percentages: two-sample z-testComparing more than two percentages: chi-square test.The course will make use of our R functions which will be made freely available. Note the course does not focus on teaching R, but on the use of the supplied R functions to perform statistical analyses covering the above syllabus.SoftwarePractical work will be done in R.Note:For practical work, participants must download and install the R software.  This must be done prior to the start of the coursePractical work is based on the Windows operating system.Extra InformationThe content of this course is identical to that of Statistics for University Administrators. The only difference is the R statistical software is used instead of Excel.  R has two advantages:It is a free dedicated statistics package and can be used for other analysesIt is a widely used software which will be maintained by the R Foundation for many years to come. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Tue, 16 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14876 https://www.ncrm.ac.uk/training/show.php?article=15035 Explore how dynamic structural equation modeling (DSEM) combines multilevel modeling, structural equation modeling, and time-series analysis to analyze intensive longitudinal data and investigate within-person change and multivariate dynamics over time. natalie@statisticalhorizons.com (Statistical Horizons) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15035 https://www.ncrm.ac.uk/training/show.php?article=14893 OverviewGetting started with NVivo: a comprehensive introduction to qualitative data analysis. Learning objectivesBy the end of the course, participants will be able to:Strategies and tactics in qualitative research – the importance of methodology and how research objectives drive the use of software toolsData formatting – transcription protocols that maximise functionality in NVivoSetting up a project – structuring the NVivo workspace in line with your objectivesExploring data – in-depth annotation and initial high-level explorationsConceptualising data – interpretive and inductive coding compared with automated coding optionsOrganising data – attaching socio-demographics or other meta-data to the units in your analysisInterrogating and visualising data – uncovering relationships and mapping ideas Who is this course for?This course is designed for postgraduate students and early career researchers in any discipline working on or planning to work with interview or focus group data.No prior knowledge of NVivo is required. Course overview and aimsNVivo can be harnessed to manage, analyse and interpret any qualitative materials. This course focuses on harnessing NVivo when working with primary data generated from conversations with participants through interviews and focus-group discussions. It is framed by the Five-Level QDA® method, which provides an adaptable framework for ensuring the tools you use in NVivo are driven by the methodological needs of your project.The course provides a comprehensive overview of NVivo by introducing its core components using sample transcripts and course participants are facilitated in using the software in guided hands-on sessions. We discuss common approaches to analysing interview and focus-group transcripts, open-up thinking about how to appropriately choose between them and provide a framework for planning and documenting your analysis. TopicsStrategies and tactics in qualitative research – the importance of methodology and how research objectives drive the use of software toolsApproaches to analysing interview and focus-group transcripts – commonly used analytic approaches for interview and focus-group data and their appropriateness to your objectivesPlanning an analysis – the purpose and use of Analytic Planning WorksheetsData formatting – transcription protocols that maximise functionality in NVivoSetting up a project – structuring the NVivo workspace in line with your objectivesExploring data – in-depth annotation and initial high-level explorationsConceptualising data – interpretive and inductive coding compared with automated coding optionsOrganising data – attaching socio-demographics or other meta-data to the units in your analysisInterrogating and visualising data – uncovering relationships and mapping ideas Format and documentationAll our workshops are hands-on, delivered through a blend of demonstration, discussion and practical exercises, rather than providing simplistic, mechanical instruction.To deliver as tailored an experience as possible, we contact you on enrolment in order to understand your research goals and analytical strategies. Additionally, our participant numbers are capped to a small group size and we run our courses with two facilitators, allowing us to cover the core topics, as well as specialist needs arising out of individual projects.Participants are provided with slide decks, reading lists and a range of resources to accompany the course and to support consolidation of the topics covered.Participants must have access to NVivo on either a Windows or Mac machine. The course will be taught on NVivo v15, however, participants running NVivo 12 or later are also able to follow the course. A two week trial version of the software is available free of charge from the NVivo website Feedback from attendees"Really helpful (and impressive!) that both hosts were very knowledgeable about how to use NVivo on both Mac and Windows.""Sarah and Christina’s style of teaching is spot on! Super clear, straightforward and to the point. What a great learning experience. Thank you.""Christina and Sarah are extremely positive and engaging and very supportive when queries are raised. They have so much knowledge and experience and explain very clearly in plain English""As a standalone course - it is very useful and helpful and they are probably the best, or at least, one of the best presenters for online learning courses that I have done. ""The training materials were effective, particularly due to the helpfulness of the instructors. The hands-on training approach made the one-day online session engaging and interactive, ensuring that it never felt boring. Time truly flew by during the training! I especially appreciated the detailed explanations about NVivo, which have significantly boosted my confidence in how to approach my research. Overall, I found the training to be extremely useful, providing me with valuable skills and insights that I can directly apply to my work. Thank you for the excellent training experience!" FacilitatorsChristina Silver, PhD is the director of Qualitative Data Analysis Services and manager of the CAQDAS Networking Project at the University of Surrey, UK. Christina’s interests relate to the relationship between technology and methodology and the effective teaching of qualitative methods and digital tools. She is co-author of Using Software in Qualitative Research: A Step-by-Step Guide (Sage publications, 2007, 2014) and Qualitative Analysis using ATLAS.ti/MAXQDA/NVivo: The Five-Level QDA® Method (Routledge 2018). Christina has trained more than 11,000 researchers around the world in qualitative methods and the use of digital tools for analysis, since 1998, and is a Fellow of the UK Academy of Social Sciences.Sarah L Bulloch, PhD is a senior associate of Qualitative Data Analysis Services and teaching fellow at the CAQDAS Networking Project at the University of Surrey, UK. Sarah has expertise in both qualitative and quantitative analysis techniques and has applied them in academic, public sector, private and third sector contexts. Her current work and research centres around enabling others to apply research methods, and the digital tools to support them, in a meaningful, robust and flexible way. Sarah first used CAQDAS packages in 2006 and started teaching them in 2010. About Qualitative Data Analysis ServicesQDA Services provide tailored and flexible training, consultancy, coaching and analysis for qualitative and mixed-methods researchers. We specialise in facilitating high-quality analysis through the powerful use of digital tools. Our website provides information about our work, including our pedagogy - the Five-Level QDA method, which underpins the way we think about, undertake and teach methods and tools. info@qdas.co.uk (QDAS | Qualitative Data Analysis Services) Tue, 23 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14893 https://www.ncrm.ac.uk/training/show.php?article=14979 Intersectional MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy) is an increasingly popular approach for studying inequalities in individual outcomes across intersections of social identity characteristics, such as gender, race, and socioeconomic background.In the first half of the session, Professor George Leckie will introduce the MAIHDA approach through an application examining intersectional inequalities in educational attainment using data from the Programme for International Student Assessment (PISA). He will explain the underlying multilevel modelling approach and discuss the interpretation of MAIHDA results.The session will also provide a high-level overview of recent substantive and methodological work using MAIHDA, including extensions for studying intersectional inequalities in longitudinal trajectory data and for examining heterogeneity of treatment effects in randomised controlled trials.In the second half, there will be a demonstration of how to conduct a standard MAIHDA analysis in both R and Stata.Familiarity with standard multilevel models would be beneficial, but is not essential (for a QMEG introduction to multilevel models, see here: https://qmeg.soton.ac.uk/events-and-resources/ ).The session is free to attend and will take place online via Microsoft Teams. J.E.Hall@Soton.ac.uk (University of Southampton) Wed, 23 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14979 https://www.ncrm.ac.uk/training/show.php?article=15042 Did you know that qualitative and mixed methods data can be accessed for research?The UK Data Service provides access to a range of qualitative and mixed-methods data, including ethnographic data and field notes, images, oral history transcripts, and essay responses. This workshop will help you get started searching for qualitative and mixed methods data with new search tools, like QualiBank, and help you quickly navigate through datasets to see if it's the right data for your project.This free introductory workshop is intended for anyone who wants to learn about the qualitative or mixed-methods data available from the UK Data Service.The workshop will cover:the types of qualitative data available from the UK Data Servicea short case study exemplifying how qualitative data has been used3 key issues to consider when using searching for qualitative datahow to access the data and getting further helpPresenter: Maureen Haaker, UK Data Service. Maureen has worked with the UK Data Service for over 12 years supporting researchers and institutions in managing, preserving, and sharing qualitative data ethically and effectively. She runs training sessions on research data management, advises on best practices in data curation and anonymisation, and has worked on special projects, including a UKRI-funded project exploring uses of synthetic data in Trusted Research Environments (TREs). She is co-editor of the IASSIST Qualitative Data Special Interest Group and a member of the DDI Working Group developing metadata standards for qualitative data.Level: IntroductoryExperience/knowledge required: NoneTarget audience: Researchers/anyone interested in qualitative or mixed methods dataThis event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.This event is part of our UK Data Service introductory training series: Autumn 2026. mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15042 https://www.ncrm.ac.uk/training/show.php?article=14917 This introductory course provides an overview of the key principles and procedures in mixed methods research. Mixed methods research can be a valuable approach when conducting research on complex topics that cannot be fully understood by quantitative or qualitative research alone. The course covers the theories behind mixed methods research, why to use mixed methods research and how to integrate the quantitative and qualitative elements of your research. We will focus on how to design and conduct mixed methods research and how to present and report mixed methods findings.Attendees are provided with guidance and will have the opportunity to discuss their projects and how they can use mixed methods research in their work. This course will benefit those involved in research in a variety of sectors and disciplines, and only a basic understanding of quantitative and qualitative research methods is required.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses training@the-sra.org.uk (Social Research Association) Wed, 29 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14917 https://www.ncrm.ac.uk/training/show.php?article=15048 Feeling overwhelmed by the command line? Confused by file pathways? Want to navigate the world of coding with confidence? Join the Code Anxiety Club!Viewers can follow along as we work through common beginner topics while coding live for a quick half hour. No prior experience, installed software or setup required. Viewers can interact via the YouTube chat (you must have a YouTube account to comment) and we will try our best to answer your questions and comments.There is no need to book a place, please follow the livestream link to join the session.Package It Up: Installing Software and LibrariesContent:Learn how to install your programming language of choice (Python and R will be covered) Understand the available package managers and why they are useful for installing coding librariesExplore the benefits of ‘virtual environment’ and when they might be usefulTo join this session, please follow the link to our livestream - 17 November 2026  mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15048 https://www.ncrm.ac.uk/training/show.php?article=14809 JBI Scoping Review Workshop (Online Short Course) This JBI-accredited course covers the key principles, steps and reporting guidelines for undertaking a Scoping Review and explores how Scoping Reviews are different from Systematic Reviews. The course is delivered by experts from the University of Nottingham Centre for Evidence Based Healthcare and will run online over two days (November 19th & 20th 2026, 09:30-13:30 on each day).  catrin.evans@nottingham.ac.uk (University of Nottingham) Wed, 15 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14809 https://www.ncrm.ac.uk/training/show.php?article=14918 Over the years the discipline of evaluation has evolved and developed and is applied to many policies and practices. It forms the core of policy, accountability and strategic planning processes of many different types of organisations, and informs evidence-based decision making about public and grant funded programmes and initiatives. Much is expected of evaluation in the public, private and voluntary sectors and people responsible for commissioning, conducting and using evaluation are increasingly required to have a broad understanding of the evaluation types, skills and competencies. The course provides an introduction to this wide field and will be suitable for those new to evaluation and others who may have some experience but are looking to widen their knowledge of evaluation options and practice.*For those with more experience or needing more specialist methods, SRA also offers courses in Impact Evaluation (Advanced), Theory-based Evaluation (Advanced) and Research and Evaluation Project Management (Introductory).Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house training. training@the-sra.org.uk (Social Research Association) Wed, 29 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14918 https://www.ncrm.ac.uk/training/show.php?article=15034 Learn how to use Claude as a systematic research partner for ideation, data collection, analysis, reproducibility, and publication-ready academic writing. natalie@statisticalhorizons.com (Statistical Horizons) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15034 https://www.ncrm.ac.uk/training/show.php?article=14967 Large language models are now embedded in research workflows across the social sciences, yet most researchers interact with these tools through consumer interfaces that obscure how they work, where data goes, and what decisions are being made on their behalf. This three-workshop series closes that gap. Across three standalone half-day sessions, participants build a working understanding of the AI stack: from how models generate text and where inference happens, through prompt engineering, retrieval-augmented generation, and API-based workflows, to the rapidly maturing ecosystem of agentic platforms, harness engineering, and autonomous research infrastructure. Each workshop combines conceptual exposition with live demonstrations and practical exercises grounded in survey research scenarios. No programming experience is required for Workshop 1; Workshops 2 and 3 assume familiarity with earlier concepts.The course covers: Workshop 1: How Large Language Models Work: tokens, training, alignment, data security, inference, open- vs closed-weights models, reproducibility challenges, and the limitations of chatbot interfaces for research.Workshop 2: Context Engineering: prompt design and optimisation, retrieval-augmented generation (RAG), API-based workflows and batch processing, memory and tool-calling, MCP servers, and evaluation engineering.Workshop 3: Agentic AI and Harness Engineering: the agentic AI ecosystem (IDE-native agents, extended-autonomy platforms, orchestration tools), harness engineering and SDKs, memory and token economics, MCP servers and hooks, oversight, auditability, and research transparency.By the end of the course participants will:Explain how LLMs generate text and assess the implications of model architecture, training, and alignment for research practiceDistinguish between open-weights and closed-weights models and evaluate their data governance implicationsApply prompt optimisation techniques and build evaluation pipelines to validate LLM outputsMake structured API calls, manage parameters, and use retrieval-augmented generation where appropriateMap the agentic AI ecosystem, explain harness engineering, and assess how platforms orchestrate memory, tools, and contextDesign human-in-the-loop safeguards and audit protocols appropriate for agentic research workflowsPre-requisitesNo prior programming experience or specialist software knowledge is required for Workshop 1. Workshops 2 and 3 assume familiarity with concepts from Workshop 1 (or equivalent knowledge of how LLMs work). Workshop 3 benefits from some comfort with reading code, but participants are not required to write any. Setup guidance for API access will be provided before Workshops 2 and 3.No software installation is required for Workshop 1. For Workshops 2 and 3, participants will benefit from having API access to a commercial LLM provider (e.g. Anthropic, OpenAI); setup guidance will be provided in advance. All demonstrations will be conducted live by the instructor. Participants do not need prior experience with any specific software, though basic familiarity with web browsers and text editors is assumed.Target AudienceSurvey researchers, methodologists, and quantitative social scientists across academia and government who use or are considering using large language models in their research. The series is designed to be accessible to researchers at all career stages, from doctoral students to senior investigators. Although the recommendation is to attend all three workshops, you are welcome to join them individually. PLEASE NOTE THESE WORKSHOPS WILL RUN ONLINE ON 23 NOVEMBER, 30 NOVEMBER AND 7 DECEMBER 2026 FROM 09:30-13:30. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14967 https://www.ncrm.ac.uk/training/show.php?article=15029 Well-designed graphics are among the most powerful tools social scientists have for exploring data and communicating findings, yet visualization is rarely taught systematically. This hands-on session introduces the principles and practice of effective data visualization using R/ggplot.We begin with the foundations: how visual perception works, which visual encodings suit which data types, and why so many published graphics fail. We then build visualizations step by step using the grammar of graphics. Topics include exploratory visualization, aggregation and uncertainty, and turning exploratory graphics into clear visual arguments for papers and presentations.Participants leave with reusable RMarkdown notebooks and a principled workflow for producing publication-quality, reproducible graphics. Basic familiarity with R is helpful but not required; all materials are provided.Session DetailsTime: 10.00 - 15.00 (12.00 - 13.00 Lunch break)Date: 23 November 2026.Mode: Hybrid: In-person at CON.1.01 and online via Zoom.If you are an ESRC funded student then we require your name, email and course to report your attendance to your home institution, please click here to see the privacy notice that this data collection is subject to. methodology.admin@lse.ac.uk (LSE) Mon, 12 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15029 https://www.ncrm.ac.uk/training/show.php?article=15043 Researchers are often at a crossroads on how to share data collected from human participants ethically while complying with the rigorous standards set by UK General Data Protection Regulation and the Data Protection Act. This introductory workshop offers a foundational understanding of data anonymisation principles and practices tailored for social sciences research.This session is designed to equip researchers with essential knowledge and skills to navigate the complexities of data anonymisation. It will cover practical approaches for safeguarding privacy in the most common data types in social sciences, with a focus on quantitative survey data, and qualitative transcripts; brief considerations for audio/visual materials are included. We will cover key concepts as defined by applicable data protection legislation and outlined by the Information Commissioner's Office (ICO) guidance, from definitions of personal data to identifiability and effective anonymisation.During this free 90-minute online workshop, participants will learn the nuances of de-identification versus anonymisation techniques, how to differentiate between directly identifying information and indirect identifiers and how to best handle special category data, with the view of sharing data ethically and legally.This session includes live exercises using Mentimeter and will conclude with a dedicated Q&A session administered via Padlet.Presenter: Maureen Haaker, UK Data Service. Maureen has worked with the UK Data Service for over 12 years supporting researchers and institutions in managing, preserving, and sharing qualitative data ethically and effectively. She runs training sessions on research data management, advises on best practices in data curation and anonymisation, and has worked on special projects, including a UKRI-funded project exploring uses of synthetic data in Trusted Research Environments (TREs). She is co-editor of the IASSIST Qualitative Data Special Interest Group and a member of the DDI Working Group developing metadata standards for qualitative data.Level: IntroductoryExperience/knowledge required: Basic understanding of research methods in the social sciences and of data protection legislation are recommended but not requiredTarget audience: Academic and non-academic data professionals such as researchers, data managers, data stewards and others involved in collecting data, or managing datasets for wider sharing and re-useThis event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.This event is part of our UK Data Service introductory training series: Autumn 2026. mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15043 https://www.ncrm.ac.uk/training/show.php?article=14667 Overview of 2-day courseMixed models have become increasingly popular, as they have many practical applications. However, the traditional linear mixed model with normally distributed errors is not appropriate for modelling discrete responses such as binary data and counts. Such responses are typically analysed using generalised linear models such as logistic regression and Poisson regression.Commonly-used generalised linear models will be extended to deal with multiple error structures, using a variety of scientific examples, mainly medical and health related applications, such as investigating the presence of adverse events in a clinical trial.The emphasis will be on practical understanding, although an outline of the theory will be presented. Practical examples will be used to illustrate the methods and participants will have the opportunity to fit and interpret models themselves in hands-on computer practicals.Practical work will be based on the R software; see https://www.r-project.org/.  