NCRM is a Hub-Node network of research groups, each conducting research and training in an area of social science research methods, coordinated by the Hub at the University of Southampton. https://www.ncrm.ac.uk/training/ Wed, 24 Jun 2026 14:05:32 +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=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=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) Mon, 22 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14796 https://www.ncrm.ac.uk/training/show.php?article=14826 Feeling overwhelmed by the command line? Confused by file pathways? Want to navigate the world of coding with confidence? Join the Code Anxiety Club!Viewers can follow along as we work through common beginner topics while coding live for a quick half hour. No prior experience, installed software or setup required. Viewers can interact via the YouTube chat (you must have a YouTube account to comment) and we will try our best to answer your questions and comments.There is no need to book a place, please follow the livestream link to join the session.Notebooks vs. IDEs7 July, 13:30-14:00Content:Understand the difference between downloading an IDE and working in a notebook environment.Get to grips with tools including: Google Colab, Positron, and Jupyter notebooks.Learn how to choose the right coding environment based on your output type, collaboration needs, pricing, and speed.To join this session, please follow the link to our livestream - 7 July 2026. bethan.jones-4@manchester.ac.uk (UKDS) Thu, 30 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14826 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=14827 Feeling overwhelmed by the command line? Confused by file pathways? Want to navigate the world of coding with confidence? Join the Code Anxiety Club!Viewers can follow along as we work through common beginner topics while coding live for a quick half hour. No prior experience, installed software or setup required. Viewers can interact via the YouTube chat (you must have a YouTube account to comment) and we will try our best to answer your questions and comments.There is no need to book a place, please follow the livestream link to join the session.SQL: Tables, databases and the shape of your data 21 July, 13:30-14:00Content:Explore the philosophy of data and how the way you store it shapes how you use it.Understand the difference between wide and long data formats and when each makes sense for your work.Learn the difference between personal and collaborative storage approaches.Follow a live demonstration of SQL and table formats to see the difference between displaying answers and storing data.To join this session, please follow the link to our livestream - 21 July 2026. bethan.jones-4@manchester.ac.uk (UKDS) Thu, 30 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14827 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=14878 A free online three-day programme of statistics training running from 28–30 July 2026. Hosted by Southampton Education School, the Summer School is designed for professionals, academics and doctoral/postgraduate researchers working with numeric data, across disciplines. Participants can attend one, some or all sessions, with each session combining conceptual introduction with practical, hands-on activity.The programme includes sessions on Structural Equation Modelling, Assessment data, Social Network Analysis, Multilevel Modelling, Machine Learning, and Causal Inference. The Summer School is intended as accessible CPD for those looking to strengthen or refresh their statistical analysis skills in a supportive online format. Registration is free via Eventbrite: https://www.eventbrite.com/e/southampton-summer-statistics-school-2026-tickets-1990997448966  J.E.Hall@Soton.ac.uk (University of Southampton) Fri, 19 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14878 https://www.ncrm.ac.uk/training/show.php?article=14879 An Introduction to Structural Equation Modelling.Session 1/6 of a free online three-day programme of statistics training running from 28–30 July 2026.   Running 9-11am on 28/7/2026Hosted by Southampton Education School, the Southampton Summer Statistics School is designed for professionals, academics and doctoral/postgraduate researchers working with numeric data, across disciplines. Participants can attend one, some or all sessions, with each session combining conceptual introduction with practical, hands-on activity.The programme includes sessions on Structural Equation Modelling, Assessment data, Social Network Analysis, Multilevel Modelling, Machine Learning, and Causal Inference. The Summer School is intended as accessible CPD for those looking to strengthen or refresh their statistical analysis skills in a supportive online format. Registration is free via Eventbrite: https://www.eventbrite.com/e/southampton-summer-statistics-school-2026-tickets-1990997448966 J.E.Hall@Soton.ac.uk (University of Southampton) Fri, 19 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14879 https://www.ncrm.ac.uk/training/show.php?article=14882 An Introduction to Multilevel ModellingSession 4/6 of a free online three-day programme of statistics training running from 28–30 July 2026.   Running 12-2pm on 29/7/2026Hosted by Southampton Education School, the Southampton Summer Statistics School is designed for professionals, academics and doctoral/postgraduate researchers working with numeric data, across disciplines. Participants can attend one, some or all sessions, with each session combining conceptual introduction with practical, hands-on activity.The programme includes sessions on Structural Equation Modelling, Assessment data, Social Network Analysis, Multilevel Modelling, Machine Learning, and Causal Inference. The Summer School is intended as accessible CPD for those looking to strengthen or refresh their statistical analysis skills in a supportive online format. Registration is free via Eventbrite: https://www.eventbrite.com/e/southampton-summer-statistics-school-2026-tickets-1990997448966 J.E.Hall@Soton.ac.uk (University of Southampton) Fri, 19 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14882 https://www.ncrm.ac.uk/training/show.php?article=14883 An Introduction to Machine LearningSession 5/6 of a free online three-day programme of statistics training running from 28–30 July 2026.   