NCRM is a Hub-Node network of research groups, each conducting research and training in an area of social science research methods, coordinated by the Hub at the University of Southampton. https://www.ncrm.ac.uk/training/ Sat, 24 Oct 2026 08:04:07 +0100 en-uk https://www.ncrm.ac.uk/incoming/furniture/images/sitewide/NCRM_new_Logo.gif https://www.ncrm.ac.uk/training/ NCRM is a Hub-Node network of research groups, each conducting research and training in an area of social science research methods, coordinated by the Hub at the University of Southampton. https://www.ncrm.ac.uk/training/show.php?article=15024 Training for PhD and MSc students in the design of social research, quantitative and qualitative analysis.Design-based causal inference in observational and experimental settings by Dr Zach Dickson - LSEThis course will introduce students to the principles and methods of causal inference. Causal inference is the process of drawing conclusions about the causal relationships between variables based on observational data. In this course, we will start with an overview of the potential outcomes framework and will cover topics such as confounding, selection bias, and causal identification. We will also discuss directed acyclic graphs (DAGs), and methods for estimating causal effects in observational settings, including difference-in-differences, instrumental variables, and regression discontinuity designs. We will use real-world examples to illustrate the concepts and methods covered in the course, and students will have the opportunity to apply these methods to their own research projects.By the end of the course, students will have a solid understanding of the principles and methods of causal inference, and will be able to apply these methods to a wide range of research questions. A basic knowledge of programming in a scripting language, such as R or Python, is recommended for this course. methodology.admin@lse.ac.uk (LSE) Mon, 05 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15024 https://www.ncrm.ac.uk/training/show.php?article=15067 This workshop covers the core concepts and applied methods of spatial statistics in R, with a focus on dependence, prediction, and regression for point-referenced and areal data. It is designed for researchers who want to build rigorous, reproducible spatial analyses for scientific publication and collaborative research. marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15067 https://www.ncrm.ac.uk/training/show.php?article=15068 This seminar provides a practical hands-on guide to setting up and running open-source Large Language Models (LLMs) locally. It covers privacy-preserving AI workflows, cost elimination, and reproducible execution of local LLMs for scientific and academic research applications. marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15068 https://www.ncrm.ac.uk/training/show.php?article=14390 Level: Intermediate (I)If you’re looking to improve the way you plan your work and improve efficiency by introducing statistical forecasting, then this course is ideal. By the end of the session you will have a firm grasp of how to summarise and measure trends, as well as how to extrapolate trends into a forecast. You will also have a good understanding of how to perform relevant calculations in Excel. This course is being delivered over two morning sessions, which will run from 9:00am to 1:00pm on both days.This course looks at one of the big questions in businesses -- finding out what is going to happen next. It would be so much easier to plan sales, purchases, production, staff and logistics if we knew the answer to this question! Many businesses know how important it is to forecast for the future, yet many fail to apply the fundamental concepts of statistical forecasting. Those that don’t use statistical forecasting face higher costs and uncertainty when reality diverges from their plans. Those that do use statistical forecasting are able plan for the future much more effectively and efficiently. Learning OutcomesUnderstand the distinction between sober & drunk time series (seriously!)Learn how to use hypothesis testing to confirm that a trend is a genuine trend.Learn how to use moving averages correctly to identify potential turning points.Discover the simplest method of identifying seasonality in your time series and to confirm it with hypothesis test.Uncover the basic principles of statistical process control and how you can use it to confirm deviations from an expected trend.Learn 4 different ways of extrapolating an existing trend to produce forecast. Topics CoveredTime series analysis, forecasting, trend identification, seasonality, moving averages, trend extrapolation, statistical process control, forecasting using extrapolations. Target AudienceAnyone involved in business planning, performance analysis and other similar roles that require analyses of historical trends and extrapolation of those trends to create forecasts. Course PrerequisitesThis course would be suitable for anyone who has completed our two day "Basic Statistics" course.Other participants should ideally have an understanding of the following:Basic statistical concepts including expectation, variance, distribution and correlation.Probability and risk including knowing what false positives and false negatives areKnow how to calculate of confidence intervals for the mean of 1 sample and the difference in means between 2 samples.Know how to calculate the slope and intercept of a simple regression models with 1 variableUse of Microsoft Excel including the use of formulae such as IF, VLOOKUP, OFFSET, etc.Produce and interpret line charts, column charts and scatter plots in Excel. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14390 https://www.ncrm.ac.uk/training/show.php?article=15051 This free online drop-in is for anyone working, or thinking of working, with UK census data from the UK Data Service. If you are looking for help and advice with your project or research, join us on 28 October. The session is targeted at academic and non-academic researchers, analysts, students, and anyone with an interest in UK census data, whether absolute beginners or more seasoned users.Come along to discuss any questions you might have about UK census data with experts from the UK Data Service census team. This is a one-hour session and it will take place on Zoom.We can help with topics such as:what census data is available from the UK Data Servicefinding variables that match your needsunderstanding the documentationkey things to consider when using census dataany other queries you might have about census dataBook your place and we will see you soon.  mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15051 https://www.ncrm.ac.uk/training/show.php?article=15063 This training will discuss key theories, methods and ethics on web scraping. We also have exercises. cq225@cam.ac.uk (University of Cambridge) Sun, 18 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15063 https://www.ncrm.ac.uk/training/show.php?article=15069 Designed specifically for European/UK time zones, this workshop provides a practical yet conceptually rigorous guide to selecting and using modern causal inference methods in empirical research. The course covers key methodologies for estimating causal effects and selecting appropriate causal modeling frameworks for scientific publication. marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15069 https://www.ncrm.ac.uk/training/show.php?article=15070 This PhD-level seminar introduces key methods for longitudinal data analysis and multilevel modeling. Designed for researchers and graduate students across business, social, and health sciences, it provides hands-on applications for analyzing repeated-measures data, estimating growth trajectories, and modeling nested or hierarchical data structures. marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15070 https://www.ncrm.ac.uk/training/show.php?article=15071 This workshop provides a comprehensive intro-to-advanced overview of statistical network analysis and network modeling using point-and-click, no-code software tools. It teaches researchers how to analyze, visualize, and model complex relational and network data without needing prior programming skills in R or Python. marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15071 https://www.ncrm.ac.uk/training/show.php?article=15039 Data documentation is essential to make sure that well-organised and well-documented research data can be produced from our research projects. This workshop provides an overview of different types of documentation depending on data type (e.g. primary or secondary, survey or transcripts), as an essential part of implementing good data management in research projects, with a focus on optimising data sharing.In this session we will explore the following:The critical role of data documentation in research integrity and data sharing, for both current and future research projects.How to develop comprehensive documentation for various data types, incorporating practical examples and templates that can be adapted to your projects.An overview of metadata: what it is, its importance in making your data discoverable and understandable, and the standards to follow for effective data sharing.During this free 90-minute online workshop, participants will learn the foundational principles and practical strategies for creating robust data documentation that enhances the accessibility, usability, and longevity of research data. Effective documentation is not a one-size-fits-all process; it varies significantly across different types of data and the workshop is tailored to address these nuances, ensuring researchers can apply best practices relevant to their specific data types.The workshop will conclude with a dedicated Q&A session.Presenter: Maureen Haaker, UK Data Service. Maureen has worked with the UK Data Service for over 12 years supporting researchers and institutions in managing, preserving, and sharing qualitative data ethically and effectively. She runs training sessions on research data management, advises on best practices in data curation and anonymisation, and has worked on special projects, including a UKRI-funded project exploring uses of synthetic data in Trusted Research Environments (TREs). She is co-editor of the IASSIST Qualitative Data Special Interest Group and a member of the DDI Working Group developing metadata standards for qualitative data.Level: IntroductoryExperience/knowledge required: Basic understanding of research methods is recommended but not required.Target