NCRM is a Hub-Node network of research groups, each conducting research and training in an area of social science research methods, coordinated by the Hub at the University of Southampton. https://www.ncrm.ac.uk/training/ Tue, 28 Apr 2026 19:59:15 +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=14683 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 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, 13 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14683 https://www.ncrm.ac.uk/training/show.php?article=14684 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 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, 13 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14684 https://www.ncrm.ac.uk/training/show.php?article=14725 Are you collecting research data involving human participants? Do you plan to share these data? Understanding the role of informed consent when working with human participants is ethically crucial.Join us for a free, 90-minute online workshop designed to guide researchers through the complexities of informed consent in the context of data archiving and future reuse.This session will clarify the difference between ethical and legal consent, emphasising that while consent can be a legal basis for processing personal data and data sharing, ethical considerations must also be addressed regardless of the legal bases used. We will explore how to ensure that consent is informed and respects participants' rights from an ethical perspective.This session will also focus on considerations for preparing documentation such as consent forms and participant information sheets when it comes to data archiving and future reuse. The session will also include extracts from real-world consent forms used by researchers and the model consent form recommended by the UK Data Service.There will be time at the end for questions and discussion.Presenter: Dr Hina Zahid. Hina has done a PhD in Health Psychology from the University of Essex. She currently works as a Senior Research Data Services Officer at the UK Data Archive, University of Essex. She provides guidance and training on ethical and legal issues related to data management and data sharing in order to promote good data practices in research and to optimise data sharing opportunities.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: Spring 2026. emma.green-3@manchester.ac.uk (University of Manchester ) Tue, 10 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14725 https://www.ncrm.ac.uk/training/show.php?article=14792 Working with mixed-mode survey data? Join this free webinar, run by CLS and Survey Futures, to understand the challenges of using mixed-mode survey data and learn statistical methods to handle these in practice.About the eventSurveys are increasingly moving to mixed-mode data collection – such as carrying out interviews via face-to-face, telephone, video and/or web modes. In this webinar, we will give an overview of issues that arise when using data collected in mixed-mode surveys. This includes the bias introduced when participants respond differently to survey items depending on the survey mode used – termed “mode effects”.We will conceptualise the bias from mode effects within a simple and intuitive empirical framework called Causal Directed Acyclic Graph (DAG). We will then describe statistical methods for handling mode effects, looking in particular at Quantitative Bias Analysis (QBA).Why attend?Learn about mixed-mode designs and the reasons they can introduce bias in data analyses.Find out how you can apply DAGs to easily conceptualise the bias from mode effects.Learn statistical methods for handling mode effects, their assumptions and the situations where they may increase bias.Learn about QBA.Who should attend?Users or managers of mixed-mode survey data, including users of CLS cohort data. radhika.jhamaria.23@ucl.ac.uk (UCL Centre for Longitudinal Studies (CLS)) Tue, 31 Mar 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14792 https://www.ncrm.ac.uk/training/show.php?article=14524 This module is part of a series of short (CPD - Continuous Professional Development) courses in Social/Official statistics delivered at the University of Southampton - Highfield campus. Evaluation and Monitoring aims to develop students' understanding of the nature of studies to monitor and evaluate intervention programmes, using examples from Government and other related areas. There is a particular focus on the contribution of statistical methods in both the design and analysis of such studies. The aim of the course is to present alternative experimental and quasi-experimental designs for evaluation programmes and the principles underlying the choice between these designs. In this regard, the main topics will include data sources and research methods, performance monitoring, theoretical framework, and randomized controlled trials. This course also aims to discuss general statistical methods that can be used in the analysis of data devised from such designs, in particular for the estimation of programme effects, allowing for potential confounding factors. Methods will include regression adjustment and other analysis methods, propensity score matching, and econometric methods of analysis.  Lectures will be complemented with exercise sessions and computer workshops in R.For more information see: Evaluation and Monitoring | STAT6089 | University of SouthamptonPre-requisitesPrevious knowledge of Regression methods is assumed. Typically, students would be familiar with the linear and logistic regression models. For an example of syllabus for suitable prerequisite, see: Regression Modelling | STAT6095 | University of SouthamptonRegistrationRegistration is by application which should be submitted at least one month before the start of the course. For further information about the application process, please get in touch with Helen Davies at helen.davies@soton.ac.ukAssessmentThis course can be taken with or without assessment. The latter offers the possibility of accumulation of credits for the MSc in Data Analytics for Government https://www.southampton.ac.uk/courses/data-analytics-for-government-masters-msc p.c.white@southampton.ac.uk (University of Southampton) Wed, 22 Oct 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14524 https://www.ncrm.ac.uk/training/show.php?article=14299 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 audiencesFormat 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"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 specialize 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. christin@qdas.co.uk (QDAS | Qualitative Data Analysis Services) Tue, 16 Sep 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14299 https://www.ncrm.ac.uk/training/show.php?article=14689 Course timings: 9.00 to 13.00 (BST)Overview:This online course provides attendees with an insight into the design and practice of advanced qualitative interviews. These can include, for example, interviews on sensitive topics, interviews involving distress, interviews with vulnerable participants, or interviews when it is difficult to build rapport. Crucially, the training provides participants with hand-on strategies, guidance and reflections on how to manage these situations as interviewer and build confidence in doing so. It provides participants with an understanding of how vulnerability, distress, sensitivities, and risk are assessed in qualitative interviewing and the importance of being able to respond to these, if, and when, necessary.The training provides guidance on, for example: defining and identifying vulnerability, distress and risk, sensitive and therapeutic interviews, the importance of empathy, the interview dynamic, boundaries and reciprocity, researcher reflexivity, identifying and responding to distress, and distress protocols. It also introduces types of participation and strategies for dealing with this (i.e. interviewees who are difficult to create rapport with or to involve in the interview).Participants will view examples of different interviewing styles and respond to various mock 'challenging interview' scenarios with their peers, in order to gain insight into the dynamics of challenging interviews and build confidence in their use of different strategies.The training is delivered on Zoom and includes a combination of presentations, discussions and practical activities. It is designed to equip participants to confidently conduct challenging interviews with vulnerable or difficult participants and on sensitive topics in applied research projects across a range of settings. By the end of the day, participants will be able to design and conduct challenging interviews and will have built their confidence in this space. Topics to be covered:· Defining and understanding distress, vulnerability, sensitivties, and risk· Sensitive and empathic interviewing· Identifying and responding to distress· Reflexivity in interviewing· Strategies including rapport and reciprocity / boundaries / active listening / the power of silence· Examples of interviews with vulnerable participants and on sensitive topics· Ethical considerations and guidelines· Examples of challenging interviews and reflecting on different scenarios with peers and trainer. Who should attend and who will benefit from this course?This course will benefit participants who wish to advance their knowledge of qualitative interviewing methods by exploring the intricacies, sensitivities, practical, and ethical considerations in conducting challenging interviews with vulnerable groups and/or different forms of participation in a range of applied and policy settings. Prior knowledge of qualitative methods is essential.The training is beneficial for doctoral students, academics and researchers. It is relevant for researchers who design and/or conduct qualitative interviews in government, policy, consultancy, social research organisations or charities.Please note: this is an interactive live course with presentation, group activities, group discussions, and opportunities to ask questions. You should be prepared to participate and have access to a working camera and mic to take part on Zoom. Trainer biography:Dr Karen Lumsden is an expert qualitative trainer, consultant, coach and mentor. She has held a number of academic posts, including most recently, Senior Lecturer in Sociology at the University of Aberdeen, UK. She was previously Associate Professor in Criminology at the University of Leicester, Assistant Professor at the University of Nottingham, and Senior Lecturer in Sociology at Loughborough University.She has over 20 years’ experience delivering qualitative methods courses and training to academics, PhD students, social researchers, and practitioners. This includes courses at universities including, for example, Aberdeen, Glasgow, Essex, Kings College London, Royal Holloway, Kingston, Cardiff ,and Bournemouth. She has delivered training for the University of Auckland, New Zealand, and the Chinese University of Hong Kong. She regularly delivers a range of qualitative methods courses via the Social Research Association, and delivers Focus Group training for the European Consortium for Political Research. She has designed and delivered bespoke training for local authorities, government departments (including DWP), ONS, NHS, charities, police organisations, and social and market research organisations.Karen has written and edited a number of books and journal articles on qualitative methods including Crafting Autoethnography (Routledge, 2023) and Reflexivity: Theory, Method, Practice (2019). She is on the Editorial Board of the journal Qualitative Research and was previously Chair of the Editorial Board of Sociological Research Online, and on the Editorial Board of Sociology. For more details of her work and services visit: www.qualitativetraining.com Bookings:Bookings for this course can be made via Eventbrite tickets. If your organisation requires payment via invoice, please contact me directly to check if this will be possible. I only issue invoices when payment terms are mutually agreed and confirmed in writing prior to the event date. Email: karen@qualitativetraining.com Refund policy:There is a 14 day 'cooling off' period from the date of booking on this course (unless there are 14 days or less between the booking date and training date, in which case, no refund is possible). The refund policy is 100% refund up to 7 working days prior to the course date. If fewer than 7 days the entire course fee is payable.If the course is fully booked and has a waiting list then transfer to another course or future course date might be possible, however this is at the discretion of the trainer.Please note that Dr Karen Lumsden delivers a range of courses for other training providers in addition to these Qualitative Training courses. She takes no responsibility or liability for individuals booking on similar courses or training with other providers which may contain similar course content. Refunds will not be given under these circumstances. karen@qualitativetraining.com (Qualitative Training) Mon, 02 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14689 https://www.ncrm.ac.uk/training/show.php?article=14710 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 those leading research 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.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, 03 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14710 https://www.ncrm.ac.uk/training/show.php?article=14733 The aim of this two-day online training course is to introduce participants to the practice and ethics of ethnographic research. Through a mix of plenary sessions, group and independent work, participants will learn the basic principles of participant observation and research design, as well as the foundations of ethical ethnographic research. The course will also examine the ways in which other qualitative and creative methods of data collection may be productively integrated in ethnographic research.The course covers:Research designQualitative methods in ethnographic researchAccess and powerResearch ethics in participant observationBy the end of the course participants will:Understand the epistemological foundations of ethnographic researchHave a solid understanding of ethnographic research in actionBe able to design and conduct research integrating qualitative and ethnographic research methodsBe able to conduct ethical ethnographic researchThe course is suitable for any professional researchers interested in learning more about using ethnographic methods – whether within or outside academia (private sector, government researchers, etc.).The course is likewise suitable for postgraduate students in any social science (human geography, sociology, business school, political sciences, area studies, education, etc.) with prior knowledge of any qualitative research methods, but not necessarily of ethnography.Some prior training in qualitative research methods, broadly defined – regardless of whether that includes ethnographic methods specifically.Day 1Morning session:•          09:30-09:45     Introduction to the course•          09:45-10:45     Plenary – The Practice of Ethnography•          10:45-11:00      Break•          11:00-12:00      Group work followed by class discussionAfternoon session:•           12:45-13:45      Plenary - Qualitative methods in ethnographic practice•           13:45-14:00      Break•           14:00-15:15      Practical exercise followed by class discussionDay 2Morning session:•           09:30-10:45     Plenary - Research ethics in ethnography•           10:45-11:00      Break•           11:00-12:00      Group work followed by class discussionAfternoon session:•           12:45-1:345       Plenary – Writing ethnography•           13:45-14:00       Break•           14:00-15:00       Practical exercise, followed by class discussion         •           15:00-15:15       Conclusions and Evaluations  jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 08 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14733 https://www.ncrm.ac.uk/training/show.php?article=14820 DESCRIPTIONTuesday 5 May 202615:00–16:30 (UK time)Online (MS Teams): Link to MeetingABSTRACTThis session focuses on a central question: when data arise from complex sampling designs, can machine learning methods support rigorous inference in the same way as traditional statistical models? We begin by contrasting the fundamental objectives of the two approaches: while traditional statistical models emphasise parameter estimation and uncertainty quantification, machine learning methods tend to prioritise predictive performance.The session then considers what this distinction implies in the context of complex survey data. In particular, it focuses on key design features such as sampling weights, clustering structures, and plausible values, which are essential for valid inference but are often not systematically addressed within machine learning frameworks. Using empirical analysis based on TIMSS 2023 data, the session illustrates how different methodological approaches handle these features, and how these choices shape our understanding of how students’ learning behaviours influence learning outcomes.SHORT BIO:Dr Yin Wang is a Lecturer in Research Methods and AI Skills in the Department of Social Statistics and Demography at the University of Southampton, and a member of the National Centre for Research Methods (NCRM). Her current work within the UKRI-funded programmes Using Artificial Intelligence Methods in Education Data and New Approaches to Digital Skills Development examines the integration of AI-based and traditional statistical methods to improve the validity, transparency, and policy relevance of quantitative research using international large-scale assessment (ILSA) data.  jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 28 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14820 https://www.ncrm.ac.uk/training/show.php?article=14747 This course aims to introduce delegates to the MATLAB programming language for data analysis. ich.statscou@ucl.ac.uk (UCL CASC) Wed, 25 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14747 https://www.ncrm.ac.uk/training/show.php?article=14752 The aim of this interactive workshop is to consider the methodologically appropriate and ethical use of Generative-AI (Gen-AI) tools to analyse qualitative data in the form of transcripts from e.g. interviews and focus groups, or other textual materials (e.g. literature, policy reports, open-ended survey responses etc.).The course will cover:Historical context of AI-assisted qualitative analysisPlanning for and setting up AI-assisted qualitative analysis projectsThe ethics of incorporating AI-assisted tools into qualitative analysisUsing AI-assisted tools to transcribe, explore and summarise qualitative dataUsing AI-assisted tools to generate ideas for, and undertake qualitative codingUsing AI-assisted tools to identify patterns and relationshipsReporting on the use of AI-assisted tools in qualitative analysisLooking 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, 26 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14752 https://www.ncrm.ac.uk/training/show.php?article=14726 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. 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: Spring 2026. emma.green-3@manchester.ac.uk (University of Manchester ) Tue, 10 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14726 https://www.ncrm.ac.uk/training/show.php?article=14753 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.The benefits of qualitative interviewsTypes of qualitative interview including structured, unstructured and semi-structured interviewsDesigning and structuring a semi-structured interview scheduleConducting a semi-structured interviewQuestioning: open and closedPractical considerations (i.e. recording and transcription)The ethics of interviewingHow to build relationships and rapport with interviewees.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, 26 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14753 https://www.ncrm.ac.uk/training/show.php?article=14409 Level: Intermediate (I)This course will introduce two technologies that will fundamentally change your use of R for data presentation on the web: htmlwidgets and Shiny. The first half of the course introduces RMarkdown and htmlwidgets, demonstrating how reports with interactive tables, maps and charts can be written and published to the web using RStudio. The second half of the course introduces the basics of Shiny, a web framework for creating sophisticated interactive applications using only the R language.Htmlwidgets and Shiny are thoroughly underused in the R community. This course will show you it is almost trivial to build interactive charts/maps/data tables with htmlwidgets and only slightly trickier to build sophisticated interactive applications with Shiny.Learning OutcomesRecognise the benefits of analysis and visualisation design in RMarkdown documentSelect appropriate htmlwidgets for data or analytical results and include them in RMarkdown documentsPublish RMarkdown reports to the web with RPubs or Github PagesProduce basic Shiny apps incorporating htmlwidgets and publish them to shinyapps.ioTopics CoveredOverview of the RStudio and tidyverse ecosystemIntroducing interactive data visualisations with R via htmlwidgets and the pipe operatorIncorporating text with code and interactive output using RMarkdownPublishing interactive RMarkdown reports to the webDesigning interactive applications in R using ShinyPublishing Shiny applications to shinyapps.io Target AudienceR users who want their audiences to interact and explore their data through interactive visualisations. Or those who wish to provide these interactive tools to others to supplement their analysis. Datasets will range from numerical observations, geolocations to colocation network data, and should therefore be interesting to anyone involved with social, physical or medical science, economics or computer science.  Knowledge AssumedA working knowledge of R is assumed. For inexperienced users you’re recommended to study the free Intro to R course here: https://campus.datacamp.com/courses/free-introduction-to-r/chapter-1-intro-to-basics-1The RSS also runs a two-day foundation level R course called 'Introduction to R' 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=14409 https://www.ncrm.ac.uk/training/show.php?article=14522 This module is part of a series of short (CPD - Continuous Professional Development) courses in Social/Official statistics delivered at the University of Southampton - Highfield campus. Statistics in Government provides an overview of issues and ideas concerning the scope and organisation of Official Statistics and its processes and products, including Statistical Acts and Codes of Practice. The module also provides a general foundation for the more detailed study of these elements and identifies links with other relevant disciplines. This course will cover the following main topics: an overview of official statistics, their importance for informing policy and identifying important trends in the economy and society; the defining documents of official statistics including: the UN Fundamental Principles of Official Statistics, statistical frameworks and national statistical laws; standards for good statistical practice, including formal codes of practice and the roles that they play; the importance of statistical quality and how quality reviews improve official statistics; conceptual frameworks, methodological standards and classification systems; the organisation and management of statistical organisations - centralised and de-centralised systems, the role of professional bodies and supranational organisations; and the history of official statistics.For more information see: Statistics in Government | STAT6088 | University of SouthamptonRegistrationRegistration is by application which should be submitted at least one month before the start of the course. For further information about the application process, please get in touch with Helen Davies at helen.davies@soton.ac.ukAssessmentThis course can be taken with or without assessment. The latter offers the possibility of accumulation of credits for the MSc in Data Analytics for Government https://www.southampton.ac.uk/courses/data-analytics-for-government-masters-msc  p.c.white@southampton.ac.uk (University of Southampton) Wed, 22 Oct 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14522 https://www.ncrm.ac.uk/training/show.php?article=14654 Overview of 2-day CourseThe most commonly used methods of dealing with survival and other time-to-event data are based on the assumption of proportional hazards. But often this assumption may not be tenable, or the data structure may be more complex. This course is concerned with models for different types of data structure, or with different underlying assumptions.Examples used will be drawn from a variety of applications in medicine and health.Practical work will be based around the statistical software R; see https://www.r-project.org/.PresentersSandro Leidi and James GallagherCost£582 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 09:00 and 17:30 (GMT+1).  The delivery platform is 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?Statisticians working in medical research in public sector institutions and in the pharmaceutical and related industries, who already have some familiarity with modelling survival data.Participants will be assumed to have a working knowledge ofModelling survival data; in particular the Cox proportional hazards regression modelThe R statistics software.How You Will BenefitIf you deal regularly with survival data and need more tools for modelling it, then this course covers a range of different survival analysis models.What Do We Cover?A review of the Cox proportional hazards regression modelThe counting process formatThe Weibull proportional hazards regression modelAccelerated Failure Time modelsTime varying covariatesNon-proportional hazardsFrailty models, i.e. inclusion of random effectsCompeting risksR packages for fitting the above models, including survival, coxme, parfm and cmprsk.The course does not cover multi-state models.