Running 9-11am on 30/7/2026Hosted by Southampton Education School, the Southampton Summer Statistics School is designed for professionals, academics and doctoral/postgraduate researchers working with numeric data, across disciplines. Participants can attend one, some or all sessions, with each session combining conceptual introduction with practical, hands-on activity.The programme includes sessions on Structural Equation Modelling, Assessment data, Social Network Analysis, Multilevel Modelling, Machine Learning, and Causal Inference. The Summer School is intended as accessible CPD for those looking to strengthen or refresh their statistical analysis skills in a supportive online format. Registration is free via Eventbrite: https://www.eventbrite.com/e/southampton-summer-statistics-school-2026-tickets-1990997448966 J.E.Hall@Soton.ac.uk (University of Southampton) Fri, 19 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14883 https://www.ncrm.ac.uk/training/show.php?article=14884 An Introduction to Modelling Causal InferenceSession 6/6 of a free online three-day programme of statistics training running from 28–30 July 2026.   Running 12-2pm on 30/7/2026Hosted by Southampton Education School, the Southampton Summer Statistics School is designed for professionals, academics and doctoral/postgraduate researchers working with numeric data, across disciplines. Participants can attend one, some or all sessions, with each session combining conceptual introduction with practical, hands-on activity.The programme includes sessions on Structural Equation Modelling, Assessment data, Social Network Analysis, Multilevel Modelling, Machine Learning, and Causal Inference. The Summer School is intended as accessible CPD for those looking to strengthen or refresh their statistical analysis skills in a supportive online format. Registration is free via Eventbrite: https://www.eventbrite.com/e/southampton-summer-statistics-school-2026-tickets-1990997448966 J.E.Hall@Soton.ac.uk (University of Southampton) Fri, 19 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14884 https://www.ncrm.ac.uk/training/show.php?article=14828 Feeling overwhelmed by the command line? Confused by file pathways? Want to navigate the world of coding with confidence? Join the Code Anxiety Club!Viewers can follow along as we work through common beginner topics while coding live for a quick half hour. No prior experience, installed software or setup required. Viewers can interact via the YouTube chat (you must have a YouTube account to comment) and we will try our best to answer your questions and comments.There is no need to book a place, please follow the livestream link to join the session.Git structure4 August, 13:30-14:00Content:Understanding the root level and top-level organisation of a Git repository.Get to grips with configuration files and why they matter.Learn why consistent naming conventions for files and folders are important.Understand the difference between versions and tagged versions, and when to use each.To join this session, please follow the link to our livestream - 4 August 2026.Presenters: Jools Kasmire, Louise Capener, Nadia Kennar and Sarwat Qureshi with interactive moderation to ensure your questions are answered in real-time. bethan.jones-4@manchester.ac.uk (UKDS) Thu, 30 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14828 https://www.ncrm.ac.uk/training/show.php?article=14821 Course Overview Focusing on hard-to-reach populations, this intensive course introduces applied research methods for conducting high-quality qualitative and quantitative research with marginal, hidden, and excluded groups. Research topics include (but are not limited to): migrants, refugees and displaced populations, children and adolescents, sex workers, homeless populations,  victims of violence, conflict or trafficking, people affected by HIV/AIDS, and drug users, as well as topics proposed by participants. The course is designed to bring together academics (researchers, PhD and master’s students) and practitioners (from NGOs, UN agencies, and government institutions), creating a unique space for experience sharing and methodological cross-fertilisation. Course Objectives Participants will: • Develop practical skills to design and conduct empirical research with hidden and marginal populations • Learn strategies to address challenges such as the lack of sampling frames and difficulties in reaching target groups • Understand concepts of impact, attribution, and contribution, and the political dimensions of research findings • Strengthen capacity to combine qualitative and quantitative methods ethically and effectively Key themes include: • Estimation and sampling techniques • Participatory research approaches • Evidence-based policy vs policy-based evidence • Innovation, crowdsourcing, and the use of technology • Ethical considerations when working with vulnerable populationsCourse Structure This full-time, intensive course is organised into morning and afternoon sessions combining lectures, applied exercises, and practical case work. Participants are encouraged to present past, ongoing, or planned research projects, which will be discussed and used throughout the course. andrea.rossi@gmail.com (Oxford Nuffield College) Tue, 28 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14821 https://www.ncrm.ac.uk/training/show.php?article=14814 A practical introduction to using AI to support coding in research. This course will help researchers understand how to use AI to help them write code effectively and responsibly. This course is designed for researchers with little to no experience coding. The course provides clear, hands-on guidance for using AI to write, debug, and understand code, while addressing key ethical, security, and reliability considerations in research contexts.The course covers: An overview of the AI landscapePractical skills for AI-assisted coding Ethics, reliability and security considerationsLearning Outcomes:AI Landscape Recall key milestones in the historical development of artificial intelligenceDescribe where ChatGPT and similar large language models fit within the broader AI landscape.Explain, at a conceptual level, what generative AI and ChatGPT are.Summarize the primary functions and intended use cases of common AI coding assistants.AI-Assisted CodingExplain why delegating full software development to AI without understanding the solution introduces technical, ethical, and reliability risks.Describe appropriate roles for AI tools as assistants rather than autonomous developers.Use ChatGPT as a reference tool to locate, summarize, and clarify technical information more precisely than traditional search methods.Apply AI tools to explain unfamiliar code to support learning.Use AI-generated suggestions to debug code and resolve errors.Generate boilerplate code using AI assistance.Use AI tools to draft technical documentation.Analyse when AI assistance enhances productivity versus when it may obscure understanding or introduce errors. Ethics, Reliability