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/ Mon, 21 Sep 2026 03:26:35 +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=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) Mon, 06 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14877 https://www.ncrm.ac.uk/training/show.php?article=14905 This webinar will introduce key approaches to causal inference when using longitudinal data and explain how to apply these methods within CLS cohort data.About the eventJoin our experts for an overview of the challenges – and opportunities – involved in making causal inferences using data from four national cohort studies based at CLS: 1958 National Child Development Study (NCDS), 1970 British Cohort Study (BCS70), Next Steps and Millennium Cohort Study (MCS). The session will cover:The importance of formulating a clear causal research question as the first step of a research project.The three main sources of bias that arise when using causal inference methods: confounding, selection, and measurement. Use of causal Directed Acyclic Graphs (DAGs) to understand these biases and the assumptions they require.Understanding how DAGs can inform subsequent analyses, focusing on the ‘Elaborate Theories’ approach that can be easily applied to CLS cohort data.The statistical method of Quantitative Bias Analysis (QBA), which allows researchers to assess whether observed associations reflect bias or true causal effects. Who should attend?This webinar is aimed at researchers who want to use CLS cohort data to answer causal research questions. Researchers at all levels and across disciplines will benefit from this webinar. No prior experience of working with the data is required.  radhika.jhamaria.23@ucl.ac.uk (UCL Centre for Longitudinal Studies) Wed, 22 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14905 https://www.ncrm.ac.uk/training/show.php?article=14971 Longitudinal research frequently seeks to understand not only whether people or other units change over time, but also whether they differ in their starting points, rates of change, and the factors associated with these differences. Growth modelling provides a family of statistical approaches for addressing such questions.This masterclass introduces longitudinal growth modelling with a particular focus on Structural Equation Modelling (SEM). The first part of the session will introduce the concepts underlying growth models, contrast multilevel and SEM approaches, and consider the distinctive possibilities offered by latent growth models. The second part will provide a worked demonstration in R, using lavaan to specify, estimate, interpret and extend a basic latent growth model.The session is intended for researchers who already have some familiarity with quantitative analysis. Previous exposure to regression and/or SEM will be helpful, but specialist knowledge of longitudinal modelling is not assumed.The session is free to attend and will take place online via Microsoft Teams. J.E.Hall@Soton.ac.uk (University of Southampton) Wed, 09 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14971 https://www.ncrm.ac.uk/training/show.php?article=14906 About the eventThis webinar highlights the value of using lifecourse data to support physical health research using relevant measures available in four British cohort studies:1958 National Child Development Study (NCDS)1970 British Cohort Study (BCS70)Next StepsMillennium Cohort Study (MCS).Why attend?Learn about the types of physical health information collected, and examples of what we’ve already learned about physical health from the cohorts to help shape your own research questions.The session will include:an outline of physical health data and measures available in the CLS cohorts, including objective and self-reported anthropometrics, biomarkers, health behaviours and linked administrative dataan explanation of the process for accessing the data and resourcesa showcase of research using physical health measures in the cohortsan overview of research opportunities that the data can offera Q&A with our expert panel.Who should attend?This webinar will be beneficial for students, academics, third sector and policy individuals from across disciplines, and all career stages, who are interested in using physical health measures from the cohorts in their research. No prior experience of the data is required.It will also be of interest to those wishing to conduct cross-cohort analyses. radhika.jhamaria.23@ucl.ac.uk (UCL Centre for Longitudinal Studies) Wed, 22 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14906 https://www.ncrm.ac.uk/training/show.php?article=14958 A three-day online course, delivered over three weeks, on using AI agents to improve survey research workflows while preserving privacy, reproducibility, review discipline, and institutional data-governance boundaries.The course adapts agentic coding methods to the survey-research lifecycle: designing survey instruments, analysing survey data, documenting metadata and derived variables, checking outputs, and preparing reproducible or release-ready packages. Participants work hands-on with Cursor, GitHub, and role-specific repository workflows, producing auditable artifacts they can reuse in their own organisations.The course covers:• Agentic coding foundations and Cursor workflows for survey research• GitHub issues, branches, pull requests, review logs, and acceptance criteria• Project guardrails: rules, AGENTS.md, .cursorignore, .gitignore, and documented data-use boundaries• Restricted-data decision rules for cloud AI, local models, metadata-only work, synthetic data, and TRE/sandboxed environments• Spec-driven development for survey workflows• Applied researcher track: literature/context, safe data exploration, cleaning, descriptives, regression or standard analysis, tables/figures, reproducible write-up support• Survey practitioner track: measure search, questionnaire specification, respondent materials, piloting, ethics documentation, testing notes, fieldwork monitoring• Survey data manager track: metadata, derived-variable specifications, longitudinal consistency, cleaning checks, disclosure checks, deposit/user documentation• Reusable skills and subagents for measure mapping, questionnaire review, metadata checks, derived-variable checks, disclosure risk review, and user-guide review• Role-specific capstone packaging: reproducible analysis, questionnaire/specification