audience: Academic and non-academic data professionals such as researchers, academic and professional services staff, data managers, data stewards and others keen on improving their data documentation practices.This event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.This event is part of our UK Data Service introductory training series: Autumn 2026. mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15039 https://www.ncrm.ac.uk/training/show.php?article=15072 Designed for researchers in the Americas and Australasia time zones, this PhD-level workshop provides a practical yet conceptually rigorous guide to selecting and using modern causal inference methods in empirical research. The seminar covers core frameworks for estimating causal effects and selecting appropriate causal modeling strategies for scientific publication. marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15072 https://www.ncrm.ac.uk/training/show.php?article=15073 This PhD-level workshop provides a comprehensive overview of machine learning methods applied to computational biology and bioinformatics. Participants learn to implement from scratch and optimize algorithms such as neural networks, random forests, k-means clustering, and Markov Chain Monte Carlo (MCMC) to advance research in biostatistics, genetics, and data science marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15073 https://www.ncrm.ac.uk/training/show.php?article=15074 A hands-on, cross-disciplinary workshop on AI-assisted qualitative coding and content analysis in R using the quallmer R package. With minimal programming, participants learn how to leverage Large Language Models (LLMs) such as OpenAI, Anthropic, Google Gemini, and local models to code qualitative text, audio, and video data reliably, reproducibly, and transparently for scientific research. marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15074 https://www.ncrm.ac.uk/training/show.php?article=15075 This practical workshop explores science communication as a process of interpretation, framing, and multimodal meaning-making in the era of artificial intelligence. It focuses on strategies for communicating complex scientific ideas engagingly and accessibly to non-expert audiences, leveraging generative AI tools ethically, and managing public trust in scientific research. marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15075 https://www.ncrm.ac.uk/training/show.php?article=15076 This seminar explores how to build a tailored AI research agent for scholarly work using agentic AI tools and practical design principles. It combines conceptual guidance with demonstrations to help participants automate research processes while retaining control, reproducibility, and critical oversight. marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15076 https://www.ncrm.ac.uk/training/show.php?article=15077 This seminar covers applied multiple regression and mediation analysis for researchers who want to move from theoretical questions to defensible statistical analysis. marc@instats.org (Instats) Thu, 22 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15077 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=14915 Artificial intelligence and large language models (LLMs) are becoming part of everyday social research, being used in everything from literature reviews to survey question development, analysis and reporting. This course will equip participants with the tools to understand the current capabilities of these models and how to use them effectively in the quantitative research workflow.The course starts by introducing key concepts such as AI, machine learning, and Large Language Models (LLMs) in non-technical language. It then discusses the stages of quantitative research and how LLMs can be used effectively at each stage. Participants will learn how to choose between different types of tools, such as chatbots, in-editor assistants, and coding agents, and why instructions, context, tools, and human review shape what LLM systems can do.The course is a mix of lectures covering key concepts in non-technical terms, workflows, and examples, as well as hands-on practicals where researchers can apply key concepts to real-world problems to better understand the strengths and limitations of these tools.Looking to book for four or more people from your organisation? Please let us know before booking by emailing: training@the-sra.org.uk training@the-sra.org.uk (Social Research Association) Wed, 29 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14915 https://www.ncrm.ac.uk/training/show.php?article=14404 Level: Intermediate (I)This course will be delivering over 4 afternoon sessions, running from 1:00pm to 5:00pm each day.This course covers the fundamentals of machine learning and the methodology for applying these to real-world analytics problems. The course outlines the stages involved in a machine learning analysis, and walks through how to perform them using the R programming language and the tidymodels suite of packages. Participants will be provided with exercises to complete through the course in order to gain hands-on experience in using the methods presented.The individual stages of: problem formulation, data preparation, feature engineering, model selection and model refinement will be walked through in detail giving participants a solid process to follow for any machine-learning analysis. This includes methods for evaluating machine-learning models in terms of a performance metric as well as assessing bias and variance.  Learning OutcomesFollowing this course the attendees will:Be familiar with the overall process of how to apply machine-learning methods in an analysis projectUnderstand the differences and similarities between statistical modelling and machine-learning theoriesHave gained hands-on experience in working with the tidymodels suite of packages in RGain an intuitive understanding of how several specific machine-learning methods solve the problems of prediction and classification Topics CoveredIntroduction to machine-learning: parsnip package; basic train and testStages of machine-learning: problem formulation; data preparation; feature engineering; model selectionHighlighted Models: Decision trees and random forests; K-nearest neighbours, linear regression and logistic regression. Target AudienceMachine Learning can be applied to data in a whole range of fields from Finance to Pharmaceutical, Retail to Marketing, Sports to Travel and many, many more! This course is aimed at anyone interested in applying machine learning methods to their data in order to: gain deeper insight, make better decisions or build data products Assumed KnowledgeThis course assumes participants are comfortable with the basic syntax and data structures in the R languageFor this online course, participants are not required to have R installed on their own laptops. A virtual environment, which can be accessed through a web browser, will be used to run R and view course materials. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14404 https://www.ncrm.ac.uk/training/show.php?article=14876 Overview of 1-day courseAre you working in Learning Analytics or Student Analytics?Ever been asked for the margin of error on a percentage?Or if the difference between your percentage and a benchmark is statistically significant?Or if the change in student retention rate from last year is statistically significant?This one-day course provides participants with hands-on experience of analysing their own type of records for data-driven planning and confidently interpreting numerical results for reports to policy makers and committees, or for returns to the Higher Education Statistics Agency.Presentations, demonstrations and hands-on computer practicals will make use of the free statistical software R; see https://www.r-project.org/.  Note the course does not focus on teaching R, but on the use of supplied R functions to perform statistical analyses of interest.  Formulae are kept to a minimum; instead, we concentrate on results, their interpretation and reporting in plain language.​PresentersSandro Leidi and James GallagherCost£282 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 17:30 (GMT).  The delivery platform is Zoom, which may be freely accessed.  Questions may be asked verbally or using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Administrators in educational establishments working in Policy, Planning and Strategy units; Data and Insight units; Business Intelligence units; those involved in learning analytics or extracting actionable insights from student records and in reporting to policy makers or committees; those involved in producing returns to the HESA.  Anyone in these positions will benefit greatly from this course.No previous experience of the R software is required.How You Will BenefitBy the end of the course you will be familiar with basic statistical methods for making sound assessments, and extracting actionable insights.  What Do We Cover?Estimation: standard errors and confidence intervals for percentagesComparing a percentage to a benchmark: one-sample z-testComparing two percentages: two-sample z-testComparing more than two percentages: chi-square test.The course will make use of our R functions which will be made freely available. Note the course does not focus on teaching R, but on the use of the supplied R functions to perform statistical analyses covering the above syllabus.SoftwarePractical work will be done in R.Note:For practical work, participants must download and install the R software.  This must be done prior to the start of the coursePractical work is based on the Windows operating system.Extra InformationThe content of this course is identical to that of Statistics for University Administrators. The only difference is the R statistical software is used instead of Excel.  R has two advantages:It is a free dedicated statistics package and can be used for other analysesIt is a widely used software which will be maintained by the R Foundation for many years to come. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Tue, 16 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14876 https://www.ncrm.ac.uk/training/show.php?article=15035 Explore how dynamic structural equation modeling (DSEM) combines multilevel modeling, structural equation modeling, and time-series analysis to analyze intensive longitudinal data and investigate within-person change and multivariate dynamics over time. natalie@statisticalhorizons.com (Statistical Horizons) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15035 https://www.ncrm.ac.uk/training/show.php?article=14979 Intersectional MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy) is an increasingly popular approach for studying inequalities in individual outcomes across intersections of social identity characteristics, such as gender, race, and socioeconomic background.In the first half of the session, Professor George Leckie will introduce the MAIHDA approach through an application examining intersectional inequalities in educational attainment using data from the Programme for International Student Assessment (PISA). He will explain the underlying multilevel modelling approach and discuss the interpretation of MAIHDA results.The session will also provide a high-level overview of recent substantive and methodological work using MAIHDA, including extensions for studying intersectional inequalities in longitudinal trajectory data and for examining heterogeneity of treatment effects in randomised controlled trials.In the second half, there will be a demonstration of how to conduct a standard MAIHDA analysis in both R and Stata.Familiarity with standard multilevel models would be beneficial, but is not essential (for a QMEG introduction to multilevel models, see here: https://qmeg.soton.ac.uk/events-and-resources/ ).The session is free to attend and will take place online via Microsoft Teams. J.E.Hall@Soton.ac.uk (University of Southampton) Wed, 23 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14979 https://www.ncrm.ac.uk/training/show.php?article=15034 Learn how to use Claude as a systematic research partner for ideation, data collection, analysis, reproducibility, and publication-ready academic writing. natalie@statisticalhorizons.com (Statistical Horizons) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15034 https://www.ncrm.ac.uk/training/show.php?article=14967 Large language models are now embedded in research workflows across the social sciences, yet most researchers interact with these tools through consumer interfaces that obscure how they work, where data goes, and what decisions are being made on their behalf. This three-workshop series closes that gap. Across three standalone half-day sessions, participants build a working understanding of the AI stack: from how models generate text and where inference happens, through prompt engineering, retrieval-augmented generation, and API-based workflows, to the rapidly maturing ecosystem of agentic platforms, harness engineering, and autonomous research infrastructure. Each workshop combines conceptual exposition with live demonstrations and practical exercises grounded in survey research scenarios. No programming experience is required for Workshop 1; Workshops 2 and 3 assume familiarity with earlier concepts.The course covers: Workshop 1: How Large Language Models Work: tokens, training, alignment, data security, inference, open- vs closed-weights models, reproducibility challenges, and the limitations of chatbot interfaces for research.Workshop 2: Context Engineering: prompt design and optimisation, retrieval-augmented generation (RAG), API-based workflows and batch processing, memory and tool-calling, MCP servers, and evaluation engineering.Workshop 3: Agentic AI and Harness Engineering: the agentic AI ecosystem (IDE-native agents, extended-autonomy platforms, orchestration tools), harness engineering and SDKs, memory and token economics, MCP servers and hooks, oversight, auditability, and research transparency.By the end of the course participants will:Explain how LLMs generate text and assess the implications of model architecture, training, and alignment for research practiceDistinguish between open-weights and closed-weights models and evaluate their data governance implicationsApply prompt optimisation techniques and build evaluation pipelines to validate LLM outputsMake structured API calls, manage parameters, and use retrieval-augmented generation where appropriateMap the agentic AI ecosystem, explain harness engineering, and assess how platforms orchestrate memory, tools, and contextDesign human-in-the-loop safeguards and audit protocols appropriate for agentic research workflowsPre-requisitesNo prior programming experience or specialist software knowledge is required for Workshop 1. Workshops 2 and 3 assume familiarity with concepts from Workshop 1 (or equivalent knowledge of how LLMs work). Workshop 3 benefits from some comfort with reading code, but participants are not required to write any. Setup guidance for API access will be provided before Workshops 2 and 3.No software installation is required for Workshop 1. For Workshops 2 and 3, participants will benefit from having API access to a commercial LLM provider (e.g. Anthropic, OpenAI); setup guidance will be provided in advance. All demonstrations will be conducted live by the instructor. Participants do not need prior experience with any specific software, though basic familiarity with web browsers and text editors is assumed.Target AudienceSurvey researchers, methodologists, and quantitative social scientists across academia and government who use or are considering using large language models in their research. The series is designed to be accessible to researchers at all career stages, from doctoral students to senior investigators. Although the recommendation is to attend all three workshops, you are welcome to join them individually. PLEASE NOTE THESE WORKSHOPS WILL RUN ONLINE ON 23 NOVEMBER, 30 NOVEMBER AND 7 DECEMBER 2026 FROM 09:30-13:30. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 23 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14967 https://www.ncrm.ac.uk/training/show.php?article=15043 Researchers are often at a crossroads on how to share data collected from human participants ethically while complying with the rigorous standards set by UK General Data Protection Regulation and the Data Protection Act. This introductory workshop offers a foundational understanding of data anonymisation principles and practices tailored for social sciences research.This session is designed to equip researchers with essential knowledge and skills to navigate the complexities of data anonymisation. It will cover practical approaches for safeguarding privacy in the most common data types in social sciences, with a focus on quantitative survey data, and qualitative transcripts; brief considerations for audio/visual materials are included. We will cover key concepts as defined by applicable data protection legislation and outlined by the Information Commissioner's Office (ICO) guidance, from definitions of personal data to identifiability and effective anonymisation.During this free 90-minute online workshop, participants will learn the nuances of de-identification versus anonymisation techniques, how to differentiate between directly identifying information and indirect identifiers and how to best handle special category data, with the view of sharing data ethically and legally.This session includes live exercises using Mentimeter and will conclude with a dedicated Q&A session administered via Padlet.Presenter: Maureen Haaker, UK Data Service. Maureen has worked with the UK Data Service for over 12 years supporting researchers and institutions in managing, preserving, and sharing qualitative data ethically and effectively. She runs training sessions on research data management, advises on best practices in data curation and anonymisation, and has worked on special projects, including a UKRI-funded project exploring uses of synthetic data in Trusted Research Environments (TREs). She is co-editor of the IASSIST Qualitative Data Special Interest Group and a member of the DDI Working Group developing metadata standards for qualitative data.Level: IntroductoryExperience/knowledge required: Basic understanding of research methods in the social sciences and of data protection legislation are recommended but not requiredTarget audience: Academic and non-academic data professionals such as researchers, data managers, data stewards and others involved in collecting data, or managing datasets for wider sharing and re-useThis event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.This event is part of our UK Data Service introductory training series: Autumn 2026. mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15043 https://www.ncrm.ac.uk/training/show.php?article=14667 Overview of 2-day courseMixed models have become increasingly popular, as they have many practical applications. However, the traditional linear mixed model with normally distributed errors is not appropriate for modelling discrete responses such as binary data and counts. Such responses are typically analysed using generalised linear models such as logistic regression and Poisson regression.Commonly-used generalised linear models will be extended to deal with multiple error structures, using a variety of scientific examples, mainly medical and health related applications, such as investigating the presence of adverse events in a clinical trial.The emphasis will be on practical understanding, although an outline of the theory will be presented. Practical examples will be used to illustrate the methods and participants will have the opportunity to fit and interpret models themselves in hands-on computer practicals.Practical work will be based on the R software; see https://www.r-project.org/.  Model fitting will mainly be done using the CRAN package GLMMadaptivePresentersSandro Leidi and James Gallagher Cost£582 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 09:00 and 17:30 (GMT). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Data analysts and statisticians working in medicine, health and related areas, who wish to have a practical introduction to Generalised Linear Mixed Models. It is assumed that participants are R users and familiar with the practical use of both generalised linear models and linear mixed models. How You Will BenefitYou will learn to formulate generalised linear models with both fixed and random effects for a range of situations, how to fit them and how to interpret their output.What Do We Cover?Review of generalised linear models and linear mixed modelsBinary and binomial outcomes: logistic regression with mixed effectsCount outcomes: Poisson and negative binomial regression with mixed effectsOrdered outcomes: proportional odds regression with mixed effectsAdaptive Gauss-Hermite Quadrature fitting method; inferential proceduresConvergence issues and solutionsInterpretation of effects in a generalised linear mixed model and predictionGLMMadaptive CRAN package for fitting generalised linear mixed models; ordinal CRAN package for fitting the proportional odds model with random effects.Notes on course content:The GLMMadaptive package can currently only fit models where the random effects part is defined by a single grouping factorThe course does not cover marginal or GEE type models for repeated measurements.SoftwarePractical work will be done in R.Note: For practical work, participants must download and install a number of CRAN packages in R.  