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) Tue, 06 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14654 https://www.ncrm.ac.uk/training/show.php?article=14727 Participants will learn about the key issues to consider when using secondary data analysis as a method. This introductory workshop will briefly cover the pros and cons of reusing data and the importance of learning about the origins of your data. Quantitative and qualitative secondary analysis will be discussed with examples and issues of context, sampling, plus ethics raised.The session is more conceptual than many of our other workshops, which are more suited to those who want a more practical introduction to our data. The practical elements of the session will focus on exploring documentation and finding suitable data to support your research.The free 90-minute workshop will consist of an interactive presentation followed by a live demonstration and a facilitated activity. There will be time at the end for questions and discussion.In order to fully take part in the workshop, attendees should already be familiar with the basic methods of qualitative or quantitative data research.Presenters: Alle Bloom and Maureen Haaker.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.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: Basic methods of qualitative or quantitative data researchTarget audience: Researchers/anyone interested in reusing 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: Spring 2026.  emma.green-3@manchester.ac.uk (University of Manchester) Tue, 10 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14727 https://www.ncrm.ac.uk/training/show.php?article=14711 Narrative inquiry is a valuable investigative technique in qualitative research. Narrative inquiry and storytelling offer us a different way of knowing, of investigating the lived experiences of individuals, and of exploring subjectivity. Narrative knowledge is created and constructed through the stories of lived experience and sense-making, the meanings people afford to them, and therefore offers valuable insight into the complexity of human lives, cultures, and behaviours. It allows us to capture the rich data within stories, including for example shedding insight into feelings, beliefs, images and time. It also takes account of the relationship between individual experience and the wider social and cultural contexts. Crucially, it also involves collaborative inquiry and co-construction of meaning between participants and the researcher. Examples of narrative inquiry in qualitative research include for instance: stories, interviews, life histories, journals, photographs and other artefacts.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, 03 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14711 https://www.ncrm.ac.uk/training/show.php?article=14576 Landing Page: RStats short courses | Nottingham Trent UniversityCourses:Introduction to Statistics Using R and RStudio – Online | Nottingham Trent UniversityIntroduction to Generalised Linear Models in R - Online | Nottingham Trent UniversityIntroduction to Multilevel and Mixed Effects Models using R - Online | Nottingham Trent UniversityIntroduction to Bayesian Data Analysis using R - Online | Nottingham Trent University kelsey.clarke@ntu.ac.uk (Nottingham Trent University) Tue, 11 Nov 2025 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14576 https://www.ncrm.ac.uk/training/show.php?article=14712 Qualitative data analysis reveals patterns and themes from the large volume of data generated by qualitative research. It is useful for gaining detailed understanding of social phenomena and individual experiences, perceptions and behaviours. However, it is often seen as a mysterious and complex stage of the research process. There are also challenges in terms of how researchers conduct analysis and the steps that they need to follow.This advanced course provides participants with the skills to conduct qualitative data analysis. While providing an overview of different analytical approaches, the focus in our activities will be on thematic analysis. It provides an introduction to qualitative data analysis. It explores ways of organising and analysing qualitative data, and the practicalities of doing so. Through a practical exercise where we analyse qualitative interview data provided by the trainer, participants will be able to gain experience of conducting their qualitative data analysis by focusing on thematic analysis.By the end of the course, participants will have knowledge of various methods and theories of qualitative data analysis and how it differs from quantitative analysis. They will be able to choose an appropriate data analysis technique for different forms of qualitative data. They will also be able to conduct their own thematic analysis, code, and organise data for analysis.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, 03 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14712 https://www.ncrm.ac.uk/training/show.php?article=14754 Have you ever discovered too late that your survey questions did not deliver useful or useable data? This course highlights ways to avoid pitfalls in the wording of individual survey questions as well as for the questionnaire whole. It also points out questionnaire design differences between face-to-face and telephone interviews, web and mobile web surveys and paper self-completion. Drawing on 30 years of the instructor’s experience and research findings from questionnaire design experiments, this course is full of practical advice.By the end of the course, participants will:Have a knowledge of the different aspects involved in writing good survey questions and questionnaires,Have the ability to write their own high quality questionnairesHave the tools to critique existing surveysHave awareness about the differences in questionnaire design between face-to-face and telephone surveys, web and mobile web surveys and paper self-completion.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, 26 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14754 https://www.ncrm.ac.uk/training/show.php?article=14767 This two-part in-person training course combines critical reflection with hands-on practice to help researchers navigate generative AI thoughtfully and responsibly. The first session explores what AI means for higher education and research at this moment of rapid change, examining both opportunities and risks. The second session is a practical workshop where participants bring their own work and AI tools to explore ethical and professional use, developing personal principles for responsible AI integration into research practice. Participants must bring their own device with access to a generative AI chatbot they already have an account with and have previously used (such as ChatGPT, Claude, Gemini, or Copilot).The course covers: The current landscape of generative AI in higher education and academic researchHow AI is reshaping academic work, including writing, analysis, and collaborationOpportunities and risks of AI adoption in research contextsEthical considerations around integrity, authorship, and responsibilityPractical exploration using participants' own research materials and AI toolsScenario-based discussions on responsible AI usePeer exchange on emerging practices and challengesDeveloping personal guiding principles for AI use in researchBy the end of the course participants will:Articulate a clearer understanding of what generative AI means for researchers and scholarshipCritically evaluate the opportunities and risks of AI in their own research contextReflect on how language models are entering their research processesIdentify key ethical considerations around integrity, authorship, and responsibility when using AIExperiment critically with AI tools using their own research materialsBegin developing their own guiding principles for responsible AI useShare and learn from peers' emerging practices and approachesScheduleWednesday 13th May 2026, 10:00 - 16:00LocationRoom 1.69, Humanities Bridgeford Street Building, The University of Manchester, M15 6ADPre-requisitesSome prior experience using a generative AI chatbotAn active account with a generative AI tool of your choice A paper they have published (open access or pre-print version)A work-in-progress paper or chapterAccess to their preferred AI chatbotPresenterDr Mark Carrigan FRSA FHEA is a Senior Lecturer in Education at the University of Manchester, where he co-leads the Digital Education Manchester group and serves as an AI Fellow at the Institute for Teaching and Learning. His work centers on three interconnected commitments: developing ontological and epistemological frameworks for understanding Large Language Models (LLMs) beyond current inadequate conceptualisations; examining higher education as a critical site where the social and cultural dynamics of LLMs unfold through practical challenges; and advancing Margaret Archer’s morphogenetic approach as a route to addressing these urgent questions.He is the author of Platform and Agency: Becoming Who We Are (Routledge, 2025), which develops a framework for understanding personal transformation in the digital age. His recent work includes Generative AI for Academics (Sage, 2024) and Social Media for Academics (Sage, 2nd edition), alongside eight other books. He co-edited Building the Post-Pandemic University (Edward Elgar, 2023), examining how universities are transforming in response to technological and social disruption.  p.c.white@southampton.ac.uk (University of Southampton) Tue, 14 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14767 https://www.ncrm.ac.uk/training/show.php?article=14770 Course overview and aimsMAXQDA 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 amongst researchers working throughout 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 MAXQDA. Using Braun & Clarke’s (2021) six phases of Reflexive Thematic Analysis as an example framework, we set up a thematic analysis in MAXQDA and experiment with a 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 for different analytic tasks, and the benefits of using MAXQDA for thematic analysis in comparison to manual methods.This course teaches the latest version of MAXQDA – currently v.24. Participants do not need to purchase a license to follow the course, as an extended trial version will be made available as part of the course registration. Learning objectivesBy the end of the course, participants will be able to:Understand the range of MAXQDA tools than can be harnessed throughout common phases of thematic analysisSet up a MAXQDA project and plan its use for thematic analysisUse MAXQDA 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 MAXQDASetting up a project – structuring the MAXQDA 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 MAXQDA 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 MAXQDA to undertake thematic analysis of qualitative materials including transcripts from interviews, group discussions, observations etc.No prior knowledge of MAXQDA is required. Format and documentationThis course is delivered in a series of live online sessions during the day that combine discussion, demonstration and hands-on exercises.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 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 MAXQDA. 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 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) Mon, 13 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14770 https://www.ncrm.ac.uk/training/show.php?article=14811 methodology.comms@lse.ac.uk (LSE Methodology) Wed, 22 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14811 https://www.ncrm.ac.uk/training/show.php?article=14338 Who is this course for?It is designed for postgraduate students and early career researchers planning to work with quantitative data in any discipline. Course overview and aimsThis course provides a high-level overview to the principles and practices of undertaking quantitative data analysis. It is designed for postgraduate students and early career researchers planning to work with quantitative data in any discipline, although many examples used draw on survey data. The course focuses on key aspects to consider before embarking upon a quantitative analysis, including considerations around data collection, as a precursor to learning statistical methods and the use of software. The course combines discussion, demonstration and hands-on exercises.Please note: the course does not teach participants how to use analysis software such as SPSS, STATA or R. Topics• Exploring phenomena of interest using quantitative measures• Types of numerical data: nominal, ordinal, interval and ratio variables• High quality sources of existing quantitative data: Overview of the UK Data Service and its offer• Guidelines for gathering and working with one’s own quantitative data• Variables and their transformation• The difference between descriptive and inferential statistics• Types of univariate analyses: descriptive statistics of individual variables• Descriptive and inferential bivariate analyses: the relationship between two variables• Evaluating the potential and limits of data & choosing the appropriate type of analysis.• Introduction to inferential analyses: hypothesis testing and interpreting p-values• Interpreting a linear regression• Reporting results Learning objectivesBy the end of the course, participants will be able to:• Design survey questions that capture quantitative data in a way that facilitates analysis.• Approach quantitative data collection confidently.• Find high quality, topical quantitative data to be re-analysed.• Understand what an analysis of individual quanitative measures looks like and what it tells us.• Understand what it means to look at the relationship between two quantitative measures and the different ways of doing that.• Identify the limits of their data and the analyses it permits.• Understand the difference between descriptive and inferential statistics• Interpret p-values, and the results of inferential bivariate analysis• Interpret the results of a linear regression. Format and documentationThis course is delivered in a series of live online sessions during the day that combine discussion, demonstration and hands-on exercises. Participants have the opportunity to discuss their quantitative 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. FacilitatorSarah L Bulloch, PhD is a senior associate of QDA Services and teaching fellow at the CAQDAS Networking Project at the University of Surrey, UK. Sarah has expertise in both quantitative and qualitative analysis techniques and has applied them in academic, public sector, private and third sector contexts. Her PhD explored the relationship between social trust and gender using large scale population datasets and applying multivariate linear regression, multivariate logistic regression, multi-level modelling and structural equation modelling. Her current work and research centres around enabling others to apply research methods in a meaningful, robust and flexible way.About QDA ServicesQDA Services provide tailored and flexible training, consultancy, coaching and analysis for qualitative, quantitative 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, 26 Jun 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14338 https://www.ncrm.ac.uk/training/show.php?article=14713 Working with quantitative data entails being proficient with data management and exploration in order to do analysis and get insights. And while there are multiple ways of doing this, R has emerged as one of the most popular tools for these tasks. R is a free and open source software for statistics and data science that is increasingly used with social survey data.Important: you will need some familiarity with quantitative data, and an understanding of variables, datasets, and re-coding. A basic knowledge of R is also a highly recommended.In this course you will be introduced to the use of R and will learn how to prepare and visualize data. We will focus on the use of the ‘Tidyverse’ package. Inspired by the concept of “tidy data” this package enables users to import, merge, recode, restructure and visualize data very efficiently.Half of the course will focus on how to efficiently transform variables and prepare them for analysis while the other half will focus on visualization. The course will combine presentations of the key concepts, hands on practical sessions and discussions of the solutions. In the practical part we will be using real world data to prepare the participants for working with their own data.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, 03 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14713 https://www.ncrm.ac.uk/training/show.php?article=14720 Research data management encompasses all stages of handling data created during a research project, either primary or secondary. It is not only about adhering to principles but also about enhancing the efficiency, impact, and integrity of research.Effective data management ensures that data are Findable, Accessible, Interoperable and Reusable (FAIR principles). The foundation of sensible data management lies in understanding and applying best practices across the data lifecycle from planning to sharing, while adhering to ethical standards and legal requirements.During this free 90-minute online workshop, we will explore the key components of effective research data management, including data management planning, organising, and documenting data, ensuring data quality, storing and backing up data, as well as strategies for data preservation and sharing. The workshop will also cover practical tools and techniques for data management, such as metadata standards for documenting data, data quality semi-automated freeware, and best practices for data sharing and archiving.The workshop will conclude with a dedicated Q&A session.Presenter: Dr. Hina Zahid. Hina has done a PhD in Health Psychology from the University of Essex. She currently works as a Senior Research Data Services Officer at the UK Data Archive, University of Essex. She provides guidance and training on ethical and legal issues related to data management and data sharing in order to promote good data practices in research and to optimise data sharing opportunities.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 advancing their research practices through effective data management.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: Spring 2026. emma.green-3@manchester.ac.uk (University of Manchester ) Fri, 27 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14720 https://www.ncrm.ac.uk/training/show.php?article=14376 In this online course, run over two mornings, we will show you how to prepare and conduct spatial analysis on a variety of spatial data in R, including a range of spatial overlays and data processing techniques. We will also cover how to use GeoDa to perform exploratory spatial data analysis, including making use of linked displays and measures of spatial autocorrelation and clustering.The course covers: Understanding and being able to interpret Spatial Autocorrelation measure Moran's IUnderstanding Local Indicators of Spatial Association statisticPerform Spatial Decision Making in RPerform Point in Polygon analysis using different approachesBe aware of the advantages and disadvantages of using point based or polygon based dataUsing buffers as a part of spatial decision makingBy the end of the course participants will:Be aware of some spatial statistics concepts and be able to apply them to their own data using GeoDaBe able to perform spatial decision makingUnderstand the limitations and benefits of working with data in this wayThis course is aimed as PhD students, post-docs and lecturers who have some existing knowledge of using R as a GIS and want to develop their knowledge of spatial stats and spatial decision making in R. Some prior knowledge of both R and GIS is required. It is also appropriate for those in public sector and industry who wish to gain similar skills.   jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 14 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14376 https://www.ncrm.ac.uk/training/show.php?article=14377 In this online course, run over two mornings, we will show you how to prepare and conduct spatial analysis on a variety of spatial data in R, including a range of spatial overlays and data processing techniques. We will also cover how to use GeoDa to perform exploratory spatial data analysis, including making use of linked displays and measures of spatial autocorrelation and clustering.The course covers: Understanding and being able to interpret Spatial Autocorrelation measure Moran's IUnderstanding Local Indicators of Spatial Association statisticPerform Spatial Decision Making in RPerform Point in Polygon analysis using different approachesBe aware of the advantages and disadvantages of using point based or polygon based dataUsing buffers as a part of spatial decision makingBy the end of the course participants will:Be aware of some spatial statistics concepts and be able to apply them to their own data using GeoDaBe able to perform spatial decision makingUnderstand the limitations and benefits of working with data in this wayThis course is aimed as PhD students, post-docs and lecturers who have some existing knowledge of using R as a GIS and want to develop their knowledge of spatial stats and spatial decision making in R. It is also appropriate for those in public sector and industry who wish to gain similar skills. Some prior knowledge of both R and GIS is required. Those with little or no experience should complete the Introductory course Introduction to Spatial Data and using R as a GIS - Online (28-29 April 2026). The Introductory course will provide the necessary knowledge required for the Advanced course. Contact Dr. Nick Bearman if you need clarification about whether your existing knowledge is sufficient for this course.  jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 28 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14377 https://www.ncrm.ac.uk/training/show.php?article=14406 Level: Intermediate (I)As spatial data sets get larger, more sophisticated software needs to be harnessed for their analysis. R is now a widely used open source software platform for working with spatial data thanks to its powerful analysis and visualisation packages. The focus of this course is providing participants with the understanding needed to apply R’s powerful suite of geographical tools to their own problems.This course will be delivered over 2 afternoon sessions, running from 1:00pm to 5:00pm on both days. Topics CoveredIntroducing R as a GISThe structure of spatial objects in RLoading and interrogating spatial dataVisualisaing spatial datasets with tmapData manipulation with spatial data using dplyrSpatial joinsCoordinate reference systems (CRS)Interactive maps with leaflet Learning OutcomesBy the end of the course, delegate will:Have an understanding of different types of spatial data.Know how to plot static maps using {tmap}, including adding multiple layers and colouring by variables.Understand how to manipulate spatial data using {dplyr} and {sf} functions.Gain knowledge of coordinate reference systems (CRS) and know how to define a CRS.Understand and be able to create interactive maps using {leaflet}.Understand how to create initial maps and add objects containing different types of data.Be able to customise their interactive maps with colours, labels and legends. Target AudienceParticipants with spatial data problems who are not making use of R and are falling behind in the ever changing world of data science. Assumed KnowledgeA basic understanding of the R software is assumed. For 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=14406 https://www.ncrm.ac.uk/training/show.php?article=14671 This exciting interactive workshop will develop your knowledge and skills in using creative and participatory research methods. Creative and participatory methods are increasingly being utilised by social researchers to tackle complex research questions, enhance participant inclusivity and to generate wide ranging research impact for a broad range of stakeholders.This session begins with an overview of developments in creative and participatory research, highlighting the opportunities and challenges in the context of social policy, research impact and advancing academic knowledge. Across the two days, the course covers how and why we use a variety of creative and participatory methods and how to bring them together in analysis, forming a constellation. The workshop will address ethics, opportunities, benefits and challenges during the research process and how to generate multi-level impact from grassroots to social policy. Participants will be given the opportunity to explore how to incorporate creative and participatory approaches (such as zines and photovoice) in their own research, and how to analyse and disseminate effectively. Over the course you will:Be introduced to key debates in creative and participatory researchUnderstand the potential for, and the challenges of, using creative and participatory research methodsExplore how to ethically engage in creative and participatory researchLearn from active peer-researchers involved in co-creating research By the end of the course participants will:Develop practical skills in different creative and participatory approaches such as Zines, Photovoice, Co-creation/co-production (including peer research)Develop skills in designing, conducting, analysing and disseminating creative and participatory researchLearn how such methods can be incorporated into the generation of meaningful research impact Indicative Schedule:The course will run across two consecutive mornings (10am - 1pm) and equates to one day of training for payment purposes. Day 1-    What do we mean by creative and/or participatory methods? -    The value of creative/participatory research methods -    Planning and setting up creative/participatory research tools. -    FOCUS ON (1): zines as creative/participatory methods-    Ethical considerations specific to creative/participatory research (part 1) Day 2-    Ethical considerations specific to creative/participatory research (part 2)-    Creative/participatory research with children and young people-    Creative/participatory research with marginalised communities    -    FOCUS ON (2): co-creation – creative and participatory research in action* -    Doing co-analysis and co-dissemination -    Creative/participatory methods for generating meaningful research impact -    Wrapping up the workshop/advice clinic*The workshop facilitators will be joined on this by two peer researchers they have trained and worked with on recent research projects. Presenters:This course will be delivered by Dr Linzi Ladlow, Senior Research Fellow from the University of Lincoln, and Dr Laura Way, Senior Lecturer from the University of Roehampton. They are experienced in engaging with creative and participatory research and facilitating training. They are editors of the book, Insights into Creative and Participatory Research: Key Issues and Innovative Developments (2026) Policy Press.  