and Security Considerations Describe common sources of bias, inaccuracy, and unreliability in AI-generated outputs.Explain data privacy, confidentiality, and security risks associated with using AI tools in coding and research contexts.Summarize intellectual property, authorship, and citation considerations related to AI-generated code and text.Analyse the potential long-term consequences of researchers relying on AI tools without developing foundational coding skills.Assess the appropriateness of AI tool usage in specific research or coding scenarios.Develop personal or team-level guidelines for responsible and ethical AI use in coding and data analysis workflows.This course is aimed at Researchers with little to no programming experience who are interested in using AI to help them write code for their research. Setup InstructionsPlease follow the instructions on this web page to download the data and install the required software before attending the workshop: https://southampton-rsg-training.github.io/coding-with-ai/index.html Note: If using a University of Southampton machine follow the instructions under the tab labelled ‘University of Southampton Computers’.  If using a personal machine or a machine from another university, please follow the instructions under the tab labelled ‘Personal Computers’.ProgrammeAn overview of the AI landscapePractical skills for AI-assisted coding Ethics, reliability and security considerationsThis course is taking place on 3rd September 2026 from 13:00 – 16:30. jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 23 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14814 https://www.ncrm.ac.uk/training/show.php?article=14402 Level: Foundation (F)This course will run over 4 afternoon sessions, from 1:00pm to 5:00pm each day.The purpose of this course is to introduce participants to the R environment for statistical computing. The course focuses on entering, working with and visualising data in R, and linear regression modelling in R. Learning OutcomesBy the end of the course, participants will be able to use R to:Have a clear understanding of R/RStudio IDE and its background.Be familiar with navigating the RStudio IDE.Understand the core fundamentals of R.Understand functions and arguments.Be able to create vectors and applying functions.Be exposed to the tibbles and {tidyverse} package.Be able to comfortably import, export, and store data in R.Have a basic introduction to graphics with {ggplot2}.Have a basic understanding of manipulating data manipulation with {dplyr}.Understand logical and relational data partitioning.Have a thorough understanding of popular statistical techniques.Have the skills to make appropriate assumptions about the structure of the data and check the validity of these assumptions in RBe able to fit regression models in R between a response variable and understand how to apply these techniques to their own data using R’s common interface to statistical functions.Be able to cluster data using standard clustering techniques. Topics CoveredIntroduction to R: A brief overview of the background and features of the R statistical programming system.Data entry: A description of how to import data.Data types: A summary of R’s data types.R environment: A description of the R environment including the R working directory, creating/using scripts, saving data and results.R graphics: Creating, editing and storing graphics in R.Summary statistics: Measures of location and spread.Manipulating data in R: Describing how data can be manipulated in R using logical operators.Basic hypothesis testing: Examples include the one-sample t-test, one-sample Wilcoxon signed-rank test, independent two-sample t-test, Mann-Whitney test, two-sample t-test for paired samples, Wilcoxon signed-rank test.ANOVA tables: One-way and two-way tables.Simple and multiple linear regression: Including model diagnostics.Clustering: Hierarchical clustering, k-means.Principal components analysis: Plotting and scaling data. Target AudienceThis course is ideally suited to anyone who:Is familiar with basic statistical methods (e.g. t-tests, boxplots) and who want to implement these methods using RHas used menu-driven statistical software (e.g. SPSS, Minitab) and who want to investigate the flexibility offered by a command line package such as RIs already familiar with basic statistical methods in R and would like to extend their knowledge to regression involving multiple predictor variables, binary, categorical and survival response variablesIs familiar with regression methods in menu-driven software (e.g. SPSS, Minitab) and who wish to migrate to using R for their analyses Assumed KnowledgeThe course requires familiarity with basic statistical methods (e.g. t-tests, box plots) but assumes no previous knowledge of statistical computing. For this online course, participants are not required to have R installed on their own laptops. A virtual environment, which can be accessed through a web browser, will be used to run R and view course materials. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14402 https://www.ncrm.ac.uk/training/show.php?article=14815 This is a hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.  This introduction aims to cover the basics of Deep Learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what next steps to take to improve the model. The course covers: What is deep learning?Classification by a neural network using KerasMonitor the training progressAdvanced layer typesReal world applicationLearning Outcomes:Introduction Define deep learningDescribe how a neural network is build upExplain the operations performed by a single neuronDescribe what a loss function isRecall the sort of problems for which deep learning is a useful toolList some of the available tools for deep learningRecall the steps of a deep learning workflowTest that you have correctly installed the Keras, Seaborn and scikit-learn librariesUse the deep learning workflow to structure the notebookClassification by a neural network using KerasExplore the dataset using pandas and seabornIdentify the inputs and outputs of a deep neural network.Use one-hot encoding to prepare data for classification in KerasDescribe a fully connected layerImplement a fully connected layer with KerasUse Keras to train a small fully connected network on prepared dataInterpret the loss curve of the training processUse a confusion matrix to measure the trained networks’ performance on a test setMonitor the training processExplain the importance of keeping your test set clean, by validating on the validation set instead of the test setUse the data splits to plot the training processExplain how optimization worksDesign