This must be done prior to the start of the course. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14667 https://www.ncrm.ac.uk/training/show.php?article=14938 One of the most popular software tools for data management and analysis is the open source package R, which is often used with the Rstudio interface. These are extremely powerful and are able to handle most types of data and analyses used in social research.In this course you will learn the basics of R and Rstudio. We will cover the main types of objects in R and how to select cases and variables. We will also discuss how to import and export data and how to describe the data using tables and summary analyses. Through the practical exercises you will get accustomed to running functions in R and using the syntax.IMPORTANT: participants will need to have a working knowledge of quantitative data.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house training. training@the-sra.org.uk (Social Research Association) Tue, 11 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14938 https://www.ncrm.ac.uk/training/show.php?article=14384 Level: Foundation (F)The course will be delivered over 5 morning sessions, running fro 9:00am to 1:00pm each day. The purpose of this course is to help participants understand some basic statistical concepts and develop a strategy for approaching simple data analysis. The course will introduce basic concepts such as hypothesis testing and confidence interval estimation. It will provide the tools to undertake simple analysis of a dataset and will include some helpful hints and tips for reading and understanding reported statistics.Learning OutcomesBy the end of this course, participants will understand basic approaches to statistical inference, including hypothesis testing and confidence interval estimation. They will be equipped with the skills necessary to undertake simple analysis and to understand some of the basic terms often used to report statistical results. The course will include some calculations by hand to aid understanding. Topics CoveredData Summary; The normal distribution; Confidence intervals; Introduction to hypothesis tests; Analysis of contingency tables – chi-squared test;  T-tests; Non-parametric tests, (Wilcoxon signed rank test, Mann-Whitney U test); Introduction to correlation and regression; Basic presentation of data and results. Target AudienceThis course is aimed at those who have either never undertaken a formal statistics course, or who have studied some statistics in the past but wish to undertake a refresher. It is ideal for statistical novices who have never had any formal training but are starting to encounter statistics in their work and wish to gain some insight.Delegate Feedback"Ellen and Jenny were extremely knowledgeable. They were also approachable and happy to give further explanations when necessary""Excellent course. Hard to fault. I can see why it's popular""This was a fantastic course, the presenters had a very high knowledge but were able to 'dumb it down' for me as I am new to this"  training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14384 https://www.ncrm.ac.uk/training/show.php?article=14978 This live online course (administered via Zoom) offers an engaging introduction to Structural Equation Modelling (SEM) using R, the popular open-source software for statistical computing and graphics. The course will run over 5 consecutive days and will combine synchronous live lectures, individual exercises, group discussions and small group supervisions to simulate a live classroom environment. High contact with our expert instructors and interactive group work will further contribute to recreating the classic Cambridge experience in an online setting.This course will present the lavaan package, rapidly becoming the tool of preference for SEM in R. Participants will actively work through practical examples to gain first-hand experience in the application of factor analysis and other more advanced latent trait models. You will learn the following topics in this course:  Introduction to R and data analysisBasic concepts of factor analysis (EFA/CFA/Categorical CFA)Basic and advanced Structural Equation Models (SEM)Application of mediation & moderation techniquesMulti-level modellingLatent growth curve modellingMultiple-group analysis and evaluation of continuous and categorical data measurement invariance, as well as longitudinal measurement invarianceYou don’t need to know R to follow the course. However, if you are not familiar with R, you will need to choose the 5 day option, which includes an introduction to the R software on Day 1. On completion, participants should have a good knowledge of the topics covered and have acquired an independent use of R and latent trait analysis.We believe in active learning and developing practical skills. Thus, the necessary theoretical introduction will be illustrated with practical examples and we will be working with real data. No prior knowledge about SEM is assumed. The pace of teaching is adjusted to suit the level of the participants. Teaching will be in small groups so that delegates can make the most of the teaching. ls523@cam.ac.uk (University of Cambridge Psychometrics Centre) Tue, 22 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14978 https://www.ncrm.ac.uk/training/show.php?article=15044 Are you wanting to understand how the world works? Have an interest in climate science, economy, trade, demographics, health or international development? Want to know about and how to access data from the IMF, World Bank, UN or OECD?This free 90-minute online workshop will introduce you to the international time series data held by the UK Data Service. We’ll look at the range of socio-economic topics covered, explore some key datasets like the World Development Indicators and OECD Economic Outlook, and cover how to access and download the data.The workshop will consist of presentations and demos with time at the end for questions.Presenters: Eve Little and Cameron Hurst, UK Data ServiceEve is a Research Impact and Engagement Manager at the UK Data Service. She works across evaluation, communications and research engagement, with a particular focus on translating complex research into engaging, accessible stories that resonate with a range of audiences. She supports early career researchers as they develop their skills and confidence in working with data for impactful social science research.Cameron is a Data and Platform Engineer at the UK Data Service.Level: IntroductoryExperience/knowledge required: Some spreadsheet knowledge would be usefulTarget audience: Anyone interested in knowing more about the UK populationThis event will be livestreamed on our UK Data Service YouTube channel but the chat will be disabled. By registering and attending the Zoom event you will be able to ask questions and interact.Recordings of UK Data Service events are made available on our YouTube channel and, together with the slides, on our past events pages soon after the event has taken place.This event is part of our UK Data Service introductory training series: Autumn 2026. mark.kelly@manchester.ac.uk (University of Manchester) Tue, 13 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15044 https://www.ncrm.ac.uk/training/show.php?article=14392 Level: Intermediate (I)Multiple linear regression is one of the most commonly used techniques in statistics and allows for the impact of multiple variables to be assessed simultaneously. The analysis of variance (ANOVA) is a related technique which allows the mean values of several groups to be compared. This course will utilize Jamovi's free software, to equip participants with the skills necessary to undertake both types of analysis, understand and interpret the output, check the assumptions that underpin each type of model, and present the results coherently. This course will be delivered over two morning sessions, running from 9:30am to 1:00pm on both days.Learning OutcomesBy the end of this course the attendees will:       Understand what is meant by the term Analysis of Variance (ANOVA) and the different ANOVA models availableAssess when it is appropriate to fit an analysis of varianceInterpret the result s of an analysis of varianceAssess model fitPresent the results of an analysis of varianceUnderstand what is meant by the term multiple linear regressionAssess when it is appropriate to fit a multiple linear regression modelCarry out a regression analysis using free softwareInterpret the results of a multiple linear regression analysisAssess model fitPresent the results of a multiple linear regression analysis      Topics CoveredThe first day will start with a brief recap on the concepts of hypothesis testing and choosing the right test. This will include the basic use of Jamovi software to carry out and interpret an independent t-test before progressing to the related technique ANOVA.  