Target audience:This short course is suitable for all qualitative researchers at any career stage, including postgraduate students. Whilst we are not expecting you to already be familiar with creative and participatory methods, familiarity with the purposes of qualitative research, as well as with qualitative methods of data generation and analysis, will be assumed. p.c.white@southampton.ac.uk (University of Southampton) Wed, 28 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14671 https://www.ncrm.ac.uk/training/show.php?article=14731 Timings: training runs from 10.00 to 15.00 (BST)Overview:Thematic analysis is useful for revealing patterns and themes in your work and for gaining detailed understanding of social phenomena and individual experiences, perceptions and behaviours. However, it is often seen as a mysterious and complex stage of the research process. Qualitative researchers can be criticised for not always making their techniques of analysis transparent when they write up their research findings. There are also challenges in terms of how researchers conduct analysis and the steps that they need to follow.This popular live online course provides you with skills on how to manually conduct reflexive thematic analysis. While providing a brief overview of different thematic approaches, the focus of the day will be on Braun and Clarke's 'reflexive thematic analysis'. Through a practical exercise in which we analyse qualitative interview data, you will gain experience of conducting a thematic analysis which includes coding and moving to themes. We cover:- Principles of qualitative data analysis- Different types of thematic analysis- Braun and Clarke's 6 steps for 'reflexive thematic analysis'- Organising your data, i.e. conceptualising, coding and categorising- Selective and complete coding- Latent and semantic coding- Generating themes and a thematic map- Examples of thematic analysis- Practical activities: coding and theme creation- Ensuring rigour and reflexivity in our analysis Who should attend?This course will be useful for researchers who are new to thematic analysis or who wish to brush up on their qualitative analysis skills. This includes doctoral students and academics. Researchers using qualitative methods in government, policy, consultancy, social research organisations and charities will also find this training useful.Please note: this is an interactive live course with presentation, group activities, group discussions, and opportunities to ask questions. It is helpful if you are prepared to participate and have use of camera and mic on Zoom. What is included?- 5 hours of live training on Zoom (inclusive of breaks) with Dr Karen Lumsden who has over 20 years experience in qualitative research and analysis, and in the design and delivery of qualitative research programmes, courses and workshops.- Group discussions, practical coding exercise, and opportunities to ask the trainer questions.- Access to resources including, for example: agenda, slides, resource list, and examples.- Recording of the presentation sections of the day (accessible for 30 days post course date).- Certificate of attendance Trainer biographyDr Karen Lumsden is a qualitative trainer, consultant, and coach. She has held a number of academic posts including Senior Lecturer in Sociology at the University of Aberdeen, Associate Professor in Criminology at Leicester University, and Assistant Professor at the University of Nottingham. Over the years she has been involved in a number of research projects and evaluations in social sciences, policing and health, for a range of partners and clients.She has over 20 years experience delivering qualitative methods courses and training to academics, PhD students, social researchers, and practitioners. This includes courses at the Universities of Aberdeen, Glasgow, Essex, Auckland, Kingston, via the Social Research Association and the European Consortium for Political Research, and also for government departments, NHS, charities, police organisations, social research and market research organisations. She has written and edited a number of books and journal articles on qualitative methods including Crafting Autoethnography (Routledge, 2023) and Reflexivity: Theory, Method, Practice (2019). She was on the Editorial Board of the journal Qualitative Research until 2026.For more info visit www.qualitativetraining.com or connect on LinkedIn here. Bookings:Bookings for this course should be made via Eventbrite. If your organisation requires payment via invoice, please contact me directly to check if this will be possible. I only accept invoice payment when the payment terms are confirmed in writing prior to event date. Email: karen@qualitativetraining.com Booking and refund policy:There is a 14 day 'cooling off' period from the date of booking on this course (unless it is less than 7 days before the event date). The refund policy is 100% refund up to 7 days prior to the course date. Less than 7 days before the course date, the entire fee is payable. Please note that in all cases, the Eventbrite ticket fee is non-refundable.If the course is fully booked and has a waiting list then transfer to another course or future course date might be possible, however this is at the discretion of the trainer. You can transfer your booking to another person in your organisation.Please note that Dr Karen Lumsden delivers a range of courses for other training providers in addition to these Qualitative Training courses. She takes no responsibility or liability for individuals booking on similar courses or training with other providers which may contain similar or the same course content. Refunds will not be given under these circumstances once the training has been partly/fully consumed karen@qualitativetraining.com (Qualitative Training) Fri, 13 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14731 https://www.ncrm.ac.uk/training/show.php?article=14728 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. 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: Spring 2026. emma.green-3@manchester.ac.uk (University of Manchester ) Tue, 10 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14728 https://www.ncrm.ac.uk/training/show.php?article=14755 Participatory Action Research (PAR) is a type of research that combines two different approaches: participatory research and action research. It is a valuable qualitative method because it empowers and involves individuals and communities in the research process, and in taking actions to improve aspects of their lives. Researchers using PAR aim to enable action on the part of the participants, and do so via a reflective process where the participants collect and analyse data, and then determine what action should be taken. When participants and researchers are equal partners in the research process, the study’s focus and results can be made more relevant to a specific community. However, in PAR there are also challenges in terms of how researchers form and maintain relationships with participants, how the data is constructed and used, and who has ownership of the data. This introductory course provides skills on how to conduct Participatory Action Research (PAR). It provides an introduction to PAR and its origins, history and theories. It explores the stages that must be followed in designing PAR, and the practicalities of doing so. Through a collaborative practical exercise, participants will be able to gain experience of designing their own PAR project.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) Thu, 26 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14755 https://www.ncrm.ac.uk/training/show.php?article=14812 Training for PhD and MSc students in the design of social research, quantitative and qualitative analysis.Modern Text Analysis and NLP with Python by Dr Patrick Gildersleve.This workshop offers students hands-on experience of modern approaches in text analysis and natural language processing (NLP) using Python. We will start with the essential preprocessing steps that allow us to apply standard machine learning models to text. Then, we will journey through more advanced methods such as text embeddings, named entity recognition, neural topic modelling, exploring transformers, and interfacing with state-of-the-art Large Language Model APIs.Participants will apply these techniques to various social science research scenarios, equipping them with a powerful toolkit for handling text data across diverse fields and providing an entry into the fast-developing methodological area of advanced NLP.Students should have a pre-existing understanding of coding in Python and general principles of machine learning. Prior experience in text analysis is optional.Session DetailsTime: 10:00 - 15:00 (12:00 - 13:00 Lunch break)Date: 20 May 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.comms@lse.ac.uk (LSE Methodology) Thu, 23 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14812 https://www.ncrm.ac.uk/training/show.php?article=14772 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 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) Tue, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14772 https://www.ncrm.ac.uk/training/show.php?article=14773 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.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 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) Tue, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14773 https://www.ncrm.ac.uk/training/show.php?article=14431 This online workshop aims to give participants a range of practical approaches they can adopt when writing about methodology in the social sciences, with a particular focus on writing a PhD methodology chapter. Using a range of exercises throughout, the course focuses on 20 or so writing strategies and thought experiments designed to provide more clarity and power to the often-difficult challenge of writing about methods. The course also looks at common mistakes and how to avoid them when writing about methods. The focus throughout is on building confidence and increasing our repertoire of writing strategies and skills.The course covers:A range of practical writing strategies for handling methodologyThe challenges of writing a PhD methodology chapter or a methods section in a research paperWriting for qualitative and quantitative research approachesUnderstanding different audiences and the needs of different academic marketsBy the end of the course participants will:Better understand who and what ‘methodology writing’ is forKnow the differences and similarities between PhD methods chapters, research paper methods sections and methods booksUnderstand and reflect on 20+ principles (or starting points) of best practice in methodology writingFocus writing on audience needs and expectationsBe aware of common mistakes and misunderstandings and so avoid themReflect on the relationship between methodology writing and other parts of your manuscriptTo develop learning and best practice through exercises and examplesThis course is aimed at PhD students, post-docs and junior researchers in the social sciences working on their doctoral theses or supervising doctoral students.Programme:09:25 - Log in to zoom09:30 - Seminar11:00 - Tea/Coffee Break12:30 - Close of Seminar13:30-15:30 - Time devoted to workbook exercises offline15:30-16:30 - Q&A and exercise feedback with Patrick jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 28 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14431 https://www.ncrm.ac.uk/training/show.php?article=14774 The aim of this interactive workshop is to consider data analysis in qualitative research with a specific focus on how to treat and deal with data that is not textual, but comes out of the use of creative methods (drawings, paintings, pick-a-card, LEGO models, etc.). Using real data from research using creative methods for data collection we explore how analysis of "messy data" can be approached.We consider the principles and process of analysis within qualitative research in general when we discuss if analysis is ever an objective process and if there is a difference between analysing data from linear texts or visual/sensory data, such as that from building LEGO models, song lists, photographs, videos and the like. Delegates have opportunities to practise analysing visual data on its own, in connection with textual data employing the "Systematic Visuo-Textual Analysis" or by employing creative forms of expression.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 following on from the workshop "Creative methods in qualitative data collection".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, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14774 https://www.ncrm.ac.uk/training/show.php?article=14784 This course offers an introduction to the uses and functions of the statistical software Stata including data entry and manipulation, do-files, and the basics of analyses and graphs. ich.statscou@ucl.ac.uk (UCL CASC) Thu, 19 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14784 https://www.ncrm.ac.uk/training/show.php?article=14340 Who is this course for?It is designed for postgraduate students, early career researchers and practitioners planning to work with survey data in any discipline. Course overview and aimsThis course provides an introduction to the principles and practices of undertaking quantitative data analysis of survey data in SPSS. The course combines discussion, demonstration and hands-on exercises.Please note: Participants will need access to SPSS in order to take part in the course. TopicsExploring phenomena of interest using quantitative measuresTypes of numerical data: nominal, ordinal, interval and ratio variablesIntroduction to the SPSS interfaceData entrySetting up an SPSS project with example dataGetting to know the sample data using descriptive statisticsRecoding and combing variablesDescribing the relationship between 2 variablesMoving beyond the descriptive: understanding hypothesis testing and p-valuesMoving beyond the descriptive: running Chi-square, independent sample t-tests and ANOVASMoving beyond the bivariate: running and interpreting a linear regression analysisTips for writing up findings and tables Learning objectivesBy the end of the course, participants will be able to:Input and import their data into SPSSExplore and recode their key measures with confidenceInterpret results of bivariate descriptive analyses and report these clearly.Set up a null hypothesis and interpret a p-valueTest the relationship between two variables using appropriate inferential statisticsUnderstand how to run and interpret a linear regression analysis. Format and documentationThis course is delivered in a series of live online sessions during the day that combine discussion, demonstration and hands-on exercises. Participants have the opportunity to discuss their quantitative projects with each other, and with the facilitators. Participants are provided with slide decks, example data, reading lists and resources to further knowledge about the topics covered during the day. FacilitatorSarah 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 quantitative and qualitative analysis techniques and has applied them in academic, public sector, private and third sector contexts. Her PhD explored the relationship between social trust and gender using large scale population datasets and applying multivariate linear regression, multivariate logistic regression, multi-level modelling and structural equation modelling. Her current work and research centres around enabling others to apply research methods in a meaningful, robust and flexible way. About Qualitative Data Analysis Services (QDAS)QDAS provide tailored and flexible training, consultancy, coaching and analysis for qualitative, quantitative 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) Mon, 19 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14340 https://www.ncrm.ac.uk/training/show.php?article=14303 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 R1, however, participants running NVivo 12 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"Having two tutors is gold standard. Helps with concentration, enabled one to deal with chat while other was teaching, gives a safety net if people are lost.""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 Qualitative Data Analysis ServicesQDA Services provide tailored and flexible training, consultancy, coaching and analysis for qualitative and mixed-methods researchers. We specialize 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) Fri, 06 Jun 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14303 https://www.ncrm.ac.uk/training/show.php?article=14796 Large language models are now embedded in research workflows across the social sciences, yet most researchers interact with these tools through consumer interfaces that obscure how they work, where data goes, and what decisions are being made on their behalf. This three-workshop series closes that gap. Across three standalone half-day sessions, participants build a working understanding of the AI stack: from how models generate text and where inference happens, through prompt engineering, retrieval-augmented generation, and API-based workflows, to the rapidly maturing ecosystem of agentic platforms, harness engineering, and autonomous research infrastructure. Each workshop combines conceptual exposition with live demonstrations and practical exercises grounded in survey research scenarios. No programming experience is required for Workshop 1; Workshops 2 and 3 assume familiarity with earlier concepts.The course covers: Workshop 1: How Large Language Models Work: tokens, training, alignment, data security, inference, open- vs closed-weights models, reproducibility challenges, and the limitations of chatbot interfaces for research.Workshop 2: Context Engineering: prompt design and optimisation, retrieval-augmented generation (RAG), API-based workflows and batch processing, memory and tool-calling, MCP servers, and evaluation engineering.Workshop 3: Agentic AI and Harness Engineering: the agentic AI ecosystem (IDE-native agents, extended-autonomy platforms, orchestration tools), harness engineering and SDKs, memory and token economics, MCP servers and hooks, oversight, auditability, and research transparency.By the end of the course participants will:Explain how LLMs generate text and assess the implications of model architecture, training, and alignment for research practiceDistinguish between open-weights and closed-weights models and evaluate their data governance implicationsApply prompt optimisation techniques and build evaluation pipelines to validate LLM outputsMake structured API calls, manage parameters, and use retrieval-augmented generation where appropriateMap the agentic AI ecosystem, explain harness engineering, and assess how platforms orchestrate memory, tools, and contextDesign human-in-the-loop safeguards and audit protocols appropriate for agentic research workflowsPre-requisitesNo prior programming experience or specialist software knowledge is required for Workshop 1. Workshops 2 and 3 assume familiarity with concepts from Workshop 1 (or equivalent knowledge of how LLMs work). Workshop 3 benefits from some comfort with reading code, but participants are not required to write any. Setup guidance for API access will be provided before Workshops 2 and 3.No software installation is required for Workshop 1. For Workshops 2 and 3, participants will benefit from having API access to a commercial LLM provider (e.g. Anthropic, OpenAI); setup guidance will be provided in advance. All demonstrations will be conducted live by the instructor. Participants do not need prior experience with any specific software, though basic familiarity with web browsers and text editors is assumed.Target AudienceSurvey researchers, methodologists, and quantitative social scientists across academia and government who use or are considering using large language models in their research. The series is designed to be accessible to researchers at all career stages, from doctoral students to senior investigators. No programming experience is required for Workshop 1; Workshops 2 and 3 assume familiarity with concepts from Workshop 1, and Workshop 3 benefits from some comfort with reading code.PLEASE NOTE THESE WORKSHOPS WILL RUN ONLINE ON 8 JUNE, 22 JUNE and 6 JULY FROM 09:30-13:30 jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 14 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14796 https://www.ncrm.ac.uk/training/show.php?article=14799 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.Course detailsMonday 8 June and Monday 15 June 202609:00 – 13:008 learning hoursthis is an online course conducted via ZoomAbout 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. Who is this course for?People with 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 students Learning outcomesThis course will not cover realist synthesis or qualitative analysis.After 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 synthesis   A.Vincent@soton.ac.uk (University of Southampton) Wed, 08 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14799 https://www.ncrm.ac.uk/training/show.php?article=14385 Level: Foundation (F)The ability to visually communicate statistics to a broad audience is an increasingly important skill for many people working in data-driven environments. Yet this skill is all too often overlooked on academic curricula.This introductory course provides the background context, essential theory and practical guidance needed to develop confidence in the effective use of data visualisation.Delegates will be expected to bring a laptop with them and access the Flourish software. Please go to https://flourish.studio/ to sign up.Learning OutcomesBy the end of this course, attendees will have gained:Proficiency in data visualisation designAwareness of various visualisation methods and when/why to use themPractical experience of applying visualisation concepts Topics CoveredDay one of the course will cover the history and purpose of data visualisation, the audience, visual perception, colour, handling and describing data, statistical relationships in data (identifying and prioritising), chart types and their uses – the visual vocabulary.Day two will examine visualisations, titles, annotations, copy, platforms (print/online/social media), tools, animation, interactivity, specific challenges -- e.g. showing uncertainty and volatility, and putting it all together -- practical exercises. Target AudienceThis course would be helpful to a broad range of professionals who need to communicate statistical information to a wide audience -- including researchers, analysts, journalists and marketing and communications professionals. Delegate Feedback"This is best training course I have been on (during the last 30 years - the length of my memory). It met all my objectives and I have already applied some of the skills learnt.""It was a very well balanced course. The tutor is clearly an authority in this subject matter and was a very engaging communicator.""Alan was a wonderful presenter, very engaging and knowledgeable and the takeaway information has been comprehensive. I am looking forward to using these new skills in my own work and developing my skills and knowledge further." 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=14385 https://www.ncrm.ac.uk/training/show.php?article=14658 Overview of 2-day CourseMixed modelling is a modern and powerful data analysis tool for modelling clustered data, typically used for modelling data collected in trials where the levels of a factor are considered to be a random selection from a wider pool, or in the presence of a multi-level structure with different levels of variability. Such models offer potential benefits such as: the ability to cope with modelling complex data structures, greater generalisability of results, accommodation of missing values and the possibility of increasing the precision of treatment comparisons. In particular, mixed models have been extensively used to analyse repeated measurements where, for example, measurements taken over time in a clinical trial naturally cluster according to patient. In general, the course will focus on medical and health related applications of mixed modelling. Specific applications include multi-centre trials and cross-over trials in addition to the analysis of repeated measurements.The course focuses on the linear mixed model, assuming normally distributed data, and on how to fit linear mixed models and interpret the results for a range of common medical and health related applications. Only essential theoretical aspects of mixed models will be summarised.Examples used will be drawn from a variety of applications in medicine and health. Practical work will be based around the statistical software R; see ​https://www.r-project.org/.PresentersSandro Leidi and James GallagherCost£582 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 09:00 and 17:30 (GMT+1).  