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/ Sat, 18 Apr 2026 05:31:47 +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=14724 Data management is essential to make sure that well-organised, well-documented, high quality and shareable research data can be produced from our research projects. A key data management component is considering the ethical and legal issues of collecting, using and/or sharing data.Researchers obtaining or using data obtained from people are expected to maintain high ethical standards and comply with the relevant legislation. Much research data - even sensitive data - can be shared ethically and legally. This session will focus on key ethical and legal guidelines researchers should consider when working with data collected from human participants.During this free 90-minute online workshop, participants will learn about the relevant data protection legislation, such as the Data Protection Act and the UK General Data Protection Regulation (UK GDPR). Participants will also learn about strategies that enable ethical and legal data sharing. These include obtaining informed consent, treating the data for example by anonymisation, and licensing data and controlling access.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 research ethics and understanding of data privacy concepts recommended but not required.Target audience: Academic and non-academic data professionals such as researchers, academic staff, ethics committee members, data managers, data stewards and others keen on navigating the ethical and legal frameworks of 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) Tue, 10 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14724 https://www.ncrm.ac.uk/training/show.php?article=14386 Level: Intermediate (I)This two-day 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=14386 https://www.ncrm.ac.uk/training/show.php?article=14389 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=14389 https://www.ncrm.ac.uk/training/show.php?article=14650 Overview of 1-day CourseAre you working in Learning Analytics or Student Analytics?Ever been asked for the margin of error on a percentage?Or if the difference between your percentage and a benchmark is statistically significant?Or if the change in student retention rate from last year is statistically significant?This one-day course provides participants with hands-on experience of analysing their own type of records for data-driven planning and confidently interpreting numerical results for reports to policy makers and committees, or for returns to the Higher Education Statistics Agency.Presentations, demonstrations and hands-on computer practicals will make use of the free statistical software R; see https://www.r-project.org/.  Note the course does not focus on teaching R, but on the use of supplied R functions to perform statistical analyses of interest.  Formulae are kept to a minimum; instead, we concentrate on results, their interpretation and reporting in plain language.PresentersSandro Leidi and James GallagherCost£282 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 17:30 (GMT+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?Administrators in educational establishments working in Policy, Planning and Strategy units; Data and Insight units; Business Intelligence units; those involved in learning analytics or extracting actionable insights from student records and in reporting to policy makers or committees; those involved in producing returns to the HESA.  Anyone in these positions will benefit greatly from this course.No previous experience of the R software is required.How You Will BenefitBy the end of the course you will be familiar with basic statistical methods for making sound assessments, and extracting actionable insights.  What Do We Cover?Estimation: standard errors and confidence intervals for percentagesComparing a percentage to a benchmark: one-sample z-testComparing two percentages: two-sample z-testComparing more than two percentages: chi-square test.The course will make use of our R functions which will be made freely available. Note the course does not focus on teaching R, but on the use of the supplied R functions to perform statistical analyses covering the above syllabus.SoftwarePractical work will be done in R.Note:For practical work, participants must download and install the R software.  This must be done prior to the start of the coursePractical work is based on the Windows operating system.Extra InformationThe content of this course is identical to that of Statistics for University Administrators. The only difference is the R statistical software is used instead of Excel.  R has two advantages:It is a free dedicated statistics package and can be used for other analysesIt is a widely used software which will be maintained by the R Foundation for many years to come. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Tue, 06 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14650 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=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=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=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=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=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=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=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=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=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=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=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=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=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=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=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=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=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=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=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=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=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=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=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=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