Assumption checking, two-way ANOVA’s and interactions will conclude the morning. The second day starts with correlation and simple linear regression to assess the relationship between two continuous variables before concentrating on multiple regression which allows multiple variables to be tested simultaneously. Both sessions will concentrate on producing and understanding outputs rather than mathematical content with regular exercises to reinforce learning. Target AudienceThis course is aimed at individuals who have some basic statistical knowledge and who wish to undertake analyses of quantitative data and who therefore wish to gain some insight into how to undertake these. Knowledge AssumedBasic statistical knowledge as the course is designed as a follow-on from our Basic Statistics course.Delegates will need to download the latest version of Jamovi onto their laptop as this will be used during the workshop: https://www.jamovi.org/download.html training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14392 https://www.ncrm.ac.uk/training/show.php?article=14397 Level: Professional (P)This online training course will be delivered over 4 afternoon sessions, running from 1:00pm to 5:00pm on each day. This course will cover R object-oriented programming techniques. It will discuss what OOP is and the different varieties within R. Beginning with the popular S3 and S4 OOP frameworks, we’ll finish with the new {R6} package that is used extensively in Shiny applications. The course will then introduce the {rlang} package as a way of parsing variables from a data set into a function. Furthermore, it cover environments and function-evaluation in R, to help you understand how the tools in {rlang} work under the hood. This course will be delivered over 4 sessions. Course Outline This course will cover the following topics:Advanced Functions: Scoping rules (including lexical scope), The … argument, Argument matchingS3 classes: Introduction to object-oriented programming, Constructing S3 objects, DrawbacksS4 classes: Creating and using S4 classes, Differences between S3 and S4 classesR6 classes: Differences between {R6} and S3/S4, Mutable states, Creating methods, Shallow and deep copiesModifying user argument in functions callsQuoting code with quosuresUsing quasi quotation Learning outcomesBy the end of this course, delegates will be able to :Select the most appropriate form of OOP for their taskLeverage encapsulation, polymorphism and inheritance to provide a nice user interface to their codeWrite functions with rich results, user-friendly display and programmer-friendly internalsExtend the functionality of functions for new object typesWrite code that is extensible by othersUse the {rlang} operators {{}}, !!, !!! and := to pass variablesModify user functions using enquo()Parse and deparse expressions Target AudienceThis course assumes that participants are comfortable with the fundamentals of R programming. As such the course will be of interest to anyone who uses R, in particular those who want to develop their computer skills to cover more advanced topics. Delegate Feedback ““Extremely good teacher, great explanations, funny examples and very flexible in terms of content and time. I got to know a lot of things, that I did not think were possible.” “Material well presented and delivered” ”I am not scared of R anymore. It was actually fun!”“Really great course! Useful content that will greatly benefit me in my future R projects.” training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14397 https://www.ncrm.ac.uk/training/show.php?article=14399 Level: Professional (P)This online training course will be delivered over 4 afternoon sessions, running from 1:00pm to 5:00pm on each day. This course will cover R object-oriented programming techniques. It will discuss what OOP is and the different varieties within R. Beginning with the popular S3 and S4 OOP frameworks, we’ll finish with the new {R6} package that is used extensively in Shiny applications. The course will then introduce the {rlang} package as a way of parsing variables from a data set into a function. Furthermore, it cover environments and function-evaluation in R, to help you understand how the tools in {rlang} work under the hood. This course will be delivered over 4 sessions. Course Outline This course will cover the following topics:Advanced Functions: Scoping rules (including lexical scope), The … argument, Argument matchingS3 classes: Introduction to object-oriented programming, Constructing S3 objects, DrawbacksS4 classes: Creating and using S4 classes, Differences between S3 and S4 classesR6 classes: Differences between {R6} and S3/S4, Mutable states, Creating methods, Shallow and deep copiesModifying user argument in functions callsQuoting code with quosuresUsing quasi quotation Learning outcomesBy the end of this course, delegates will be able to :Select the most appropriate form of OOP for their taskLeverage encapsulation, polymorphism and inheritance to provide a nice user interface to their codeWrite functions with rich results, user-friendly display and programmer-friendly internalsExtend the functionality of functions for new object typesWrite code that is extensible by othersUse the {rlang} operators {{}}, !!, !!! and := to pass variablesModify user functions using enquo()Parse and deparse expressions Target AudienceThis course assumes that participants are comfortable with the fundamentals of R programming. As such the course will be of interest to anyone who uses R, in particular those who want to develop their computer skills to cover more advanced topics. Delegate Feedback ““Extremely good teacher, great explanations, funny examples and very flexible in terms of content and time. I got to know a lot of things, that I did not think were possible.” “Material well presented and delivered” ”I am not scared of R anymore. It was actually fun!”“Really great course! Useful content that will greatly benefit me in my future R projects.” training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14399 https://www.ncrm.ac.uk/training/show.php?article=15021 Date: January 11th through to lunchtime on January 15th, 2027By: Jakub Bijak and GuestsLocation: Reuben College, University of OxfordCourse description: This comprehensive, interdisciplinary short course provides foundations in applied Bayesian Statistics with a focus on methods for Population Data Science – a dynamically-growing area of research, connecting statistical rigour with fascinating real-life applications. Led by one of the pioneers of Bayesian thinking for Demographic Research, and illustrated by examples of Bayesian methods for estimation and prediction, the course will help the participants understand the main concepts and methods unlocking the study of uncertain processes. In addition, the participants will be able to appreciate the breadth of possible applications, such as local planning, national policy setting or human rights monitoring. The course is primarily intended for Masters students, doctoral and post-doctoral researchers, and other academics, but it is open to anyone with interest in population topics, who would like to enter into the world of population uncertainty equipped with state-of-the-art tools for dealing with unknowns – known and unknown alike.Objectives: To familiarise the participants with the philosophy and methodology of Bayesian Statistics, and to enable them to use selected software tools and packages to application Bayesian methods in practice.Learning outcomes: By the end of the course, the participants will be able to:1. Understand the philosophical and statistical basics of Bayesian statistics.2. Appreciate the different sources of uncertainty for various applications and the way in which they are reflected in models.3. Specify a Bayesian model for a given problem, estimate it, and assess its quality and sensitivity to various assumptions.4. Carry out an applied piece of Bayesian analysis, by using freely-available dedicated statistical software, and interpret the results.Timetable (Subject to change): AM session 10:00-13:00, Lunch 13:00-14:00, PM Session 14:00-17:00Lectures in the morning, practicals/computer workshops in the afternoon.Day 1. AM: History and philosophy of Bayesian statistics. Bayes Theorem: prior, posterior, likelihood. Applications of Bayesian methods in demography. PM: Pen-and-paper exercises: deriving posterior probabilities for a few simple discrete distributions. Point-and-click Bayesian estimation in JASP. Day 2. AM: Sources of uncertainty. Hierarchical models. Prior selection and elicitation. Brief introduction to numerical methods (MCMC, HMC). PM: Getting started: introduction to Stan. Coding simple models on pre-prepared data. Guided choice of individual mini-project topics; collection of data.Day 3. AM: Model comparison, selection and averaging, Bayesian model critique. Sensitivity analysis: not only priors. PM: Guided work on mini-projects – Part 1: Data preparation, model design, coding, estimation and troubleshooting. Day 4. AM: Guest lectures: real-life examples of Bayesian demographic models (details tbc). PM: Guided work on mini-projects – Part 2: Model critique and sensitivity analysis. Informative versus non-informative priors.Day 5. AM: Building theory: complex models and uncertainty quantification. New directions of Bayesian demography. Finalisation of mini-projects and individual presentations (five minutes, five slides). Conclusions to the course. PM: course end.Prerequisites: Undergraduate-level knowledge of statistics (any paradigm). Knowledge of calculus (integration) is beneficial, but not necessarily required.Teaser: A short video featuring Jakub Bijak and John Bryant discussing Bayesian demography back in 2016. Preliminary Reading Gelman A et al. (2013/2025) Bayesian Data Analysis: Third edition. CRC/Chapman & Hall.Bijak J and Bryant J (2016) Bayesian demography 250 years after Bayes. Population Studies, 70(1), 1–19.Bryant J and Zhang J (2018) Bayesian Demographic Estimation and Forecasting. CRC/Chapman & Hall.Attendance will be recognised through Accredible badges. A shop link to register for the course will be forthcoming in the immediate future. The course currently costs £600 for the entire 4.5 days of sessions. A 50% fee-waiver is available for internal Oxford students and participants from low and middle income countries upon request: please email teaching@demography.ox.ac.uk for more details. Lunch will be provided on Monday the 11th of January by Reuben College.To register, please click [here].For any other or additional queries, please kindly contact teaching@demography.ox.ac.uk. charles.rahal@demography.ox.ac.uk (University of Oxford) Thu, 01 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15021 https://www.ncrm.ac.uk/training/show.php?article=14989 To be held Online, via Zoom SummaryThis three-day course provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN, R, or Stata. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or hierarchical). These models can be viewed as an extension of conventional linear and logistic regression models to account for and learn from the clustering in the data. Such models are appropriate when, for example, analysing exam scores of students nested within schools, or health outcomes of patients nested within hospitals. Special interest lies in disentangling social processes operating at different levels of analysis by decomposing the within- from the between-cluster effects of covariates (explanatory or predictor variables). Longitudinal data are also clustered, with repeated measurements on individuals or multiple panel waves per survey respondent. Throughout the course we emphasize how to interpret multilevel models and the types of research question they can be used to explore. Full course information and excerpts can be viewed here: https://www.bristol.ac.uk/cmm/learning/introworkshop.html  Testimonials“The course was excellent - far exceeded expectations. The course has given me the confidence to use MLM, something I very much lacked before. I feel I understand the theory behind MLM, why each stage is so important, and the various interpretations. Without this course I would be lost. I cannot thank you all enough.”