The delivery platform is 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 linear mixed models. It will be assumed that participants are R users and are familiar with the practical use of linear models, covering regression models and ANOVA. How You Will BenefitThe course will give you the skills to formulate, fit and interpret linear mixed models for a range of practical situations, as well as an appreciation of some of the benefits of mixed modelling.What Do We Cover?Concept of fixed versus random effectsSimple random effects and variance components models for modelling clustered dataIntra-class correlation coefficient; R-squared; shrinkage A summary of the important theoretical aspects of mixed models: maximum likelihood versus REML for fitting mixed models, estimating and testing fixed effects, degrees of freedom options and the Kenward-Roger methodMultilevel modelling for hierarchical data structuresNested vs crossed random effectsModel checkingMulti-centre analysesMixed models for cross-over designsRepeated measurements analysis: random coefficient modelsPractical experience: fitting models and interpreting R outputConvergence issueslmerTest CRAN package, which extends lme4, for fitting mixed models; use of other CRAN packages including emmeans for summarising results from a mixed model.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) Tue, 06 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14658 https://www.ncrm.ac.uk/training/show.php?article=14775 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) Tue, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14775 https://www.ncrm.ac.uk/training/show.php?article=14785 This course introduces basic ideas of machine learning with a focus on the most popular machine learning algorithms for supervised and unsupervised learning. ich.statscou@ucl.ac.uk (UCL CASC) Thu, 19 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14785 https://www.ncrm.ac.uk/training/show.php?article=14760 Overview of 0.5-day CourseThe statistical package R (www.r-project.org) is a freely available statistical software. R is part of an international collaboration and has become very popular; it is used for data analysis in many application areas and is used extensively in research and academia. In particular, it has flexible facilities for advanced modern statistical modelling and is equally well suited to more elementary statistics and graphics. In this hands-on tutorial we access R via RStudio which provides a user-friendly interface to R in an integrated development environment and is very popular among R users.  Participants will be supplied with a R script file containing R code which will be used for the entirety of the tutorial to demonstrate and illustrate the use of R.The tutorial begins with a general overview of RStudio and the concepts involved in using R, such as object orientation.  It then moves onto importing data from Excel, utilising the extension of R’s capabilities through the installation of third party CRAN packages.  Next the calculation of summary statistics for numerical data are illustrated using functions in the base installation of R, along with the production of some common graphics. The tutorial will finish with a discussion of some common sources of errors when using R.The majority of the tutorial will make use of a small educational dataset and a larger health study example.  The tutorial will use the freely available RStudio Desktop version on a Windows operating system. Note the R language is not based on using point-and-click mode, but a written command syntax.PresentersSandro Leidi and James GallagherCost£126 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 12:30 (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?Anyone involved in the analysis of data, either on a regular or irregular basis, who wants a practical introduction to R. No previous experience of the R software or RStudio is required.The tutorial is also suitable for anyone wanting brief refresher training.How You Will BenefitYou will be able to describe the essential concepts related to using R via RStudio and use R to import data and for basic statistical analysis including graphics.  In particular, you will also be able to:Write a script to (a) efficiently reproduce output and (b) have an auditable record of your analysis in your workflowInstall CRAN packages for specific analysesMake effective use of the many resources which are freely available to prepare for a deep dive into the R software applied to your particular area of interest. What Do We Cover?Introductory concepts of R and the RStudio interface: windows/panes, objects, data frames, working directory, attributes of an object in particular class, functions, R as a calculator, scripts, workspaces and CRAN packages; help systemImporting data from other applications: CSV, Excel into a data frame; the class of a data column and its importance; factors and their labelling. Missing valuesExploratory data analysis: summary statistics, statistical graphics for numerical data. Labelling of graphicsCommon errors and remedies.SoftwareThe tutorial will be conducted using the free version of RStudio Desktop on a Window operating system.Participants must download and install the R Gui and RStudio Desktop applications and install a number of CRAN packages prior to the start of the tutorial.​Course teaching materialsThere are no formal slides for this tutorial. Presentations will be based around a pre-supplied R script file, containing code to be executed live by both the presenters and participants in parallelA pdf file containing the output generated during the course of the tutorial will be supplied for future use along with a small number of ad hoc files such as a reference list. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 09 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14760 https://www.ncrm.ac.uk/training/show.php?article=14776 Understanding key statistics and choosing an appropriate statistical test are an essential part of a quantitative researcher’s skill set. The course will provide a foundation in statistical concepts and testing options. After completing the course participants will have the tools to understand the statistics described in quantitative research papers and reports and be able to evaluate the appropriateness of each test for the data and research objective.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, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14776 https://www.ncrm.ac.uk/training/show.php?article=14394 Level: Foundation (F)It has been asserted that ‘the purpose of a scientific paper is to communicate, and within the paper this applies especially to the presentation of data’ (Altman & Bland, 1996) and effective data presentation is an essential skill for anybody wishing to display or publish research results. However, good display does not always happen in practice and when done poorly, it can convey a misleading or confusing message. This 3-hour course is aimed at individuals who need to understand how to display and interpret data, and it is hoped that by the end of the course they will have a greater understanding of the methods available for displaying data and the tools to display data appropriately and clearly, with the use both graphs and tables. Learning OutcomesThe purpose of this course is to introduce the principles of good design and layout to the presentation of data, as an aid to better understanding and to learn about appropriate methods for displaying and summarising data. Topics CoveredTypes of dataSummarising dataDisplaying dataTufte’s principles for the visual display of quantitative dataCommon problems/errors in displaying dataPresentations and postersThe course will incorporate lectures and discussion, with several practical sessions. The practical sessions will involve both individual exercises on tabulating and displaying data and small group work critically appraising the tables and graphs in published papers. Participants may bring their own data to discuss. Target Audience This course is for anyone who is involved in communicating statistics to non-statisticians or who reports on data – of any description – within organisation, to specific communities and to the general public where a message is being given. Prior knowledge/experience requiredNone, however, basic numeracy will be assumed. 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=14394 https://www.ncrm.ac.uk/training/show.php?article=14626 Practical Statistics for Medical Research is a comprehensive training course designed for healthcare professionals and researchers who seek to enhance their understanding of study design and statistical methods. This course introduces the basic principles of quantitative research methods and illustrates their application in medical science. The course syllabus is at an intermediate level and assumes no prior in-depth knowledge of statistical terminology or concepts. baptiste.leurent@ucl.ac.uk (University College London) Wed, 10 Dec 2025 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14626 https://www.ncrm.ac.uk/training/show.php?article=14756 Overview:This live one-day Zoom training course provides participants with an introduction to autoethnography and writing the self. Autoethnography is a powerful methodological approach which combines autobiography and ethnography to explore personal experiences within broader social and cultural contexts. This day serves as an introduction to the theory, practice, and application of autoethnography, inviting participants to engage deeply with their own lived experiences while critically examining the intersections of self and society. Through a combination of presentations, group discussions, and hands-on activities, participants will develop the skills necessary to begin their own autoethnographic inquiries and produce meaningful narratives that contribute to personal growth and scholarly inquiry.By signing up for this training you also receive complimentary access to our Autoethnography Support Community - monthly virtual meetings and WhatsApp group. And an electronic copy of the book, Crafting Autoethnography (see below for more info). Topics we cover:· Principles of autoethnography· Autoethnography: methodology and method· Types of autoethnography: e.g. critical, evocative, analytical, performative· Examples of autoethnography and its application· Autoethnographic practice: from writing the self to crafting the self· Autoethnography and reflexivity· Ethics in autoethnography· Debates and critiques of autoethnography Who is this course for?This is a foundational course which is designed for doctoral students, academics and researchers who are new to autoethnography, who are considering using autoethnography in their research, or are in the early stages of conducting an autoethnography. It is beneficial, but not essential, if participants have some prior foundational training or knowledge of qualitative research.Please note: this is live interactive training with presentations, group activities, group discussions, and opportunities to ask questions. You should be prepared to participate in these and it is helpful to have use of camera and mic on Zoom. What is included?- 5 hours of live training on Zoom (inclusive of breaks) with Dr Karen Lumsden who has over 20 years experience in qualitative research and analysis, and in the design and delivery of qualitative research programmes, courses and workshops.- Complimentary access to our Autoethnography Support Community - monthly virtual meetings and WhatsApp group.- Group discussions, practical exercise, and opportunities to ask the trainer questions.- Access to resources including, for example: agenda, slides, resource list, and examples of autoethnography.- Recording of the presentation sections of the day (accessible for 30 days post course date).- Certificate of completion- PDF copy of Karen's co-edited book Crafting Autoethnography (Winner of the International Association of Autoethnography and Narrative Inquiry 'Outstanding Edited Book' Award 2026) Trainer biographyDr Karen Lumsden is a qualitative trainer, consultant, coach and mentor. She has held a number of academic posts including Senior Lecturer in Sociology at the University of Aberdeen, Associate Professor in Criminology at Leicester University, and Assistant Professor at the University of Nottingham. Over the years she has been involved in a number of research projects and evaluations in social sciences, policing and health, for a range of partners and clients.She has over 20 years experience delivering qualitative methods courses and training to academics, PhD students, social researchers, and practitioners. This includes courses at the Universities of Aberdeen, Glasgow, Essex, Auckland, Kingston, via the Social Research Association and the European Consortium for Political Research, and also for government departments, NHS, charities, police organisations, social research and market research organisations. She has written and edited a number of books and journal articles on qualitative methods including Crafting Autoethnography (Routledge, 2023) and Reflexivity: Theory, Method, Practice (2019). She was on the Editorial Board of the journal Qualitative Research until 2026.For more info visit www.qualitativetraining.com or connect on LinkedIn here. Bookings:Bookings for this course should be made via Eventbrite. If your organisation requires payment via invoice, please contact me directly to check if this will be possible. I only accept invoice payment when the payment terms are confirmed in writing prior to event date. Email: karen@qualitativetraining.com Booking and refund policy:There is a 14 day 'cooling off' period from the date of booking on this course. The refund policy is 100% refund up to 7 days prior to the course date. This does not include the Eventbrite ticket fee which is non-refundable. Less than 7 days before the course date, and the entire course fee is payable.If the training is fully booked and has a waiting list then transfer to another course or future course date might be possible, however this is at the discretion of the trainer.Please note that Dr Karen Lumsden delivers a range of courses and training for other providers in addition to these Qualitative Training courses. She takes no responsibility or liability for individuals booking on similar courses or training with other providers which may contain similar or the same course content. Refunds will not be given under these circumstances once the training has been consumed. karen@qualitativetraining.com (Qualitative Training) Wed, 04 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14756 https://www.ncrm.ac.uk/training/show.php?article=14659 Following eleven very successful courses in previous years, we are pleased to offer an in-person two-day course designed to develop understanding and skills in the theory and practice of participatory action research (PAR). PAR is increasingly popular, involving people affected by/interested in a research topic taking an active part in designing, carrying out and putting research into practice. The aim of PAR is to bring about change – for example, in people’s living conditions, service provision or public policy. Doctoral students taking a PAR approach face many challenges, including negotiating how to work with partner organisations, handling co-ownership of research findings in relation to the thesis, and responsibilities for working for social change.The course will cover key values, ethical/political issues, theorising and critiquing PAR, working with partner organisations to influence change, and participatory approaches to research design, process, analysis, dissemination and implementation. The course will be participatory, using small groups to focus on specific questions and evaluate learning. Community partners and academics will act as tutors. Ten places will be reserved for members of community organisations, enabling a process of mutual learning for doctoral students and community partners.The objectives of the course are to:1.Enable participants to develop critical understandings of the uses, advantages and limitations of PAR, and an ability to draw on a range of theoretical and practical insights.2.Develop participants’ awareness of ethical and political challenges in PAR, particularly in community-university partnership working, and strategies for handling these.3.Facilitate the development of participants’ confidence in working with the complexities of PAR within different disciplines and settings.4.Offer participants the experience of learning and understanding through active participation during the course, particularly through the co-inquiry group model.5.Develop participants’ understanding of the impacts that PAR may have, and processes for creating and capturing these. The course will be facilitated by a team of academic and community-based practitioners of PAR, with a variety of areas of expertise and experience, including: Professor Sarah Banks, Centre for Social Justice and Community Action, Durham University, UK; Professor Mary Brydon Miller, University of Louisville, USA; Professor Tina Cook, Liverpool Hope University, UK; and Professor Kristin Kalsem, University of Cincinnati, USA.Who should come: The course will be of interest to doctoral students and members of community organisations who do, or are interested in doing, research that is participatory. In selecting participants from community organisations, priority will be given to those based in North East England or Northern Ireland. PLEASE NOTE: This course is only available to doctoral students and members of community organisations.Timing: the course will run from 10.30 to 17.00 on Day 1 and 9.30 to 16.00 on Day 2.Places are limited to 50, so early booking is advisable. Please complete the online booking form. Demand is usually high, so selection is based on the case you make in the application for why you will benefit from the course. The closing date is 31 March 2026. Early booking is advisable as the course is likely to fill up quickly. research.nine@durham.ac.uk (NINE DTP / Durham university) Tue, 06 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14659 https://www.ncrm.ac.uk/training/show.php?article=14757 Overview:This live Zoom course provides you with an overview of the main principles of focus groups as a qualitative research method, factors to consider in the design of your focus groups, and key facilitator skills for moderating your focus groups in person and online.Focus groups are a popular qualitative research method but it is vital to understand the kind of data they generate and the importance of group interaction for generating insightful and useful data. As a research method, focus groups require considerable questioning and moderation skills.This interactive online course helps you to improve the quality of your focus group research. It provides participants with a clear understanding of when and how to use focus groups as a qualitative method. We also consider how to modify the style and approach of focus groups depending on the sensitivity of the research topic, the nature of the participants, levels of comparison, and the mode of delivery (i.e. in person or online).By the end of the course participants will be equipped to independently design, undertake, and plan for moderation of focus group discussions.You will receive a certificate of completion after attending the day's training, access to resources, and a recording of the training accessible for 30-days post training date. We cover:- The principles of focus groups as qualitative research method- The importance of group interaction- Designing your focus group agenda, questions and activities- Composition of focus groups- Focus groups in person and online- Dynamics and logistics of focus groups- How to effectively facilitate your focus groups and deal with challenging situations Who should attend?This course will be useful for researchers who are new to focus groups or who wish to brush up on their focus group design skills and knowledge. It is also useful for those who wish to more generally improve their workshop or group meeting design skills. This includes doctoral students and professional researchers. Researchers using qualitative methods in government, policy, consultancy, social research organisations and charities will also find this training useful.Please note: this is an interactive course with presentations, group activities, discussions, and opportunities to ask questions. It is helpful if you have access to a working camera and mic to take part on Zoom. Trainer biographyDr Karen Lumsden is an expert qualitative trainer, consultant, coach and mentor. Before leaving academia, she climbed the academic career ladder to Associate Professor level and conducted research and teaching in social sciences (sociology and criminology). She has held a number of academic posts, including Senior Lecturer in Sociology at the University of Aberdeen, Associate Professor in Criminology at the University of Leicester, Assistant Professor at the University of Nottingham, and Senior Lecturer in Sociology at Loughborough University.She has over 20 years’ experience delivering qualitative methods courses and training to academics, PhD students, social researchers, and practitioners. This includes courses at universities including, for example, Aberdeen, Glasgow, Essex, Kings College London, Royal Holloway, Kingston, Cardiff ,and Bournemouth. She has delivered training for the University of Auckland, New Zealand, and the Chinese University of Hong Kong. She regularly delivers a range of qualitative methods courses via the Social Research Association, and delivers Focus Group training for the European Consortium for Political Research. She has designed and delivered bespoke training for local authorities, government departments, ONS, NHS, charities, police organisations, and social and market research organisations.Karen has written and edited a number of books and journal articles on qualitative methods including Crafting Autoethnography (Routledge, 2023) and Reflexivity: Theory, Method, Practice (2019). She was on the Editorial Board of the journal Qualitative Research (until 2026), was Chair of the Editorial Board of Sociological Research Online, and was on the Editorial Board of Sociology. For more details of her work and services visit: www.qualitativetraining.com Bookings:Bookings for this course can be made via Eventbrite tickets. If your organisation requires payment via invoice, please contact me directly to check if this will be possible. I only issue invoices when payment terms are mutually agreed and confirmed in writing prior to the event date. Email: karen@qualitativetraining.com for invoice queries. Refund policy:There is a 14 day 'cooling off' period from the date of booking on this course (unless there are 7 days or less between the booking date and training date, in which case, no refund is possible). The refund policy is 100% refund up to 7 working days prior to the course date. Less than 7 days the entire course fee is payable.If the course is fully booked and has a waiting list then transfer to another course or future course date might be possible, however this is at the discretion of the trainer. You may be able to transfer your place to another person at your organisation, at the discretion of the trainer.Please note that Dr Karen Lumsden delivers a range of courses for other training providers in addition to these Qualitative Training courses. She takes no responsibility or liability for individuals booking on similar courses or training with other providers which may contain similar course content. Refunds will not be given under these circumstances. karen@qualitativetraining.com (Qualitative Training) Wed, 04 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14757 https://www.ncrm.ac.uk/training/show.php?article=14777 Capturing the views and experiences of children and young people directly, rather than by proxy (through parents or professionals), is increasingly recognised as essential for research, evaluation and policy development. Drawing on the tutors’ extensive experience and expertise in this field, this long-running, and highly interactive, training which provides an opportunity for participants to explore the ethical, methodological and practical considerations of doing research and evaluation with children and young people, both face-to-face and remotely, and consider how to apply this learning to their own work.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) Tue, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14777 https://www.ncrm.ac.uk/training/show.php?article=14788 PAR aims to create a space for researcher and participants to co-produce knowledge and where relevant, action for change. PAR is considered as a research paradigm in itself, that embodies a particular set of concepts under which researchers operate (Minkler and Wallerstein 2008). These include respect for diversity, community strengths, reflection of cultural identities, power-sharing, and co-learning (Minkler 2000).During this two day online course we will explore these principles, the cyclical approach to PAR and what this means in practice. Participants will be given the opportunity to learn terminology, understand participation in community engaged research, explore how power and positionality can change health outcomes in PAR, and learn about a variety of participatory methods and how they have been applied in different contexts, globally and within the UK. Participants will also be provided with the space to explore challenges they are facing in designing or implementing community engaged collaborative research within a discussion clinic forum.   