a neural network for a regression taskMeasure the performance of your deep neural networkInterpret the training plots to recognize overfittingUse normalization as preparation step for deep learningImplement basic strategies to prevent overfittingAdvanced layer typesUnderstand why convolutional and pooling layers are useful for image dataImplement a convolutional neural network on an image datasetUse a dropout layer to prevent overfittingBe able to tune the hyperparameters of a Keras modelTransfer learningAdapt a state-of-the-art pre-trained network to your own datasetOutlookUnderstand that what we learned in this course can be applied to real-world problemsUse best practices for organising a deep learning projectIdentify next steps to take after this coursePre-requisites:Learners are expected to have the following knowledge:Basic Python programming skills and familiarity with the Pandas package.Basic knowledge on machine learning, including the following concepts: Data cleaning, train & test split, type of problems (regression, classification), overfitting & underfitting, metrics (accuracy, recall, etc.).Setup InstructionsPlease follow the setup instructions here: https://carpentries-lab.github.io/deep-learning-intro/index.html#software-setup Note that software installation can take some time.  Please set up your python environment at least a day in advance of the workshop. If you encounter problems with the installation procedure, ask your workshop organizers via email for assistance so you are ready to go as soon as the workshop begins.ProgrammeWhat is deep learning?Classification by a neural network using KerasMonitor the training progressAdvanced layer typesReal world applicationThis course is taking place on 15-17 September from 09:00 - 17:00.  jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 23 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14815 https://www.ncrm.ac.uk/training/show.php?article=14383 Level: Foundation (F)The course will be delivered over 5 morning sessions, running fro 9:00am to 1:00pm each day. The purpose of this course is to help participants understand some basic statistical concepts and develop a strategy for approaching simple data analysis. The course will introduce basic concepts such as hypothesis testing and confidence interval estimation. It will provide the tools to undertake simple analysis of a dataset and will include some helpful hints and tips for reading and understanding reported statistics.Learning OutcomesBy the end of this course, participants will understand basic approaches to statistical inference, including hypothesis testing and confidence interval estimation. They will be equipped with the skills necessary to undertake simple analysis and to understand some of the basic terms often used to report statistical results. The course will include some calculations by hand to aid understanding. Topics CoveredData Summary; The normal distribution; Confidence intervals; Introduction to hypothesis tests; Analysis of contingency tables – chi-squared test;  T-tests; Non-parametric tests, (Wilcoxon signed rank test, Mann-Whitney U test); Introduction to correlation and regression; Basic presentation of data and results. Target AudienceThis course is aimed at those who have either never undertaken a formal statistics course, or who have studied some statistics in the past but wish to undertake a refresher. It is ideal for statistical novices who have never had any formal training but are starting to encounter statistics in their work and wish to gain some insight.Delegate Feedback"Ellen and Jenny were extremely knowledgeable. They were also approachable and happy to give further explanations when necessary""Excellent course. Hard to fault. I can see why it's popular""This was a fantastic course, the presenters had a very high knowledge but were able to 'dumb it down' for me as I am new to this"  training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14383 https://www.ncrm.ac.uk/training/show.php?article=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=14877 This short course is designed to give participants a practical introduction to ECHILD (Educational and Child Health Insights from Linked Data). ECHILD is a collection of linked, longitudinal administrative datasets covering health, education and children’s social care. More information about the ECHILD, and resources for researchers and the public can be found on the ECHILD website. The course is aimed at both analysts intending to use ECHILD and researchers who want to understand more about how the data can be used for policy relevant research. This course includes a mixture of lectures and practical sessions that will enable participants to put theory into practice.   Day 1 will provide information on the strengths and limitations of the different component datasets of ECHILD, through case studies of the National Pupil Database, Hospital Episode Statistics, Maternity Services Data, Mental Health Services Data, and the Community Services Dataset. Interactive lectures / tutorials will teach participants how to design a research study to answer a specific research question in ECHILD, focusing on the power and complexity of working with linked datasets. We will also discuss how to extract ECHILD data from SQL tables on the ONS Secure Research Service platform and provide an overview of access arrangements. Day 2 will focus on a series of practical sessions (in Stata and R) allowing participants to progress through an exemplar research study using ECHILD, covering phenotyping, developing cohorts, and analysing ECHILD cohort data. The current course builds on our previous in person and online training courses with the inclusion of some updated information reflecting newer developments of ECHILD. ECHILD users who have previously attended this training course and have an interest in learning about emerging ECHILD research and newly available data are encouraged to attend the next ECHILD User Day, which is available for anyone with an interest in ECHILD.The course covers: Overview of the component datasets of ECHILD, with reference to case studies Accessing the data on the ONS SRS Strengths and limitations of ECHILD Development of electronic cohorts in ECHILD Phenotyping in ECHILD Analysing ECHILD data By the end of the course participants will: Understand what data are available in ECHILD Know how to access ECHILD data Understand the strengths and limitations of ECHILD Know how to developing electronic cohorts in ECHILD The course is aimed at academic or government analysts and researchers who would like to know more about ECHILD and how ECHILD could be used in their own research, or who would