Model fitting will mainly be done using the CRAN package GLMMadaptivePresentersSandro Leidi and James Gallagher Cost£582 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 09:00 and 17:30 (GMT). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Data analysts and statisticians working in medicine, health and related areas, who wish to have a practical introduction to Generalised Linear Mixed Models. It is assumed that participants are R users and familiar with the practical use of both generalised linear models and linear mixed models. How You Will BenefitYou will learn to formulate generalised linear models with both fixed and random effects for a range of situations, how to fit them and how to interpret their output.What Do We Cover?Review of generalised linear models and linear mixed modelsBinary and binomial outcomes: logistic regression with mixed effectsCount outcomes: Poisson and negative binomial regression with mixed effectsOrdered outcomes: proportional odds regression with mixed effectsAdaptive Gauss-Hermite Quadrature fitting method; inferential proceduresConvergence issues and solutionsInterpretation of effects in a generalised linear mixed model and predictionGLMMadaptive CRAN package for fitting generalised linear mixed models; ordinal CRAN package for fitting the proportional odds model with random effects.Notes on course content:The GLMMadaptive package can currently only fit models where the random effects part is defined by a single grouping factorThe course does not cover marginal or GEE type models for repeated measurements.SoftwarePractical work will be done in R.Note: For practical work, participants must download and install a number of CRAN packages in R.  This must be done prior to the start of the course. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14667 https://www.ncrm.ac.uk/training/show.php?article=14919 Project leaders, analysts, contract managers and others involved in commissioning or funding ‘external’ research, or evaluation studies face special challenges in managing for their effective delivery. They typically need to successfully navigate the appropriate scoping and specification of the project, the commissioning process and selection of well-placed contractors as well as subsequent contract scrutiny and management to provide for responsive delivery and where pressures on budgets and often intensive timetables add to the challenges of doing this well. This course provides an intensive introduction to practical processes for managing externally contracted projects, working relationships and minimising delivery risks and challenges. It is aimed at participants from governmental and other public bodies, research councils, larger charities and other organisations commissioned research and evaluation studies.NB. A parallel course, Research and Evaluation Project Management for Project Leaders, focuses on the different project management challenges and skills needed for those designing and delivering commissioned or funded projects. The two separate courses can be taken separately over one day or together over two consecutive days.Looking to book for four or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Wed, 29 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14919 https://www.ncrm.ac.uk/training/show.php?article=14920 Anyone asked to conduct a research or evaluation project or study - small or large - faces the challenge of how best to manage this. University and professional training in research (and evaluation) principles and methods have long neglected this vital aspect of successfully delivering projects. More recently, shrinking budgets and timetables (but not expectations) have added to the challenges for principal investigators and others leading the design and delivery of projects and intensified the need for more structured approaches to project management. Using a mixture of shared slides, interactive sessions and exercises in small groups, this practical course provides an intensive introduction to how to rise to these challenges using tried and tested methods. It is aimed at participants from all sectors, those new to project management in research and evaluation, and those who may have some experience but are looking to widen their knowledge of structured approaches.NB. A parallel course, Management for Commissioned Research and Evaluation, focuses on the different project management challenges and skills needed for those specifying and commissioning externally contracted projects. The two separate courses can be taken separately over one day or together over two consecutive days.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house training. training@the-sra.org.uk (Social Research Association) Wed, 29 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14920 https://www.ncrm.ac.uk/training/show.php?article=14938 One of the most popular software tools for data management and analysis is the open source package R, which is often used with the Rstudio interface. These are extremely powerful and are able to handle most types of data and analyses used in social research.In this course you will learn the basics of R and Rstudio. We will cover the main types of objects in R and how to select cases and variables. We will also discuss how to import and export data and how to describe the data using tables and summary analyses. Through the practical exercises you will get accustomed to running functions in R and using the syntax.IMPORTANT: participants will need to have a working knowledge of quantitative data.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house training. training@the-sra.org.uk (Social Research Association) Tue, 11 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14938 https://www.ncrm.ac.uk/training/show.php?article=14384 Level: Foundation (F)The course will be delivered over 5 morning sessions, running fro 9:00am to 1:00pm each day. The purpose of this course is to help participants understand some basic statistical concepts and develop a strategy for approaching simple data analysis. The course will introduce basic concepts such as hypothesis testing and confidence interval estimation. It will provide the tools to undertake simple analysis of a dataset and will include some helpful hints and tips for reading and understanding reported statistics.Learning OutcomesBy the end of this course, participants will understand basic approaches to statistical inference, including hypothesis testing and confidence interval estimation. They will be equipped with the skills necessary to undertake simple analysis and to understand some of the basic terms often used to report statistical results. The course will include some calculations by hand to aid understanding. Topics CoveredData Summary; The normal distribution; Confidence intervals; Introduction to hypothesis tests; Analysis of contingency tables – chi-squared test;  T-tests; Non-parametric tests, (Wilcoxon signed rank test, Mann-Whitney U test); Introduction to correlation and regression; Basic presentation of data and results. Target AudienceThis course is aimed at those who have either never undertaken a formal statistics course, or who have studied some statistics in the past but wish to undertake a refresher. It is ideal for statistical novices who have never had any formal training but are starting to encounter statistics in their work and wish to gain some insight.Delegate Feedback"Ellen and Jenny were extremely knowledgeable. They were also approachable and happy to give further explanations when necessary""Excellent course. Hard to fault. I can see why it's popular""This was a fantastic course, the presenters had a very high knowledge but were able to 'dumb it down' for me as I am new to this"  training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14384 https://www.ncrm.ac.uk/training/show.php?article=14978 This live online course (administered via Zoom) offers an engaging introduction to Structural Equation Modelling (SEM) using R, the popular open-source software for statistical computing and graphics. The course will run over 5 consecutive days and will combine synchronous live lectures, individual exercises, group discussions and small group supervisions to simulate a live classroom environment. High contact with our expert instructors and interactive group work will further contribute to recreating the classic Cambridge experience in an online setting.This course will present the lavaan package, rapidly becoming the tool of preference for SEM in R. Participants will actively work through practical examples to gain first-hand experience in the application of factor analysis and other more advanced latent trait models. You will learn the following topics in this course:  Introduction to R and data analysisBasic concepts of factor analysis (EFA/CFA/Categorical CFA)Basic and advanced Structural Equation Models (SEM)Application of mediation & moderation techniquesMulti-level modellingLatent growth curve modellingMultiple-group analysis and evaluation of continuous and categorical data measurement invariance, as well as longitudinal measurement invarianceYou don’t need to know R to follow the course. However, if you are not familiar with R, you will need to choose the 5 day option, which includes an introduction to the R software on Day 1. On completion, participants should have a good knowledge of the topics covered and have acquired an independent use of R and latent trait analysis.We believe in active learning and developing practical skills. Thus, the necessary theoretical introduction will be illustrated with practical examples and we will be working with real data. No prior knowledge about SEM is assumed. The pace of teaching is adjusted to suit the level of the participants. Teaching will be in small groups so that delegates can make the most of the teaching. ls523@cam.ac.uk (University of Cambridge Psychometrics Centre) Tue, 22 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14978 https://www.ncrm.ac.uk/training/show.php?article=14939 Qualitative interviewing is a popular method in social research and it is often described as a conversation between interviewer and interviewee. It allows us to collect detailed and rich information about individuals’ lives, their experiences, behaviours, and how they understand and make sense of the world. The rich insight it provides into people’s lives is one of the benefits which the method offers over standardised surveys or questionnaires.This introductory level course introduces participants to the method of qualitative interviewing. This includes its benefits, examples of effective interviewing, and the key ethical and practical issues to be considered. We look at types of qualitative interview which include structured, unstructured and semi-structured interviews. In particular, we explore the benefits of semi-structured interviewing which involves a combination of pre-set open ended questions with room for the exploration of other (sometimes unanticipated) topics. Participants gain experience of designing their own interview schedule and of conducting a semi-structured interview.By the end of the workshop, participants will have knowledge of various forms of qualitative interview and theories of interviewing. They will be able to distinguish between various types of interviews and questioning. They will be aware of practical and ethical issues which must be considered prior to interviews. They will also be able to design their own semi-structured interview schedule and conduct a semi-structured interview.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Tue, 11 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14939 https://www.ncrm.ac.uk/training/show.php?article=14992 This course aims to introduce the statistical software package Stata, including its use for data management and data exploration.This course will give an overview of basic Stata syntax to enable the user to manage and manipulate data and produce summary statistics and graphs.This course will cover:1. getting started with Stata (loading and saving files, finding help, etc.);2. listing, describing and summarising data;3. naming and labelling variables;4. subsetting data;5. generating and replacing variables;6. creating tables;7. importing data from other programs and merging datasets;8. logs and Do files to document output and code;9. creating graphs; and10. working with dates (optional). short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14992 https://www.ncrm.ac.uk/training/show.php?article=15049 Feeling overwhelmed by the command line? Confused by file pathways? Want to navigate the world of coding with confidence? Join the Code Anxiety Club!Viewers can follow along as we work through common beginner topics while coding live for a quick half hour. No prior experience, installed software or setup required. Viewers can interact via the YouTube chat (you must have a YouTube account to comment) and we will try our best to answer your questions and comments.There is no need to book a place, please follow the livestream link to join the session.Getting started with Git: Version Control BasicsContent:Understand the importance of tracking changes to files over time Install git and master basic git commands such as: git init, git clone, git status, git add, git commit, and git push Practical exercises to build confidence in using git and GitHub To join this session, please follow the link to our livestream - 1 December 2026 mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15049 https://www.ncrm.ac.uk/training/show.php?article=14894 OverviewLearn how to review literature and analyse documents efficiently and systematically with NVivo Learning objectivesBy the end of the course, participants will be able to:Differentiate types of literature review and document analysisMake an informed decision as to an appropriate methodology for their needsDevelop a framework for undertaking a literature review and/or document analysis systematicallyNavigate digital tools designed to facilitate literature reviewing and/or document analysisKnow where to go for more resources and advice in undertaking a literature review and/or document analysis Who is this course for?This course is designed for postgraduate students and early career researchers in any discipline working on or planning for a literature review or documentary analysis. No prior knowledge of NVivo is required. Course overview and aimsNVivo can be harnessed to organise a range of types of materials and our ideas about them. This includes electronic copies of journal articles, and documents such as reports, public records and policy statements, amongst other materials. Framed by reviewing and documentary analysis methodologies, this course provides an introduction to NVivo’s powerful tools that facilitate in-depth analysis of individual items, as well as cross-item comparison; all with a view to enabling structured, efficient writing of findings. TopicsTypes of literature review & document analysis – differentiating review methodologies and understanding their purposesReview and analysis questions – developing focused questions to guide reading and appraising literature and documentsPlanning a literature review or document analysis – considerations in ensuring reviews and analyses are efficient and systematicTasks involved in reviewing literature and analysing documents - working directly and indirectly with literature and documentsDigital tools for reviewing and analysing literature and documents – understanding the different role of bibliographic software and the tools available in NVivoStructuring notes and writing up – tying report-writing with review objectives and sharing with different audiences Format and documentationAll our workshops are hands-on, delivered through a blend of demonstration, discussion and practical exercises, rather than providing simplistic, mechanical instruction.To deliver as tailored an experience as possible, we contact you on enrolment in order to understand your research goals and analytical strategies. Additionally, our participant numbers are capped to a small group size and we run our courses with two facilitators, allowing us to cover the core topics, as well as specialist needs arising out of individual projects.Participants are provided with slide decks, reading lists and a range of resources to accompany the course and to support consolidation of the topics covered.Participants must have access to NVivo on either a Windows or Mac machine. The course will be taught on NVivo v15, however, participants running NVivo 12 or later are also able to follow the course.A two-week trial version of the software is available free of charge from the NVivo website. Feedback from previous attendees"The trainers are knowledgeable, passionate and witty . They were able to carry the group and have our full attention during the entire session. Highly recommend to anyone who is doing or planning to do a literature review""Working hands-on in the software in tandem with the course leaders, cementing the learning. This makes me confident that I will be able to use the information going forward in my own work.""All of it was really useful thank you so much! Really practical and meaningful." FacilitatorsChristina Silver, PhD is the director of Qualitative Data Analysis Services and manager of the CAQDAS Networking Project at the University of Surrey, UK. Christina’s interests relate to the relationship between technology and methodology and the effective teaching of qualitative methods and digital tools. She is co-author of Using Software in Qualitative Research: A Step-by-Step Guide (Sage publications, 2007, 2014) and Qualitative Analysis using ATLAS.ti/MAXQDA/NVivo: The Five-Level QDA® Method (Routledge 2018). Christina has trained more than 11,000 researchers around the world in qualitative methods and the use of digital tools for analysis, since 1998, and is a Fellow of the UK Academy of Social Sciences.Sarah L Bulloch, PhD is a senior associate of Qualitative Data Analysis Services and teaching fellow at the CAQDAS Networking Project at the University of Surrey, UK. Sarah has expertise in both qualitative and quantitative analysis techniques and has applied them in academic, public sector, private and third sector contexts. Her current work and research centres around enabling others to apply research methods, and the digital tools to support them, in a meaningful, robust and flexible way. Sarah first used CAQDAS packages in 2006 and started teaching them in 2010. About QDAS | Qualitative Data Analysis ServicesQDAS provide tailored and flexible training, consultancy, coaching and analysis for qualitative and mixed-methods researchers. We specialise in facilitating high-quality analysis through the powerful use of digital tools. Our website provides information about our work, including our pedagogy - the Five-Level QDA method, which underpins the way we think about, undertake and teach methods and tools. info@qdas.co.uk (QDAS | Qualitative Data Analysis Services) Tue, 23 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14894 https://www.ncrm.ac.uk/training/show.php?article=14940 Focus groups are a popular qualitative research method which allow us to explore a variety of views and experiences from participants. They produce a particular type of qualitative data via the interaction which participations have with each other and the activities they engage in. As a qualitative method, focus groups also rely on effective moderation and facilitation skills. This online interactive course helps participants to improve the quality of their focus groups and moderation skills, and provides strategies to help them generate a participative and effective focus group discussion.The course aims to give participants a clear understanding of when and how to use focus groups as a qualitative method and to provide first-hand experience of one of the key roles: moderator and group member. We also consider research ethics, how to modify the style and approach depending on the sensitivity of the topic, and the nature of the participants. Although the focus is primarily on in person focus groups, participants will consider strategies for conducting focus groups in both in-person and online settings, and the different challenges these focus group styles present for moderators. Through practical hands-on activities on designing focus group schedules and moderation, participants will gain skills in focus group design, questioning, moderation, and facilitation.By the end of the course, participants will have knowledge of focus groups as a qualitative method and the type of data they generate. They will have knowledge of the role of the moderator and how to effectively design, plan and conduct a focus group. They will also have an awareness of the different ways in which focus groups can be facilitated (i.e. in-person, online, text) although the focus of this course will be on designing and facilitating in-person focus groups.Looking to book for four or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Tue, 11 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14940 https://www.ncrm.ac.uk/training/show.php?article=15036 Build custom AI research agents in R that can search, retrieve, analyze, and verify information to support literature reviews, research design, and more. natalie@statisticalhorizons.com (Statistical Horizons) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15036 https://www.ncrm.ac.uk/training/show.php?article=14907 New to the CLS cohort studies? This webinar will give you an overview of four internationally renowned national cohort studies and the wide range of opportunities they offer to researchers.About the eventThis flagship CLS webinar will give first-time users an insight into four world-leading cohort studies. These studies follow generations born between 1958 and 2002 and are a powerful resource for addressing a wide range of scientific questions in various social, economic, health, political and geographical sciences. The data are free to download from the UK Data Service. What’s covered?You will learn about the data collected in each study, possible research topics, how to access and get started with the data and examples of the types of analyses that can be undertaken using the following studies: 1958 National Child Development Study 1970 British Cohort Study Next Steps Millennium Cohort Study. Why attend?Gain an overview of the type of research topics that the data could offer.Find out about the types of analyses that can be performed with the data.Learn how the cohort studies could be used in coursework or dissertations.Discover how to access and get started handling the data.Get answers to specific questions about the cohort studies and how to use them.Who should attend?This webinar is suitable for a wide range of researchers including: postgraduate students (Masters and PhD) interested in using the cohorts for a dissertation or thesisundergraduate students looking for data resources for their final year dissertationresearchers in academia (particularly early career researchers)researchers in the public or the third sector new to the cohort studies.  