Programme of ActivitiesDay 1 - The history of PAR and underpinning orientationPlanning and setting up a PAR projectSkills required for a PAR studyEthical considerations specific to a PAR studyParticipatory research with children and young peoplePhotovoice methodologyIndependent activityGroup discussion  Day  2 - Doing co-analysisParticipatory research methods (examples of other visual methods, social mapping, seasonal calendars and other non-visual methods but still participatory such as narratives and others that have been used)Participation and inclusion Dissemination and writing for PAR projects – different approaches, narratives/thematic analysis, thesis, publications, policy briefs, blogs and othersGroup discussion on pre-workshop task Advice clinicAfternoon independent learning and practical exercises Practical activities: Day 1: Photovoice activity and reflectionsDay 2: Individual PAR project outline and feedback  Engage@liverpool.ac.uk (University of Liverpool) Thu, 26 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14788 https://www.ncrm.ac.uk/training/show.php?article=14670 Longitudinal data is essential in a number of research fields as it enables analysts to concurrently understand aggregate and individual level change in time, the occurrence of events and improves our understanding of causality in the social sciences. In this course you will learn both how to clean longitudinal data as well as the main statistical models used to analyse it. The course will cover three fundamental frameworks for analysing longitudinal data: multilevel modelling, structural equation modelling and event history analysis. The course is organized as a mixture of lectures and hands on practicals using real world data. During the course there will also be opportunities to discuss also how to apply these models in your own research.Objectives:To gain competence in the concepts, designs and terms of longitudinal research;To be able to apply a range of different methods for longitudinal data analysis;To have a general understanding of how each method represents different kinds of longitudinal processes;To be able to choose a design, a plausible model and an appropriate method of analysis for a range of research questions.The course consists of five sessions (all Fridays) spread over six weeks (note there is no session on 15th May).Topics covered by day:22.06.2026  - Data cleaning and visualization of longitudinal data23.06.2026 - Cross-lagged models (covering also an introduction to Structural Equation Modelling and auto-regressive models)24.06.2026 - Multilevel model of change (covering also an introduction to multilevel modelling)25.06.2026 -  Latent Growth Modelling26.06.2026  - Survival models (also known as event history analysis)Teaching will take place online (using Zoom) between 09:00 to 16:00 UK time. There will be 1 hour lunch break from 12:00 to 13:00.IMPORTANT: Please note that this course includes computer workshops. Before registering please check that you will be able to access the software noted below. Please bear in mind minimum system requirements to run software and administration restrictions imposed by your institution or employer with may block the installation of software.PrerequisitesGood knowledge of regression modellingBasic knowledge of R or good programming experience with a different statistical softwareRecommended readingCernat, A. (in press). Longitudinal Data Analysis using R. LeanPub.Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data (First edition). O’Reilly. (also available free online)Singer, J., & Willett, J. (2003). Applied longitudinal data analysis: modeling change and event occurrence. Oxford University Press.Newsom, J. T. (2015). Longitudinal Structural Equation Modeling: A Comprehensive Introduction. Routledge. p.c.white@southampton.ac.uk (University of Southampton) Mon, 13 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14670 https://www.ncrm.ac.uk/training/show.php?article=14400 Level: Intermediate (I)This course will be delivered over 2 afternoons, running from 1:00pm to 5:00pm on both days.This virtual course will take you through the fundamental approach of R Programming to creating automated reports. The course commences with an introduction to R Markdown and how you can embed R code directly into a document.  By the end of this course you will be able to work on your own data, importing and exporting data from spreadsheets and other data sources. You will learn how to build automated reports using data, text and graphics using R Markdown.Course attendees are encouraged to bring their own data or sample data they would like to create a report with, however that is not essential.For 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. Learning OutcomesBy the ends of the day participants will:be able to use Markdown formatting syntax to customise a documenthave the ability to embed R code into reports using {knitr}have learned how to dynamically embed images, plots and tables to their documentsbe able to re-render a report by defining default parameter valuesunderstand how dashboards and HTML widgets can give them new interactive techniques to view their data Topics CoveredR Markdown: Creating documents using rmarkdownKnitr: Running dynamic R codeKableExtra & DT: Embedding tabular data into output documentsBookdown: Writing books and long-form reports with R MarkdownFlexdashboard: Creating interactive dashboardsParameterised reports: Creating flexible reportsWidgets: Exploring interactive HTML widgets Target AudienceThis course is suitable for data analysts. It is expected that participants are already familiar with R. In particular they should be familiar with basic data manipulations, functions, if statements and for loops. These concepts are covered in our Introduction to R course. 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=14400 https://www.ncrm.ac.uk/training/show.php?article=14778 Qualitative data analysis reveals patterns and themes from the large volume of data generated by qualitative research. It is useful for gaining detailed understanding of social phenomena and individual experiences, perceptions and behaviours. However, it is often seen as a mysterious and complex stage of the research process. There are also challenges in terms of how researchers conduct analysis and the steps that they need to follow.This advanced course provides participants with the skills to conduct qualitative data analysis. While providing an overview of different analytical approaches, the focus in our activities will be on thematic analysis. It provides an introduction to qualitative data analysis. It explores ways of organising and analysing qualitative data, and the practicalities of doing so. Through a practical exercise where we analyse qualitative interview data provided by the trainer, participants will be able to gain experience of conducting their qualitative data analysis by focusing on thematic analysis.By the end of the course, participants will have knowledge of various methods and theories of qualitative data analysis and how it differs from quantitative analysis. They will be able to choose an appropriate data analysis technique for different forms of qualitative data. They will also be able to conduct their own thematic analysis, code, and organise data for 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, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14778 https://www.ncrm.ac.uk/training/show.php?article=14600 Date: 29th June through 3rd July, 2026By: Daniel Valdenegro (Teaching Assistant to be Confirmed)Location: Nuffield College, University of OxfordCourse Description: Artificial Intelligence (AI) and Large Language Models (LLMs) have become ubiquitous concepts in research and everyday life. The demand for a clear, grounded understanding of the capacities and shortcomings of these tools will only grow as their popularity increases. In this 5-day intensive course, we will explore the fundamental theoretical and practical aspects of using Large Language Models in health and social science research, maintaining a grounded and critical stance. We will examine the origins and development of the core architectures powering today’s most widely used language models, as well as their current applications in research, complemented by practical sessions on how to work with both commercial and locally hosted models.Each day runs from 9:30 to 16:30, with:Morning lectures (9:30–12:30): Concepts, theory, and methods.Lunch (12:30–13:30): Provided at Nuffield College.Afternoon practicals (13:30–16:30): Guided coding sessions, data activities, research talks, and group exercises By the end of this course, you will gain:1. A working knowledge of how LLMs are trained, evaluated, and applied in research.2. Practical skills in accessing, fine-tuning, and applying LLMs to real-world datasets.3. Critical tools to assess bias, ethics, and reproducibility in LLM-driven research.4. Exposure to interdisciplinary projects at the intersection of AI, health, and the social sciences.Lunch and refreshments are provided daily, fostering an informal, collaborative learning environment in the heart of Oxford’s academic community. Course content by day:Day 1: Foundations – Introduction to NLP and initial language models: tokenization, embeddings, RNN-LSTM vs Attention, Text-generation Transformer Models.Day 2: Applications – Advent of Large, transformer-based, text-generation language models: Emerging capacities in summarization, classification and information extraction. Review of application in health and social science research. Practical session on this tasks.Day 3: Use: Review of current commercial and open-source models. Review of the API use and local hosting options.Day 4: Ethical considerations: Review of current research on technical limitation of LLMs. Review of current research con ethical challenges of on the Use of LLMs. Practical session exploring those limitations.Day 5: Future Directions – Research frontiers: multimodal models, causal inference, and integrating LLMs into scientific workflows.Attendance will be recognised through Accredible badges.Information on how to register for this course and information on course fees can be found [here]!For any queries, contact teaching@demography.ox.ac.uk. charles.rahal@demography.ox.ac.uk (University of Oxford) Fri, 05 Dec 2025 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14600 https://www.ncrm.ac.uk/training/show.php?article=14735 This five-day summer school introduces experimental methods in the Social Sciences, covering lab, field, and survey experiments. Participants will gain a solid foundation in experimental methodology and practical skills for designing, implementing, analysing, and presenting experiments. The interdisciplinary team of the Centre for Behavioural Experimental Action and Research (C-BEAR) will lead the five-day course, using examples from Politics, Economics, Business, and Psychology. Days 1 and 2 cover the basics of designing, analysing, and presenting different types of experimental designs, while Days 3, 4, and 5 will provide in-depth knowledge and insights on survey, field, and laboratory experiments. The hands-on activities throughout the week ensure that participants not only understand the theoretical aspects of experimental methods but also acquire the practical skills necessary to apply these methods in their own research.Coffee and light refreshments will be served every day during dedicated breaks in the morning and afternoon sessions. Lunch and planned dinners are self-catered.Software requirementsStudents should bring their own laptop and install R, the free version of Stata, and Excel (or an open alternative).Day 1: Foundations in experimental methods Instructors: Dr. Jana Sadeh, Dr. Vanessa Cheng-Matsuno, Prof. Tereza CapelosDay 1 provides a balanced mix of theory, storytelling, discussion, and hands-on practice to engage participants and build a strong foundation in experimental methods. The learning outcomes for the program encompass both theoretical and practical aspects. Participants will delve into the rich history and foundational definitions of experiments, exploring the diverse types that have shaped research across disciplines. They will gain insights into the advantages and disadvantages of using experiments compared to other research designs, providing a comprehensive understanding of when and why to employ experimental methods. On the practical side, participants will have the opportunity to implement a simple experiment themselves. This hands-on experience will guide them through designing the experiment, then analysing and presenting the results.Morning Session: 9:30 AM – 12:30 PM (refreshments break 11:00am)Lunch Break: 12:30 PM - 2:00 PM (buy/bring your own)Afternoon Session: 2:00 PM - 5:00 PM (refreshments break 3:30pm) Highlights include:• Welcome and Introduction• Engaging stories that illustrate the value of experiments• Group work and discussion on the fundamental aspects of experimental design• Introduction to different types of designs• Research questions suitable for experimentation• Practical session on designing a simple experiment• Welcome GROUP DINNER (self-catering)Day 2: Key concepts and essential experimental techniques Instructors: Dr. Paolo Spada and Prof. Konstantinos KatsikopoulosDay 2 delves into both the theoretical underpinnings and practical applications of experimental methods in social sciences. The day is structured to enhance participants' understanding of key concepts and provide hands-on experience with essential research techniques. We will review the Potential Outcome Model, a fundamental framework for causal inference in experiments, and discuss the ethical principles that govern experimental research, ensuring that participants understand the importance of conducting studies responsibly and ethically. The practical sessions on Day 2 are designed to reinforce the theoretical concepts through hands-on activities. We will design experiments, learn how to calculate average treatment effects and determine statistical power, review pre-registration examples, and engage in replication exercises.Morning Session: 9:30 AM – 12:30 PM (refreshments break 11:00am)Lunch Break: 12:30 PM - 2:00 PM (buy/bring your own)Afternoon Session: 2:00 PM - 5:00 PM (refreshments break 3:30pm) Highlights include:• Understand the Potential Outcome Model• Design experiments• Calculate average treatment effects and statistical power• Learn about pre-registration with examples• Discuss ethical principles and guidelines• Analyse data and report experimental results with graphs and plotsDay 3: Survey Experiments Instructors: Professor Robert JohnsDay 3 focuses on survey experiments, offering participants a blend of theoretical knowledge and practical skills. The day is designed to deepen their understanding of survey-based experimental methods and provide hands-on experience with designing and analysingsurvey experiment data. We will introduce survey experiments and how they differ from other experimental methods, and we will highlight key studies that have shaped the field. In the practical sessions, participants will focus on designing and analysing conjoint experiments and presenting the results clearly and effectively. By the end of Day 3, participants will be equipped with the theoretical understanding and practical skills needed to design, implement, and analyse survey experiments, with a particular focus on conjoint analysis.Morning Session: 9:30 AM – 12:30 PM (refreshments break 11:00am)Lunch Break: 12:30 PM - 2:00 PM (buy/bring your own)Afternoon Session: 2:00 PM - 5:00 PM (refreshments break 3:30pm) Highlights include:• Understand the significance and applications of survey experiments in social sciences.• Overview of classic survey experiments• Design a conjoint experiment• Replicate the analysis of a conjoint experiment• Interpret the findings, draw conclusions, present resultsDay 4: Field Experiments Instructors: Dr. Monica Beeder and Dr. Paolo SpadaDay 4 is dedicated to field experiments, providing participants with a comprehensive understanding of this essential research method. The day's agenda combines theoretical insights with practical exercises to ensure participants can effectively design, manage, and analyse field experiments. The introduction to field experiments is followed by an overview of the strengths and challenges of field experiments, a discussion of classic studies that have made significant contributions to the field, offering appreciation for the methodological rigour and practical implications of field experiments. The practical sessions focus on the design and analysis of field experiments, and the review of the logistical considerations involved will offer a real-world perspective on managing field experiments. We will replicate the analysis and presentation of a simple field experiment, calculate treatment effects, test hypotheses, and communicate findings through visual aids.Morning Session: 9:30 AM – 12:30 PM (refreshments break 11:00am)Lunch Break: 12:30 PM - 2:00 PM (buy/bring your own)Afternoon Session: 2:00 PM - 5:00 PM (refreshments break 3:30pm) Highlights include:• Understand the significance and characteristics of field experiments• Overview of classic field experiments• Project management of field experiments• Hands-on analysis of data from a field experiment• Perform statistical analyses to interpret results and test hypotheses• Presentation of field experiment results using visual aidsDay 5: Laboratory Experiments & Online Incentivized Experiments Instructors: Dr. João V. FerreiraDay 5 marks the culmination of the C-BEAR summer school with a session on laboratory and online incentivized experiments. We will introduce these experiments and explore their unique features, with a focus on ways to incentivize the truthful revelation of preferences and beliefs and the design of paradigmatic games used by experimental economists. The practical sessions will focus on the tools and techniques necessary to design your own lab or online incentivized experiment, with an opportunity to design a simple experiment in groups. We will also replicate the analysis and presentation of a simple lab experiment. Using real data, we will calculate treatment effects, conduct hypothesis tests, and learn how to present results clearly and concisely. The day is designed to equip participants with the necessary knowledge and skills to leverage a range of tools used in laboratory and online incentivized experiments in their own research endeavours.Morning Session: 9:30 AM – 12:30 PM (refreshments break 11:00am)Lunch Break: 12:30 PM - 2:00 PM (buy/bring your own)Afternoon Session: 2:00 PM - 5:00 PM (refreshments break 3:30pm) Highlights include:• Understand the significance and features of laboratory and online incentivized experiments• Overview of classic lab experiments and paradigmatic games used by experimental economists• Learn about methods and tools to incentivize the truthful revelation of preferences and beliefs• Participate in paradigmatic games• Design your own simple experiment• Replicate the analysis of a lab experiment• Perform statistical analyses to interpret results and test hypotheses• Presentation of lab experimental results• Farewell GROUP DINNER (self-catering)The target audience of the course are professionals, members of public institutions and researchers that are approaching experimental methods for the first time and are interested to implement an experiment for the first time or to commission an experiment to a survey company or other service provider.The course does not require any previous knowledge of experimental design or statistics and is open to anybody with basic high school knowledge of mathematics.  The level (junior, senior, etc.) of the course is open. The first two days will provide the students the mathematical and statistical tools to engage effectively with the rest of the course.The workshop is taught by a team of faculty members from Politics, Economics, Psychology and Business, and it is targeted to people with interests in any discipline in the social sciences. Participants need to bring their own device that can run basic office suites, and free versions of R and Stata.  PLEASE NOTE REFRESHMENTS WILL BE PROVIDED BUT PARTICIPANTS WILL NEED TO BRING/BUY THEIR OWN LUNCH. jmh6@soton.ac.uk (NCRM, University of Southampton ) Wed, 18 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14735 https://www.ncrm.ac.uk/training/show.php?article=14779 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.  It will equip participants with the knowledge, skills and confidence required to begin their own qualitative investigations.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, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14779 https://www.ncrm.ac.uk/training/show.php?article=14787 Introduction to Multilevel Modelling Using MLwiN, R, or Stata30th June – 2nd July 2026, Online via Zoom-------------------------------------------------------------------------------------------------------Deadline for applications: 17th May 2026Full course information and excerpts can be viewed here: https://www.bristol.ac.uk/cmm/learning/introworkshop.html  Go to booking form >>-------------------------------------------------------------------------------------------------------InstructorsProfessor George Leckie and Professor William Browne SummaryThis three-day course provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN, R, or Stata. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or hierarchical). These models can be viewed as an extension of conventional linear and logistic regression models to account for and learn from the clustering in the data. Such models are appropriate when, for example, analysing exam scores of students nested within schools, or health outcomes of patients nested within hospitals. Special interest lies in disentangling social processes operating at different levels of analysis by decomposing the within- from the between-cluster effects of covariates (explanatory or predictor variables). Longitudinal data are also clustered, with repeated measurements on individuals or multiple panel waves per survey respondent. Throughout the course we emphasize how to interpret multilevel models and the types of research question they can be used to explore. Testimonials“The course was excellent - far exceeded expectations. The course has given me the confidence to use MLM, something I very much lacked before. I feel I understand the theory behind MLM, why each stage is so important, and the various interpretations. Without this course I would be lost. I cannot thank you all enough.”“This was a beautifully constructed course. It was clear throughout that careful thought had been given to providing a balance between lecture content, time for questions and discussion, and practical sessions. Both George and Bill delivered fantastic lectures - explanations were clear and thorough (including critiques of each approach) and content built up in complexity over time with plenty of worked examples of different kinds. The course was superb - can't rate it highly enough.”“I thought it was a really good double act between George and Bill - they are both hugely knowledgeable so having one person focused on the slides and the other manning the chat was a good approach as it meant the teaching didn't get derailed by people's questions.” TopicsOverview of multilevel modellingVariance-components modelsRandom-intercept models with covariatesBetween- and within-effects of level-1 covariatesRandom-coefficient modelsGrowth-curve modelsThree-level modelsReview of single-level logistic regressionTwo-level logistic regression FormatThe course will consist of a 2:1 mix of lectures and hands-on practical sessions applying the taught methods to real datasets. The lectures are software independent and are delivered live via Zoom, but recordings of the lectures will be made available shortly afterwards for twelve weeks following the course if participants are unable to attend at the scheduled time. The instructors alternate the lecturing. Participants can ask questions via Zoom’s text-based chat facility and these will be monitored and answered by the instructor not presenting or relayed to the instructor presenting to answer live.Each lecture is immediately followed by a self-directed practical, offered in participants’ choice of MLwiN, R, or Stata, giving participants the chance to replicate the presented analyses and to consolidate their knowledge. At the end of each practical session the instructors demo the different software, each in a different breakout room. FeesFor UK-registered MSc and PhD students - £180For UK university academics, UK public sector staff, and staff at UK registered charity organisations - £360For all other participants - £660Please note, in order to be eligible for the reduced pricing brackets please submit your application using your UK academic/organisational email address. ApplicationsIf you would like to attend the workshop, please complete and submit the online booking form (see below). Please note the closing date for applications is 17th May 2026.Applications will be processed on a rolling basis, once a week, until the application deadline. A link to the University of Bristol’s online shop will be provided and your place on the course will be confirmed upon successful payment.If you have any queries, please email info-cmm@bristol.ac.uk.Go to booking form >> info-cmm@bristol.ac.uk (Centre for Multilevel Modelling, University of Bristol) Tue, 24 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14787 https://www.ncrm.ac.uk/training/show.php?article=14382 Level: Foundation (F)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.Delegates will be given a small amount of precourse materials to help them prepare for the course, and will be expected to bring a laptop to the course.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 mainly use calculations by hand to aid understanding, but will include outputs from Excel and other free statistical software for some statistical tests.Topics CoveredDay 1: The normal distribution, basic study design,data summary, confidence intervals, introduction to hypothesis tests,analysis of contingency tables - the chi-squared test.Day 2: T-tests, non parametric tests, (Wilcoxon signed rank test, Mann-Whitney U test), 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. Experience of using Excel is an advantage but not essential as some examples of analysis in Excel and other free software will be demonstrated.