like to know how it is currently being used to generate policy-relevant research. This is an intermediate level course that requires some prior knowledge of epidemiological research methods and statistical analysis, and familiarity with R or Stata.ECHILD users who have previously attended this training course and have an interest in learning about emerging ECHILD research and newly available data are encouraged to attend the next ECHILD User Day, which is open to anyone with an interest in ECHILD.Although there is no preparatory reading is required, although participants are encouraged to familiarise themselves with the data resources for researchers available on the ECHILD website.Participants should have a basic understanding of epidemiological research methods and statistical analysis. They should be comfortable in using R or Stata. Experience using administrative data is not required but would be an advantage.ProgrammeDay 1: Introduction Case Studies using each component dataset Developing cohorts in ECHILD Getting started with ECHILD data ECHILD best practice and user communityDay 2: Developing e-cohorts Part 1 Phenotyping to define exposures, outcomes and covariates Developing e-cohorts Part 2 Analysing cohort data Developing e-cohorts Part 3 Leveraging longitudinal administrative data for policy evaluation and population researchThis is an in-person course and will take place at Friend's House (Elizabeth Fry Suite) on 28-29 September from 09:30-17:00. jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 17 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14877 https://www.ncrm.ac.uk/training/show.php?article=14831 Date: October 5th through to October 9th, 2026Registration Closes: 23:59 BST, 25th September 2026By: Jakub Bijak and GuestsLocation: Nuffield College, University of OxfordCourse description: This comprehensive, interdisciplinary short course provides foundations in applied Bayesian Statistics with a focus on methods for Population Data Science – a dynamically-growing area of research, connecting statistical rigour with fascinating real-life applications. Led by one of the pioneers of Bayesian thinking for Demographic Research, and illustrated by examples of Bayesian methods for estimation and prediction, the course will help the participants understand the main concepts and methods unlocking the study of uncertain processes. In addition, the participants will be able to appreciate the breadth of possible applications, such as local planning, national policy setting or human rights monitoring. The course is primarily intended for Masters students, doctoral and post-doctoral researchers, and other academics, but it is open to anyone with interest in population topics, who would like to enter into the world of population uncertainty equipped with state-of-the-art tools for dealing with unknowns – known and unknown alike.Objectives: To familiarise the participants with the philosophy and methodology of Bayesian Statistics, and to enable them to use selected software tools and packages to application Bayesian methods in practice.Learning outcomes: By the end of the course, the participants will be able to:1. Understand the philosophical and statistical basics of Bayesian statistics.2. Appreciate the different sources of uncertainty for various applications and the way in which they are reflected in models.3. Specify a Bayesian model for a given problem, estimate it, and assess its quality and sensitivity to various assumptions.4. Carry out an applied piece of Bayesian analysis, by using freely-available dedicated statistical software, and interpret the results.Timetable (Subject to change): AM session 10:00-13:00, Lunch 13:00-14:00, PM Session 14:00-17:00Lectures in the morning, practicals/computer workshops in the afternoon.Day 1. AM: History and philosophy of Bayesian statistics. Bayes Theorem: prior, posterior, likelihood. Applications of Bayesian methods in demography. PM: Pen-and-paper exercises: deriving posterior probabilities for a few simple discrete distributions. Point-and-click Bayesian estimation in JASP. Day 2. AM: Sources of uncertainty. Hierarchical models. Prior selection and elicitation. Brief introduction to numerical methods (MCMC, HMC). PM: Getting started: introduction to Stan. Coding simple models on pre-prepared data. Guided choice of individual mini-project topics; collection of data.Day 3. AM: Model comparison, selection and averaging, Bayesian model critique. Sensitivity analysis: not only priors. PM: Guided work on mini-projects – Part 1: Data preparation, model design, coding, estimation and troubleshooting. Day 4. AM: Guest lectures: real-life examples of Bayesian demographic models (details tbc). PM: Guided work on mini-projects – Part 2: Model critique and sensitivity analysis. Informative versus non-informative priors.Day 5. AM: Building theory: complex models and uncertainty quantification. New directions of Bayesian demography. Finalisation of mini-projects and individual presentations (five minutes, five slides). Conclusions to the course.Prerequisites: Undergraduate-level knowledge of statistics (any paradigm). Knowledge of calculus (integration) is beneficial, but not necessarily required.Teaser: A short video featuring Jakub Bijak and John Bryant discussing Bayesian demography back in 2016. Preliminary Reading Gelman A et al. (2013/2025) Bayesian Data Analysis: Third edition. CRC/Chapman & Hall.Bijak J and Bryant J (2016) Bayesian demography 250 years after Bayes. Population Studies, 70(1), 1–19.Bryant J and Zhang J (2018) Bayesian Demographic Estimation and Forecasting. CRC/Chapman & Hall.Attendance will be recognised through Accredible badges. Please kindly see the registration link onm the LCDS homepage to sign up for this couse. The course currently costs £600 for the entire 4.5 days of sessions. A 50% fee-waiver is available for internal Oxford students and participants from low and middle income countries upon request: please email teaching@demography.ox.ac.uk for more details.For any other or additional queries, please kindly contact teaching@demography.ox.ac.uk. charle.rahal@demgraphy.ox.ac.uk (Demographic Science Unit, Universty of Oxford) Sun, 03 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14831 https://www.ncrm.ac.uk/training/show.php?article=14395 Level: Professional (P)Standard methods of survival analysis based on the Kaplan-Meier estimate of a survivor function, the log rank test and Cox regression modelling are widely used in many different areas of application.   But often, the assumptions that underlie these techniques may not be valid, or the data structure may be more complex.  