radhika.jhamaria.23@ucl.ac.uk (UCL Centre for Longitudinal Studies) Wed, 22 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14907 https://www.ncrm.ac.uk/training/show.php?article=14941 The aim of this interactive workshop is to explore creativity within research, to identify opportunities to use creative methods within the research process and to explore the foundations and theoretical underpinning related to these methods in qualitative research.We discuss what creativity is, why we should be creative in research and how we can introduce creativity and creative methods in our existing paradigms and methods. Subsequently, delegates actively experiment with "pick a card" and "diamond 9" activities, photo elicitation, and the process of creating representations of experiences. Delegates also have opportunities to consider creativity within diary methods and observations as data collection. Creative research methods have been found particularly helpful in yielding rich qualitative data and thus provide a deeper insight into research participants' experiences. All tasks are explored in view of 4 guiding questions allowing delegates to focus on practical, methodological and ethical considerations regarding the approaches presented.In line with the pedagogical principles of social constructivism the course is delivered as a mixture of interactive group tasks, discussions and lectures to enable active and experiential learning. This workshop can be taken on its own or in conjunction with the workshop "Creative Data Analysis".Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Tue, 11 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14941 https://www.ncrm.ac.uk/training/show.php?article=14838 Learn the essential principles of research project management at your own pace.This practical online self-study course will equip you to better manage any type of research – at whatever stage of your career – so you can:Identify the key skills every research project/programme manager needsUnderstand which skills are most in demand – inside and outside of academiaExplore the four core research project management stages necessary for delivering research objectives on time and on budgetFind out how to get up to speed on any project – at whatever stage you join itDiscover the Top 10 challenges research project managers face and tried and tested solutions for handling themShow funders that your project will complete within timeframe and resource limitations.Take away simple tools, templates and checklists to help you:Identify project stakeholders and prioritize their needsAssess the impact of potential risks to your research and discover tried and tested strategies for overcoming the most common threatsIdentify what skills and resources your project needs to achieve its objectivesBreak your project into manageable tasksWork out how long it should take to complete any given projectIdentify the critical activities you’ll need to monitor closely to make sure you complete your research as plannedTrack any project through to completion...…and much more!Additional benefits:Eight modules, fully recorded over 27 video lectures (no live component)Take away simple tools, templates and checklists you can use to manage any research projectSix months access to all video lectures and course materials. contact@evaluationworks.co.uk (Evaluation Works) Wed, 06 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14838 https://www.ncrm.ac.uk/training/show.php?article=14960 This two-day live online course provides participants with an in depth understanding of focus groups as qualitative research method including their principles, how to effectively design and run focus groups, and how to how to prepare for analysis and writing up of focus group data. Crucially, it covers moderation skills for different group dynamics and challenging participants, and provides participants with the opportunity to practice their moderation skills via practical group activities including mock focus groups. The course will be useful for social researchers who intend to run focus groups and/or would like to revisit their moderation skills.Day 1 focuses on the principles of focus groups, benefits and limitations, sampling and composition, discussion guide design and questioning techniques, as well as suggestions for focusing activities and stimulus material. We also consider methodological, practical and ethical aspects of designing face-to-face or online focus groups.Day 2 focuses on effective moderation skills and group dynamics, different types of participant (including how to deal with challenging participants and sensitive issues), note-taking, and how to prepare for analysis and writing up of focus group data. It also provides participants with the opportunity to practice their moderation skills in mock focus groups online.Looking for something more introductory? We also offer a 1-day focus group course, Introduction to Focus Groups. Take a look to see if the next date is on sale, or email training@the-sra.org.uk to enquire about future dates.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house training. training@the-sra.org.uk (Social Research Association) Thu, 20 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14960 https://www.ncrm.ac.uk/training/show.php?article=14993 R is an open source statistical programming language, created specifically for data analysis. R is extremely versatile and this makes it a popular choice for health researchers. Many of the researchers working in the Bristol Medical School use R to implement their analyses.This course aims to introduce the statistical software package R, focusing on providing the starting point needed to exploit R’s huge potential for statistical analysis.This course will cover:conducting basic calculations;using variables, functions, scripts and control structures (e.g. if statements and for loops);working with packages;using data structures including vectors, matrices and data frames;reading and writing data into / out of R; andcreating some basic plots.  short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14993 https://www.ncrm.ac.uk/training/show.php?article=15044 Are you wanting to understand how the world works? Have an interest in climate science, economy, trade, demographics, health or international development? Want to know about and how to access data from the IMF, World Bank, UN or OECD?This free 90-minute online workshop will introduce you to the international time series data held by the UK Data Service. We’ll look at the range of socio-economic topics covered, explore some key datasets like the World Development Indicators and OECD Economic Outlook, and cover how to access and download the data.The workshop will consist of presentations and demos with time at the end for questions.Presenters: Eve Little and Cameron Hurst, UK Data ServiceEve is a Research Impact and Engagement Manager at the UK Data Service. She works across evaluation, communications and research engagement, with a particular focus on translating complex research into engaging, accessible stories that resonate with a range of audiences. She supports early career researchers as they develop their skills and confidence in working with data for impactful social science research.Cameron is a Data and Platform Engineer at the UK Data Service.Level: IntroductoryExperience/knowledge required: Some spreadsheet knowledge would be usefulTarget audience: Anyone interested in knowing more about the UK populationThis event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.This event is part of our UK Data Service introductory training series: Autumn 2026. mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15044 https://www.ncrm.ac.uk/training/show.php?article=14392 Level: Intermediate (I)Multiple linear regression is one of the most commonly used techniques in statistics and allows for the impact of multiple variables to be assessed simultaneously. The analysis of variance (ANOVA) is a related technique which allows the mean values of several groups to be compared. This course will utilize Jamovi's free software, to equip participants with the skills necessary to undertake both types of analysis, understand and interpret the output, check the assumptions that underpin each type of model, and present the results coherently. This course will be delivered over two morning sessions, running from 9:30am to 1:00pm on both days.Learning OutcomesBy the end of this course the attendees will:       Understand what is meant by the term Analysis of Variance (ANOVA) and the different ANOVA models availableAssess when it is appropriate to fit an analysis of varianceInterpret the result s of an analysis of varianceAssess model fitPresent the results of an analysis of varianceUnderstand what is meant by the term multiple linear regressionAssess when it is appropriate to fit a multiple linear regression modelCarry out a regression analysis using free softwareInterpret the results of a multiple linear regression analysisAssess model fitPresent the results of a multiple linear regression analysis      Topics CoveredThe first day will start with a brief recap on the concepts of hypothesis testing and choosing the right test. This will include the basic use of Jamovi software to carry out and interpret an independent t-test before progressing to the related technique ANOVA.  Assumption checking, two-way ANOVA’s and interactions will conclude the morning. The second day starts with correlation and simple linear regression to assess the relationship between two continuous variables before concentrating on multiple regression which allows multiple variables to be tested simultaneously. Both sessions will concentrate on producing and understanding outputs rather than mathematical content with regular exercises to reinforce learning. Target AudienceThis course is aimed at individuals who have some basic statistical knowledge and who wish to undertake analyses of quantitative data and who therefore wish to gain some insight into how to undertake these. Knowledge AssumedBasic statistical knowledge as the course is designed as a follow-on from our Basic Statistics course.Delegates will need to download the latest version of Jamovi onto their laptop as this will be used during the workshop: https://www.jamovi.org/download.html training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14392 https://www.ncrm.ac.uk/training/show.php?article=14899 In this one-day online training workshop you will be introduced to four qualitative research methods to better understand diverse lives - Photo Go-Alongs, Collage, Life History Interviews and Participant Packs. When researching social groups, researchers may focus on categories such as age, gender, sexuality and so on. These categories can turn catch-all terms into catch-all agendas. Treating groups of people with one shared characteristic as homogenous risks a cookie-cutter approach which overlooks diverse lives and needs. Given the complexity of what it means to be a person, a one-size fits all approach to engagement cannot suffice. The methods introduced in this training workshop are beneficial in exploring diverse lives and can be used when researching with any group. The session is aimed at PhD students and academics of all career stages across the UK who want to better understand: The specific place-based needs of people The everyday practices of peopleThe world from participants’ perspectivesHow to work with people in an inclusive and accessible wayThis online training workshop will be structured as follows:  IntroductionsOrigins and Approach Methods deep dive: * Photo Go-Alongs* Participant packs* Collage * Life Histories Workshops Learnings and close By the end of the course participants will:Be able to think critically about how creative, participatory methods might be incorporated into their research and/ or teaching. Have broadened their understanding of research methods from tools of data collection to techniques for capacity building.Have workshopped four qualitative methods for creatively engaging with people (Photo Go-Alongs, Collage, Life Histories and Participant packs).This online course will take place on Wednesday 9 December from 10:00 - 16:00, with 1 hour for lunch from 12:30 - 13:30.  jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14899 https://www.ncrm.ac.uk/training/show.php?article=14959 It is essential that participants on this advanced course are already familiar with the principles and practice of thematic analysis, or have attended the SRA Qualitative Analysis course. This one-day advanced course covers the transition from interpretive thematic analysis to writing up qualitative findings. It shows how key themes can be extrapolated to form the structure of a written piece and presented with confidence and style. It reviews different formats of presentation, for different audiences, and includes examples from both academic and applied/policy research. Emphasis is on higher-level abstractions, achieving coherence, and writing in a confident qualitative style, expressing range and diversity as opposed to incidence and statistics.Looking to book for four or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses training@the-sra.org.uk (Social Research Association) Thu, 20 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14959 https://www.ncrm.ac.uk/training/show.php?article=14892 OverviewEmbarking on qualitative data analysis: a beginner’s guide to critical considerations for your qualitative research Learning objectivesBy the end of the course, participants will be able to:Develop appropriate research questions;Focus a literature review to ensure it is efficient and effective;Understand the range of qualitative methodologies and their underlying assumptions;Make appropriate choices between methodologies, data collection strategies and analytic methodsIdentify the audiences of their work and frame their dissemination accordingly. Who is this course for?This course is designed for postgraduate students and early career researchers in any discipline who are new to working with qualitative data (e.g. literature and documents, transcripts of interviews and focus groups, visual materials, social media and online content, open-ended survey responses etc.) Course overview and aimsThis course provides a thorough overview of the principles and practices of undertaking qualitative data analysis. Framed around the research cycle, we discuss and illustrate the interrelated activities undertaken as analysis progresses – from the paradigms and assumptions that underlie qualitative projects, through the practicalities of planning and implementing an analysis, to writing up, visualising and sharing findings. The aim of this course is to open up thinking about the range and flexibility of qualitative data analysis approaches and to equip participants with the necessary mindsets and frameworks to plan and undertake their own projects. The course is led by two facilitators and the number of participants is limited to ensure everyone has the opportunity to discuss their work-in-progress. TopicsThe qualitative research cycle – planning and managing the iterative and cyclical nature of qualitative researchResearch in the wider landscape – research topics and objectives, reviewing the state of the art, developing research questionsThe role of methodology – underlying paradigms and assumptions, differences between methodologies and methodsThe ethics of working qualitatively – considerations for involving participants and representing their contributionsFrom methodology to data collection – types of qualitative data, ways of collecting qualitative data, integrating different types of data in an analysisAnalytic activities – integrate, organise, explore, reflect, interrogateQualitative analytic strategies – qualitative approaches, mixed-methods analysis and quantifying qualitative data, focusing on the examples of Grounded theory, Content Analysis, Thematic Analysis, Discourse Analysis, Action ResearchAnalysis exercise – small groups collaboratively analyse sample qualitative data using different strategies and compare and discuss their findingsCollating insights and presenting a narrative – sharing findings with different audiences Format and documentationAll our workshops are hands-on, delivered through a blend of demonstration, discussion and practical exercises, rather than providing simplistic, mechanical instruction.To deliver as tailored an experience as possible, we contact you on enrolment in order to understand your research goals and analytical strategies. Additionally, our participant numbers are capped to a small group size and we run our courses with two facilitators, allowing us to cover the core topics, as well as specialist needs arising out of individual projects.Participants are provided with slide decks, reading lists and a range of resources to accompany the course and to support consolidation of the topics covered. Feedback from previous attendees"Since beginning my doctoral journey this training is what I have been searching for! The trainers are exceptional and have a way of delivering complicated theory and methods in an accessible and engaging manner. This has been the very best experience - thank you! ""I've only just come out of the session, and it was a HUGE amount of information, delivered extremely clearly. I have studied qual methodologies for my masters, and this was a far clearer guide than anything else I have experienced.""A fantastic session that helped a lot of learning click into place. I feel a lot more confident about embarking on my PhD and have further areas of inquiry to focus on my direction." FacilitatorsChristina Silver, PhD is the director of Qualitative Data Analysis Services and manager of the CAQDAS Networking Project at the University of Surrey, UK. Christina’s interests relate to the relationship between technology and methodology and the effective teaching of qualitative methods and digital tools. She is co-author of Using Software in Qualitative Research: A Step-by-Step Guide (Sage publications, 2007, 2014) and Qualitative Analysis using ATLAS.ti/MAXQDA/NVivo: The Five-Level QDA® Method (Routledge 2018). Christina has trained more than 11,000 researchers around the world in qualitative methods and the use of digital tools for analysis, since 1998, and is a Fellow of the UK Academy of Social Sciences.Sarah L Bulloch, PhD is a senior associate of Qualitative Data Analysis Services and teaching fellow at the CAQDAS Networking Project at the University of Surrey, UK. Sarah has expertise in both qualitative and quantitative analysis techniques and has applied them in academic, public sector, private and third sector contexts. Her current work and research centres around enabling others to apply research methods, and the digital tools to support them, in a meaningful, robust and flexible way. Sarah first used CAQDAS packages in 2006 and started teaching them in 2010. About Qualitative Data Analysis ServicesQDA Services provide tailored and flexible training, consultancy, coaching and analysis for qualitative and mixed-methods researchers.We specialise in facilitating high-quality analysis through the powerful use of digital tools.Our website provides information about our work, including our pedagogy - the Five-Level QDA method, which underpins the way we think about, undertake and teach methods and tools. info@qdas.co.uk (QDAS | Qualitative Data Analysis Services) Tue, 29 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14892 https://www.ncrm.ac.uk/training/show.php?article=14994 This course aims to provide an introduction to the importance of inclusivity in health research, key issues to consider while designing your study and tools to optimise inclusivity. We will draw from work led by academics at the University of Bristol and other institutions, the literature on developing inclusive methodology and strategies used to reduce inequities in health research. Course content will provide an overview covering multiple under-served groups (e.g., people with autism, those experiencing socio-economic disadvantage), with a focus on strategies that enable inclusivity of ethnic minority groups.This course aims to provide a broad understanding of the why and how of considering inclusivity throughout the life cycle of a research project, with an added emphasis on ethnic minority group inclusion.The course will specifically cover:introduction to inclusivity and why this is an important consideration in health research;how to decide who should be in a research study and how to collect data to monitor inclusion;community engagement and measures to redress barriers to research participation, such as mistrust in research amongst ethnic minority groups;tools and strategies that can be employed to design and conduct inclusive research; andhow to design and budget for an inclusive research study. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14994 https://www.ncrm.ac.uk/training/show.php?article=15030 Training for PhD and MSc students in the design of social research, quantitative and qualitative analysis.Machine learning and probability: missing data solutions and analyses by Dr Patrick EnglishIn this short course we will learn how to deal with missing data in surveys by applying machine learning driven imputation models. We will then look at how we can use probability modelling to sanity check the imputations, and then analyse the full sets of data. Previous experience with R and a familiarity with regression analysis is required.Session DetailsTime: 10.00 - 15.00 (12.00 - 13.00 Lunch break)Date: 23 November 2026.Mode: Hybrid: In-person at CON.1.01 and online via Zoom.If you are an ESRC funded student then we require your name, email and course to report your attendance to your home institution, please click here to see the privacy notice that this data collection is subject to. methodology.admin@lse.ac.uk (LSE) Mon, 12 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15030 https://www.ncrm.ac.uk/training/show.php?article=14961 For government and its agencies, local government, the European Commission, the Lottery, and charitable Trusts, impact evaluation has become a cornerstone in the use, accountability and effectiveness of new policies, programmes and initiatives. Different theories, options and alternative methods have proliferated, and those setting up and running programmes across the public and voluntary sector, and outside, find themselves confronted with difficult choices and rising expectations among policy makers and funding bodies for measuring programme effects, effectiveness and impacts. Pressure on the public purse has intensified those demands - with new calls among policy makers and others for sophisticated evaluation strategies to show impacts, ‘what works’, to demonstrate added-value and cost-effectiveness. This online course provides a more flexible opportunity for an introduction to impact evaluation. It shares much of the same content as the ‘face to face’ SRA course and is led by the same tutor.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Thu, 20 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14961 https://www.ncrm.ac.uk/training/show.php?article=14397 Level: Professional (P)This online training course will be delivered over 4 afternoon sessions, running from 1:00pm to 5:00pm on each day. This course will cover R object-oriented programming techniques. It will discuss what OOP is and the different varieties within R. Beginning with the popular S3 and S4 OOP frameworks, we’ll finish with the new {R6} package that is used extensively in Shiny applications. The course will then introduce the {rlang} package as a way of parsing variables from a data set into a function. Furthermore, it cover environments and function-evaluation in R, to help you understand how the tools in {rlang} work under the hood. This course will be delivered over 4 sessions. Course Outline This course will cover the following topics:Advanced Functions: Scoping rules (including lexical scope), The … argument, Argument matchingS3 classes: Introduction to object-oriented programming, Constructing S3 objects, DrawbacksS4 classes: Creating and using S4 classes, Differences between S3 and S4 classesR6 classes: Differences between {R6} and S3/S4, Mutable states, Creating methods, Shallow and deep copiesModifying user argument in functions callsQuoting code with quosuresUsing quasi quotation Learning outcomesBy the end of this course, delegates will be able to :Select the most appropriate form of OOP for their taskLeverage encapsulation, polymorphism and inheritance to provide a nice user interface to their codeWrite functions with rich results, user-friendly display and programmer-friendly internalsExtend the functionality of functions for new object typesWrite code that is extensible by othersUse the {rlang} operators {{}}, !!, !!! and := to pass variablesModify user functions using enquo()Parse and deparse expressions Target AudienceThis course assumes that participants are comfortable with the fundamentals of R programming. As such the course will be of interest to anyone who uses R, in particular those who want to develop their computer skills to cover more advanced topics. Delegate Feedback ““Extremely good teacher, great explanations, funny examples and very flexible in terms of content and time. I got to know a lot of things, that I did not think were possible.” “Material well presented and delivered” ”I am not scared of R anymore. It was actually fun!”