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=14382 https://www.ncrm.ac.uk/training/show.php?article=14780 The course introduces you to various techniques and strategies for the writing up of your qualitative data.This introductory course is mainly suitable for researchers who have completed data collection or at the least are currently in the midst of data collection and/or analysis. It provides participants with the skills needed to write up and present their qualitative data and findings. In addition to presentations, this training includes practical elements such as workshop discussions, examples of qualitative data, and application of various strategies for writing up.By the end of the course participants will have knowledge of various ways of writing up and presenting qualitative data and how this differs from the presentation of quantitative data. They will be able to align their writing to the principles of qualitative research in order to effectively communicate their insights.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, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14780 https://www.ncrm.ac.uk/training/show.php?article=14781 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 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) Tue, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14781 https://www.ncrm.ac.uk/training/show.php?article=14782 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 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) Tue, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14782 https://www.ncrm.ac.uk/training/show.php?article=14408 Level: Intermediate (I)This course will introduce how to create interactive dashboards and web applications using R and Shiny. The first day will focus on the core components of a Shiny app; inputs, outputs and how to send data between the client and server. It will also cover how to design responsive webs applications that seamlessly work on mobile devices. By the end of the day you'll be able to design and deploy a Shiny app from your local machine to shinyapps.io.The second day of the course will cover the following advanced topics:Using reactive expressions to control when and where a Shiny app updatesDesigning data-driven controls through the use of reactive expressions.Embedding interactive charts/maps/tables using the following htmlwidget libraries; leaflet, highcharter and DT.Allow users to download files/images from a Shiny appAdvice and guidance on structuring large/complex Shiny apps Learning OutcomesConfidently design user interfaces in Shiny with appropriately selected controls/inputsUnderstand reactivity to effectively update outputs based on specific inputsDesign responsive Shiny apps that work on both desktop and mobile devicesConfidently embed htmlwidgets into Shiny apps and extract user interactions (click/touch)Effectively structure your code in Shiny apps to simplify growing your app with additional content. Topics CoveredR, Data Visualisation, Shiny, Data Presentation and Exploratory Data Analysis Knowledge AssumedFamiliarity with the R language is required as a number of fairly complicated concepts will be introduced in this course:Reactive expressionsControlling how data moves between the client and serverDeploying Shiny apps to the web 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=14408 https://www.ncrm.ac.uk/training/show.php?article=14415 This popular internationally recognised course is accredited by the JBI and provides a comprehensive understanding of all steps involved in systematic reviewing. The course covers quantitative and qualitative approaches to evidence synthesis and is delivered by experts from the University of Nottingham Centre for Evidence Based Healthcare. The course will equip you with the skills to undertake a review yourself or to supervise students doing systematic review projects. By the end of the course, you will be ready to register your review, submit a protocol for publication and get started. The course will be delivered in-person over 5 days. catrin.evans@nottingham.ac.uk (University of Nottingham) Fri, 29 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14415 https://www.ncrm.ac.uk/training/show.php?article=14783 Putting together a Theory of Change (ToC) which is specific to a policy, initiative or programme is increasingly seen as a foundation for effective problem-centred programme development and innovation by central and local government, NGOs, voluntary and community sector bodies and others. This course aims to demystify the confusion about what constitutes an effective ToC and to raise the understanding and ambition of researchers and evaluators about how to best go about developing and harnessing these. A ‘good’ ToC is a valuable framework for shaping ‘novel’ programmes and for putting in place focussed, cost-effective and proportionate methodologies to monitor progression, assess their effects and effectiveness and inform lessons for improved practice. This intensive course provides a broad understanding of how ‘theory building’ contributes to policy and programme development, and how to go about developing a cause-effect chain to underpin a fit for purpose ToC. It will provide participants with the confidence and practical skills to work with colleagues, partners and stakeholders to help put together practical ToCs for policy and programme development, monitoring, research and evaluation.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) Tue, 17 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14783 https://www.ncrm.ac.uk/training/show.php?article=14718 A short course designed for researchers and doctoral students to critically engage with the theory and practice of ethnography in healthcare settings.You will be taught by staff from SAPPHIRE, University of Leicester, who have an international reputation for expertise in using ethnographic methods in healthcare and improvement research8-10 July, 2026, at the Clubhouse, Leicester Tigers Rugby Club, Aylestone Road Leicester.  Detailed DescriptionThe three day course (18 hrs) will employ a variety of lectures, workshops, group work and student presentations.The course will cover the following:·         Ethnography in and of healthcare, managing tensions in improvement and evaluation·         Use, variation and value associated with the ethnographic label, including non-traditional, digital, visual and remote approaches·         The roles and positions of the ethnographer in healthcare ‘fields’·         Critical appraisal of ethnographic contributions to healthcare improvement·         Cross-cultural variation, comparative studies of healthcare using ethnographic methods –·         sensitivity to local context, time, place and complexity·         Designing and conducting ethnographic research in healthcare improvement; tips from the field, the importance of reflexivity and ethical conduct inside and outside healthcare settings·         Analysis of ethnographic data·         Ethnographic writing and publication, influencing policy and practice By the end of the course the student will be able to:·         identify scope and practical application of ethnography for healthcare improvement·         identify sociological / anthropological origins of ethnography and key philosophical concepts involved with using ethnography for healthcare improvement·         outline approaches to recording field notes, interviews, and debriefs, and to managing data·         describe approaches to the analysis of ethnographic data·         consider the challenges and benefits of team working in ethnographic studies·         discuss challenges in gaining ethical approval, access to sites, data collection in healthcare settings (particularly around securing consent), exiting the field and writing up for publication·         understand ways to generate theoretically informed insights ‘telling cases’ with implications for healthcare improvement·         consider methods of ethnographic writing, and ways of disseminating findings to different·         audiences using different media·         link with other researchers and doctoral students to share good practice and foster·         development of an ‘ethnography in healthcare improvement’ community of practice jennifer.creese@leicester.ac.uk (University of Leicester) Thu, 05 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14718 https://www.ncrm.ac.uk/training/show.php?article=14800 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 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, 08 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14800 https://www.ncrm.ac.uk/training/show.php?article=14388 Level: Foundation (F)This course will be delivered over three sessions, running from 9:30am to 3:00pm each day.This course looks at each of the main steps in having a successful quantitative survey. Drawing on both practical experience and research findings from survey experiments, this course is full of insightful and pragmatic advice.Course OutlineThe course covers:An overview of survey design (including types of survey designs, project management, time-tabling and budgeting) (with DISCUSSION)Ethical considerations (with WORKSHOP)Mode of data collection differences (including appendix on how respondents answer differently between interview-administered and web surveys)Mixed mode and ‘push to web’ designsWhat to look for in a good sample design (types of probability and non-probability samples, internet panels, confidence intervals and 7 considerations for sample size)Key principles of questionnaire design (for individual questions and the questionnaire as a whole) (with two WORKSHOPS)Appendix on why demographic questions are the most difficult to writeAppendix on various ways to test your questionnaire even with little time or budgetUnderstanding response rates and non-response bias; ways to minimise non-response/non-response bias during fieldworkAn overview of coding open-ended questions (with WORKSHOP) and other data processing stepsIntroduction to basic weighting (with WORKSHOP)Appendix with more complex weighting issues and imputationHighlights on what makes a good survey reportThe course does not cover:Analysis of survey data as this is covered by other RSS courses Learning OutcomesParticipants will:Have a better awareness of the key aspects involved in the creation and implementation of a quantitative surveyBe able to critique aspects of existing surveysHave knowledge to conduct or improve the quality of their own surveys Knowledge AssumedIt is helpful, but not required, that participants have a basic understanding of producing surveys/doing research. For the sampling part of the course, it is useful for participants to have some familiarity with basic statistical concepts such as normal distribution, mean, standard deviation and standard error. 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=14388 https://www.ncrm.ac.uk/training/show.php?article=14798 Overview:This course introduces participants to the intersection of trauma and qualitative research. It explores the value of a trauma-informed approach and how we incorporate trauma-informed principles into our research. It is appropriate for social researchers and professionals seeking a deeper understanding of trauma-informed research methods and their practical implications.The course outlines the theoretical frameworks that underpin trauma-based research including categories and types of trauma, trauma responses, and how trauma can influence the research process (including the risks of vicarious trauma, re-traumatisation and participant distress). From study design considerations to data collection methods and analysis techniques, participants will gain insights into how trauma can affect research.Interactive sessions, case studies, and group discussions provide opportunities for practical application of the principles explored, allowing participants to engage with real-world scenarios and ethical dilemmas encountered in research. We highlight the importance of cultural considerations and inclusivity in conducting trauma-based research, promoting a holistic and respectful approach to data collection and analysis.By the end of the course, participants will have a detailed understanding of trauma-informed research and how to incorporate these principles into their own qualitative work. They will be equipped with knowledge and tools to ethically and sensitively conduct research with trauma-affected populations. Learning outcomes· Have an understanding of the principles of trauma-informed research and practice;· Be able to incorporate trauma-informed principles into their qualitative research (including i.e. study design, data collection, analysis);· Understand the different categories and types of trauma;· Understand different trauma responses;· Be aware of the potential risks in research of: re-traumatisation, vicarious trauma, and participant distress;· Be aware of the ethical challenges specific to trauma-informed research;· Practical exercises, case studies, examples and discussions will enable participants to reflect on trauma in social research and begin to practice a trauma-informed approach. Topics· Principles and theories of trauma-informed practice· Categories and types of trauma· Trauma responses· Principles of trauma-informed response: safety, trust worthiness and transparency, peers support, voice and choice, collaboration and mutuality, empowerment, and cultural issues· Potential risks: re-traumatisation, vicarious trauma, participant distress· Research design considerations· Case studies, examples and practical exercises on trauma-informed research Who should attend?This advanced course will be of value to researchers who have some experience in qualitative research and are looking to incorporate a trauma-informed approach into their research practice. It will be beneficial for researchers who wish to update their skills and knowledge in relation to research ethics. It is also designed to support those who need to know what to look for when commissioning good quality research. Participants come from diverse academic, social research and policy backgrounds and very diverse topic areas. Knowledge of social research and/or attendance on introductory methods course is advisable (particularly qualitative research courses).Please note: this is an interactive live course with presentation, group activities, group discussions, and opportunities to ask questions. You should be prepared to participate and have use of camera and mic on Zoom. Trainer biographyDr Karen Lumsden is an experienced trainer in Qualitative Research Methods and Academic Skills, Qualified Professional Coach and Mentor (she holds an ICF, EMCC & AC Accredited Professional Diploma from Optimus), and consultant in qualitative research & evaluations.She has held a number of academic posts over the years including Senior Lecturer in Sociology at the University of Aberdeen, Associate Professor in Criminology at the University of Leicester, and at the Universities of Nottingham and Loughborough.She has over 20 years' experience delivering qualitative methods courses and training to academics, PhD students, social researchers, and practitioners. This includes courses at, for example, the Universities of Aberdeen, Glasgow, Kings College London, Essex, Cardiff, Kingston, Auckland, and City University Hong Kong. She has delivered training for the Social Research Association since 2016 and for the European Consortium for Political Research since 2024. She regularly delivers bespoke training for government departments, NHS, charities, police organisations, and social and market research organisations.She has written and edited a number of books and journal articles on qualitative methods including Crafting Autoethnography (Routledge, 2023) and Reflexivity: Theory, Method, Practice (2019). She is on the Editorial Board of the journal Qualitative Research. Visit www.qualitativertraining.com for full details of the services she provides to students, researchers and universities. Bookings:Bookings for this course can be made via Eventbrite tickets. If your organisation requires payment via invoice, please contact me directly to check if this will be possible. I only issue invoices when payment terms are mutually agreed and confirmed in writing prior to the event date. Email: karen@qualitativetraining.com Booking and refund policy:There is a 14 day 'cooling off' period from the date of booking on this course. The refund policy is 100% refund up to 7 working days prior to the course date. Less than 7 days the entire fee is payable.If within the 7 days and the course is fully booked and has a waiting list, then transfer to another course or future course date might be possible, however this is at the discretion of the trainer.Please note that Dr Karen Lumsden delivers a range of courses for other training providers in addition to these Qualitative Training courses. She takes no responsibility or liability for individuals booking on similar courses or training with other providers which may contain similar or the same course content. Refunds will not be given under these circumstances once the training has been partly/fully consumed. karen@qualitativetraining.com (Qualitative Training) Wed, 08 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14798 https://www.ncrm.ac.uk/training/show.php?article=14813 This course introduces researchers to version control using Git and GitHub through an accessible graphical interface, requiring no prior experience with Git or the command line. Participants will learn the core concepts of version control and work through the full Git workflow - from setting up Git and creating repositories, to tracking files, working with remote repositories, and managing branches. By the end of the course, researchers will be able to manage their project files using Git and collaborate with others through GitHub.The course covers: What is version control?Setting up GitCreating a repositoryTracking changesExploring historyRemote repositoriesBranchingIgnoring things in version controlBy the end of the course participants will:Understand the benefits of an automated version control systemUnderstand the basics of how automated version control systems workConfigure Git and GitHub on their computerCreate a repository from a templateClone and use a Git repositoryGo through the modify-add-commit cycle for one or more filesDescribe where changes are stored at each stage in the modify-add-commit cycleCompare files with previous versions of themselvesRestore old versions of filesUnderstand git push and git pullEncounter and resolve a conflictUnderstand why you would use a branchMerge together two modified version of a fileUse a gitignore file to ignore specific files and explain why this is usefulThis course is aimed at academic researchers at all career stages, across all disciplines. No prior experience with Git, GitHub, or the command line is required. This course is relevant to any researchers who want to adopt better practices for tracking and organising their work.Setup InstructionsGitHubWe’ll be using the website GitHub (https://github.com/) to host, back up, and distribute our code. You’ll need to create an account there. As your GitHub username will appear in the URLs of your projects there, it’s best to use a short, clear version of your name if you can.Go to https://github.com and follow the “Sign up” link at the top-right of the window. Follow the instructions to create an account. Verify your email address with GitHub. Configure multifactor authentication (if necessary)GitHub DesktopVisit the download page for GitHub Desktop at https://desktop.github.com/download/ Click the relevant button to download GitHub Desktop for your operating system. In your computer’s Downloads folder, double-click the GitHub Desktop setup file and follow the on-screen prompts to complete installation.ProgrammeWhat is version control?Setting up GitCreating a repositoryTracking changesExploring historyRemote repositoriesBranchingIgnoring things in version controlThis course will run on 15th July 2026 from 13:00 – 16:30. jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 23 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14813 https://www.ncrm.ac.uk/training/show.php?article=14609 This summer school is ideal for anyone wanting to explore a range of contemporary topics in psychology, whilst getting a taste of what it’s like to study psychology at university. Through seminar and practical workshop sessions you will explore a range of contemporary everyday psychology beyond that taught at school.As part of the 5-day summer school, you will:explore key psychological theories across a range of everyday and socially relevant topicsunderstand the mechanisms surrounding human behaviour, including cognitive, emotional, and social processesget hands-on with our specialist software commonly used in psychological studiesgain experience in conducting psychological research including the latest scientific methods and data analysisapply psychological thinking to every day life, enhancing your personal insight and reflective skills. kelsey.clarke@ntu.ac.uk (Nottingham Trent University ) Fri, 28 Nov 2025 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14609 https://www.ncrm.ac.uk/training/show.php?article=14801 Participatory Action Research (PAR) is a type of research that combines two different approaches: participatory research and action research. It is a valuable qualitative method because it empowers and involves individuals and communities in the research process, and in taking actions to improve aspects of their lives. Researchers using PAR aim to enable action on the part of the participants, and do so via a reflective process where the participants collect and analyse data, and then determine what action should be taken. When participants and researchers are equal partners in the research process, the study’s focus and results can be made more relevant to a specific community. However, in PAR there are also challenges in terms of how researchers form and maintain relationships with participants, how the data is constructed and used, and who has ownership of the data. This introductory course provides skills on how to conduct Participatory Action Research (PAR). It provides an introduction to PAR and its origins, history and theories. It explores the stages that must be followed in designing PAR, and the practicalities of doing so. Through a collaborative practical exercise, participants will be able to gain experience of designing their own PAR project.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, 08 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14801 https://www.ncrm.ac.uk/training/show.php?article=14608 This summer school is ideal for anyone wanting to explore a range of contemporary topics in psychology, whilst getting a taste of what it’s like to study psychology at university. Through seminar and practical workshop sessions you will explore a range of contemporary everyday psychology beyond that taught at school including:The Psychology of the SelfThe Psychology of Body ImageOccupational PsychologyPsychology in SportPsychology of Gambling and GamingCounsellingQuantitative research methods -  learn some statistical coding (in R)Qualitative research methods - learn interview and analysis techniques. kelsey.clarke@ntu.ac.uk (Nottingham Trent University) Fri, 28 Nov 2025 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14608 https://www.ncrm.ac.uk/training/show.php?article=14802 By the end of the workshop, participants will:Have a critical awareness of the range of AI-assisted qualitative analysis toolsBe equipped to consider their ethical incorporation into analysis practiceHave practical, hands-on experience with using general-purpose AI Chatbots, and bespoke qualitative analysis tools for GenAI-assisted analysis that can be transferred to other applicationsParticipants do not need licenses for any qualitative analysis software to attend this course. Trial versions will be made available for the purpose of the sessions. However, you will need to install the trial version of MAXQDA so will need to leave plenty of time to arrange this, especially if you will be using an organisational computer with admin rights / security. Upon registration you will be provided with installation information.