Extensions of these basic methods allow particular features of data that occur in practice to be handled appropriately.  This course will begin with an overview of standard methods and then move on to some of the more advanced techniques.   Their practical application will be illustrated using the R software, with an emphasis on interpreting output rather than on writing R code.  The course will consist of a series of presentations and practical sessions. Learning OutcomesAn appreciation of how the methods of survival analysis can be used in a variety of situations.Topics CoveredOverview of standard methods for summarising survival data and the Cox regression model.  Types of censoring in survival data, including interval and dependent censoring.  Time dependent variables and the counting process formulation of survival data.  Parametric models for survival data, including flexible models based on splines.  Incorporating random effects into a survival analysis; frailty models.  Analysis of data where there is more than one type of event; models for competing risks.  Detecting and handling non proportional hazards. Target AudienceStatisticians and epidemiologists in public sector research organisations, pharmaceutical companies and related organisations.  University research students and fellows. Assumed KnowledgeSome familiarity with basic methods for summarising survival data, including estimates of the survivor function and the log rank test.  Some experience in using the Cox regression model would be advantageous.  While knowledge of the R software is not essential, participants generally find it useful to be able to undertake the practical work using R on their laptop.   training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14395 https://www.ncrm.ac.uk/training/show.php?article=14666 Overview of 2-day courseBayesian statistics have become very popular in recent years. Modern software has made this possible and Bayesian methods are now applied in a wide range of scientific application areas from medicine to ecology. This course provides an introduction to the motivation, methods and applications of Bayesian statistics. The analysis tool is R (https://www.r-project.org/); prior knowledge of this software is assumed.The course is a mixture of presentations and hands-on computer exercises. It begins with an overview of the rationale and methodology underpinning Bayesian analysis, and the Markov chain Monte Carlo (MCMC) computational tools behind the methodology are outlined. An introduction to the JAGS engine within the R software is then provided, followed by data analysis applications, including linear models and generalised linear models. The advantages of Bayesian approach applied to the latter are emphasised and considered in detail. For example, the question “What is the chance that method A better than method B?” can be easily addressed in a Bayesian framework, but not in classical statistics.The emphasis in this course is on practical data analysis, although the essential theory will be outlined. Examples are drawn from a range of scientific disciplines.PresentersSandro Leidi and James Gallagher Cost£582 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 09:00 and 17:30 (GMT+1). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Data analysts and statisticians who want an introduction to Bayesian methods for statistical analysis. No prior knowledge of Bayesian statistics is required. Participants are expected to have:An A-level mathematics qualification or equivalent, including knowledge of probability density functions and probability mass functions for describing distributionsA working knowledge of linear models and generalised linear modelsA working knowledge of the R statistics software.How You Will BenefitBy the end of the course you will have a firm understanding of Bayesian methods and their flexibility. You will also have acquired a working knowledge of specialised software for Bayesian data analysis and will be able to fit and interpret linear and generalised linear models in a Bayesian framework. You will also appreciate the practical benefits of Bayesian methods.What Do We Cover?Bayesian versus classical frequentist statisticsLikelihood, prior and posterior distributions and the use of Bayes' theoremBayesian analysis of single-parameter models and multi-parameter modelsConjugate, vague and informative priorsSimulation of posterior distributions; posterior summariesMarkov chain Monte Carlo (MCMC) methods and MCMC diagnosticsLinear models, generalised linear models and model selectionQuestions that classical statistics find difficult to answer or cannot answerUse of the JAGS software via R and the CRAN packages rjags, runjags and coda.SoftwarePractical work will be done in R.Note: For practical work, participants must download and install (i) the JAGS software and (ii) a number of CRAN packages in R.  This must be done prior to the start of the course. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14666 https://www.ncrm.ac.uk/training/show.php?article=14396 Level: Professional (P)This course introduces the Bayesian approach to meta-analysis. Attendees will learn practical ways in which they can combine multiple sources of published evidence while accounting for uncertainties such as response bias, publication bias, confounding, and missing information, using either BUGS, JAGS or Stan as software. With Bayesian models, this can be transparent and reproducible.This two-day course begins by reviewing classic meta-analysis methods and expressing them as statistical models. Once attendees understand meta-analysis in this larger context, they are able to extend the model flexibly to account for common problems such as papers that report only change from baseline. A series of problems will be tackled in this course, and attendees will leave with model code that they can immediately start using with their own projects. Learning OutcomesAfter attending, participants will be able to:Write out standard meta-analyses as statistical modelsUse BUGS, JAGS or Stan to fit such models to dataRecognise several common problems in meta-analysisExtend these models to account for these problemsUnderstand and communicate their findings Topics CoveredDay 1:A review of statistical models of meta-analysis​Introduction to Bayesian analysis problems in meta-analysis, and sources of uncertaintyModels for basic DerSimonian-Laird and Biggerstaff-Tweedie meta-analysesIntroduction to Bayesian software options: BUGS, JAGS and StanDay 2:Models for network meta-analysisModels for missing statisticsModels for reporting biasModels for publication biasModels for a mixture of statisticsModels for a mixture of study typesReporting Bayesian meta-analyses Target AudienceThis course will be of interest to evidence-based healthcare researchers, including those writing guidelines and evaluating policies. Attendees should be comfortable conducting simple meta-analyses in some software but do not have to have experience of Bayesian methods. Assumed KnowledgeThis course assumes that all participants have a basic grounding in Bayesian statistics, to the level covered by the RSS courses "Introduction to Bayesian Statistics" or "Introduction to Bayesian Analysis using Stan". There is no specific software expertise required, but examples will be written in BUGS and Stan, using R as an interface. We also assume that participants are familiar with the principles of systematic reviews, for example from reading relevant parts of the Cochrane Collaboration Handbook online. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14396 https://www.ncrm.ac.uk/training/show.php?article=14410 Level: Intermediate (I)This course will be delivered over 4 afternoon sessions, running from 1:00pm to 5:00pm each day.The intensive course on programming principles in R. This course covers the fundamental techniques such as functions, for loops and conditional expressions. It also covers the {tidyverse} package, {purrr}. {purrr} is a very powerful package that gives great flexibility to analysts, by enhancing R’s functional programming toolkit. By the end of this course, you will understand what these techniques are and when to use them. The course will also demonstrate how to use functions such as map(), map2() and pmap(), to iteratively map functions over multi-element objects like vectors and lists. Emphasis will also be placed on how to manipulate list outputs and how this can be applied to data..Learning OutcomesBy the end of the course, delegates will: Understand basic functions, multiple arguments and variable scopes.Have a thorough understanding for loops.Be able to apply basic functions.Have a thorough understanding of conditionals such as if, else and else if statements.Be familiar with possible R workflows such as directory structure and working with directories.Understand how the aforementioned techniques can be applied to their own data.Understand how these techniques will improve efficiency and results.Understand where to find help in R using resources and the help() function.Understand lists in R and know how to use {purrr} to map functions.Know what nested loops are and use {magrittr} to extract elements from them.Be able to create list columns and know how to access the data in them.Iteratively loop two or more objects to a function of choice using functions such as map2(), pmap() and imap().Recognize the advantages of using {purrr}.Understand how to extract elements from nested lists to achieve a desired output object class.Be able to effectively debug their code using multiple {purrr} functions for the debugging process.Save precious debugging time using e.g. safely() Topics CoveredConditionals: using if and else statements in RFunctions: what a function is, how are they used, and how can we construct our own functions.Looping in R: an introduction to the concept of looping in R. In particular for and while loops.Help: The help system in R can at first glance appear daunting, however, after the initial shock, R’s documentation is second to none.Project structure: Practical tips on how to structure a project.Data manipulation and aggregation using dplyrIntroduction to {purrr} and Lists: Introduction to lists in R and using {purrr} to map a function across a list.List-Columns and Nesting: Exploring nested data in list columns and using the mapping functions to manipulate them.Parallel Mapping: Using {purrr} functions to map over multiple lists in parallel.Manipulating {purrr} Output: Using {purrr} to efficiently extract elements from lists into vector and dataframe format, and change the hierarchy within nested lists.Best Practices in {purrr}: Showcase of functions from {purrr} which aid in the debugging process.  Target AudienceThis course is idea for anyone who would like to extend their basic familiarity with using R, and using R to write their own bespoke functions or optimizing their code. Assumed KnowledgeBasic prior experience with the R programming language is assumed. Namely that participants have some experience of R data structures, such as vectors, data frames, and experience in using pre-made functions from R packages.The course is aimed as a follow up the 'Introduction to R and Regression Modelling in R' training course.Whilst no statistical knowledge will be assumed, some of the examples will be statistical in nature.For this online course, participants are not required to have R installed on their own laptops. A virtual environment, which can be accessed through a web browser, will be used to run R and view course materials. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14410 https://www.ncrm.ac.uk/training/show.php?article=14791 Overview of 1-day courseIn survival analysis in medical research, the proportional hazards assumption and the hazard ratio effect measure have been popular for decades, fuelled by extensive application of the log-rank test and the Cox regression model.  However, the hazard ratio can be clinically awkward to interpret, or the proportional hazards assumption may not hold, rendering the use of a hazard ratio effect measure questionable at best.In this course we introduce the restricted mean survival time (RMST), which is a well established, but under-used summary of the survival experience.  In recent years there has been a surge of interest in the RMST, particularly in oncology, but also in many other areas. We begin with a review of the definitions of the RMST, approaches to estimation and different RMST-based effect measures which are clinically meaningful alternatives to the hazard ratio and are not based on a proportional hazards assumption.For the practical analysis of survival data, which includes right-censoring, the course focuses on a non-parametric analysis for comparing treatments and a generalised linear model (GLM) -type modelling approach based on the use of pseudo-values.  The latter provides a flexible method for directly modelling the RMST in a regression framework, where a treatment effect may be adjusted for covariates.  Model checking is also considered. The course concludes with a brief consideration of an extension to the RMST known as the window mean survival time (WMST) or the long-term RMST (LT-RMST).The course is a practical introduction to analysing survival data using a RMST-based effect measure.  Only essential theoretical aspects of the methodology will be summarised.  