“Really great course! Useful content that will greatly benefit me in my future R projects.” training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14397 https://www.ncrm.ac.uk/training/show.php?article=14399 Level: Professional (P)This online training course will be delivered over 4 afternoon sessions, running from 1:00pm to 5:00pm on each day. This course will cover R object-oriented programming techniques. It will discuss what OOP is and the different varieties within R. Beginning with the popular S3 and S4 OOP frameworks, we’ll finish with the new {R6} package that is used extensively in Shiny applications. The course will then introduce the {rlang} package as a way of parsing variables from a data set into a function. Furthermore, it cover environments and function-evaluation in R, to help you understand how the tools in {rlang} work under the hood. This course will be delivered over 4 sessions. Course Outline This course will cover the following topics:Advanced Functions: Scoping rules (including lexical scope), The … argument, Argument matchingS3 classes: Introduction to object-oriented programming, Constructing S3 objects, DrawbacksS4 classes: Creating and using S4 classes, Differences between S3 and S4 classesR6 classes: Differences between {R6} and S3/S4, Mutable states, Creating methods, Shallow and deep copiesModifying user argument in functions callsQuoting code with quosuresUsing quasi quotation Learning outcomesBy the end of this course, delegates will be able to :Select the most appropriate form of OOP for their taskLeverage encapsulation, polymorphism and inheritance to provide a nice user interface to their codeWrite functions with rich results, user-friendly display and programmer-friendly internalsExtend the functionality of functions for new object typesWrite code that is extensible by othersUse the {rlang} operators {{}}, !!, !!! and := to pass variablesModify user functions using enquo()Parse and deparse expressions Target AudienceThis course assumes that participants are comfortable with the fundamentals of R programming. As such the course will be of interest to anyone who uses R, in particular those who want to develop their computer skills to cover more advanced topics. Delegate Feedback ““Extremely good teacher, great explanations, funny examples and very flexible in terms of content and time. I got to know a lot of things, that I did not think were possible.” “Material well presented and delivered” ”I am not scared of R anymore. It was actually fun!”“Really great course! Useful content that will greatly benefit me in my future R projects.” training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14399 https://www.ncrm.ac.uk/training/show.php?article=14995 Co-producing a research project is an approach in which researchers, practitioners and the public work together, sharing power and responsibility from the start to the end of the project (NIHR, 2021). This course focuses on involving members of the public, to ensure more democratic, relevant research/ services that make a difference in practice. This course will be particularly useful for researchers considering using co-production, co-design or participatory action research, people co-designing services/ interventions or anyone wanting to find out more.This course will provide an introductory overview of approaches where researchers and practitioners work in partnership with members of the public to co-produce/ co-design research, services or interventions. Participants will learn about the theory of these approaches and apply principles into action through case studies and their own/ others work. We draw on the expertise within the University of Bristol and beyond, with course tutors who are researchers, public involvement specialists and community members, who all have extensive experience in this field.This course will cover:Background to co-production:a.    What’s in a name? Co-production, co-design, and participatory action researchb.    Building relationships and trustc.    Why it matters: working with under-served communities and tackling health inequalities d.    What does it look like?  Co-designing health interventions:a.    Experience-based co-design and participatory designb.    Co-producing evidence-based health interventions c.    Community led work, different frameworks and toolkitsCo-producing research and participatory action research:a.    Planning and working with community researchers b.    Using creative approachesc.    Pragmatics, funding, ethics and intellectual propertyEvaluating co-production and impact/ implementation. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14995 https://www.ncrm.ac.uk/training/show.php?article=15021 Date: January 11th through to lunchtime on January 15th, 2027By: Jakub Bijak and GuestsLocation: Reuben College, University of OxfordCourse description: This comprehensive, interdisciplinary short course provides foundations in applied Bayesian Statistics with a focus on methods for Population Data Science – a dynamically-growing area of research, connecting statistical rigour with fascinating real-life applications. Led by one of the pioneers of Bayesian thinking for Demographic Research, and illustrated by examples of Bayesian methods for estimation and prediction, the course will help the participants understand the main concepts and methods unlocking the study of uncertain processes. In addition, the participants will be able to appreciate the breadth of possible applications, such as local planning, national policy setting or human rights monitoring. The course is primarily intended for Masters students, doctoral and post-doctoral researchers, and other academics, but it is open to anyone with interest in population topics, who would like to enter into the world of population uncertainty equipped with state-of-the-art tools for dealing with unknowns – known and unknown alike.Objectives: To familiarise the participants with the philosophy and methodology of Bayesian Statistics, and to enable them to use selected software tools and packages to application Bayesian methods in practice.Learning outcomes: By the end of the course, the participants will be able to:1. Understand the philosophical and statistical basics of Bayesian statistics.2. Appreciate the different sources of uncertainty for various applications and the way in which they are reflected in models.3. Specify a Bayesian model for a given problem, estimate it, and assess its quality and sensitivity to various assumptions.4. Carry out an applied piece of Bayesian analysis, by using freely-available dedicated statistical software, and interpret the results.Timetable (Subject to change): AM session 10:00-13:00, Lunch 13:00-14:00, PM Session 14:00-17:00Lectures in the morning, practicals/computer workshops in the afternoon.Day 1. AM: History and philosophy of Bayesian statistics. Bayes Theorem: prior, posterior, likelihood. Applications of Bayesian methods in demography. PM: Pen-and-paper exercises: deriving posterior probabilities for a few simple discrete distributions. Point-and-click Bayesian estimation in JASP. Day 2. AM: Sources of uncertainty. Hierarchical models. Prior selection and elicitation. Brief introduction to numerical methods (MCMC, HMC). PM: Getting started: introduction to Stan. Coding simple models on pre-prepared data. Guided choice of individual mini-project topics; collection of data.Day 3. AM: Model comparison, selection and averaging, Bayesian model critique. Sensitivity analysis: not only priors. PM: Guided work on mini-projects – Part 1: Data preparation, model design, coding, estimation and troubleshooting. Day 4. AM: Guest lectures: real-life examples of Bayesian demographic models (details tbc). PM: Guided work on mini-projects – Part 2: Model critique and sensitivity analysis. Informative versus non-informative priors.Day 5. AM: Building theory: complex models and uncertainty quantification. New directions of Bayesian demography. Finalisation of mini-projects and individual presentations (five minutes, five slides). Conclusions to the course. PM: course end.Prerequisites: Undergraduate-level knowledge of statistics (any paradigm). Knowledge of calculus (integration) is beneficial, but not necessarily required.Teaser: A short video featuring Jakub Bijak and John Bryant discussing Bayesian demography back in 2016. Preliminary Reading Gelman A et al. (2013/2025) Bayesian Data Analysis: Third edition. CRC/Chapman & Hall.Bijak J and Bryant J (2016) Bayesian demography 250 years after Bayes. Population Studies, 70(1), 1–19.Bryant J and Zhang J (2018) Bayesian Demographic Estimation and Forecasting. CRC/Chapman & Hall.Attendance will be recognised through Accredible badges. A shop link to register for the course will be forthcoming in the immediate future. The course currently costs £600 for the entire 4.5 days of sessions. A 50% fee-waiver is available for internal Oxford students and participants from low and middle income countries upon request: please email teaching@demography.ox.ac.uk for more details. Lunch will be provided on Monday the 11th of January by Reuben College.To register, please click [here].For any other or additional queries, please kindly contact teaching@demography.ox.ac.uk. charles.rahal@demography.ox.ac.uk (University of Oxford) Thu, 01 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15021 https://www.ncrm.ac.uk/training/show.php?article=14989 To be held Online, via Zoom SummaryThis three-day course provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN, R, or Stata. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or hierarchical). These models can be viewed as an extension of conventional linear and logistic regression models to account for and learn from the clustering in the data. Such models are appropriate when, for example, analysing exam scores of students nested within schools, or health outcomes of patients nested within hospitals. Special interest lies in disentangling social processes operating at different levels of analysis by decomposing the within- from the between-cluster effects of covariates (explanatory or predictor variables). Longitudinal data are also clustered, with repeated measurements on individuals or multiple panel waves per survey respondent. Throughout the course we emphasize how to interpret multilevel models and the types of research question they can be used to explore. Full course information and excerpts can be viewed here: https://www.bristol.ac.uk/cmm/learning/introworkshop.html  Testimonials“The course was excellent - far exceeded expectations. The course has given me the confidence to use MLM, something I very much lacked before. I feel I understand the theory behind MLM, why each stage is so important, and the various interpretations. Without this course I would be lost. I cannot thank you all enough.”“This was a beautifully constructed course. It was clear throughout that careful thought had been given to providing a balance between lecture content, time for questions and discussion, and practical sessions. Both George and Bill delivered fantastic lectures - explanations were clear and thorough (including critiques of each approach) and content built up in complexity over time with plenty of worked examples of different kinds. The course was superb - can't rate it highly enough.”“I thought it was a really good double act between George and Bill - they are both hugely knowledgeable so having one person focused on the slides and the other manning the chat was a good approach as it meant the teaching didn't get derailed by people's questions.”Topics Overview of multilevel modellingVariance-components modelsRandom-intercept models with covariatesBetween- and within-effects of level-1 covariatesRandom-coefficient modelsGrowth-curve modelsThree-level modelsReview of single-level logistic regressionTwo-level logistic regression FormatThe course will consist of a 2:1 mix of lectures and hands-on practical sessions applying the taught methods to real datasets. The lectures are software independent and are delivered live via Zoom, but recordings of the lectures will be made available shortly afterwards for twelve weeks following the course if participants are unable to attend at the scheduled time. The instructors alternate the lecturing. Participants can ask questions via Zoom’s text-based chat facility and these will be monitored and answered by the instructor not presenting or relayed to the instructor presenting to answer live.Each lecture is immediately followed by a self-directed practical, offered in participants’ choice of MLwiN, R, or Stata, giving participants the chance to replicate the presented analyses and to consolidate their knowledge. At the end of each practical session the instructors demo the different software, each in a different breakout room.ApplicationsIf you would like to attend the workshop, please complete and submit the online booking form (see below). Please note the closing date for applications is 22nd November 2026.Applications will be processed on a rolling basis, once a week, until the application deadline. A link to the University of Bristol’s online shop will be provided and your place on the course will be confirmed upon successful payment.If you have any queries, please email info-cmm@bristol.ac.uk. info-cmm@bristol.ac.uk (Centre for Multilevel Modelling, University of Bristol) Fri, 09 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14989 https://www.ncrm.ac.uk/training/show.php?article=14996 Health research, whether population based, epidemiological or randomised controlled trials, must comply with a host of standards and legislation. The course is led by staff who have many years' experience in health research and have been involved in a wide range of high impact health research projects. Invited tutors provide specialist expertise for complex areas such as Data Protection.This course is designed for researchers who are new to health research, providing an overview of all you need to consider, and signposting further information and support. You will leave the course knowing which standards and legislation apply to your work, where to go for further support and armed with practical tips on how to fulfil your obligations as a health researcher. Please note that this is an introductory course and not designed for those already experienced in this field who may have specific, complex queries.This course will cover:What do we mean by research governance?Who is responsible for research governance?When in the research process should you start thinking about it?Where can you get help and advice?  short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14996 https://www.ncrm.ac.uk/training/show.php?article=14911 The aim of this two-day online training course is to introduce participants to the practice and ethics of ethnographic research. Through a mix of plenary sessions, group and independent work, participants will learn the basic principles of participant observation and research design, as well as the foundations of ethical ethnographic research. The course will also examine the ways in which other qualitative and creative methods of data collection may be productively integrated in ethnographic research.The course covers:Research designQualitative methods in ethnographic researchAccess and powerResearch ethics in participant observationBy the end of the course participants will:Understand the epistemological foundations of ethnographic researchHave a solid understanding of ethnographic research in actionBe able to design and conduct research integrating qualitative and ethnographic research methodsBe able to conduct ethical ethnographic researchThe course is suitable for any professional researchers interested in learning more about using ethnographic methods – whether within or outside academia (private sector, government researchers, etc.).The course is likewise suitable for postgraduate students in any social science (human geography, sociology, business school, political sciences, area studies, education, etc.) with prior knowledge of any qualitative research methods, but not necessarily of ethnography.Some prior training in qualitative research methods, broadly defined – regardless of whether that includes ethnographic methods specifically.Day 1Morning session:•          09:30-09:45     Introduction to the course•          09:45-10:45     Plenary – The Practice of Ethnography•          10:45-11:00      Break•          11:00-12:00      Group work followed by class discussionAfternoon session:•           12:45-13:45      Plenary - Qualitative methods in ethnographic practice•           13:45-14:00      Break•           14:00-15:15      Practical exercise followed by class discussionDay 2Morning session:•           09:30-10:45     Plenary - Research ethics in ethnography•           10:45-11:00      Break•           11:00-12:00      Group work followed by class discussionAfternoon session:•           12:45-1:345       Plenary – Writing ethnography•           13:45-14:00       Break•           14:00-15:00       Practical exercise, followed by class discussion         •           15:00-15:15       Conclusions and Evaluations jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14911 https://www.ncrm.ac.uk/training/show.php?article=14991 This online course on questionnaire design, explores question wording issues and the questionnaire-as-a-whole with a focus on web surveys and mobile-friendly web surveys. The course is full of practical advice. It also provides tips for anyone moving from interviewer-administered surveys to web surveys. Mirroring in-person training, this course will be interactive. There will also be 6 small group workshops to facilitate putting the course concepts into practice.Questionnaire DesignThe popularity of web surveysGetting started with a new questionnaireTrade-offs – short and simple versus clearFour cognitive stages a respondent goes through in answering a survey questionSolutions to ambiguous term, understanding recall error and reducing question sensitivityQuestion wording guidelines - This about the do's and don'ts of writing survey questions for any context.Workshop 1 - Critiquing a survey questionSome additional issues with factual questionsHighlights from mini appendix: Demographic questions are always the most difficult to writeWorkshop 2 - Writing a survey questionMini appendix on actual versus usual behaviourHighlights from mini appendix on some additional issues with subjective questionsKnow the deeper issues with open and closed questionsProblematic question formats to be aware of or avoid (eg. agree / disagree)Mini appendix on other problematic formats (satisfaction, tick all that apply, ranking and hypothetical questions)Web surveysDon't rely on web survey software templatesWorkshop 3 - Critiquing web survey software templatesModes of quantitative data collectionModes of quantitative data collection: Mixing modesModes of quantitative data collection: Overall mode differences (the obvious ones)Mini Appendix on mode effects due to satisficingWorkshop 4 - Interpreting data from a mixed mode experimentBack to web surveysDesigning the web survey itselfDay 2 appendix - 8 question testing methods for web surveysThe special things that web surveys can do, but should we?Visual versus not visualMini appendix on tips for paper self-completionWorkshop 5 - Visual problemsWeb surveys for mobile phones - earlier evidence, current thinkingHighlights from mini appendix on data collection differences: What should you do?Mini appendix on "push to web"Back to questionnaire designExamples of question revisions based on testing resultsWorkshop 6 - Revising survey questionsHighlights from mini appendix on extra tasks on mobile phones By the end of the course participants will:Have greater questionnaire design skills in general and the ability to critique existing web survey software templatesHave the ability to create effective web survey questionnaires as well as mobile-friendly onesHave better knowledge about questionnaire-related mode differences and effectsThis course is for anyone interested in questionnaire design for web and mobile web surveys. Ideally participants need some familiarity with surveys and questionnaire design.Preparatory ReadingDesirable for participants to read:https://web.stanford.edu/dept/communication/faculty/krosnick/docs/2009/2009_handbook_krosnick.pdfhttps://www.gesis.org/fileadmin/admin/Dateikatalog/pdf/guidelines/mixed_device_mobile_web_surveys_beuthner_2021.pdfPLEASE NOTE THIS COURSE IS TAUGHT OVER THREE DAYS (10:00-15:00), AND EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14991 https://www.ncrm.ac.uk/training/show.php?article=14997 The development of technology to generate and analyse large, quantitative health datasets provides unprecedented opportunity to improve public health and clinical medical practices. Unfortunately, the analytical complexity of these datasets accompanied by poor data science practices has contributed to a reproducibility crisis. There is now a growing movement to improve practice in data science by integrating concepts, tools and strategies that are widely used in software engineering. Critically, understanding how to organise complex projects is also fundamental to safely and effectively leveraging new AI coding tools.This hands-on course will introduce participants to how these essential practices are now being used to improve reproducibility in research. Each practice, skill and tool will be introduced in lectures delivered by active data scientists at the MRC Integrative Epidemiology Unit (IEU) and the University of Bristol with a strong track record of both impactful and reproducible research.  