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, 08 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14802 https://www.ncrm.ac.uk/training/show.php?article=14803 Qualitative data analysis reveals patterns and themes from the large volume of data generated by qualitative research. It is useful for gaining detailed understanding of social phenomena and individual experiences, perceptions and behaviours. However, it is often seen as a mysterious and complex stage of the research process. There are also challenges in terms of how researchers conduct analysis and the steps that they need to follow.This advanced course provides participants with the skills to conduct qualitative data analysis. While providing an overview of different analytical approaches, the focus in our activities will be on thematic analysis. It provides an introduction to qualitative data analysis. It explores ways of organising and analysing qualitative data, and the practicalities of doing so. Through a practical exercise where we analyse qualitative interview data provided by the trainer, participants will be able to gain experience of conducting their qualitative data analysis by focusing on thematic analysis.By the end of the course, participants will have knowledge of various methods and theories of qualitative data analysis and how it differs from quantitative analysis. They will be able to choose an appropriate data analysis technique for different forms of qualitative data. They will also be able to conduct their own thematic analysis, code, and organise data for 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) Wed, 08 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14803 https://www.ncrm.ac.uk/training/show.php?article=14814 A practical introduction to using AI to support coding in research. This course will help researchers understand how to use AI to help them write code effectively and responsibly. This course is designed for researchers with little to no experience coding. The course provides clear, hands-on guidance for using AI to write, debug, and understand code, while addressing key ethical, security, and reliability considerations in research contexts.The course covers: An overview of the AI landscapePractical skills for AI-assisted coding Ethics, reliability and security considerationsLearning Outcomes:AI Landscape Recall key milestones in the historical development of artificial intelligenceDescribe where ChatGPT and similar large language models fit within the broader AI landscape.Explain, at a conceptual level, what generative AI and ChatGPT are.Summarize the primary functions and intended use cases of common AI coding assistants.AI-Assisted CodingExplain why delegating full software development to AI without understanding the solution introduces technical, ethical, and reliability risks.Describe appropriate roles for AI tools as assistants rather than autonomous developers.Use ChatGPT as a reference tool to locate, summarize, and clarify technical information more precisely than traditional search methods.Apply AI tools to explain unfamiliar code to support learning.Use AI-generated suggestions to debug code and resolve errors.Generate boilerplate code using AI assistance.Use AI tools to draft technical documentation.Analyse when AI assistance enhances productivity versus when it may obscure understanding or introduce errors. Ethics, Reliability and Security Considerations Describe common sources of bias, inaccuracy, and unreliability in AI-generated outputs.Explain data privacy, confidentiality, and security risks associated with using AI tools in coding and research contexts.Summarize intellectual property, authorship, and citation considerations related to AI-generated code and text.Analyse the potential long-term consequences of researchers relying on AI tools without developing foundational coding skills.Assess the appropriateness of AI tool usage in specific research or coding scenarios.Develop personal or team-level guidelines for responsible and ethical AI use in coding and data analysis workflows.This course is aimed at Researchers with little to no programming experience who are interested in using AI to help them write code for their research. Setup InstructionsPlease follow the instructions on this web page to download the data and install the required software before attending the workshop: https://southampton-rsg-training.github.io/coding-with-ai/index.html Note: If using a University of Southampton machine follow the instructions under the tab labelled ‘University of Southampton Computers’.  If using a personal machine or a machine from another university, please follow the instructions under the tab labelled ‘Personal Computers’.ProgrammeAn overview of the AI landscapePractical skills for AI-assisted coding Ethics, reliability and security considerationsThis course is taking place on 3rd September 2026 from 13:00 – 16:30. jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 23 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14814 https://www.ncrm.ac.uk/training/show.php?article=14402 Level: Foundation (F)This course will run over 4 afternoon sessions, from 1:00pm to 5:00pm each day.The purpose of this course is to introduce participants to the R environment for statistical computing. The course focuses on entering, working with and visualising data in R, and linear regression modelling in R. Learning OutcomesBy the end of the course, participants will be able to use R to:Have a clear understanding of R/RStudio IDE and its background.Be familiar with navigating the RStudio IDE.Understand the core fundamentals of R.Understand functions and arguments.Be able to create vectors and applying functions.Be exposed to the tibbles and {tidyverse} package.Be able to comfortably import, export, and store data in R.Have a basic introduction to graphics with {ggplot2}.Have a basic understanding of manipulating data manipulation with {dplyr}.Understand logical and relational data partitioning.Have a thorough understanding of popular statistical techniques.Have the skills to make appropriate assumptions about the structure of the data and check the validity of these assumptions in RBe able to fit regression models in R between a response variable and understand how to apply these techniques to their own data using R’s common interface to statistical functions.Be able to cluster data using standard clustering techniques. Topics CoveredIntroduction to R: A brief overview of the background and features of the R statistical programming system.Data entry: A description of how to import data.Data types: A summary of R’s data types.R environment: A description of the R environment including the R working directory, creating/using scripts, saving data and results.R graphics: Creating, editing and storing graphics in R.Summary statistics: Measures of location and spread.Manipulating data in R: Describing how data can be manipulated in R using logical operators.Basic hypothesis testing: Examples include the one-sample t-test, one-sample Wilcoxon signed-rank test, independent two-sample t-test, Mann-Whitney test, two-sample t-test for paired samples, Wilcoxon signed-rank test.ANOVA tables: One-way and two-way tables.Simple and multiple linear regression: Including model diagnostics.Clustering: Hierarchical clustering, k-means.Principal components analysis: Plotting and scaling data. Target AudienceThis course is ideally suited to anyone who:Is familiar with basic statistical methods (e.g. t-tests, boxplots) and who want to implement these methods using RHas used menu-driven statistical software (e.g. SPSS, Minitab) and who want to investigate the flexibility offered by a command line package such as RIs already familiar with basic statistical methods in R and would like to extend their knowledge to regression involving multiple predictor variables, binary, categorical and survival response variablesIs familiar with regression methods in menu-driven software (e.g. SPSS, Minitab) and who wish to migrate to using R for their analyses Assumed KnowledgeThe course requires familiarity with basic statistical methods (e.g. t-tests, box plots) but assumes no previous knowledge of statistical computing. For 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=14402 https://www.ncrm.ac.uk/training/show.php?article=14815 This is a hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.  This introduction aims to cover the basics of Deep Learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what next steps to take to improve the model. The course covers: What is deep learning?Classification by a neural network using KerasMonitor the training progressAdvanced layer typesReal world applicationLearning Outcomes:Introduction Define deep learningDescribe how a neural network is build upExplain the operations performed by a single neuronDescribe what a loss function isRecall the sort of problems for which deep learning is a useful toolList some of the available tools for deep learningRecall the steps of a deep learning workflowTest that you have correctly installed the Keras, Seaborn and scikit-learn librariesUse the deep learning workflow to structure the notebookClassification by a neural network using KerasExplore the dataset using pandas and seabornIdentify the inputs and outputs of a deep neural network.Use one-hot encoding to prepare data for classification in KerasDescribe a fully connected layerImplement a fully connected layer with KerasUse Keras to train a small fully connected network on prepared dataInterpret the loss curve of the training processUse a confusion matrix to measure the trained networks’ performance on a test setMonitor the training processExplain the importance of keeping your test set clean, by validating on the validation set instead of the test setUse the data splits to plot the training processExplain how optimization worksDesign a neural network for a regression taskMeasure the performance of your deep neural networkInterpret the training plots to recognize overfittingUse normalization as preparation step for deep learningImplement basic strategies to prevent overfittingAdvanced layer typesUnderstand why convolutional and pooling layers are useful for image dataImplement a convolutional neural network on an image datasetUse a dropout layer to prevent overfittingBe able to tune the hyperparameters of a Keras modelTransfer learningAdapt a state-of-the-art pre-trained network to your own datasetOutlookUnderstand that what we learned in this course can be applied to real-world problemsUse best practices for organising a deep learning projectIdentify next steps to take after this coursePre-requisites:Learners are expected to have the following knowledge:Basic Python programming skills and familiarity with the Pandas package.Basic knowledge on machine learning, including the following concepts: Data cleaning, train & test split, type of problems (regression, classification), overfitting & underfitting, metrics (accuracy, recall, etc.).Setup InstructionsPlease follow the setup instructions here: https://carpentries-lab.github.io/deep-learning-intro/index.html#software-setup Note that software installation can take some time.  Please set up your python environment at least a day in advance of the workshop. If you encounter problems with the installation procedure, ask your workshop organizers via email for assistance so you are ready to go as soon as the workshop begins.ProgrammeWhat is deep learning?Classification by a neural network using KerasMonitor the training progressAdvanced layer typesReal world applicationThis course is taking place on 15-17 September from 09:00 - 17:00.  jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 23 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14815 https://www.ncrm.ac.uk/training/show.php?article=14383 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=14383 https://www.ncrm.ac.uk/training/show.php?article=14768 This workshop will introduce Institutional Ethnography (IE), an interdisciplinary feminist approach to social research that focuses on how texts and language organise our everyday lives. IE is not just a methodology, but an entire approach to research with a specific ontology of how the social world works and the organising role of texts and language. In IE, the researcher ‘takes sides’ using a specific version of standpoint to explore how institutions work in practice rooted in peoples’ experiences. This often involves researching as, with, or alongside marginalised groups and making visible how institutions exclude or make invisible certain groups of people and experiences.The overall aim of the workshop is to provide attendees with a comprehensive overview of institutional ethnography as an approach and the opportunity to translate their own research ideas and projects into an IE research proposal and do a small piece of text-focused analysis. This hands-on workshop is suitable for students, academics, and anyone else interested in feminist methodologies, text and discourse analysis, and institutional or organisational ethnographies. No prior training in, or knowledge of, IE is required.The course covers:· An overview of Institutional Ethnography and the work of feminist sociologist, Dorothy Smith, who developed Institutional Ethnography· Case studies of Institutional Ethnography research projects to show how it works in practice in different disciplines· How to translate your research into an Institutional Ethnography project using a research proposal framework· Practical explanation of how to do text and discourse analysis within Institutional Ethnography through a short text analysis activityBy the end of the course participants will:· understand of the origin and development of Institutional Ethnography· know how to use Institutional Ethnography to analyse texts, processes, and discourses· have an outline of how their research ideas could become an Institutional Ethnography projectThe course is aimed at Academics, students, any other qualitative researchers, including policymakers, organisers, and activists interested in analysing organisational processes.Participants must have at least some experience in qualitative research methods, but no experience of Institutional Ethnography is required. Preparatory ReadingRequired:· 1 hour lecture by Dorothy Smith summarising Institutional Ethnography -https://www.youtube.com/watch?v=1RI2KEy9NDw · Murray, Ó.M., 2020. Text, Process, Discourse: Doing feminist text analysis in institutional ethnography, Available at: https://doi.org/10.1080/13645579.2020.1839162  Desirable: · Earles, J., & Crawley, S. L. 2020. Institutional ethnography. In P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug, & R. A. Williams (Eds.), Foundation: SAGE research methods. Retrieved July 17, 2020, from: http://dx.doi.org/10.4135/9781526421036759274  · Smith, D.E. & Griffith, A.I., 2022. Simply Institutional Ethnography: Creating a Sociology for People. Toronto: University of Toronto Press.ProgrammeDay One: 21 September 202610:00 - 10:15 Introductions10:15 - 11:30 Series of short introductory video lectures + 1 case study11:30 - 11:45 Short break11:45 - 12:45 Q&A on the videos and institutional ethnography in general12:45 - 13:00 Explain afternoon task and split everyone into small groups based on research interests 13:00 - 14:00 Lunch break 14:00 - 15:00 Small group discussions divided up by discipline/area of interest; participants collectively discuss how their research projects would translate into Institutional Ethnographies, aided by a research proposal template and guiding questions - each group is facilitated by one of the three organisers 15:00 - 15:15 Short break 15:15 - 16:00 Three groups come back together to highlight key points of discussions and any final questions before explaining what will happen on Day 2 - participants will have to choose a 'text' related to their research to bring to Day 2 to analyse. Day 2: 22 September 2026 10:00 - 11:30 Brief introductions and 2 short case studies with Q&A 11:30 - 11:45 Short break 11:45 - 13:00 Any further questions and introduction to the text analysis methods we will use in the afternoon 13:00 - 14:00 Lunch break 14:00 - 15:00 Small groups work facilitated by three organisers in which participants using text analysis methods on their research-related 'text' (in groups or individually) 15:00 - 15:15 Short break 15:00 - 16:00 Everyone comes back together to discuss their text analysis and ask any final questions about how to do Institutional Ethnography text analysis, the overall approach, and distribution of follow-up resources. Completion of online evaluation survey. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 13 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14768 https://www.ncrm.ac.uk/training/show.php?article=14739 The problem: Your team collected thousands of words of data. You try a traditional thematic analysis of the text. Soon, colour coding, close reading, writing ad hoc reflections about the text become too onerous a task. You doubt the validity of your observations. You wish there was another way to streamline the process, that would extract key themes in data in a faster and empirically-valid way.Solution: Join us for a session in which we showcase empirical methods for the extraction and analysis of meaning, concepts, and themes in texts. The session will provide training in corpus linguistics and mixed-method tools that enable the analysis of texts in an empirical, bottom-up fashion. Through a range of case-studies, you will be guided to extract meaning and other thematic patterns from texts to gain insight into thoughts and behaviours of authors of those texts. We will share best practises on the thematic analysis of various data types, such as diaries, interview transcripts, data scraped from the web, and outputs of both new and traditional media. We also demonstrate ways of building the results of such analyses into answering research questions, developing business strategy, or a public policy.This session will be run by researchers from the University of Sussex’s Concept Analytics Lab (https://conceptanalytics.org.uk/) using texts from Mass Observation Archive to showcase approaches to thematic analysis. We will demonstrate solutions developed for a variety of problems and text types coming from our work with medical sciences, psychology, economics, and the energy industry. We will also show how linguistic patterns within or between texts (e.g. those that differ demographically or diachronically) can be explored, particularly through the use of new visualisation techniques. The workshop will conclude with a showcase of next-generation textual analysis tools that have been developed at Concept Analytics Lab.This will be a practical session, enabling attendees to develop hands-on experience with using corpus analysis tools. The course will consist of six hours of training over the course of one day [9.30am - 5pm] and will be delivered online. The course covers: How to extract meaning from large textual dataHow to build a corpus using textual data How to engage with existing corpora, such as multi-billion word corpora scraped from the webHow to use corpus methods for bottom-up and top-down researchTechniques for the visualisation of unstructured language dataAn introduction to discourse analysis and its application to corpora (corpus-assisted discourse analysis)By the end of the course participants will:Know how to engage a suite of mixed-method corpus linguistic tools to extract meaning from a corpusBe able to use corpora to answer a variety of research questionsBe able to build their own corporaConduct comparative corpus analysis (e.g. between texts that differ demographically or diachronically)Programme:9:30: Welcome and introduction to corpus linguistics10:00: Interrogating existing corpora - quantitative analysis12:00: Lunch13:00: Interrogating existing corpora - qualitative analysis15:00: Break15:15: Building your own corpus16:15: The Concept Cruncher: The next generation of text analysis16:45: Final remarksSpeakers:Dr Justyna Robinson is a Director of Concept Analytics Lab at the University of Sussex. She researches meaning in language and is interested in methods of analysing meaning empirically. Her publications focus on ways of researching meaning from historical perspectives (2012), from cognitive angles (2014), using socio-demographic information and other text metadata (2012, 2022), using corpus and statistical methods (2014, 2022). She researches meaning represented by words (2010), concepts and themes (2017, 2023). With the research team at Concept Analytics Lab, she delivered a range of projects investigating current meanings of loneliness, aging, UK trade deals post Brexit, political manifestos, recycling practises, or post-covid behaviour changes. Dr Rhys Sandow is a Senior Research Associate at Concept Analytics Lab, University of Sussex. He specialises in applying corpus methods to answer applied research questions, such as in collaborative work with economists, psychologists, historians, and medical humanities researchers, as well as organisations in the private sector. He also specialises in sociolinguistic variation and change, including its intersection with corpus linguistics, where he has worked as an expert witness in a legal context. He has published academic articles and book chapters on corpus linguistics and sociolinguistics and has a forthcoming co-edited book on Sociolinguistic Approaches to Lexical Variation in English to be published by Routledge. p.c.white@southampton.ac.uk (University of Southampton) Thu, 19 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14739 https://www.ncrm.ac.uk/training/show.php?article=14669 Overview of 1-day courseChoosing an appropriate sample size is a common problem and should be given due consideration in any research proposal, as an inadequate sample size invariably leads to wasted resources. Hence objective sample size determination is increasingly requested by regulatory authorities in a number of disciplines. This course aims to give a practical introduction to sample size determination, or power calculations, in the context of some commonly used hypothesis tests. Examples from a scientific background will be used to highlight the problems associated with sample size determination, and suggest potential solutions. Practical work will be based around the free statistical software R; see https://www.r-project.org/. Formulae and algebraic notation will be kept to a minimum.PresentersSandro Leidi and James Gallagher Cost£282 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 17:30 (GMT+1). 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?Scientists and related who need to address the problem of sample size determination or power calculations in planning a study. Participants will be assumed to have a working knowledge of sampling distributions, confidence intervals and hypothesis tests for both means and proportions.  No previous experience of the R software is required.How You Will BenefitThis course will give you a sound introduction to sample size determination, and be able to conduct common power calculations using the free R software. What Do We Cover?Concepts of significance and power in relation to hypothesis testsSample size determination (power calculations) with one sample, two samples and paired samples for comparing means with a t-testSample size determination (power calculations) with one sample, two samples and paired samples for comparing proportions with a z-testPractical problems associated with sample size determination and possible solutionsOther issues such as the role of confidence intervals and why it can be difficult to determine sample size.The course will make use of our R functions which will be made freely available.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. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14669 https://www.ncrm.ac.uk/training/show.php?article=14387 Level: Intermediate (I)This course will be delivered over 4 morning sessions, running from 9:30am to 12:30pm each day. This course is ideal for beginners or intermediate users of Bayesian modelling, who want to learn how to use Stan software within R (the material we cover can easily be applied to other Stan interfaces, such as Python or Julia). We will learn about constructing a Bayesian model in a flexible and transparent way, and the benefits of using a probabilistic programming language for this. The language in question, Stan, provides the fastest and most stable algorithms available today for fitting your model to your data. Participants will get lots of hands-on practice with real-life data, and lots of discussion time. We will also look at ways of validating, critiquing and improving your models. Learning OutcomeUse Stan to fit various models to dataCheck outputs for computational problems, and know what to do to fix themCompare and critique competing modelsJustify their modelling choices, including prior probability distributionsUnderstand what Stan can and cannot do Topics CoveredA quick overview of Bayesian analysisSimulation is useful for statistical inferenceWhat is a probabilistic programming language?Parts of a Stan modelUnivariate models; exploring priors and likelihoodsPrior predictive checkingBivariate regression modelsPredictions and posterior predictive checkingHierarchical modelsLatent variable models including item-response theoryWorking with missing and coarse dataGaussian processesLimitations of Stan Target AudienceAnyone with some statistics training who is aware of the advantages of Bayesian modelling could benefit from attending. Fields where this may be most popular are: insurance, political pollsters, finance, marketing, healthcare, education research, psychology, econometrics.Assumed KnowledgeAttendees should be comfortable with using R, Python, Julia or Stata. They should understand probability distributions and basic regression models, though this can be intuitive and doesn’t have to be mathematically rigorous. They do not need to have used Stan before. 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=14387 https://www.ncrm.ac.uk/training/show.php?article=14665 Overview of 2-day courseIn a repeated measures experiment a response variable is repeatedly measured for each subject or unit over time under the same treatment. These observations are likely to be correlated over time, rendering conventional linear model methods either inappropriate for analysis or of limited use. Linear mixed models are commonly used to analyse repeated measurements, or longitudinal data, which are normally distributed. In this course we begin with a brief overview of repeated measures before moving onto the random coefficient model formulation of a linear mixed model (also known as subject-specific models). For the remainder of the course we focus on applying marginal models, sometimes known as covariance pattern models. Marginal models are particular useful for situations where the primary interest lies in studying mean trend through fixed effects, with variation in correlated errors about the trend treated as a nuisance.The course will emphasise the practicalities associated with choosing, fitting and interpreting linear mixed models in the context of analysing repeated measures. Examples will be drawn from medical and health related applications.Practical work will be based on the R software; see https://www.r-project.org/. Relevant models will be fitted using the CRAN packages lmerTest and mmrm.PresentersSandro Leidi and James Gallagher Cost£582 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 10:00 and 16:30 (GMT+1). 