“This was a beautifully constructed course. It was clear throughout that careful thought had been given to providing a balance between lecture content, time for questions and discussion, and practical sessions. Both George and Bill delivered fantastic lectures - explanations were clear and thorough (including critiques of each approach) and content built up in complexity over time with plenty of worked examples of different kinds. The course was superb - can't rate it highly enough.”“I thought it was a really good double act between George and Bill - they are both hugely knowledgeable so having one person focused on the slides and the other manning the chat was a good approach as it meant the teaching didn't get derailed by people's questions.”Topics Overview of multilevel modellingVariance-components modelsRandom-intercept models with covariatesBetween- and within-effects of level-1 covariatesRandom-coefficient modelsGrowth-curve modelsThree-level modelsReview of single-level logistic regressionTwo-level logistic regression FormatThe course will consist of a 2:1 mix of lectures and hands-on practical sessions applying the taught methods to real datasets. The lectures are software independent and are delivered live via Zoom, but recordings of the lectures will be made available shortly afterwards for twelve weeks following the course if participants are unable to attend at the scheduled time. The instructors alternate the lecturing. Participants can ask questions via Zoom’s text-based chat facility and these will be monitored and answered by the instructor not presenting or relayed to the instructor presenting to answer live.Each lecture is immediately followed by a self-directed practical, offered in participants’ choice of MLwiN, R, or Stata, giving participants the chance to replicate the presented analyses and to consolidate their knowledge. At the end of each practical session the instructors demo the different software, each in a different breakout room.ApplicationsIf you would like to attend the workshop, please complete and submit the online booking form (see below). Please note the closing date for applications is 22nd November 2026.Applications will be processed on a rolling basis, once a week, until the application deadline. A link to the University of Bristol’s online shop will be provided and your place on the course will be confirmed upon successful payment.If you have any queries, please email info-cmm@bristol.ac.uk. info-cmm@bristol.ac.uk (Centre for Multilevel Modelling, University of Bristol) Fri, 09 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14989 https://www.ncrm.ac.uk/training/show.php?article=14991 This online course on questionnaire design, explores question wording issues and the questionnaire-as-a-whole with a focus on web surveys and mobile-friendly web surveys. The course is full of practical advice. It also provides tips for anyone moving from interviewer-administered surveys to web surveys. Mirroring in-person training, this course will be interactive. There will also be 6 small group workshops to facilitate putting the course concepts into practice.Questionnaire DesignThe popularity of web surveysGetting started with a new questionnaireTrade-offs – short and simple versus clearFour cognitive stages a respondent goes through in answering a survey questionSolutions to ambiguous term, understanding recall error and reducing question sensitivityQuestion wording guidelines - This about the do's and don'ts of writing survey questions for any context.Workshop 1 - Critiquing a survey questionSome additional issues with factual questionsHighlights from mini appendix: Demographic questions are always the most difficult to writeWorkshop 2 - Writing a survey questionMini appendix on actual versus usual behaviourHighlights from mini appendix on some additional issues with subjective questionsKnow the deeper issues with open and closed questionsProblematic question formats to be aware of or avoid (eg. agree / disagree)Mini appendix on other problematic formats (satisfaction, tick all that apply, ranking and hypothetical questions)Web surveysDon't rely on web survey software templatesWorkshop 3 - Critiquing web survey software templatesModes of quantitative data collectionModes of quantitative data collection: Mixing modesModes of quantitative data collection: Overall mode differences (the obvious ones)Mini Appendix on mode effects due to satisficingWorkshop 4 - Interpreting data from a mixed mode experimentBack to web surveysDesigning the web survey itselfDay 2 appendix - 8 question testing methods for web surveysThe special things that web surveys can do, but should we?Visual versus not visualMini appendix on tips for paper self-completionWorkshop 5 - Visual problemsWeb surveys for mobile phones - earlier evidence, current thinkingHighlights from mini appendix on data collection differences: What should you do?Mini appendix on "push to web"Back to questionnaire designExamples of question revisions based on testing resultsWorkshop 6 - Revising survey questionsHighlights from mini appendix on extra tasks on mobile phones By the end of the course participants will:Have greater questionnaire design skills in general and the ability to critique existing web survey software templatesHave the ability to create effective web survey questionnaires as well as mobile-friendly onesHave better knowledge about questionnaire-related mode differences and effectsThis course is for anyone interested in questionnaire design for web and mobile web surveys. Ideally participants need some familiarity with surveys and questionnaire design.Preparatory ReadingDesirable for participants to read:https://web.stanford.edu/dept/communication/faculty/krosnick/docs/2009/2009_handbook_krosnick.pdfhttps://www.gesis.org/fileadmin/admin/Dateikatalog/pdf/guidelines/mixed_device_mobile_web_surveys_beuthner_2021.pdfPLEASE NOTE THIS COURSE IS TAUGHT OVER THREE DAYS (10:00-15:00), AND EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 23 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14991 https://www.ncrm.ac.uk/training/show.php?article=15022 Why do our friends often know one another, even though we are only a few connections away from people in very different social circles? Why does information spread easily through some networks, while changes in behaviour require repeated encouragement? Who should be targeted in an intervention, and how does the answer depend on what we want to achieve?Social network analysis offers a theoretical framework and a methodology to answer questions like these by focusing on the relationships between and among social entities. A network is a structure composed of units and the relationships that connect them. The position of these units and the overall structure help us understand how social ties form, how information and behaviour spread, and how individuals gain access to resources and opportunities.The focus of the course is on the connection between the mathematical foundations of networks and the social phenomena we want to understand. We will introduce concepts from matrix algebra and graph theory across both days, and use empirical examples and practical exercises to examine what these concepts tell us about social life. We will also discuss how network data are collected and what evidence is needed to distinguish between different explanations for the patterns we observe.The course covers:Mathematical foundations of social networks: matrix algebra and graph theory;Network structure, cohesion and models of network formation;Centrality, betweenness and brokerage;Contagion, diffusion and network interventions;Network data, sampling and research designLearning outcomes:Develop an understanding of social phenomena through social network theory;Learn how to use and interpret graph-theoretic and matrix algebra concepts with real-world data;Understand how local patterns of connection contribute to the overall structure of a network;Examine how network position and structure affect social processes;Acquire knowledge of network data collection and the implications of sampling and missing data;Assess competing explanations for observed network patterns and identify the evidence needed to investigate them.The course is aimed researchers and analysts interested in using social network analysis to understand social phenomena, including students, academics, government researchers and researchers in third sector organisations. The course is relevant to work in sociology, survey research, public health and related disciplines.Pre-requisitesBasic familiarity with data matrices and introductory statistical concepts. No previous training in social network analysis is required. Mathematical concepts will be introduced as they are used throughout the course. Practical exercises and demonstrations will accompany the theoretical sessions. Software requirements will be circulated before the course.THIS COURSE IS BEING DELIVERED OVER TWO DAYS. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 23 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15022 https://www.ncrm.ac.uk/training/show.php?article=15064 This online course introduces innovative sampling methods that are intuitive and feasible to apply. Rather than sampling from a predefined list, which is often not available, we sample from network connections instead, even if the connections are not known in advance. Whether you are a statistician, data scientist, biologist, or sociologist, you may find yourself studying data that has an inherent network structure. Have you ever opened a standard textbook on sampling, but could not find any methods that seem to help you? Or have you wondered how your data might be generalised to the real-world population it represents?For example, you may be interested in social networks, but you are not sure how to collect the data in a way that capitalises on the links among the individuals. Or, during an epidemic, one knows one must track the people in contact with the confirmed cases, but you may have wondered what one needs to do to be able to estimate the case prevalence at the same time. For a third example, you may have heard that Google calculates PageRank based on randomly crawling the hyperlinks from one webpage to another, but you might want other measures of connectivity, such as small cliques of webpages with mutual hyperlinks to each other. Or perhaps you work with spatial data and are used to treat space continuously. Here, it is also possible to introduce a network structure among all the actual locations and represent the spatial data on a graph. If your problem resembles any of these examples, this is the course that will help you. Unlike the standard sampling methods, where units are selected from a list at each stage, we will introduce you to novel approaches where the sampling is based on the connections among the units, i.e. network-driven, even when the connections are not known in advance. You will be introduced to many such sampling methods as well as the unifying ideas that enable them, which have applications to various socio-economic, technical, biological or environmental phenomena.Each method will be explained using small datasets that can be easily visualised so that the level of abstraction is kept to a minimum, and the accompanying computer workshops shall allow one to explore the underlying ideas directly through numerical results without having to labour with the relevant mathematical formulae and equations. The course covers the following topics, organised across 5 sessions.Multiplicity sampling, such as indirect sampling or line intercept sampling. Some examples: customer-to-product transaction data, sampling siblings via households for gene study, line-in-space transecting animal-tracks for the purpose of wildlife conservation.Adaptive network sampling, where a connection is sampled only if it satisfies a threshold condition. Some examples: persons-in-contact during an epidemic, contiguous cells-in-space in species abundance study.Probability snowball sampling, where the sample grows wave-by-wave by the new connections encountered. Some examples: sampling friendship networks, actor-actor co-casting for equitable professionalism. Walk sampling by random, targeted and lagged walks. Some examples: Google PageRank, efficient sampling of spatial objects, dynamic monitoring of a specific subpopulation.Graph sampling as the unifying framework, where the interest may be any specific subgraphs such as nodes, edges, triangles, squares, K-stars. Generally applicable sampling strategies.By the end of the course participants will:Have been introduced to a range of network-driven sampling methodsHave obtained first-hand experience with various associated probability-based estimators Be aware of the unifying ideas of graph sampling strategies applicable to a variety of situationsPre-requisitesBasic knowledge of finite population sampling methods, such as simple random sampling, Poisson sampling would be ideal. Ideally also knowledge of two basic inverse-probability-weighted population total estimators, i.e. the Horvitz-Thompson estimator based on sampling without replacement, and the Hansen-Hurwitz estimator based on sampling with replacement. Basic skills with R and Rstudio - ideally knowledge of: Using R to perform calculations or simulations, output or plot results.Use of given scripts in R to execute simple algorithms including loops. Modify the given R scripts or write own codes to explore alternatives and different ideas (if interested).It is possible to follow the course without attending the computer workshops, but they help to understand implementation and provide illustrations of the learned material.Target AudienceAnyone working with data that has an inherent network structure. The course is suitable for people from all career stages with a quantitative or statistical background, including both senior and junior researchers, post-graduate and doctoral researchers. You may be a statistician by profession or a researcher from an applied area. You may be a data scientist, biologist, sociologist, psychologist, economist and so on, who uses sampling methods, or simply would like to find out whether network-driven sampling methods might help you to tackle the problems in your profession.THIS COURSE IS BEING TAUGHT EVERY THURSDAY OVER 5 WEEKS FROM 18 FEBRUARY - 18 MARCH (10:00-12:00, hour break 13:00-14:00) AND EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15064 https://www.ncrm.ac.uk/training/show.php?article=14986 This live online course (administered via Zoom) offers an engaging introduction to Structural Equation Modelling (SEM) using R, the popular open-source software for statistical computing and graphics. The course will run over 5 consecutive days and will combine synchronous live lectures, individual exercises, group discussions and small group supervisions to simulate a live classroom environment. High contact with our expert instructors and interactive group work will further contribute to recreating the classic Cambridge experience in an online setting.This course will present the lavaan package, rapidly becoming the tool of preference for SEM in R. Participants will actively work through practical examples to gain first-hand experience in the application of factor analysis and other more advanced latent trait models. You will learn the following topics in this course:  Introduction to R and data analysisBasic concepts of factor analysis (EFA/CFA/Categorical CFA)Basic and advanced Structural Equation Models (SEM)Application of mediation & moderation techniquesMulti-level modellingLatent growth curve modellingMultiple-group analysis and evaluation of continuous and categorical data measurement invariance, as well as longitudinal measurement invarianceYou don’t need to know R to follow the course. However, if you are not familiar with R, you will need to choose the 5 day option, which includes an introduction to the R software on Day 1. On completion, participants should have a good knowledge of the topics covered and have acquired an independent use of R and latent trait analysis.We believe in active learning and developing practical skills. Thus, the necessary theoretical introduction will be illustrated with practical examples and we will be working with real data. No prior knowledge about SEM is assumed. The pace of teaching is adjusted to suit the level of the participants. Teaching will be in small groups so that delegates can make the most of the teaching. ls523@cam.ac.uk (University of Cambridge Psychometrics Centre) Mon, 28 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14986 https://www.ncrm.ac.uk/training/show.php?article=15052 The size, structure and dynamics of the population in which we live provide some of the most fundamental facts about our society. This course aims to give participants a practical introduction to the techniques of demography, including methods for dealing with each of the components of population change (mortality, fertility, and migration). The course will include hands-on computer workshops using the R programming language.This course is intended as an introduction for and a companion to a related NCRM course Population Estimation and Forecasting, which will run from 7-9 April 2027. Participants may choose to attend one or both courses as their prior experience and time allows. The course covers: Introduction to demographic measurementSources of demographic dataThe life table; stable population theoryApproaches for dealing with unreliable dataThe analysis of fertility Migration estimationBy the end of the course participants will:Understand the basics of demographic measurement and the key sources of demographic dataUnderstand and employ lifetables and the Kaplan-Meier estimatorUnderstand and employ methods for the analysis of migrationBe able to apply these approaches to a range of demographic data across different contexts and at different scalesPre-requisitesThis practical course will include workshops that use the R programming language to demonstrate the techniques introduced in the course. Basic knowledge of R would be beneficial, but is not essential. You will be able to follow the course just by attending the lecture sessions and carefully reading and digesting the workshop material, although of course, you will get more out of the course by completing the workshop exercises in R. Pre-course material will also provide links to free online introductory R courses to aid preparation.Target AudienceThe  course is of interest to anybody with an interest in quantitative methods from across disciplines (e.g. social sciences, economists etc), sectors and from across the career life-course (student, junior or senior researchers). The  course is particularly suited to:  Analysts working at or with, for example, National Statistical Offices, International Non-Governmental Organisations, and Government Departments with an interest in population estimation and projection. Examples include the ONS, the DHCLG; local and devolved government bodies; UN agencies. Employees of private sector organisations who have an interest in population change:Consumer-focussed industries; RetailersMarket research agenciesUtility companiesConstruction companiesPhD students working on population-related topics including demography, population health, human geography, etc.THIS COURSE IS BEING DELIVERED IN-PERSON AT THE UNIVERSITY OF SOUTHAMPTON (BUILDING 39, ROOM 2015) AND WILL RUN FROM 10:00 to 17:00 ON 5 AND 6 APRIL. IT EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton ) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15052 https://www.ncrm.ac.uk/training/show.php?article=15053 At any time horizon, policy depends upon estimates of population size and structure. Without population forecasts, planning for pension spending, housing demand, healthcare provision, and school place requirements becomes impossible. To know what the size and structure of our population will look like in the future, we need to have the tools to make demographic projections confidently. This course introduces the methods for forecasting the key components of population change, dealing in turn with mortality, fertility and migration. The course also includes an introduction to matrix models for multi-population forecasting is provided, together with a discussion of how to validate and assess uncertainty in demographic projections.The course will include hands-on computer workshops using the R programming language.This course is intended to follow on from a related NCRM course Introduction to Demographic Methods, which will run on 5-6 April 2027. Participants may choose to attend one or both courses as their prior experience and time allows. The course covers: The cohort component projection modelMatrix population models Principal component methods for population estimation and forecastingValidating and assessing uncertainty in population forecastsBy the end of the course participants will:Appreciate contemporary issues in demographic forecastingBe able to produce projections of each of the demographic components (mortality, fertility, and migration)Produce full population projections using the cohort component modelUse matrix models for multi-population projectionPre-requisitesThis practical course will include workshops that use the R programming language to demonstrate the techniques introduced in the course. Basic knowledge of R would be beneficial, but is not essential. You will be able to follow the course just by attending the lecture sessions and carefully reading and digesting the workshop material, although of course, you will get more out of the course by completing the workshop exercises in R.  