The course aims to demonstrate that reproducible research practices are accessible, will save time in the longer term, and will improve the reliability and trustworthiness of the scientific work that we create. While the use of AI coding tools is not covered in this course, the concepts covered are critical for safe and effective use of AI tools. This course will cover: the importance of and pitfalls preventing reproducibility;how concepts of reproducibility borrow from software engineering, and how those same concepts are critical for AI based software development; setting up your compute environment, modern coding environments (VS Code) and relevant plugins;transparent, portable and scalable organisation of project files and data (including project planning, data hygiene and security, directory management, modularity, portability);integrating version control into daily practice using git and GitHub;techniques to improve readability and reliability using code review, code linting, unit testing;managing analytical software reproducibly;using Quarto and Jupyter to create dynamic and shareable analysis reports;reproducibility within trusted research environments;(advanced) creating R packages that adhere to standards to share analytical functions and processes;(advanced) pipelining tools to formalise the documentation and running of more complex pipelines (Snakemake); and(advanced) containerization (Apptainer, Docker) and other approaches to software management (conda, mamba, renv). short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14997 https://www.ncrm.ac.uk/training/show.php?article=14929 Course Overview:Navigating Qualitative AI: Ethical, Methodological and Practical ConsiderationsThe Navigating Qualitative AI course provides an introduction into developments, debates and practicalities in the role and use of Generative AI in Qualitative Research. The course is split into three modules combining synchronous and asynchronous learning to provide a comprehensive introduction to the principles, practices and ethics of using AI tools to facilitate the analysis of qualitative materials. Module 1: Mindsets for considering the use of AI for Qualitative ResearchModule 2: Harnessing Generative-AI tools for qualitative analysisModule 3. Integrating Generative-AI tools into qualitative analysis processesChoose from three pathways through the course according to your interests and needs (note, to take Module 2 or 3 it is a pre-requisite to have first completed Module 1):Pathway 1 – Module 1 onlyPathway 2 – Module 1 and 2Pathway 3 – Module 1 and 3Module 1. Mindsets for considering the use of AI for Qualitative Research (Synchronous Delivery = lectures, illustration, and discussion)This module provides essential context to considering the potential use of AI in qualitative projects, focusing on the current landscape of AI tools and their impact on qualitative research practice. Practical, ethical and methodological implications are discussed in relation to the contexts in which research and evaluation happen. The course covers:The landscape of Qualitative AI - 26 January 2027 (13:30-16:45)development of AI tools in qualitative research spacesethical considerations and how to navigate themmethodological consequences when using AI for qualitative analysisCritically engaging with the Qualitative AI space - 27 January 2027 (13:30-16:45)potential uses of AI throughout the qualitative workflowgenres of AI tools for qualitative data analysiswhen the use of AI is - and is not - methodologically appropriate By the end of the course participants will:Understand historical contexts of current developments in AI within in the Qualitative Research fieldUnderstand the range of ethical issues involved in using AI for Qualitative Research and how to navigate use in appropriate waysPossess critical thinking skills around the role of AI in different Qualitative Research contextsThis course is aimed at students and researchers interested in qualitative analysis, within any sector or discipline.The course takes places over two afternoons (26 and 27 January) and equates to one teaching day for payment purposes. This page is for Module 1: Mindsets for considering the use of AI for Qualitative Research. If you wish to register for Modules 2 or 3, please do so here:Module 2 - Harnessing Generative-AI tools for qualitative analysis - 3 and 4 FebModule 3 - Integrating Generative-AI tools into qualitative analysis processes - 22 and 23 Feb jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14929 https://www.ncrm.ac.uk/training/show.php?article=14935 These courses provide an introduction into developments, debates and practicalities in the role and use of Generative AI in Qualitative Research. The courses are delivered as three modules combining synchronous and asynchronous learning to provide a comprehensive introduction to the principles, practices and ethics of using AI tools to facilitate the analysis of qualitative materials. Choose from three pathways through the course according to your interests and needs (note, to take Module 2 or 3 it is a pre-requisite to have first completed Module 1):Pathway 1 – Module 1 onlyPathway 2 – Module 1 and 2Pathway 3 – Module 1 and 3 YOU CAN FIND OUT MORE INFORMATION ON EACH MODULE, INCLUDING HOW TO REGISTER HERE:Module 1: Mindsets for considering the use of AI for Qualitative Research Module 2: Harnessing Generative-AI tools for qualitative analysisModule 3. Integrating Generative-AI tools into qualitative analysis processesEach module is taught over two afternoons and equates to one teaching day for payment purposes. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14935 https://www.ncrm.ac.uk/training/show.php?article=14999 Genetic epidemiology examines the effect of genetic variation on a disease or a health-related outcome. Since 2007, genome wide association studies have been widely used to detect associations between genetic variants and a disease or a health-related outcome in populations. It has facilitated a wide range of discoveries in the genetic epidemiology field. Genetic epidemiology is used in a large amount of the applied research in the MRC Integrative Epidemiology Unit (IEU) and throughout Population Health Sciences at the University of Bristol Medical School.This course aims to provide a grounding in a wide range of cutting-edge genetic epidemiological methods for complex traits.This course will cover:Introduction to genetics (optional pre-course videos): molecular structure; andvariation & population structure.Introduction to data types and formats: genotyping;imputed genetic data;sequence data;other ‘omics (e.g. expression, methylation); anduse of summary statistics.Heritability and genetic architecture of complex traits: twin studies; andwhole genome approaches.Genetic association of complex traits: statistical tests for association;genome-wide association study (GWAS) analysis, multi-ancestry GWAS;study design, confounding, statistical power;construction & use of polygenic risk scores;Next Generation Sequence analysis; andbioinformatic follow-up of results to explore functional mechanisms.Related study designs: other ‘omics (e.g. expression, methylation);Mendelian randomization for causal inference in epidemiology; andrecall by genotype studies. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14999 https://www.ncrm.ac.uk/training/show.php?article=15022 Why do our friends often know one another, even though we are only a few connections away from people in very different social circles? Why does information spread easily through some networks, while changes in behaviour require repeated encouragement? Who should be targeted in an intervention, and how does the answer depend on what we want to achieve?Social network analysis offers a theoretical framework and a methodology to answer questions like these by focusing on the relationships between and among social entities. A network is a structure composed of units and the relationships that connect them. The position of these units and the overall structure help us understand how social ties form, how information and behaviour spread, and how individuals gain access to resources and opportunities.The focus of the course is on the connection between the mathematical foundations of networks and the social phenomena we want to understand. We will introduce concepts from matrix algebra and graph theory across both days, and use empirical examples and practical exercises to examine what these concepts tell us about social life. We will also discuss how network data are collected and what evidence is needed to distinguish between different explanations for the patterns we observe.The course covers:Mathematical foundations of social networks: matrix algebra and graph theory;Network structure, cohesion and models of network formation;Centrality, betweenness and brokerage;Contagion, diffusion and network interventions;Network data, sampling and research designLearning outcomes:Develop an understanding of social phenomena through social network theory;Learn how to use and interpret graph-theoretic and matrix algebra concepts with real-world data;Understand how local patterns of connection contribute to the overall structure of a network;Examine how network position and structure affect social processes;Acquire knowledge of network data collection and the implications of sampling and missing data;Assess competing explanations for observed network patterns and identify the evidence needed to investigate them.The course is aimed researchers and analysts interested in using social network analysis to understand social phenomena, including students, academics, government researchers and researchers in third sector organisations. The course is relevant to work in sociology, survey research, public health and related disciplines.Pre-requisitesBasic familiarity with data matrices and introductory statistical concepts. No previous training in social network analysis is required. Mathematical concepts will be introduced as they are used throughout the course. Practical exercises and demonstrations will accompany the theoretical sessions. Software requirements will be circulated before the course.THIS COURSE IS BEING DELIVERED OVER TWO DAYS. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15022 https://www.ncrm.ac.uk/training/show.php?article=15023 Next Virtual Cohort Starts: Monday 1 February 2027 (for 4 weeks):Mondays 10.00 - 12.00 (GMT): Live training sessionThursdays 13.00 - 14.30 (GMT): Group coaching & practice*Limited places *Hosted on ZoomTo book:Visit the webpage here: https://reflexivethematicanalysis.subscribepage.io/Reduced student rate = £600 (VAT inclusive)Professional researcher and organisational rate = £900 (VAT inclusive)Why this RTA Bootcamp?- Are you struggling with your thematic analysis?- Asking yourself if you are doing this correctly?- Lacking confidence in your work?- Perhaps you've been to a short lecture on reflexive thematic analysis, but have no idea how to put this into practice?- Or you are missing out on support and guidance from your supervisors and/or institution?- Or just feel that analysis is a lonely process and would prefer to work alongside peers? The first of its kind: This cohort programme's unique and ground-breaking approach supports and guides students and professional researchers through the reflexive thematic analysis process.In only 4 weeks you will go from struggling with your data and analysis, to successfully progress with your coding and themes. You will have the ability to defend your work with confidence and certainty.You can choose to work on your own qualitative data, or to use a dataset provided by the trainer.We implement Virginia Braun and Victoria Clarke's (2021) six-phase approach for Reflexive Thematic Analysis, and cover structured coding, reflexive documentation, and systematic theme development.You will strengthen methodological rigour, enhance transparency, and produce publication and/or viva ready qualitative research that you are proud to have created. 4 Week Programme Content and JourneyGo on a journey of analysis and interpretation with peers and expert guidance.Week 1: Position Yourself as a Reflexive Researcher and Understand RTAUnderstand RTA foundations, epistemological positioning, and the reflexive mindset essential for qualitative analysis.Week 2: Learn to Generate Meaningful CodesDevelop and refine your coding process.Move from data familiarisation to semantic and latent code development.Consider inductive and deductive coding.Week 3: Transform Codes into Robust ThemesOrganise coded data, identify overarching patterns, and synthesise candidate themes with strong analytical grounding.Develop and review your themes.Develop your thematic map.Week 4: Create Publication and Viva Ready AnalysisRefine, define and name your themes.Generate a final map.Demonstrate methodological rigour.Leave with a substantial portion of your analysis complete and concrete plans for remaining work Weekly Live Training Sessions2 hours of expert led content each week, including coding and analysis training. Deep dives into each phase of Braun & Clarke's framework with practical examples, live Q&A, and methodological guidance to strengthen your analytical foundation. Sessions are recorded for you to watch back and for those who are unable to join each live weekly session.Mondays 10.00 - 12.00 (GMT) Weekly Group Coaching & Practice1.5 hours of live group coaching with peers and coach. Work through real data extracts, troubleshoot analytical challenges, and refine your coding and thematic decisions alongside fellow doctoral and professional researchers in a supportive cohort setting.Thursdays 13.00 - 14.30 (GMT) Trainer BiographyDr Karen Lumsden is a qualitative research specialist with over 25 years' experience in thematic analysis, qualitative research design, doctoral supervision, mentoring and coaching. She has supported thousands of researchers across social sciences, healthcare, and education in developing rigorous, publication-ready qualitative studies.She set up Qualitative Training in 2019 to better train and support students and professional researchers on their research journeys. For more info visit: www.qualitativetraining.comOr connect on LinkedIn here.Why work with Karen? →- Decades of experience in the use of Braun & Clarke's Reflexive Thematic Analysis including delivery of highly rated training in the approach.- Published researcher with expertise in qualitative methodology and methods, reflexivity, and research ethics.- Experienced supervisor and mentor supporting doctoral candidates, professional researchers and interdisciplinary teams to improve and defend their qualitative work. Bookings and RatesTo book:Visit the webpage here: https://reflexivethematicanalysis.subscribepage.io/Reduced student rate = £600 (VAT inclusive)Professional researcher and organisational rate = £900 (VAT inclusive) Payment and Refund PolicyFull payment must be received in advance of the first live session date in order to secure your place. Payments must be made above via the URL link. You are trusted to select the rate that applies to you. Organisations who are funding places should book at the professional and organisational rate. Student rate is for individual students who are funding their studies/training.Places are limited.Refund policy is 100% refund up to 7 days prior to the first live training session. 50% refund 3-6 days prior to first session. Less than 2 days before no refund (unless there is a waiting list and it is at trainer discretion to refund under these circumstances).Transfer to a future programme date may also be possible (at trainer discretion). karen@qualitativetraining.com (Qualitative Training) Mon, 05 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15023 https://www.ncrm.ac.uk/training/show.php?article=14932 Course OverviewNavigating Qualitative AI: Ethical, Methodological and Practical ConsiderationsThe Navigating Qualitative AI course provides an introduction into developments, debates and practicalities in the role and use of Generative AI in Qualitative Research. The course is split into three modules combining synchronous and asynchronous learning to provide a comprehensive introduction to the principles, practices and ethics of using AI tools to facilitate the analysis of qualitative materials. Module 1: Mindsets for considering the use of AI for Qualitative ResearchModule 2: Harnessing Generative-AI tools for qualitative analysisModule 3. Integrating Generative-AI tools into qualitative analysis processesChoose from three pathways through the course according to your interests and needs (note, to take Module 2 or 3 it is a pre-requisite to have first completed Module 1):Pathway 1 – Module 1 onlyPathway 2 – Module 1 and 2Pathway 3 – Module 1 and 3Module 2. Harnessing Generative-AI tools for qualitative analysis(Synchronous Delivery = hands-on guided exercises using selected tools, and discussion)This module comprises demonstrations and hands-on exercises using selected AI tools for Qualitative Data Analysis, and discussion about their potential for research and evaluation. Using sample data, we consider a series of analytic tasks and methodological implications of use in different contexts, with different types of data and at different stages in the analytic process. The tools-focus in this module is on general-purpose Generative-AI tools and new online Apps designed for GenAI-driven qualitative analysis. The course covers:Using general-purpose Generative-AI tools - 3 February 2027 (13:30-16:45)Overview of the capabilities of general-purpose Generative-AI toolsHands-on exercises following process of Query-Based Analysis (Morgan 2025)Chatbots – e.g. ChatGPT, Claude, Gemini etc.AI Research Assistant platforms – e.g. Notebook LMGenerative-AI driven analysis with bespoke online Apps - 4 February 2027 (13:30-16:45)Overview of the capabilities of bespoke online Apps for qualitative analysisHands-on exercises using Reveal platform (analysis of transcripts)Hands-on exercises using Blix platform (analysis of open-ended survey responses)By the end of the course participants will:Understand the potential uses of AI tools at different stages of the qualitative analysis workflowUnderstand the methodological consequences of using AI tools (potentials and risks)Identify skills in harnessing AI tools in ways that enable human-centred approachesThis course is aimed at students and researchers interested in qualitative analysis, within any sector or discipline. Participants must have attended Module 1 in order to attend this module.The course takes place over two afternoons (3 and 4 February) and equates to one teaching day for payment purposes. This page is for Module 2: Harnessing Generative-AI tools for qualitative analysis.If you wish to register for Modules 1 or 3 please do so here:Module 1 - Mindsets for considering the use of AI for Qualitative Research - 26 and 27 JanModule 1 - Mindsets for considering the use of AI for Qualitative Research - 11 and 12 FebModule 3 - Integrating Generative-AI tools into qualitative analysis processes - 22 and 23 Feb jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14932 https://www.ncrm.ac.uk/training/show.php?article=14998 This course is the starting point for learning about statistical analysis in clinical and public health research. Attendees will learn the key concepts from statisticians working with the many internationally recognized research groups at the Bristol Medical School, gaining a firm foundation for further learning.The field of statistics is a fundamental cornerstone of clinical and public health research, playing a key role in improving scientific understanding and developing successful health policy. This short course will provide students with a thorough grounding in the understanding and application of statistics.In terms of well-known statistical methods, this course covers t-tests, chi-square tests, correlation coefficients, and an introduction to linear regression. But attendees should be prepared to gain a deeper understanding of the concepts underlying these labels. If all you want to do is fire out p-values from SPSS, there are plenty of freely available YouTube videos that will tell you how to do that. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14998 https://www.ncrm.ac.uk/training/show.php?article=14947 This exciting interactive workshop will develop your knowledge and skills in using creative and participatory research methods. Creative and participatory methods are increasingly being utilised by social researchers to tackle complex research questions, enhance participant inclusivity and to generate wide ranging research impact for a broad range of stakeholders.This session begins with an overview of developments in creative and participatory research, highlighting the opportunities and challenges in the context of social policy, research impact and advancing academic knowledge. Across the two days, the course covers how and why we use a variety of creative and participatory methods and how to bring them together in analysis, forming a constellation. The workshop will address ethics, opportunities, benefits and challenges during the research process and how to generate multi-level impact from grassroots to social policy. Participants will be given the opportunity to explore how to incorporate creative and participatory approaches (such as zines and photovoice) in their own research, and how to analyse and disseminate effectively.Over the course you will:Be introduced to key debates in creative and participatory researchUnderstand the potential for, and the challenges of, using creative and participatory research methodsExplore how to ethically engage in creative and participatory researchLearn from active peer-researchers involved in co-creating researchBy the end of the course participants will:Develop practical skills in different creative and participatory approaches such as Zines, Photovoice, Co-creation/co-production (including peer research)Develop skills in designing, conducting, analysing and disseminating creative and participatory researchLearn how such methods can be incorporated into the generation of meaningful research impactIndicative Schedule:The course will run across two consecutive mornings (10am - 1pm) and equates to one day of training for payment purposes.Day 1-    What do we mean by creative and/or participatory methods? -    The value of creative/participatory research methods -    Planning and setting up creative/participatory research tools. -    FOCUS ON (1): zines as creative/participatory methods-    Ethical considerations specific to creative/participatory research (part 1) Day 2-    Ethical considerations specific to creative/participatory research (part 2)-    Creative/participatory research with children and young people-    Creative/participatory research with marginalised communities    -    FOCUS ON (2): co-creation – creative and participatory research in action* -    Doing co-analysis and co-dissemination -    Creative/participatory methods for generating meaningful research impact -    Wrapping up the workshop/advice clinic*The workshop facilitators will be joined on this by two peer researchers they have trained and worked with on recent research projects.Presenters:This course will be delivered by Dr Linzi Ladlow, Senior Research Fellow from the University of Lincoln, and Dr Laura Way, Senior Lecturer from the University of Roehampton. They are experienced in engaging with creative and participatory research and facilitating training. They are editors of the book, Insights into Creative and Participatory Research: Key Issues and Innovative Developments (2026) Policy Press. Target audience:This short course is suitable for all qualitative researchers at any career stage, including postgraduate students. Whilst we are not expecting you to already be familiar with creative and participatory methods, familiarity with the purposes of qualitative research, as well as with qualitative methods of data generation and analysis, will be assumed. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14947 https://www.ncrm.ac.uk/training/show.php?article=15031 Learn the fundamentals of large language models (LLMs), like ChatGPT, Claude, Gemini, Deepseek, and Llama, and how to integrate these tools into your own research. ashley@statisticalhorizons.com (AI Horizons) Mon, 12 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15031 https://www.ncrm.ac.uk/training/show.php?article=14931 Course OverviewNavigating Qualitative AI: Ethical, Methodological and Practical ConsiderationsThe Navigating Qualitative AI course provides an introduction into developments, debates and practicalities in the role and use of Generative AI in Qualitative Research. The course is split into three modules combining synchronous and asynchronous learning to provide a comprehensive introduction to the principles, practices and ethics of using AI tools to facilitate the analysis of qualitative materials. Module 1: Mindsets for considering the use of AI for Qualitative ResearchModule 2: Harnessing Generative-AI tools for qualitative analysisModule 3. Integrating Generative-AI tools into qualitative analysis processesChoose from three pathways through the course according to your interests and needs (note, to take Module 2 or 3 it is a pre-requisite to have first completed Module 1):Pathway 1 – Module 1 onlyPathway 2 – Module 1 and 2Pathway 3 – Module 1 and 3Module 1. Mindsets for considering the use of AI for Qualitative Research (Synchronous Delivery = lectures, illustration, and discussion)This module provides essential context to considering the potential use of AI in qualitative projects, focusing on the current landscape of AI tools and their impact on qualitative research practice. Practical, ethical and methodological implications are discussed in relation to the contexts in which research and evaluation happen. The course covers:The landscape of Qualitative AI - 11 February 2027 (13:30-16:45)development of AI tools in qualitative research spacesethical considerations and how to navigate themmethodological consequences when using AI for qualitative analysisCritically engaging with the Qualitative AI space - 12 February 2027 (13:30-16:45)potential uses of AI throughout the qualitative workflowgenres of AI tools for qualitative data analysiswhen the use of AI is - and is not - methodologically appropriate By the end of the course participants will:Understand historical contexts of current developments in AI within in the Qualitative Research fieldUnderstand the range of ethical issues involved in using AI for Qualitative Research and how to navigate use in appropriate waysPossess critical thinking skills around the role of AI in different Qualitative Research contextsThis course is aimed at students and researchers interested in qualitative analysis, within any sector or discipline.The course takes places over two afternoons (11 and 12 February) and equates to one teaching day for payment purposes. This page is for Module 1: Mindsets for considering the use of AI for Qualitative Research. If you wish to register for Modules 2 or 3, please do so here:Module 2 - Harnessing Generative-AI tools for qualitative analysis - 3 and 4 FebruaryModule 3 - Integrating Generative-AI tools into qualitative analysis processes - 22 and 23 February jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14931 https://www.ncrm.ac.uk/training/show.php?article=15032 An 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.Participants will need 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. 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.Setup InstructionsYou 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.See below for details: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 prerequisites:pip install numpy pandas matplotlib opencv-python scikit-learn scikit-image seabornNote: If you’re using WSL, you will also need to pip install the PyQT5 package in your virtual environment.PLEASE NOTE THIS COURSE IS BEING TAUGHT OVER TWO AFTERNOONS (13:00-16:30) AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15032 https://www.ncrm.ac.uk/training/show.php?article=15064 This online course introduces innovative sampling methods that are intuitive and feasible to apply. Rather than sampling from a predefined list, which is often not available, we sample from network connections instead, even if the connections are not known in advance. Whether you are a statistician, data scientist, biologist, or sociologist, you may find yourself studying data that has an inherent network structure. Have you ever opened a standard textbook on sampling, but could not find any methods that seem to help you? Or have you wondered how your data might be generalised to the real-world population it represents?For example, you may be interested in social networks, but you are not sure how to collect the data in a way that capitalises on the links among the individuals. Or, during an epidemic, one knows one must track the people in contact with the confirmed cases, but you may have wondered what one needs to do to be able to estimate the case prevalence at the same time. For a third example, you may have heard that Google calculates PageRank based on randomly crawling the hyperlinks from one webpage to another, but you might want other measures of connectivity, such as small cliques of webpages with mutual hyperlinks to each other. Or perhaps you work with spatial data and are used to treat space continuously. Here, it is also possible to introduce a network structure among all the actual locations and represent the spatial data on a graph. If your problem resembles any of these examples, this is the course that will help you. Unlike the standard sampling methods, where units are selected from a list at each stage, we will introduce you to novel approaches where the sampling is based on the connections among the units, i.e. network-driven, even when the connections are not known in advance. You will be introduced to many such sampling methods as well as the unifying ideas that enable them, which have applications to various socio-economic, technical, biological or environmental phenomena.Each method will be explained using small datasets that can be easily visualised so that the level of abstraction is kept to a minimum, and the accompanying computer workshops shall allow one to explore the underlying ideas directly through numerical results without having to labour with the relevant mathematical formulae and equations. The course covers the following topics, organised across 5 sessions.Multiplicity sampling, such as indirect sampling or line intercept sampling. Some examples: customer-to-product transaction data, sampling siblings via households for gene study, line-in-space transecting animal-tracks for the purpose of wildlife conservation.Adaptive network sampling, where a connection is sampled only if it satisfies a threshold condition. Some examples: persons-in-contact during an epidemic, contiguous cells-in-space in species abundance study.Probability snowball sampling, where the sample grows wave-by-wave by the new connections encountered. Some examples: sampling friendship networks, actor-actor co-casting for equitable professionalism. Walk sampling by random, targeted and lagged walks. Some examples: Google PageRank, efficient sampling of spatial objects, dynamic monitoring of a specific subpopulation.Graph sampling as the unifying framework, where the interest may be any specific subgraphs such as nodes, edges, triangles, squares, K-stars. Generally applicable sampling strategies.By the end of the course participants will:Have been introduced to a range of network-driven sampling methodsHave obtained first-hand experience with various associated probability-based estimators Be aware of the unifying ideas of graph sampling strategies applicable to a variety of situationsPre-requisitesBasic knowledge of finite population sampling methods, such as simple random sampling, Poisson sampling would be ideal. Ideally also knowledge of two basic inverse-probability-weighted population total estimators, i.e. the Horvitz-Thompson estimator based on sampling without replacement, and the Hansen-Hurwitz estimator based on sampling with replacement. Basic skills with R and Rstudio - ideally knowledge of: Using R to perform calculations or simulations, output or plot results.Use of given scripts in R to execute simple algorithms including loops. Modify the given R scripts or write own codes to explore alternatives and different ideas (if interested).It is possible to follow the course without attending the computer workshops, but they help to understand implementation and provide illustrations of the learned material.Target AudienceAnyone working with data that has an inherent network structure. The course is suitable for people from all career stages with a quantitative or statistical background, including both senior and junior researchers, post-graduate and doctoral researchers. You may be a statistician by profession or a researcher from an applied area. You may be a data scientist, biologist, sociologist, psychologist, economist and so on, who uses sampling methods, or simply would like to find out whether network-driven sampling methods might help you to tackle the problems in your profession.THIS COURSE IS BEING TAUGHT EVERY THURSDAY OVER 5 WEEKS FROM 18 FEBRUARY - 18 MARCH (10:00-12:00, hour break 13:00-14:00) AND EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15064 https://www.ncrm.ac.uk/training/show.php?article=14933 Course OverviewNavigating Qualitative AI: Ethical, Methodological and Practical ConsiderationsThe Navigating Qualitative AI course provides an introduction into developments, debates and practicalities in the role and use of Generative AI in Qualitative Research. The course is split into three modules combining synchronous and asynchronous learning to provide a comprehensive introduction to the principles, practices and ethics of using AI tools to facilitate the analysis of qualitative materials. Module 1: Mindsets for considering the use of AI for Qualitative ResearchModule 2: Harnessing Generative-AI tools for qualitative analysisModule 3. Integrating Generative-AI tools into qualitative analysis processesChoose from three pathways through the course according to your interests and needs (note, to take Module 2 or 3 it is a pre-requisite to have first completed Module 1):Pathway 1 – Module 1 onlyPathway 2 – Module 1 and 2Pathway 3 – Module 1 and 3Module 3. Integrating Generative-AI tools into qualitative analysis processes(Synchronous Delivery = hands-on guided exercises using selected tools, and discussion)This module comprises demonstrations and hands-on exercises focused on integrating the use of GenAI capabilities into existing qualitative analysis workflows, using selected qualitative analysis programs. The sessions combine demonstrations, hands-on exercises, and discussion about how integrating GenAI may impact practice. Using sample data, we consider a series of analytic tasks and methodological implications of use in different contexts, with different types of data and at different stages in the analytic process. The tools-focus in this module is on existing qualitative software programs, using NVivo and MAXQDA as core examples. The course covers:Principles of integrating AI tools into qualitative analysis processes - 22 February 2027 (13:30-16:45)Overview of the capabilities of established qualitative software programs (AI and non-AI tools)Ensuring analytic methods drive the use of AI tools Hands-on exercises using NVivoExploring MAXQDA’s AI tools for qualitative analysis - 23 February 2027 (13:30-16:45)Overview of MAXQDA’s tools Hands-on exercises integrating AI tools into a qualitative analysis process (using literature, transcripts and open-ended survey responses),By the end of the course participants will:Understand opportunities for the use of AI tools at different stages of the qualitative analysis workflowUnderstand the methodological consequences of using AI tools (potentials and risks)Identify skills in harnessing AI tools in ways that enable human-centred approachesThis course is aimed at students and researchers interested in qualitative analysis, within any sector or discipline. Participants must have attended Module 1 in order to attend this module.The course takes place over two afternoons (22 and 23 February) and equates to one teaching day for payment purposes. This page is for Module 3: Integrating Generative-AI tools into qualitative analysis processesIf you wish to register for Modules 1 or 2, please do so here:Module 1 - Mindsets for considering the use of AI for Qualitative Research - 26 and 27 JanuaryModule 1 - Mindsets for considering the use of AL for Qualitative Research - 11 and 12 FebruaryModule 2 - Harnessing Generative-AI tools for qualitative analysis - 3 and 4 February jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14933 https://www.ncrm.ac.uk/training/show.php?article=15000 Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply them allows students to comprehend the results presented in research papers and interrogate their own data. These models also form the building blocks for more advanced statistical techniques taught in other short courses offered by Bristol Medical School. The tutors of this course have extensive experience teaching applied statistics to a wide range of healthcare researchers, both clinical and non-clinical, using real-world data in demonstrations.This course aims to provide an understanding of the statistical principles behind, and the practical application of, univariable and multivariable linear and logistic regression in medical, epidemiological and health services research.This course will cover:1. revision of basic methods in a statistical modelling framework;2. terminology in regression modelling;3. indicator variables to incorporate categorical explanatory variables;4. univariable and multivariable regression;5. common features of linear and logistic regression models;6. interpretation of model coefficients as differences in means or odds ratios;7. adjustment for confounding and exploring effect modification (interaction) in multivariable regression;8. comparing models with Wald tests and likelihood ratio tests;9. model assumptions; and10. analytical strategies. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15000 https://www.ncrm.ac.uk/training/show.php?article=15001 Epidemiology is the study and analysis of the distribution, patterns and determinants of health and disease conditions within populations; and is a cornerstone of clinical and public health. Academics within the MRC Integrative Epidemiology Unit, Bristol Trials Centre and elsewhere throughout Bristol Medical School (including those who are tutors on this course) have been at the forefront of applied and methodological epidemiological research.This course aims to provide a grounding in epidemiological study designs and measures of disease risk used in aetiological epidemiology and health services research.This course will cover:exposure measurement and measures of disease occurrence;measures of exposure effect (e.g., risk and odds ratios);study designs (randomised controlled trials, cohort studies, case-control studies; ecological studies and cross-sectional studies);bias and confounding;simple regression and interaction effects;sample size calculations;an introduction to causal inference; andthe future of epidemiology. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15001 https://www.ncrm.ac.uk/training/show.php?article=15020 Qualitative Diary Methods (QDMs) are increasingly recognised as a valuable and important method in social science research, due to concern across disciplines with an overreliance on cross-sectional research, a lack of focus on temporality, and the need to capture evolving processes and the daily dynamics of phenomena. QDMs offer a range of innovative approaches and tools for social science researchers that enable us to capture and subsequently begin to understand, how phenomena are experienced in the moment, as well as how they evolve over time. However, they remain a methodological blindspot in much postgraduate research training.This workshop will provide researchers with a new range of methods to add to their methodological toolkit, as well as support and guidance in managing some of the challenges associated with these methods, including insights into qualitative diary (longitudinal) analysis approaches.  The course covers: Intro to Qualitative Diary Methods (QDMs) When and why to use different qualitative diary methodsAddressing challenges associated with QDMsEthical considerations when using QDMsDesigning pen and paper diariesDesigning audio and video diariesDesigning photo-elicitation diariesDesigning qualitative ‘App’ diariesAnalysing data from QDMs: Moving beyond thematic analysisBy the end of the course participants will:Have a comprehensive understanding of the benefits and challenges of QDMs, and how to manage these challengesBe able to evaluate when different QDMs should be used and whyBe able to design appropriate QDM research projects to address particular research questionsHave a critical awareness of particular ethical issues associated with the use of QDMsHave a comprehensive understanding of analytical approaches suited to data gathered using QDMsPlease note that this is a full day event running from 10:30am to 4:30pm Engage@liverpool.ac.uk (University of Liverpool) Wed, 07 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15020 https://www.ncrm.ac.uk/training/show.php?article=15002 This course aims to introduce participants to the methodology of systematic reviews and meta-analysis. It is taught by a team of systematic reviewers, research synthesis methodologists, information retrieval specialists and statisticians, including those at the forefront of developing and applying systematic review and meta-analysis methods.This course will cover:why we need systematic reviews and meta-analyses;the systematic review process;identifying relevant studies;selecting studies and data extraction;assessing risk of bias in primary studies;statistical methods for meta-analysis of dichotomous and numerical (continuous) outcomes;explaining heterogeneity: subgroup analysis and meta-regression;meta-analysis and meta-regression in R;synthesis without meta-analysis;brief introduction to network meta-analysis;understanding, investigating and dealing with bias in systematic reviews;systematic reviews and meta-analysis of non-randomized studies;assessing certainty of the evidence in a systematic review;reporting a systematic review; andcritical appraisal of a systematic review. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15002 https://www.ncrm.ac.uk/training/show.php?article=14912 This course will explain when and why to use documents as data for research, show you how to gather documentary data, and consider some ways to analyse that data. It will also cover some of the associated ethical issues and include case studies of documents as data in practice.The course covers: When and why to use documents as dataHow to find personal and official documents, historical documents, mainstream print media documents, virtual documents, and self-published documentsHow to assess the quality and usefulness of documents for research purposesSome methods of analysing documentary dataAn introduction to the ethical issues of using documents as dataBy the end of the course participants will:Know how to find the documents they need for their researchKnow how to assess the quality and usefulness of documentsUnderstand how to approach the analysis of documentary dataHave an action plan for using documents in their own researchThis is an intermediate level course assuming a good basic knowledge of research methods. It would suit postgraduate students, early career researchers in academia, practice-based and independent researchers.Preparatory ReadingDesirable: Grant, A. (2022) Doing Your Research Project With Documents: A Step-By-Step Guide to Take You from Start to Finish. Bristol: Policy Press.Grant, A. and Kara, H. (eds) (2026) Using Documents in Research: When, Where, Why and How. Bristol: Policy Press.Please note the course will run from 09:30 - 12:30 on the 9th and 10th March and equates to one full teaching day for payment purposes. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14912 https://www.ncrm.ac.uk/training/show.php?article=15026 Within current contexts of heightened political polarisation, evolving digital environments and growing scrutiny of research on contentious issues, social science researchers are increasingly required to navigate complex safeguarding considerations. Exposure to distressing and sometimes hateful content can be emotionally taxing and can impact on researchers’ mental health. Researchers who work on sensitive and potentially ‘controversial’ subjects have to contend with the risks of doxxing and online/offline harassment, especially when choosing to become professionally visible in/on (social) media. Risks are significantly heightened for minoritised and marginalised researchers, including female(-presenting), gender non-conforming, neurodiverse, disabled, LGBTQ+ and racialised researchers.The aim of this course is to support participants in understanding and addressing the ethical, safeguarding, emotional and psychological dimensions of their work, as well as the challenges that come with safely navigating professional visibility – paying particular attention to how risks intersect with identity and career stage. The course will further provide participants with practical guidance how to protect themselves both online and offline, how to create and establish support networks and communities, and to engage constructively with their institutions, including ethical review processes. We will further discuss how researchers can look after their wellbeing, set boundaries and engage in self-care whilst doing high risk and sensitive research.Please note that this course will be facilitated on Microsoft Teams and will run over two consecutive mornings with the afternoon of Day 1 being used for an individual task to feed back to the group on Day 2. PLEASE NOTE THIS COURSE EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES. Engage@liverpool.ac.uk (University of Liverpool) Fri, 09 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15026 https://www.ncrm.ac.uk/training/show.php?article=14986 This live online course (administered via Zoom) offers an engaging introduction to Structural Equation Modelling (SEM) using R, the popular open-source software for statistical computing and graphics. The course will run over 5 consecutive days and will combine synchronous live lectures, individual exercises, group discussions and small group supervisions to simulate a live classroom environment. High contact with our expert instructors and interactive group work will further contribute to recreating the classic Cambridge experience in an online setting.This course will present the lavaan package, rapidly becoming the tool of preference for SEM in R. Participants will actively work through practical examples to gain first-hand experience in the application of factor analysis and other more advanced latent trait models. You will learn the following topics in this course:  Introduction to R and data analysisBasic concepts of factor analysis (EFA/CFA/Categorical CFA)Basic and advanced Structural Equation Models (SEM)Application of mediation & moderation techniquesMulti-level modellingLatent growth curve modellingMultiple-group analysis and evaluation of continuous and categorical data measurement invariance, as well as longitudinal measurement invarianceYou don’t need to know R to follow the course. However, if you are not familiar with R, you will need to choose the 5 day option, which includes an introduction to the R software on Day 1. On completion, participants should have a good knowledge of the topics covered and have acquired an independent use of R and latent trait analysis.We believe in active learning and developing practical skills. Thus, the necessary theoretical introduction will be illustrated with practical examples and we will be working with real data. No prior knowledge about SEM is assumed. The pace of teaching is adjusted to suit the level of the participants. Teaching will be in small groups so that delegates can make the most of the teaching. ls523@cam.ac.uk (University of Cambridge Psychometrics Centre) Mon, 28 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14986 https://www.ncrm.ac.uk/training/show.php?article=15003 This course enables participants to develop skills for identifying the causes and consequences of molecular variation within population-based studies. Causes of molecular variation explored include genotype, developmental processes, exposures, phenotypes, and disease processes. Consequences examined include health outcomes such as disease onset, disease progression and response to therapy.The course will be led by molecular epidemiologists in the MRC Integrative Epidemiology Unit at the University of Bristol. Their research utilises biological samples from a variety of established cohort resources and applies bioinformatic and statistical approaches for biomarker development and validation.This course aims to provide an overview of epidemiological principles that are relevant to population-based molecular studies and provide participants with the knowledge and skills necessary to design, execute and interpret population-based molecular studies.This course will cover:the various uses of high-throughput molecular data in epidemiology and medicine (including as an exposure, outcome, mediator, indicator and predictor);key considerations in the design of molecular studies (including choosing appropriate technologies and statistical analyses);practical analysis of molecular data;interpreting the biological function some of the most popular molecular data types (including DNA methylation, metabolite abundance, gene expression, protein abundance and genetics);methods for deriving and evaluating the performance of molecular biomarkers for indexing exposure and predicting health outcomes;causality of molecular phenotypes (including the importance of establishing causality to address certain research questions, examples of causal inference techniques, applying Mendelian randomization); andcritical appraisal of the molecular epidemiological literature. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15003 https://www.ncrm.ac.uk/training/show.php?article=15052 The size, structure and dynamics of the population in which we live provide some of the most fundamental facts about our society. This course aims to give participants a practical introduction to the techniques of demography, including methods for dealing with each of the components of population change (mortality, fertility, and migration). The course will include hands-on computer workshops using the R programming language.This course is intended as an introduction for and a companion to a related NCRM course Population Estimation and Forecasting, which will run from 7-9 April 2027. Participants may choose to attend one or both courses as their prior experience and time allows. The course covers: Introduction to demographic measurementSources of demographic dataThe life table; stable population theoryApproaches for dealing with unreliable dataThe analysis of fertility Migration estimationBy the end of the course participants will:Understand the basics of demographic measurement and the key sources of demographic dataUnderstand and employ lifetables and the Kaplan-Meier estimatorUnderstand and employ methods for the analysis of migrationBe able to apply these approaches to a range of demographic data across different contexts and at different scalesPre-requisitesThis practical course will include workshops that use the R programming language to demonstrate the techniques introduced in the course. Basic knowledge of R would be beneficial, but is not essential. You will be able to follow the course just by attending the lecture sessions and carefully reading and digesting the workshop material, although of course, you will get more out of the course by completing the workshop exercises in R. Pre-course material will also provide links to free online introductory R courses to aid preparation.Target AudienceThe  course is of interest to anybody with an interest in quantitative methods from across disciplines (e.g. social sciences, economists etc), sectors and from across the career life-course (student, junior or senior researchers). The  course is particularly suited to:  Analysts working at or with, for example, National Statistical Offices, International Non-Governmental Organisations, and Government Departments with an interest in population estimation and projection. Examples include the ONS, the DHCLG; local and devolved government bodies; UN agencies. Employees of private sector organisations who have an interest in population change:Consumer-focussed industries; RetailersMarket research agenciesUtility companiesConstruction companiesPhD students working on population-related topics including demography, population health, human geography, etc.THIS COURSE IS BEING DELIVERED IN-PERSON AT THE UNIVERSITY OF SOUTHAMPTON (BUILDING 39, ROOM 2015) AND WILL RUN FROM 10:00 to 17:00 ON 5 AND 6 APRIL. IT EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton ) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15052 https://www.ncrm.ac.uk/training/show.php?article=15053 At any time horizon, policy depends upon estimates of population size and structure. Without population forecasts, planning for pension spending, housing demand, healthcare provision, and school place requirements becomes impossible. To know what the size and structure of our population will look like in the future, we need to have the tools to make demographic projections confidently. This course introduces the methods for forecasting the key components of population change, dealing in turn with mortality, fertility and migration. The course also includes an introduction to matrix models for multi-population forecasting is provided, together with a discussion of how to validate and assess uncertainty in demographic projections.The course will include hands-on computer workshops using the R programming language.This course is intended to follow on from a related NCRM course Introduction to Demographic Methods, which will run on 5-6 April 2027. Participants may choose to attend one or both courses as their prior experience and time allows. The course covers: The cohort component projection modelMatrix population models Principal component methods for population estimation and forecastingValidating and assessing uncertainty in population forecastsBy the end of the course participants will:Appreciate contemporary issues in demographic forecastingBe able to produce projections of each of the demographic components (mortality, fertility, and migration)Produce full population projections using the cohort component modelUse matrix models for multi-population projectionPre-requisitesThis practical course will include workshops that use the R programming language to demonstrate the techniques introduced in the course. Basic knowledge of R would be beneficial, but is not essential. You will be able to follow the course just by attending the lecture sessions and carefully reading and digesting the workshop material, although of course, you will get more out of the course by completing the workshop exercises in R.  Pre-course material will also provide links to free online introductory R courses to aid preparation.Knowledge of basic demographic techniques is recommended, including: The distinction between rates and probabilities in a demographic contextThe life tableBasic fertility measuresCompletion of the companion course Introduction to Demographic Methods would be advantageous but is not required.Target AudienceThe  course is of interest to anybody with an interest in quantitative methods from across disciplines (e.g. social sciences, economists etc), sectors and from across the career life-course (student, junior or senior researchers). The  course is particularly suited to:  Analysts working at or with, for example, National Statistical Offices, International Non-Governmental Organisations, and Government Departments with an interest in population estimation and projection. Examples include the ONS, the DHCLG; local and devolved government bodies; UN agencies. Employees of private sector organisations who have an interest in population change:Consumer-focussed industries; RetailersMarket research agenciesUtility companiesConstruction companiesPhD students working on population-related topics including demography, population health, human geography, etc.THIS COURSE IS BEING DELIVERED IN-PERSON AT THE UNIVERSITY OF SOUTHAMPTON (BUILDING 39, ROOM 2015) AND WILL RUN FROM 10:00 to 17:00 ON 5 AND 6 APRIL. IT EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton and Department of Social Statistics and Demography) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15053 https://www.ncrm.ac.uk/training/show.php?article=15004 Questionnaires are widely used for data collection in health research and evaluation. Their design, use and analysis are key to accurate, comprehensive and complete data. This course is an introduction to questionnaire design, application and data interpretation delivered by researchers and healthcare professionals experienced in developing, conducting and analysing questionnaire-based research for use in health services research and healthcare.This course provides an introduction to the theoretical and practical approaches to questionnaire design, application and data interpretation. It covers an overview of processes involved in questionnaire development, testing, administration and data management to provide a basic understanding of the key concepts and approaches when using questionnaires to collect healthcare data and measure outcomes. This course will cover an introduction to the key topics of:questionnaire construction: using qualitative methods to inform content and design, developing concepts to be measured, question type, wording and layout, and piloting;using quantitative methods to assess the reliability of scales, validity, sensitivity to change and interpreting multi-item scales;questionnaire application: choosing a questionnaire, maximising response rates, examples from case studies;using different methods and technologies for questionnaire administration;qualitative analysis of open responses; andquantitative data management, data coding and cleaning.Opportunities for delegates to discuss their own projects with course tutors and other delegates will be included. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15004 https://www.ncrm.ac.uk/training/show.php?article=15005 This course provides a broad introduction to carrying out qualitative research and how to apply qualitative methods to a range of contexts and study designs. We draw on the expertise within the University of Bristol. Course tutors have extensive experience of applied qualitative research including expertise in ethnography, optimising trial methods and recruitment, sensitive health topics, intervention development, and health care research.This course aims to give an overview and introduction to the major qualitative research methods based in the social sciences as used in health services, public health and other health-related research.This course is intended to give an overview and hands-on introduction to qualitative research methods, as used in the social sciences and health-related research. Methods introduced include individual interviews, focus groups and participant observation, as well as research design, ethical issues, data analysis, mixing qualitative and quantitative methods and writing up findings for publication.Day 1 introduces qualitative research and interviewing techniques.Day 2 covers study design (including qualitative sampling) and focus groups.Day 3 focuses on ethnographic and observational methods.Day 4 covers data analysis (including coding, thematic analysis and computer-assisted qualitative data analysis software).Day 5 covers mixing and integrating methods and writing up research findings for publication. Students will also have the opportunity to plan a qualitative project of their choice through a structured workshop. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15005 https://www.ncrm.ac.uk/training/show.php?article=14913 Ethical research is better quality research. This course is designed to raise your awareness of why and how you need to think and act ethically in practice throughout your research work. The current system of ethical review by committee can lead to the misleading sense of having ‘done ethics’. This course shows you how to conduct research which is truly ethical. It also provides the opportunity for discussion of your own ethical dilemmas, if you wish.The course covers: Research ethics in context – ethical breaches past and present, ethics activism, trauma-informed research, debiasing, ethics of technologyPotential ethical pitfalls at each stage of the research process, from question setting to aftercareHow to think and act ethically throughout researchBy the end of the course participants will:Recognise the importance of context for ethical decision-makingUnderstand why they need to think and act ethically throughout research workBe clearer about potential ethical pitfalls at different stages of the research processKnow how to approach ethical thought and action at any point in their research This course is aimed at Doctoral students, early career researchers (any discipline), practice-based/applied researchers and possibly government researchers and independent researchers.Note: If you are currently facing an ethical dilemma in your research work, or have faced one in the past that you would like to review, you can bring it for discussion on the second day. If you would like to do this anonymously you can email it to the course leader (via NCRM) and she will present it for you.THIS COURSE IS TAUGHT OVER TWO MORNINGS AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES.Programme:Day One09:30    Welcome and introductions09:40    Research ethics in context: presentation10:00    Discussion, Q&A10.15    When do we need research ethics?10:20    Video and discussion10:40    Trauma-informed research: presentation10:50    Debiasing techniques: presentation11.00    Discussion, Q&A11.10    Break time11.25    Ethical research design: discussion11.35    Ethical context-setting: discussion         11:45    Ethical data gathering: discussion11:55    Ethical data analysis: discussion12:05    Video and discussion12:20    Q&A12:30    CloseDay Two09:30    Welcome, questions arising from Day 109:40    Ethical research reporting: discussion09:50    Ethical research presenting: discussion10.00    Ethical research dissemination: discussion10.10    Ethical aftercare: discussion10.20    Researcher wellbeing10.30    Unethical research today – presentation10.45    Video and discussion11.10    Break time11.25    Real-life ethical dilemmas from research #111:40    Real-life ethical dilemmas from research #211:55    Real-life ethical dilemmas from research #312.10    Discussion, Q&A, evaluation12.30    Close jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14913 https://www.ncrm.ac.uk/training/show.php?article=14921 In this online one day course (taught over two mornings) you will learn what GIS is, how it works and how you can use it to create maps. We assume no prior knowledge of GIS and you will learn how to get data into the GIS, how to produce maps using your own data and what you can and cannot do with spatial data. You will gain practical experience in data importation, map creation, and analysis techniques, empowering them to enhance their research insights with compelling spatial visualisations.The course covers: What is GIS and spatial data?How to classify data for a choropleth mapHow to create a publication ready mapHow to work with different data sources including XY coordinate and postcode dataBy the end of the course participants will:Be able to set up QGIS and add dataUnderstand how to add data with latitude & longitude coordinatesKnow how to classify data for a choropleth mapBe able to join tabular data to spatial dataDesigning and producing a publication ready map in QGISUnderstand how to import a range of data types into QGISThis course is ideal for anyone who wishes to use spatial data in their role. This includes students, academic, government & other public sector researchers who have data with some spatial information (e.g. address, postcode, etc.) which they wish to show on a map. This course is also suitable for those who wish to have an overview of what GIS and spatial data can be used for, and how you can better represent your data with maps. No previous experience of spatial data is required.THIS COURSE IS RUN OVER TWO MORNINGS (10:00-13:00) AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14921 https://www.ncrm.ac.uk/training/show.php?article=15006 This overview of qualitative evidence synthesis will provide an introduction to the key methods for searching for, appraising and synthesizing qualitative evidence. This course provides a quick introduction to core methods, with sign posting to key tools and literature.This course will cover:Introduction to qualitative evidence synthesis, including:nature of qualitative evidence;types of qualitative syntheses andformulating a qualitative synthesis research question.Searching, Screening & Data Extraction for QES, including:tailoring a search for QES;screening issues in QES anddata extraction issues in QES.Quality appraisal for QES, including:nature of qualitative evidence & implications for critical appraisal;available tools for appraisal of qualitative evidence anddebate & evidence around critical appraisal in QES.Synthesis Methods for QES, including:different approaches to synthesis;nature of qualitative evidence & implications for synthesis andthematic synthesis (process, challenges, worked example). short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15006 https://www.ncrm.ac.uk/training/show.php?article=15007 This course introduces the analysis of survival-time outcomes, consisting of the time each person "survives" until some specified event occurs. Bristol Medical School staff teaching on this course use diverse survival analysis methods in their research.This course aims to give students a grounding in the theory behind the methods most commonly used to analyse rates and survival-time data, as well as extensive hands-on experience of their application in Stata software.The course will cover:1. definition of rates and the relation between risks and rates;2. manipulating person-time data in Stata using the st commands;3. analysis of rates using Mantel-Haenszel methods and Poisson regression;4. splitting follow-up time to allow for exposures that change with time;5. introduction to survival analysis;6. log rank tests and Cox proportional hazards regression;7. testing for proportional hazards and modelling non-proportional hazards;8. survival-time data management; and9. choosing between survival analysis methods.Please note that 'repeated measures' analyses, in which multiple events or measurements are recorded in the same person over time, are not covered in this course. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15007 https://www.ncrm.ac.uk/training/show.php?article=15008 This course aims to provide an introduction to the challenges of recruiting people to randomised controlled trials (RCTs) and strategies to address these. The course draws on evidence generated by the QuinteT research programme, which specialises in optimising recruitment to RCTs based on research conducted in over 70 trials to date. Course content and examples will be drawn primarily from trials set in secondary care hospital settings that span a range of medical specialities.This course aims to equip attendees with insight into the common sources of recruitment difficulty in RCTs and ways of mitigating or overcoming these.The course content has been informed by two decades of research into optimising RCT recruitment, conducted by the University of Bristol's QuinteT research group. The QuinteT group have worked on over seventy challenging RCTs, most of which have compared very different types of treatment or 'non-treatment' groups in secondary care NHS settings.The day long course will cover the following areas, using examples from real RCTs:common organisational and logistic difficulties that can impede recruitment;the purpose of RCT screening logs and their potential to monitor inclusivity and identify obstacles throughout the recruitment process;an overview of the concepts of individual and community equipoise, and their implications for recruitment;strategies for engaging with patient preferences for or against trial treatments;the implications of language and terminology on recruitment when discussing RCTs with potential participants; andan overview of inclusivity concepts, rationale for inclusive research, existing guidance, and team examples for optimising inclusivity in RCTs. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15008 https://www.ncrm.ac.uk/training/show.php?article=14922 In this one day online course (taught over 2 mornings) we will explore how to use R to import, manage and process spatial data. We will also cover the process of making choropleth maps, as well as some basic spatial analysis. Finally, we will cover the use of loops to make multiple maps quickly and easily, one of the major benefits of using a scripting language to make maps, rather than traditional graphic point-and-click interface.The course covers: Using R to import, manage and process spatial dataDesign and creation of choropleth mapsBasic spatial analysisWorking with loops in R to create multiple mapsBy the end of the course participants will:Use R to read in CSV data & spatial dataKnow how to plot spatial data using RJoin spatial data to attribute dataCustomize colour and classification methodsUnderstand how to use loops to make multiple mapsKnow how to reproject spatial dataBe able to perform point in polygon operationsKnow how to write shapefiles This course is ideal for anyone who wishes to use spatial data in their role. This includes government & other public sector researchers who have data with some spatial information (e.g. address, postcode, etc.) which they wish to show on a map. This course is also suitable for those who wish to have an overview of what spatial data can be used for. No previous experience of spatial data is required.No previous experience of coding is required, although participants would benefit from some experience of using spatial data (e.g. Google Maps).THIS COURSE IS BEING TAUGHT OVER TWO MORNINGS AND EQUATES TO ONE TEACHING DAY FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14922 https://www.ncrm.ac.uk/training/show.php?article=14949 This one-day course is aimed at those who need to learn about the very basics of analysing and presenting statistical data.The topics covered in this course are summarising data with numbers, tables and graphs (including the Normal distribution and confidence intervals). These topics will be demonstrated using Excel and SPSS.If you are interested in this course you may also be interested in our Survey Design & Analysis courseSee our full range of courses (and options for bespoke courses) at https://go.herts.ac.uk/sscu or contact the course organiser, Professor Neil Spencer, at statistics@herts.ac.uk.No prior knowledge of statistics is required. statistics@herts.ac.uk (University of Hertfordshire) Wed, 19 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14949 https://www.ncrm.ac.uk/training/show.php?article=15009 Missing data are almost inevitable in medical research. This leads to a loss of power and potential bias. Multiple imputation is a widely-used and flexible approach for handling missing data.This course aims to provide a theoretical and practical introduction to multiple imputation methods for dealing with missing data in straightforward situations.This course will cover:an introduction to the problems caused by missing data, including when a complete case analysis is likely to result in bias;an introduction to multiple imputation;practical sessions performing multiple imputation, including interactions and non-linear associations as well as simple diagnostic checks; anda practical session on how to present multiple imputation methods and results in journal articles. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15009 https://www.ncrm.ac.uk/training/show.php?article=15010 Pragmatic Randomised Controlled Trial (RCTs) are the ‘gold standard’ test for evaluating whether a new intervention is better than an existing one and are routinely used in both real-world healthcare and public health settings. Academics working in the Bristol Trials Centre (BTC) and the University of Bristol (including those who are tutors on this course) have all been at the forefront of designing and implementing high quality pragmatic RCTs for this purpose.This course aims to provide an understanding of the essentials of designing, conducting and analysing pragmatic randomised controlled trials (RCTs). The course examines RCTs evaluating health and public health interventions in primary, secondary and community settings with individual and cluster randomised designs. Please note this course does not go into lots of detail about regulatory approvals and research governance.The course will specifically cover:need for randomised trials;design of trials;randomisation;sample size;feasibility and pilot studies;trial planning, resourcing and working with the NHS;inclusion in trials;trial initiation;qualitative research in trials;Patient and Public Involvement;optimising trial recruitment;trial conduct and closure;protocol adherence and missing data;cluster and public health trials, including in schools;outcome assessment and Patient Reported Outcome Measures;health economics overview;primary and secondary trial analyses;experience of being a Chief Investigator;Clinical Trials Unit support for trialists; andCRF and database design. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15010 https://www.ncrm.ac.uk/training/show.php?article=15011 Survival analysis, also known as time-to-event analysis, uses duration data to estimate rates of events and associations of time with possible explanatory variables. University of Bristol academics (including those tutoring on this course) have wide-ranging experience in applying these methods within health research. This course will cover:parametric survival models;flexible parametric survival models; andcompeting risks in survival analysis. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15011 https://www.ncrm.ac.uk/training/show.php?article=15065 The course aims to provide participants with an understanding of the key issues involved in the design, implementation and analysis of sample surveys, with particular emphasis on sample design, data collection procedures, non-response, questionnaire design and testing, and the handling of missing data. Participants will gain practical insights into survey design and management, as well as the strengths and limitations of probability-based and non-probability-based surveys.The course is designed for a broad range of professionals, including data analysts working across different disciplinary fields, government analysts, policy professionals, survey designers, researchers and PhD students. It will be particularly valuable for anyone involved in analysing survey data, designing or commissioning surveys, interpreting survey findings, or developing policies and making decisions that rely on statistical analysis of survey data. The course will help participants critically assess survey quality, understand the limitations of survey evidence and make more informed use of survey data in research, policy development and practice.The course covers: Total Survey Error FrameworkSurvey samplingSurvey non-responseProbability-based vs non-probability-based surveysSurvey modeQuestionnaire design and testingBy the end of the course participants will:Demonstrate understanding of the key principles and practices involved in survey design, implementation and data analysis.Explain and evaluate the principles of questionnaire design and testing, drawing on relevant survey methodology literature and best practice.Identify, describe and critically evaluate a range of sampling designs, including probability-based and non-probability-based surveys, and understand their respective strengths and limitations.Identify and evaluate potential sources of survey error, including issues relating to sampling, non-response and data collection, and understand approaches to assessing and minimising their impact on survey quality.Demonstrate understanding of approaches to identifying and handling missing data in survey analysis, and assess their implications for the interpretation of survey findings.Critically assess the quality, limitations and suitability of survey data for particular research questions, policy development and decision-making.Interpret survey findings critically and understand the implications of survey design and data quality for statistical analysis and evidence-based policy.Target AudienceThe course is designed for a broad range of professionals, including data analysts working across different disciplinary fields, government analysts, policy professionals, survey designers, researchers and PhD students. It will be particularly valuable for anyone involved in analysing survey data, designing or commissioning surveys, interpreting survey findings, or developing policies and making decisions that rely on statistical analysis of survey data.THIS COURSE WILL TAKE PLACE ON 24 MAY (10:00-17:00), 25 MAY (9:00-17:00) AND 26 MAY (09:00-16:00) AND EQUATES TO THREE TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 21 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15065 https://www.ncrm.ac.uk/training/show.php?article=14950 This two-day course is aimed at those who need to plan and design questionnaire-based surveys and those who need to analyse the data collected. The first day covers the vital activities of designing a quality survey and obtaining a good sample. It covers the following topics.Defining your sample Choosing the right sampling methods to obtain a representative sample (e.g. stratified sampling, cluster sampling, multi-stage sampling,Deciding on the best way to collect your data (e.g. online, face-to-face, postal questionnaire,Designing questions to get good quality answers Deciding on the sample size needed to get estimates that have sufficient accuracy Using effect sizes and power to decide the sample size needed for statistical testsThe software G*Power will be used to demonstrate sample size calculations.The second day will cover summarising the results of your survey, presenting the results and undertaking statistical tests. It concentrates on those analyses which are particularly appropriate for the sort of data commonly collected using questionnaires. It covers the following topics.Creating graphs and chartsCalculating summary statisticsTesting for differences between groupsInvestigating relationships between variablesFitting regression modelsThe software Excel and SPSS will be used to demonstrate sample size calculations.No prior knowledge of statistics is required.See our full range of courses (and options for bespoke courses) at https://go.herts.ac.uk/sscu or contact the course organiser, Professor Neil Spencer, at statistics@herts.ac.uk. statistics@herts.ac.uk (University of Hertfordshire) Wed, 19 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14950 https://www.ncrm.ac.uk/training/show.php?article=15012 Economic evaluation is used to compare health and social care interventions and treatment options in terms of both costs and outcomes in order to assess value for money and support decision makers. The Health Economics and Health Policy at Bristol team (including those who are tutors on this course) has a well-established reputation in the economics of health and healthcare.This course aims to provide an introduction to the theory and practice of assessing value for money in health and care.This course will cover:why we need economic evaluation of health care interventions;key concepts in using health and care resources efficiently;identification, measurement and valuation of resource use to create costs;identification, measurement and valuation of health benefits and benefits beyond health, including the use of quality-adjusted life years (QALYs) and capability measures;presenting and interpreting the results of economic evaluations;applying decision analysis and Markov models in economic evaluation;critically appraising the quality of published economic evaluations; andtranslating economic evidence into health policy. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15012 https://www.ncrm.ac.uk/training/show.php?article=14951 This intensive two-day course will provide an introduction to R, aimed at those who have previously used other software for statistical analysis and want to switch to using R. The course uses the RStudio development environment for and will focus on the practical operation of the software and obtaining useful output.The first day covers the basics of R and how it operates. You will learn how to get your data into R, manipulate it to obtain basic summary statistics, tables and charts and get to know how the R environment works.  You will also see how the impressive graphics capabilities of R can be used to produce customised charts.  As well as getting to know the R language, we will show you how RStudio can be used as a front-end for managing your R programming.The second day will show you how to use R to apply commonly used statistical techniques, including tabulation, hypothesis testing, linear regression and methods for categorical variables. You may bring your own data for analysis or use the example datasets we provide.We recommend downloading the free software in advance of the course and can give instructions if needed. Alternatively, remote access to computers in our labs can be arranged.No prior knowledge of computer programming is required but familiarity with basic statistical methods would be advantageous.See our full range of courses (and options for bespoke courses) at https://go.herts.ac.uk/sscu or contact the course organiser, Professor Neil Spencer, at statistics@herts.ac.uk. statistics@herts.ac.uk (University of Hertfordshire) Wed, 19 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14951 https://www.ncrm.ac.uk/training/show.php?article=15013 The R statistical software provides an efficient, flexible, transparent, and extensible tool for building models for economic evaluation in healthcare. It is an increasingly popular alternative to less efficient, generalisable and powerful software such as spreadsheets. The tutors of this course have been at the forefront of developing R models and tools for economic evaluation.This course aims to teach the use of R for building Markov, semi-Markov, and discrete event simulation models for economic evaluation and how to conduct a value of information analysis in R.This course will cover :Day 1reminder on R;advanced Markov models (more states, more treatments, modularised code); andsemi-Markov models.Day 2value of information analysis; anddiscrete event simulation. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15013 https://www.ncrm.ac.uk/training/show.php?article=15014 This short course offers academics a thorough grounding in creating publication-ready graphics, interactive web resources, and applications using R. Designed for researchers at any stage of the R learning curve, the course takes you from a freshly collected and cleaned dataset through to polished, readily interpretable outputs. Participants will leave equipped to produce clear, compelling visualisations and build simple interactive applications that bring their data to life, whether for publication, teaching, or public engagement.This course aims to teach participants how to clearly and effectively communicate research findings. It will familiarise participants with data visualisation tools and techniques used in R for the creation of high-quality, dynamic, clear and reproducible graphics and research reports.This course will cover:1. an introduction to creating plots in R using ggplot2;2. advanced graphics techniques, including facets, themes, arranging plots and creating interactive plots;3. the use of examples from published papers to identify plots that present results clearly and intuitively (whether a figure clearly communicates its intended message);4. using RMarkdown to produce reports, tables and templates; and5. an introduction to Shiny and further applications for building interactive web applications. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15014 https://www.ncrm.ac.uk/training/show.php?article=15015 Repeated measures allow researchers to monitor how participants change over time. These repeated measures are correlated and require appropriate statistical modelling. In this course, you will understand when and why repeated measures data require appropriate analysis, and learn methods for analysing longitudinal/repeated continuous data.This course aims to provide an introduction to methods for analysing longitudinal/repeated continuous data, with a focus on methods for analysing individual trajectories over time. Throughout the course there will be an emphasis on what kinds of research question different types of method can be used to explore, and the interpretation of results.This course will cover:background: Why to collect, and how to analyse longitudinal data?;an overview of mixed-effects modelling approach;simple linear growth model for continuous outcomes;regression spline models for nonlinear trajectories in continuous outcomes; andpenalised regression splines, SITAR, and latent trajectory models. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15015 https://www.ncrm.ac.uk/training/show.php?article=15016 Protocols are essential for many areas of health research and a quality protocol is key to the successful delivery of a study. Academics working in the Bristol Trials Centre (BTC) and the University of Bristol (including those who are tutors on this course) have many years of experience in writing and delivering a wide range of protocols for high impact health research projects.This course is aimed at health researchers new to writing protocols, and will cover the essentials of writing a good quality protocol for a range of health research projects. Core knowledge will be supplemented by practical tips and illustrated by real life examples, with signposting of relevant guidance and support.  This course will cover: what is a protocol, why is it important and what information should be included? who should be involved and when, and what are the expectations of external stakeholders? what are the key considerations and potential challenges of writing a protocol? what guidance should be followed, and where can you get help and advice?  short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15016 https://www.ncrm.ac.uk/training/show.php?article=15017 Health research is increasingly turning to high-throughput molecular datasets (also known as ‘omic’ datasets) to discover novel biomarkers of disease risk and outcome. Unfortunately, the size and complexity of these datasets makes them difficult to manage and prone to many pitfalls. In this course, we introduce you to the latest approaches from data science for interpreting and extracting useful and reliable biomarkers from these challenging datasets.This course aims provide an overview of the principles and methods of epidemiology and data science that are relevant to high-throughput omic studies and provide students with the knowledge and skills necessary to design and utilize population-based omic studies to gain insight and to derive robust biomarkers of exposures and health outcomes.The course will cover:examples of published omic analyses and models for epidemiological and medical applications;statistical methods for preprocessing, discovering patterns and testing associations in omic datasets;interpreting the biological relevance of omic patterns and associations;estimating the heritability and proportion of variation explained by omic data;approaches from machine learning for deriving reliable omic biomarkers for indexing exposures and predicting health outcomes; application and interpretation of appropriate metrics for evaluating biomarker performance; andethical challenges of developing, interpreting and applying molecular biomarkers. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15017 https://www.ncrm.ac.uk/training/show.php?article=15018 Many observational studies aim to make causal inferences about effects of interventions or exposures on health outcomes. This course defines causation, describes how emulating a ‘target trial’ can clarify the research question and guide analysis choices, introduces methods to make causal inferences from observational data and explains the assumptions underpinning them, which can be encoded using directed acyclic graphs (DAGs). Learning is consolidated by interactive discussion-based and computer practical sessions. The course is taught by academics and researchers from the University of Bristol’s Department of Population Health Sciences, MRC Integrative Epidemiology Unit and NIHR Bristol Biomedical Research Centre who are experts in the field with extensive experience of developing and applying relevant methods. This is an advanced course. Familiarity with regression models including Cox models for time-to-event data and their implementation in statistical software (R or Stata) is essential.This course aims to define causation in biomedical research, describe methods to make causal inferences in epidemiology and health services research, and demonstrate the practical application of these methods.The course will cover:potential (counterfactual) outcomes;causal diagrams (DAGs);confounding and methods to control for confounding (stratification, regression, propensity scores and inverse probability weighting);selection and information biases;inverse probability weighting to deal with informative censoringtarget trials to define a causal question about health interventionsinstrumental variable estimation;intention-to-treat and per-protocol effects in randomized trials and observational studies;time-varying confounding, marginal structural models and other g-methods;sequential approaches to emulating a target trial using observational data;avoiding bias caused by immortal time: the clone-censor-IP weight approach;model selection for causal inference studiesstudy designs for causal inference; andreporting and triangulating causal inference studies short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15018 https://www.ncrm.ac.uk/training/show.php?article=14987 This live online course (administered via Zoom) offers an engaging introduction to Structural Equation Modelling (SEM) using R, the popular open-source software for statistical computing and graphics. The course will run over 5 consecutive days and will combine synchronous live lectures, individual exercises, group discussions and small group supervisions to simulate a live classroom environment. High contact with our expert instructors and interactive group work will further contribute to recreating the classic Cambridge experience in an online setting.This course will present the lavaan package, rapidly becoming the tool of preference for SEM in R. Participants will actively work through practical examples to gain first-hand experience in the application of factor analysis and other more advanced latent trait models. You will learn the following topics in this course:  Introduction to R and data analysisBasic concepts of factor analysis (EFA/CFA/Categorical CFA)Basic and advanced Structural Equation Models (SEM)Application of mediation & moderation techniquesMulti-level modellingLatent growth curve modellingMultiple-group analysis and evaluation of continuous and categorical data measurement invariance, as well as longitudinal measurement invarianceYou don’t need to know R to follow the course. However, if you are not familiar with R, you will need to choose the 5 day option, which includes an introduction to the R software on Day 1. On completion, participants should have a good knowledge of the topics covered and have acquired an independent use of R and latent trait analysis.We believe in active learning and developing practical skills. Thus, the necessary theoretical introduction will be illustrated with practical examples and we will be working with real data. No prior knowledge about SEM is assumed. The pace of teaching is adjusted to suit the level of the participants. Teaching will be in small groups so that delegates can make the most of the teaching. ls523@cam.ac.uk (University of Cambridge Psychometrics Centre) Mon, 28 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14987 https://www.ncrm.ac.uk/training/show.php?article=15019 This course will demonstrate how qualitative research can be used to address particular trial design and conduct issues, enhance understanding of how qualitative methods can be integrated at various stages of a trial, show how a range of qualitative methods can be used within trials, and facilitate greater confidence in including qualitative research in grant applications for randomised trials. It is not a course on how to do qualitative research but rather a course on how to appreciate its value within trials.This course aims to illustrate how qualitative research can be used to optimise the design and conduct of randomised trials, particularly pragmatic trials of complex interventions.The course will cover:using qualitative research to address particular randomised trial design and conduct issues;integrating qualitative research at various stages of the development and running of a trial;how a range of qualitative methods can be used within a trial; andincorporating qualitative research in grant applications for randomised trials. short-course@bristol.ac.uk (University of Bristol Medical School) Wed, 30 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15019 https://www.ncrm.ac.uk/training/show.php?article=14934 Ethnographic methods are increasingly popular with researchers across the social sciences, but the full potential and possible pitfalls of this complex practice are often overlooked in favour of catch-phrase definitions. This course moves beyond standard understandings of ethnography that depict it as a generic qualitative method founded on ‘participant observation’ to provide learners with a sophisticated, state-of-the-art approach based on cutting-edge academic research. The course will blend theorical and practical considerations. On the one hand, the course examines the theoretical scaffolding of ethnography, recognising that a thorough understanding of the epistemological foundations of the methods we use is essential to conducting rigorous and ethical research. On the other, the spirit of the course is inherently practical and pragmatic, as it aims at preparing researchers to design and conduct ethnographic fieldwork, as well as writing it up for academic and non-academic audiences.The course covers:Epistemology, method, and research design: ethnography beyond participant observationPreparing for fieldwork: a pragmatic approach to designing research projectsEthics and power: access, collaboration, co-production, and the possibility of decolonising researchWriting ethnography: from the practical to the politicalBy the end of the course participants will:Grasp the practice of ethnographic research beyond participant observationUnderstand the potential of ethnography beyond the traditional ‘study of culture’Have a sophisticated understanding of ethnographic research, from the design stage to its execution and writing up, including an overview of sensorial considerations and visual methodsBe able to appreciate the ethical and power dimensions of ethnographic researchUnderstand the ethics and politics of writing, publishing, and representing ethnographicallyThis advanced course is suitable for any researchers equipped with some prior knowledge/experience using both standard qualitative methods (interviews, focus groups, life histories, etc.) as well as ethnographic methods but is interested in advancing their understanding of ethnographic research to a professional level. Researchers working within and outside academia (private sector, government, charitable institutions, etc.) are equally welcome to apply. The course is likewise suitable for postgraduate students in any social science (human geography, sociology, business school, political sciences, area studies, education, etc.), particularly if enrolled or intending to enrol in a research degree (e.g., PhD, Masters by Research, Masters in Research Methods). Please note that this course is also suitable for postgraduate researchers with an UG background in anthropology, as the course if pitched to an advanced level.Pre-requisitesExperience using ethnographic research methods and qualitative research methods.Preparatory ReadingDemetriou, O. (2023), ‘Reconsidering the vignette as method. Art, ethnography, and refugee studies’, American Ethnologist, 50(2): 208-222.Hage, G. (2005), ‘A not so multi-sited ethnography of a not so imagined community’, Anthropological Theory 5, no.4: 463-475.Ingold, T. (2014), ‘That’s enough about ethnography!’, HAU Journal of Ethnographic Theory 4, no 1: 383–395Stefanelli, A. (forthcoming 2024) ‘Reading ethnography in the classroom: complementary strategies to develop students’ ethnographic imagination.’ Learning and Teaching in the Social Sciences.This online course will run from 09:30 to 15:15 both days. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14934