Examples used will be drawn from applications in medicine and health, particularly clinical trials.Practical work will be based around the statistical software R; see https://www.r-project.org/.PresentersSandro Leidi and James GallagherCost£312 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 17:30 (GMT). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked verbally or using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter.​  We also use Zoom meetings rather than webinars to encourage further interaction during an online course.Who Should Attend?Statisticians and data analysts working with survival data in medical research. Participants will be assumed to have a working knowledge of:Survival analysis techniques applicable to right censoringRegression modellingThe R statistics software.How You Will BenefitYou will acquire practical experience in the use of RMST-based effect measures as an alternative to the hazard ratio.  You will also be able carry out adjusted as well as unadjusted analyses.What Do We Cover?Problems with proportional hazards and hazard ratio effect measure.  Introduction to the RMST: definition, approaches to estimation, RMST-based effect measures as an alternative to the hazard ratio.  The restricted mean time lost (RMTL)Non-parametric analysis for comparing two groups. Statistical inference: estimation, confidence intervals and hypothesis testing for different effect measures. Advantage of RMST-based effect measures over the hazard ratioAdjusting a treatment effect for covariates. Modelling the RMST using a GLM-type model based on pseudo-values; choice of link functions and effect measures; model fitting and comparison of modelsModel checking and the use of pseudo-residualsExtending the RMST: window mean survival time (WMST)CRAN packages, including geepack for modelling, emmeans for post-processing and survRM2 for non-parametric analysis.The course does not cover time dependent covariates.SoftwarePractical work will be done in R.Note: For practical work, participants must download and install a number of CRAN packages in R.  This must be done prior to the start of the course. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 30 Mar 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14791 https://www.ncrm.ac.uk/training/show.php?article=14668 Overview of 2-day courseAre you working in Learning Analytics or Student Analytics?Ever been asked if the average mark is changing significantly over academic years, or if the difference between the rate of change for females and males is statistically significant? Or which factors are associated with non-continuation?Or which factors are associated with accepting an offer?Or if the chance of achieving a first class honours degree is associated with tariff points on entry? This two-day course provides participants with hands-on experience of analysing their own type of records for data-driven planning and confidently interpreting numerical results for reports to policy makers and committees. The focus of the course is on the use of two statistical modelling techniques:Linear regressionLogistic regressionLinear regression is used to examine how the mean of a numerical outcome, like final year mark, might be associated with different characteristics. If the outcome is binary, such as drop-out, logistic regression is used to investigate how the chance of failing to continue to the second year is associated with different characteristics.  Logistic regression is a popular modelling technique, for example it is advocated by the Office for Students in their Financial support evaluation toolkit.The course also illustrates how these modelling techniques may be used for one-step-ahead forecasting into next year.Presentations, demonstrations and hands-on computer practicals are based around the free statistical software R; see https://www.r-project.org/. Formulae are kept to a minimum; instead, we concentrate on results, their interpretation and reporting in plain language.PresentersSandro Leidi and James Gallagher Cost£516 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 10:00 and 16:30 (GMT+1). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Administrators in educational establishments working in Policy, Planning and Strategy units; Data and Insight units; Business Intelligence units; those involved in learning analytics or extracting actionable insights from student records and in reporting to policy makers or committees. Anyone in these positions needing to answer questions around how student outcomes may be associated with different factors will benefit greatly from this course.It is assumed that participants will, prior to the course, have:An understanding of mathematical functions and equations. In particular, the natural logarithmic and exponential functions (loge() and exp() respectively), the equation of a straight line and its geometrical representationAttended the one-day course Statistics for University Administrators, or Statistics for University Administrators using R, or have equivalent knowledge.No previous experience of the R software is required; a brief introduction for the purpose of the course will be given.How You Will BenefitBy the end of the course you will be familiar with two common statistical modelling methods for investigating associations and extracting actionable insights, be able to report the results in plain language, and be able to perform analyses using free statistical software. You will also be able to follow official guidance on the use of such models, e.g. the Office for Students’ guidance on the use of binary logistic regression for investigating the effectiveness of financial support with respect to student outcomes.What Do We Cover?Introduction to the R software·Simple linear regression for relating a numerical outcome to a numerical explanatory variableExtending the linear regression model to incorporate categorical explanatory variables and interactions to allow for effect modificationUsing binary logistic regression in place of linear regression when modelling binary outcomesOne-step-ahead forecasting.SoftwarePractical work will be done in R.Note:For practical work, participants must download and install the R software prior to the start of the coursePractical work is based on the Windows operating system.Extra InformationThe R software is used on the course for two reasons:It is a free dedicated statistics package and can be used for other analysesIt is a widely used software which will be maintained by the R Foundation for many years to come. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14668 https://www.ncrm.ac.uk/training/show.php?article=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