workflow, or data-release documentation• 30-day adoption planning for post-course implementationBy the end of the course participants will:• Set up a practical agentic workflow using Cursor, GitHub, and a role-specific repository structure• Use issues, branches, pull requests, review logs, and acceptance criteria to make survey workflow tasks auditable• Apply restricted-data decision rules for cloud AI, local models, metadata-only work, synthetic data, and TRE/sandboxed environments• Configure project guardrails with rules, AGENTS.md, .cursorignore, .gitignore, and documented data-use boundaries• Turn a survey workflow into a spec-driven plan with a safe input map, role-specific deliverables, and verification checks• Build or adapt reusable skills and subagents for survey-specific work such as measure mapping, questionnaire review, metadata checks, derived-variable checks, disclosure risk review, and user-guide review• Package a reviewed output for their role track: reproducible analysis, questionnaire/specification workflow, or data-release documentationTarget AudienceThe course is designed for a mixed audience across the survey research lifecycle:• Applied researchers using existing survey data for empirical social science (economics, sociology, political science, and related disciplines)• Survey practitioners designing questionnaires, respondent materials, pilot tests, and fieldwork processes• Survey data managers preparing, documenting, checking, and releasing survey datasets• Government researchers, academic researchers, and staff at survey agencies and research institutions• Mixed seniority: junior and senior researchers, analysts, and operational staffTechnical levels range from participants comfortable in R/Stata/Python to participants who mainly work through specifications, documents, and operational workflows. Recommended cohort cap: 20 participants unless additional facilitators support breakouts and troubleshooting.Pre-requisitesNo specialist prior knowledge of agentic coding or AI tools is required. Participants should:• Be comfortable using a computer, web browser, and video conferencing• Have interest in survey research workflows (design, analysis, or data management)• Be willing to create GitHub and Cursor accounts before Day 1• Complete the pre-course setup checklist or attend the setup clinic• Choose one role track before or during prework: applied researcher, survey practitioner, or survey data manager• Bring a non-sensitive workflow description, public dataset, synthetic example, questionnaire specification, or metadata extractSoftware prerequisites:• Cursor and GitHub accounts (required)• Python 3.10+ recommended for examples; not required for participants using R/Stata/SPSS patterns only• Participants must avoid bringing real restricted microdata unless explicit permission exists for the exact tool workflowPreparatory readingRequired before Day 1:• Create GitHub and Cursor accounts• Complete the course setup guide and prework checklist (course repository)• Choose one role track: applied researcher, survey practitioner, or survey data manager• Select a non-sensitive workflow or use the provided synthetic survey example• Read the restricted-data safety guidanceDesirable:• Attend the pre-course setup clinic if you have limited admin rights or limited Git/GitHub experience• Review basic GitHub concepts: repositories, issues, branches, and pull requests• Install minimum viable toolset: Cursor, browser access to GitHub, ability to download/unzip files• Optional recommended setup: Git, Python 3.10+, Node.js 18+, and any role-specific tools (R, Stata, SPSS)Course materialsParticipants will receive course slides and access to course repository materials.Participants should have/bring:• A computer with internet access and ability to join live video sessions and share screen• GitHub and Cursor accounts (set up before Day 1)• A non-sensitive workflow description, public dataset, synthetic example, questionnaire specification, or metadata extract for their capstone work• Headphones/microphone recommended for breakout-room workParticipants should NOT bring real restricted microdata unless explicit permission exists for the exact tool workflow.The course will run over 3 live online sessions on 5, 12 and 19 November (13:00-17:00) and there will be asynchronous assignments between sessions. This course equates to two teaching days for payment purposes. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 21 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14958 https://www.ncrm.ac.uk/training/show.php?article=14907 New to the CLS cohort studies? This webinar will give you an overview of four internationally renowned national cohort studies and the wide range of opportunities they offer to researchers.About the eventThis flagship CLS webinar will give first-time users an insight into four world-leading cohort studies. These studies follow generations born between 1958 and 2002 and are a powerful resource for addressing a wide range of scientific questions in various social, economic, health, political and geographical sciences. The data are free to download from the UK Data Service. What’s covered?You will learn about the data collected in each study, possible research topics, how to access and get started with the data and examples of the types of analyses that can be undertaken using the following studies: 1958 National Child Development Study 1970 British Cohort Study Next Steps Millennium Cohort Study. Why attend?Gain an overview of the type of research topics that the data could offer.Find out about the types of analyses that can be performed with the data.Learn how the cohort studies could be used in coursework or dissertations.Discover how to access and get started handling the data.Get answers to specific questions about the cohort studies and how to use them.Who should attend?This webinar is suitable for a wide range of researchers including: postgraduate students (Masters and PhD) interested in using the cohorts for a dissertation or thesisundergraduate students looking for data resources for their final year dissertationresearchers in academia (particularly early career researchers)researchers in the public or the third sector new to the cohort studies.  radhika.jhamaria.23@ucl.ac.uk (UCL Centre for Longitudinal Studies) Wed, 22 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14907