Pre-course material will also provide links to free online introductory R courses to aid preparation.Knowledge of basic demographic techniques is recommended, including: The distinction between rates and probabilities in a demographic contextThe life tableBasic fertility measuresCompletion of the companion course Introduction to Demographic Methods would be advantageous but is not required.Target AudienceThe  course is of interest to anybody with an interest in quantitative methods from across disciplines (e.g. social sciences, economists etc), sectors and from across the career life-course (student, junior or senior researchers). The  course is particularly suited to:  Analysts working at or with, for example, National Statistical Offices, International Non-Governmental Organisations, and Government Departments with an interest in population estimation and projection. Examples include the ONS, the DHCLG; local and devolved government bodies; UN agencies. Employees of private sector organisations who have an interest in population change:Consumer-focussed industries; RetailersMarket research agenciesUtility companiesConstruction companiesPhD students working on population-related topics including demography, population health, human geography, etc.THIS COURSE IS BEING DELIVERED IN-PERSON AT THE UNIVERSITY OF SOUTHAMPTON (BUILDING 39, ROOM 2015) AND WILL RUN FROM 10:00 to 17:00 ON 5 AND 6 APRIL. IT EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton and Department of Social Statistics and Demography) Tue, 20 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15053 https://www.ncrm.ac.uk/training/show.php?article=14949 This one-day course is aimed at those who need to learn about the very basics of analysing and presenting statistical data.The topics covered in this course are summarising data with numbers, tables and graphs (including the Normal distribution and confidence intervals). These topics will be demonstrated using Excel and SPSS.If you are interested in this course you may also be interested in our Survey Design & Analysis courseSee our full range of courses (and options for bespoke courses) at https://go.herts.ac.uk/sscu or contact the course organiser, Professor Neil Spencer, at statistics@herts.ac.uk.No prior knowledge of statistics is required. statistics@herts.ac.uk (University of Hertfordshire) Wed, 19 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14949 https://www.ncrm.ac.uk/training/show.php?article=15065 The course aims to provide participants with an understanding of the key issues involved in the design, implementation and analysis of sample surveys, with particular emphasis on sample design, data collection procedures, non-response, questionnaire design and testing, and the handling of missing data. Participants will gain practical insights into survey design and management, as well as the strengths and limitations of probability-based and non-probability-based surveys.The course is designed for a broad range of professionals, including data analysts working across different disciplinary fields, government analysts, policy professionals, survey designers, researchers and PhD students. It will be particularly valuable for anyone involved in analysing survey data, designing or commissioning surveys, interpreting survey findings, or developing policies and making decisions that rely on statistical analysis of survey data. The course will help participants critically assess survey quality, understand the limitations of survey evidence and make more informed use of survey data in research, policy development and practice.The course covers: Total Survey Error FrameworkSurvey samplingSurvey non-responseProbability-based vs non-probability-based surveysSurvey modeQuestionnaire design and testingBy the end of the course participants will:Demonstrate understanding of the key principles and practices involved in survey design, implementation and data analysis.Explain and evaluate the principles of questionnaire design and testing, drawing on relevant survey methodology literature and best practice.Identify, describe and critically evaluate a range of sampling designs, including probability-based and non-probability-based surveys, and understand their respective strengths and limitations.Identify and evaluate potential sources of survey error, including issues relating to sampling, non-response and data collection, and understand approaches to assessing and minimising their impact on survey quality.Demonstrate understanding of approaches to identifying and handling missing data in survey analysis, and assess their implications for the interpretation of survey findings.Critically assess the quality, limitations and suitability of survey data for particular research questions, policy development and decision-making.Interpret survey findings critically and understand the implications of survey design and data quality for statistical analysis and evidence-based policy.Target AudienceThe course is designed for a broad range of professionals, including data analysts working across different disciplinary fields, government analysts, policy professionals, survey designers, researchers and PhD students. It will be particularly valuable for anyone involved in analysing survey data, designing or commissioning surveys, interpreting survey findings, or developing policies and making decisions that rely on statistical analysis of survey data.THIS COURSE WILL TAKE PLACE ON 24 MAY (10:00-17:00), 25 MAY (9:00-17:00) AND 26 MAY (09:00-16:00) AND EQUATES TO THREE TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 23 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15065 https://www.ncrm.ac.uk/training/show.php?article=14950 This two-day course is aimed at those who need to plan and design questionnaire-based surveys and those who need to analyse the data collected. The first day covers the vital activities of designing a quality survey and obtaining a good sample. It covers the following topics.Defining your sample Choosing the right sampling methods to obtain a representative sample (e.g. stratified sampling, cluster sampling, multi-stage sampling,Deciding on the best way to collect your data (e.g. online, face-to-face, postal questionnaire,Designing questions to get good quality answers Deciding on the sample size needed to get estimates that have sufficient accuracy Using effect sizes and power to decide the sample size needed for statistical testsThe software G*Power will be used to demonstrate sample size calculations.The second day will cover summarising the results of your survey, presenting the results and undertaking statistical tests. It concentrates on those analyses which are particularly appropriate for the sort of data commonly collected using questionnaires. It covers the following topics.Creating graphs and chartsCalculating summary statisticsTesting for differences between groupsInvestigating relationships between variablesFitting regression modelsThe software Excel and SPSS will be used to demonstrate sample size calculations.No prior knowledge of statistics is required.See our full range of courses (and options for bespoke courses) at https://go.herts.ac.uk/sscu or contact the course organiser, Professor Neil Spencer, at statistics@herts.ac.uk. statistics@herts.ac.uk (University of Hertfordshire) Wed, 19 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14950 https://www.ncrm.ac.uk/training/show.php?article=14987 This live online course (administered via Zoom) offers an engaging introduction to Structural Equation Modelling (SEM) using R, the popular open-source software for statistical computing and graphics. The course will run over 5 consecutive days and will combine synchronous live lectures, individual exercises, group discussions and small group supervisions to simulate a live classroom environment. High contact with our expert instructors and interactive group work will further contribute to recreating the classic Cambridge experience in an online setting.This course will present the lavaan package, rapidly becoming the tool of preference for SEM in R. Participants will actively work through practical examples to gain first-hand experience in the application of factor analysis and other more advanced latent trait models. You will learn the following topics in this course:  Introduction to R and data analysisBasic concepts of factor analysis (EFA/CFA/Categorical CFA)Basic and advanced Structural Equation Models (SEM)Application of mediation & moderation techniquesMulti-level modellingLatent growth curve modellingMultiple-group analysis and evaluation of continuous and categorical data measurement invariance, as well as longitudinal measurement invarianceYou don’t need to know R to follow the course. However, if you are not familiar with R, you will need to choose the 5 day option, which includes an introduction to the R software on Day 1. On completion, participants should have a good knowledge of the topics covered and have acquired an independent use of R and latent trait analysis.We believe in active learning and developing practical skills. Thus, the necessary theoretical introduction will be illustrated with practical examples and we will be working with real data. No prior knowledge about SEM is assumed. The pace of teaching is adjusted to suit the level of the participants. Teaching will be in small groups so that delegates can make the most of the teaching. ls523@cam.ac.uk (University of Cambridge Psychometrics Centre) Mon, 28 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14987