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 the analysis of repeated measures using linear mixed models. It is assumed that participants are R users and have some familiarity with the practical use of linear mixed models in general.  No prior knowledge of analysing repeated measures is assumed.How You Will BenefitThe course will give you the skills to use linear mixed models to analyse normally distributed repeated measurement data. You will also appreciate the distinction between random coefficient (subject-specific) models and marginal models, and their advantages and disadvantages.What Do We Cover?Overview of repeated measuresRandom coefficient models; lmerTest CRAN packageMarginal models and covariance structuresFitting marginal models using the mmrm CRAN packageRandom coefficient models versus marginal modelsModel selection for marginal modelsInferential methods; Kenward-Roger for fixed effects and likelihood ratio testing and AIC for covariance structuresModel checking for marginal modelsFurther complexities associated with the analysis of repeated measures, e.g. relationship between random coefficient and marginal formulations of the mixed model, negatively correlated repeated measures data, convergence issues.The course does not cover GEE type models.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=14665 https://www.ncrm.ac.uk/training/show.php?article=14764 This online course, taught over four mornings, aims to teach participants how to conduct qualitative field research, particularly participant observation and ordinary language interviewing. The course provides an understanding of the distinctiveness of ethnographic fieldwork compared to other data collection methods. By the end of the course, students should be able to understand how to conduct ethnography rigorously and the skills needed to produce high-quality ethnographic research. Students will be able to practice data collection methods associated with ethnography, such as participant observation, field notes, and ordinary language interviews. Finally, the course will discuss how to use fieldwork data to produce new and general theoretical insights.The course covers:Introduction to EthnographyOrdinary Language InterviewParticipant ObservationDigital EthnographyTheory building with qualitative dataBy the end of the course participants will:Explain the distinctive features of ethnographic fieldwork, particularly how participant observation and ordinary language interviewing differ from other qualitative research methods.Apply core ethnographic methods such as participant observation, field notes, digital ethnography, and interviews in their own research projectsCritically assess the methodological and ethical considerations involved in designing and conducting ethnographic research.Analyse fieldwork data to generate theoretical insightsTarget AudiencePostgraduate students (Master’s and PhD) in political science, sociology, anthropology, international relations, cultural studies, linguistics, arts, geography, archaeology, anthropology, and development studies, and related fields who are interested in incorporating ethnographic methods into their research;Early-career researchers and practitioners studying political or social dynamics who wish to strengthen their qualitative fieldwork skills—especially in participant observation and interviewing;Students planning or currently conducting fieldwork, particularly those working on topics like political parties, social movements, state institutions, or the everyday practices of politics.Preparatory ReadingBorges Martins da Silva, Mariana, 2025. "Notes from the Classroom: Lessons and Best Practices for Teaching Digital Ethnography", Qualitative and Multi-Method Research.Schatz, Edward. 2009. “Ethnography Immersion and the Study of Politics.” In Political Ethnography: What Immersion Contributes to the Study of Power. University of Chicago Press.Hammersley, G., M. Hammersley, and P. Atkinson. 1995. Ethnography: Principles in Practice. Research Methods, Sociological Theory, Ethnography. Routledge. (Chapter 1)Jerolmack, Colin, and Shamus Khan. 2014. ‘Talk Is Cheap: Ethnography and the Attitudinal Fallacy’. Sociological Methods & Research 43 (2): 178–209.Schaffer, F.C. 2014. Elucidating Social Science Concepts: An Interpretivist Guide. Routledge Series on Interpretive Methods. Routledge. (Chapter 1, 2)Schaffer, Frederic Charles. 2006. ‘Ordinary Language Interviewing’. In Interpretation and Method: Empirical Research Methods and the Interpretive Turn, edited by Dvora Yanow and Peregrine Schwartz-Shea, 150–60. Armonk, London: M.E. Sharpe.Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up. Chicago Guides to Writing, Editing, and Publishing. Chicago ; London: The University of Chicago Press. (chapter 4 and 5)Walt, Kathleen M., and Billie R. DeWalt. 2011. Participant Observation: A Guide for Fieldworkers. Rowman Altamira. (chapter 2-5)Emerson, R.M., R.I. Fretz, and L.L. Shaw. 2011. Writing Ethnographic Fieldnotes, Second Edition. Chicago Guides to Writing, Editing, and Publishing. University of Chicago Press. (Chapters 1-3)Fujii, Lee Ann. 2012. “Research Ethics 101: Dilemmas and Responsibilities.” PS: Political Science & Politics 45 (4): 717–23. https://doi.org/10.1017/S1049096512000819.Fu, Diana. 2017. “Disguised Collective Action in China.” Comparative Political Studies 50 (4): 499–527. (Please also read the methodological appendix)Borges Martins da Silva, Mariana. 2023. “Weapons of Clients: Why Do Voters Support Bad       Patrons? Ethnographic Evidence from Rural Brazil.” Latin American Politics and Society 65 (1): 22–46.Timmermans, Stefan, and Iddo Tavory. 2012. ‘Theory Construction in Qualitative Research: From Grounded Theory to Abductive Analysis’. Sociological Theory 30 (3): 167–86There are no prerequisites. The course is designed to be accessible to those new to ethnographic research, though some familiarity with qualitative methods may enhance your experience.PLEASE NOTE THIS COURSE EQUATES TO 1.5 DAYS FOR PAYMENT PURPOSES.Programme2 October – 10AM-12PMIntroduction to Ethnography and Ordinary Language Interview9 October - 10AM-12PMParticipant Observation16 October 10AM-12PMWriting Fieldnotes; Digital Ethnography23 October - 10AM-12PMConstructing Theory with Ethnographic Data   jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 11 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14764 https://www.ncrm.ac.uk/training/show.php?article=14395 Level: Professional (P)Standard methods of survival analysis based on the Kaplan-Meier estimate of a survivor function, the log rank test and Cox regression modelling are widely used in many different areas of application.   But often, the assumptions that underlie these techniques may not be valid, or the data structure may be more complex.  Extensions of these basic methods allow particular features of data that occur in practice to be handled appropriately.  This course will begin with an overview of standard methods and then move on to some of the more advanced techniques.   Their practical application will be illustrated using the R software, with an emphasis on interpreting output rather than on writing R code.  The course will consist of a series of presentations and practical sessions. Learning OutcomesAn appreciation of how the methods of survival analysis can be used in a variety of situations.Topics CoveredOverview of standard methods for summarising survival data and the Cox regression model.  Types of censoring in survival data, including interval and dependent censoring.  Time dependent variables and the counting process formulation of survival data.  Parametric models for survival data, including flexible models based on splines.  Incorporating random effects into a survival analysis; frailty models.  Analysis of data where there is more than one type of event; models for competing risks.  Detecting and handling non proportional hazards. Target AudienceStatisticians and epidemiologists in public sector research organisations, pharmaceutical companies and related organisations.  University research students and fellows. Assumed KnowledgeSome familiarity with basic methods for summarising survival data, including estimates of the survivor function and the log rank test.  Some experience in using the Cox regression model would be advantageous.  While knowledge of the R software is not essential, participants generally find it useful to be able to undertake the practical work using R on their laptop.   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=14395 https://www.ncrm.ac.uk/training/show.php?article=14666 Overview of 2-day courseBayesian statistics have become very popular in recent years. Modern software has made this possible and Bayesian methods are now applied in a wide range of scientific application areas from medicine to ecology. This course provides an introduction to the motivation, methods and applications of Bayesian statistics. The analysis tool is R (https://www.r-project.org/); prior knowledge of this software is assumed.The course is a mixture of presentations and hands-on computer exercises. It begins with an overview of the rationale and methodology underpinning Bayesian analysis, and the Markov chain Monte Carlo (MCMC) computational tools behind the methodology are outlined. An introduction to the JAGS engine within the R software is then provided, followed by data analysis applications, including linear models and generalised linear models. The advantages of Bayesian approach applied to the latter are emphasised and considered in detail. For example, the question “What is the chance that method A better than method B?” can be easily addressed in a Bayesian framework, but not in classical statistics.The emphasis in this course is on practical data analysis, although the essential theory will be outlined. Examples are drawn from a range of scientific disciplines.PresentersSandro 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+1). 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 who want an introduction to Bayesian methods for statistical analysis. No prior knowledge of Bayesian statistics is required. Participants are expected to have:An A-level mathematics qualification or equivalent, including knowledge of probability density functions and probability mass functions for describing distributionsA working knowledge of linear models and generalised linear modelsA working knowledge of the R statistics software.How You Will BenefitBy the end of the course you will have a firm understanding of Bayesian methods and their flexibility. You will also have acquired a working knowledge of specialised software for Bayesian data analysis and will be able to fit and interpret linear and generalised linear models in a Bayesian framework. You will also appreciate the practical benefits of Bayesian methods.What Do We Cover?Bayesian versus classical frequentist statisticsLikelihood, prior and posterior distributions and the use of Bayes' theoremBayesian analysis of single-parameter models and multi-parameter modelsConjugate, vague and informative priorsSimulation of posterior distributions; posterior summariesMarkov chain Monte Carlo (MCMC) methods and MCMC diagnosticsLinear models, generalised linear models and model selectionQuestions that classical statistics find difficult to answer or cannot answerUse of the JAGS software via R and the CRAN packages rjags, runjags and coda.SoftwarePractical work will be done in R.Note: For practical work, participants must download and install (i) the JAGS software and (ii) 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=14666 https://www.ncrm.ac.uk/training/show.php?article=14396 Level: Professional (P)This course introduces the Bayesian approach to meta-analysis. Attendees will learn practical ways in which they can combine multiple sources of published evidence while accounting for uncertainties such as response bias, publication bias, confounding, and missing information, using either BUGS, JAGS or Stan as software. With Bayesian models, this can be transparent and reproducible.This two-day course begins by reviewing classic meta-analysis methods and expressing them as statistical models. Once attendees understand meta-analysis in this larger context, they are able to extend the model flexibly to account for common problems such as papers that report only change from baseline. A series of problems will be tackled in this course, and attendees will leave with model code that they can immediately start using with their own projects. Learning OutcomesAfter attending, participants will be able to:Write out standard meta-analyses as statistical modelsUse BUGS, JAGS or Stan to fit such models to dataRecognise several common problems in meta-analysisExtend these models to account for these problemsUnderstand and communicate their findings Topics CoveredDay 1:A review of statistical models of meta-analysis​Introduction to Bayesian analysis problems in meta-analysis, and sources of uncertaintyModels for basic DerSimonian-Laird and Biggerstaff-Tweedie meta-analysesIntroduction to Bayesian software options: BUGS, JAGS and StanDay 2:Models for network meta-analysisModels for missing statisticsModels for reporting biasModels for publication biasModels for a mixture of statisticsModels for a mixture of study typesReporting Bayesian meta-analyses Target AudienceThis course will be of interest to evidence-based healthcare researchers, including those writing guidelines and evaluating policies. Attendees should be comfortable conducting simple meta-analyses in some software but do not have to have experience of Bayesian methods. Assumed KnowledgeThis course assumes that all participants have a basic grounding in Bayesian statistics, to the level covered by the RSS courses "Introduction to Bayesian Statistics" or "Introduction to Bayesian Analysis using Stan". There is no specific software expertise required, but examples will be written in BUGS and Stan, using R as an interface. We also assume that participants are familiar with the principles of systematic reviews, for example from reading relevant parts of the Cochrane Collaboration Handbook online. 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=14396 https://www.ncrm.ac.uk/training/show.php?article=14410 Level: Intermediate (I)This course will be delivered over 4 afternoon sessions, running from 1:00pm to 5:00pm each day.The intensive course on programming principles in R. This course covers the fundamental techniques such as functions, for loops and conditional expressions. It also covers the {tidyverse} package, {purrr}. {purrr} is a very powerful package that gives great flexibility to analysts, by enhancing R’s functional programming toolkit. By the end of this course, you will understand what these techniques are and when to use them. The course will also demonstrate how to use functions such as map(), map2() and pmap(), to iteratively map functions over multi-element objects like vectors and lists. Emphasis will also be placed on how to manipulate list outputs and how this can be applied to data..Learning OutcomesBy the end of the course, delegates will: Understand basic functions, multiple arguments and variable scopes.Have a thorough understanding for loops.Be able to apply basic functions.Have a thorough understanding of conditionals such as if, else and else if statements.Be familiar with possible R workflows such as directory structure and working with directories.Understand how the aforementioned techniques can be applied to their own data.Understand how these techniques will improve efficiency and results.Understand where to find help in R using resources and the help() function.Understand lists in R and know how to use {purrr} to map functions.Know what nested loops are and use {magrittr} to extract elements from them.Be able to create list columns and know how to access the data in them.Iteratively loop two or more objects to a function of choice using functions such as map2(), pmap() and imap().Recognize the advantages of using {purrr}.Understand how to extract elements from nested lists to achieve a desired output object class.Be able to effectively debug their code using multiple {purrr} functions for the debugging process.Save precious debugging time using e.g. safely() Topics CoveredConditionals: using if and else statements in RFunctions: what a function is, how are they used, and how can we construct our own functions.Looping in R: an introduction to the concept of looping in R. In particular for and while loops.Help: The help system in R can at first glance appear daunting, however, after the initial shock, R’s documentation is second to none.Project structure: Practical tips on how to structure a project.Data manipulation and aggregation using dplyrIntroduction to {purrr} and Lists: Introduction to lists in R and using {purrr} to map a function across a list.List-Columns and Nesting: Exploring nested data in list columns and using the mapping functions to manipulate them.Parallel Mapping: Using {purrr} functions to map over multiple lists in parallel.Manipulating {purrr} Output: Using {purrr} to efficiently extract elements from lists into vector and dataframe format, and change the hierarchy within nested lists.Best Practices in {purrr}: Showcase of functions from {purrr} which aid in the debugging process.  Target AudienceThis course is idea for anyone who would like to extend their basic familiarity with using R, and using R to write their own bespoke functions or optimizing their code. Assumed KnowledgeBasic prior experience with the R programming language is assumed. Namely that participants have some experience of R data structures, such as vectors, data frames, and experience in using pre-made functions from R packages.The course is aimed as a follow up the 'Introduction to R and Regression Modelling in R' training course.Whilst no statistical knowledge will be assumed, some of the examples will be statistical in nature.For 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=14410 https://www.ncrm.ac.uk/training/show.php?article=14791 Overview of 1-day courseIn survival analysis in medical research, the proportional hazards assumption and the hazard ratio effect measure have been popular for decades, fuelled by extensive application of the log-rank test and the Cox regression model.  However, the hazard ratio can be clinically awkward to interpret, or the proportional hazards assumption may not hold, rendering the use of a hazard ratio effect measure questionable at best.In this course we introduce the restricted mean survival time (RMST), which is a well established, but under-used summary of the survival experience.  In recent years there has been a surge of interest in the RMST, particularly in oncology, but also in many other areas. We begin with a review of the definitions of the RMST, approaches to estimation and different RMST-based effect measures which are clinically meaningful alternatives to the hazard ratio and are not based on a proportional hazards assumption.For the practical analysis of survival data, which includes right-censoring, the course focuses on a non-parametric analysis for comparing treatments and a generalised linear model (GLM) -type modelling approach based on the use of pseudo-values.  The latter provides a flexible method for directly modelling the RMST in a regression framework, where a treatment effect may be adjusted for covariates.  Model checking is also considered. The course concludes with a brief consideration of an extension to the RMST known as the window mean survival time (WMST) or the long-term RMST (LT-RMST).The course is a practical introduction to analysing survival data using a RMST-based effect measure.  Only essential theoretical aspects of the methodology will be summarised.  Examples used will be drawn from applications in medicine and health, particularly clinical trials.Practical work will be based around the statistical software R; see https://www.r-project.org/.PresentersSandro Leidi and James GallagherCost£312 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 17:30 (GMT). Delivery platform: 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?Statisticians and data analysts working with survival data in medical research. Participants will be assumed to have a working knowledge of:Survival analysis techniques applicable to right censoringRegression modellingThe R statistics software.How You Will BenefitYou will acquire practical experience in the use of RMST-based effect measures as an alternative to the hazard ratio.  You will also be able carry out adjusted as well as unadjusted analyses.What Do We Cover?Problems with proportional hazards and hazard ratio effect measure.  Introduction to the RMST: definition, approaches to estimation, RMST-based effect measures as an alternative to the hazard ratio.  The restricted mean time lost (RMTL)Non-parametric analysis for comparing two groups. Statistical inference: estimation, confidence intervals and hypothesis testing for different effect measures. Advantage of RMST-based effect measures over the hazard ratioAdjusting a treatment effect for covariates. Modelling the RMST using a GLM-type model based on pseudo-values; choice of link functions and effect measures; model fitting and comparison of modelsModel checking and the use of pseudo-residualsExtending the RMST: window mean survival time (WMST)CRAN packages, including geepack for modelling, emmeans for post-processing and survRM2 for non-parametric analysis.The course does not cover time dependent covariates.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, 30 Mar 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14791 https://www.ncrm.ac.uk/training/show.php?article=14668 Overview of 2-day courseAre you working in Learning Analytics or Student Analytics?Ever been asked if the average mark is changing significantly over academic years, or if the difference between the rate of change for females and males is statistically significant? Or which factors are associated with non-continuation?Or which factors are associated with accepting an offer?Or if the chance of achieving a first class honours degree is associated with tariff points on entry? This two-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. The focus of the course is on the use of two statistical modelling techniques:Linear regressionLogistic regressionLinear regression is used to examine how the mean of a numerical outcome, like final year mark, might be associated with different characteristics. If the outcome is binary, such as drop-out, logistic regression is used to investigate how the chance of failing to continue to the second year is associated with different characteristics.  Logistic regression is a popular modelling technique, for example it is advocated by the Office for Students in their Financial support evaluation toolkit.The course also illustrates how these modelling techniques may be used for one-step-ahead forecasting into next year.Presentations, demonstrations and hands-on computer practicals are based around the free statistical software R; see https://www.r-project.org/. Formulae are kept to a minimum; instead, we concentrate on results, their interpretation and reporting in plain language.PresentersSandro Leidi and James Gallagher Cost£516 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 10:00 and 16:30 (GMT+1). 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?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. Anyone in these positions needing to answer questions around how student outcomes may be associated with different factors will benefit greatly from this course.It is assumed that participants will, prior to the course, have:An understanding of mathematical functions and equations. In particular, the natural logarithmic and exponential functions (loge() and exp() respectively), the equation of a straight line and its geometrical representationAttended the one-day course Statistics for University Administrators, or Statistics for University Administrators using R, or have equivalent knowledge.No previous experience of the R software is required; a brief introduction for the purpose of the course will be given.How You Will BenefitBy the end of the course you will be familiar with two common statistical modelling methods for investigating associations and extracting actionable insights, be able to report the results in plain language, and be able to perform analyses using free statistical software. You will also be able to follow official guidance on the use of such models, e.g. the Office for Students’ guidance on the use of binary logistic regression for investigating the effectiveness of financial support with respect to student outcomes.What Do We Cover?Introduction to the R software·Simple linear regression for relating a numerical outcome to a numerical explanatory variableExtending the linear regression model to incorporate categorical explanatory variables and interactions to allow for effect modificationUsing binary logistic regression in place of linear regression when modelling binary outcomesOne-step-ahead forecasting.SoftwarePractical work will be done in R.Note:For practical work, participants must download and install the R software prior to the start of the coursePractical work is based on the Windows operating system.Extra InformationThe R software is used on the course for two reasons: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) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14668 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, 22 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14692 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=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=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=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=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=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=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=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