NCRM is a Hub-Node network of research groups, each conducting research and training in an area of social science research methods, coordinated by the Hub at the University of Southampton. https://www.ncrm.ac.uk/training/ Tue, 09 Jun 2026 12:30:08 +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=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=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=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=14838 Learn the essential principles of research project management at your own pace.This practical online self-study course will equip you to better manage any type of research – at whatever stage of your career – so you can:Identify the key skills every research project/programme manager needsUnderstand which skills are most in demand – inside and outside of academiaExplore the four core research project management stages necessary for delivering research objectives on time and on budgetFind out how to get up to speed on any project – at whatever stage you join itDiscover the Top 10 challenges research project managers face and tried and tested solutions for handling themShow funders that your project will complete within timeframe and resource limitations.Take away simple tools, templates and checklists to help you:Identify project stakeholders and prioritize their needsAssess the impact of potential risks to your research and discover tried and tested strategies for overcoming the most common threatsIdentify what skills and resources your project needs to achieve its objectivesBreak your project into manageable tasksWork out how long it should take to complete any given projectIdentify the critical activities you’ll need to monitor closely to make sure you complete your research as plannedTrack any project through to completion...…and much more!Additional benefits:Eight modules, fully recorded over 27 video lectures (no live component)Take away simple tools, templates and checklists you can use to manage any research projectSix months access to all video lectures and course materials. contact@evaluationworks.co.uk (Evaluation Works) Wed, 06 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14838 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=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=14809 JBI Scoping Review Workshop (Online Short Course) This JBI-accredited course covers the key principles, steps and reporting guidelines for undertaking a Scoping Review and explores how Scoping Reviews are different from Systematic Reviews. The course is delivered by experts from the University of Nottingham Centre for Evidence Based Healthcare and will run online over two days (November 19th & 20th 2026, 09:30-13:30 on each day).  catrin.evans@nottingham.ac.uk (University of Nottingham) Wed, 15 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14809 https://www.ncrm.ac.uk/training/show.php?article=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=14797 Course detailsDate: Monday 2nd & Monday the 9th November 2026Time: 09:00 – 13:00Location: This is an online course conducted via ZoomDuration: 8 learning hoursWho this course is forThis course is ideal for people who have an awareness of evidence-based health. It's also useful for people who are in the process of or about to undertake a systematic review. Previous delegates have been:ResearchersHealthcare professionalsAcademic cliniciansEducation and policy commissionersMedical studentsPhD or MSc studentsLearning outcomesAfter completing this course, you will understand:What a systematic review isScoping the research question and writing a protocolLiterature searchingInclusion/exclusion screeningData extraction and critical appraisalData synthesisPlease note this course will NOT cover realist synthesis or qualitative analysis.This course is delivered by Southampton Health Technology Assessments Centre (SHTAC)Course Leaders:Jonathan ShepherdCourse TutorsGeoff FramptonEmma MaundKaren PickettJonathan ShepherdLois Woods  A.Vincent@soton.ac.uk (University of Southampton) Wed, 27 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14797 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=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=14692 Want to learn more about mixed methods research? Need support or advice with your mixed methods research? Our online course could help you!The ‘Introduction to Mixed Methods Research’ course from (Methodical) will be delivered by experienced mixed methods researchers Dr Sarah Jasim, a senior research fellow at University College London and London School of Economics and Dr Ruth Plackett, a senior research fellow at King’s College London.The course will cover:Key principles and procedures in mixed methods researchWhat is mixed methods research and why do we use it?How to plan a mixed methods research project.Understanding models of sequence and priority used in mixed methods research.How to analyse data and combine results.There will be opportunity to discuss your own research questions, methods, and desired outcomes.Relevant for PhD students, post-docs and researchers across disciplines and industries.The course will also be available on Monday Mar 2nd or Monday October 19th, 2025, 10-4pm online.   ruth.l.plackett@kcl.ac.uk (KCL) Thu, 22 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14692 https://www.ncrm.ac.uk/training/show.php?article=14767 This two-part in-person training course combines critical reflection with hands-on practice to help researchers navigate generative AI thoughtfully and responsibly. The first session explores what AI means for higher education and research at this moment of rapid change, examining both opportunities and risks. The second session is a practical workshop where participants bring their own work and AI tools to explore ethical and professional use, developing personal principles for responsible AI integration into research practice. Participants must bring their own device with access to a generative AI chatbot they already have an account with and have previously used (such as ChatGPT, Claude, Gemini, or Copilot).The course covers: The current landscape of generative AI in higher education and academic researchHow AI is reshaping academic work, including writing, analysis, and collaborationOpportunities and risks of AI adoption in research contextsEthical considerations around integrity, authorship, and responsibilityPractical exploration using participants' own research materials and AI toolsScenario-based discussions on responsible AI usePeer exchange on emerging practices and challengesDeveloping personal guiding principles for AI use in researchBy the end of the course participants will:Articulate a clearer understanding of what generative AI means for researchers and scholarshipCritically evaluate the opportunities and risks of AI in their own research contextReflect on how language models are entering their research processesIdentify key ethical considerations around integrity, authorship, and responsibility when using AIExperiment critically with AI tools using their own research materialsBegin developing their own guiding principles for responsible AI useShare and learn from peers' emerging practices and approachesScheduleWednesday 14th October 2026, 10:00 - 16:00LocationRoom 1.69, Humanities Bridgeford Street Building, The University of Manchester, M15 6ADPre-requisitesSome prior experience using a generative AI chatbotAn active account with a generative AI tool of your choice A paper they have published (open access or pre-print version)A work-in-progress paper or chapterAccess to their preferred AI chatbotPresenterDr Mark Carrigan FRSA FHEA is a Senior Lecturer in Education at the University of Manchester, where he co-leads the Digital Education Manchester group and serves as an AI Fellow at the Institute for Teaching and Learning. His work centers on three interconnected commitments: developing ontological and epistemological frameworks for understanding Large Language Models (LLMs) beyond current inadequate conceptualisations; examining higher education as a critical site where the social and cultural dynamics of LLMs unfold through practical challenges; and advancing Margaret Archer’s morphogenetic approach as a route to addressing these urgent questions.He is the author of Platform and Agency: Becoming Who We Are (Routledge, 2025), which develops a framework for understanding personal transformation in the digital age. His recent work includes Generative AI for Academics (Sage, 2024) and Social Media for Academics (Sage, 2nd edition), alongside eight other books. He co-edited Building the Post-Pandemic University (Edward Elgar, 2023), examining how universities are transforming in response to technological and social disruption.  p.c.white@southampton.ac.uk (University of Southampton) Mon, 11 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14767 https://www.ncrm.ac.uk/training/show.php?article=14668 Overview of 2-day courseAre you working in Learning Analytics or Student Analytics?Ever been asked if the average mark is changing significantly over academic years, or if the difference between the rate of change for females and males is statistically significant? Or which factors are associated with non-continuation?Or which factors are associated with accepting an offer?Or if the chance of achieving a first class honours degree is associated with tariff points on entry? This two-day course provides participants with hands-on experience of analysing their own type of records for data-driven planning and confidently interpreting numerical results for reports to policy makers and committees. The focus of the course is on the use of two statistical modelling techniques:Linear regressionLogistic regressionLinear regression is used to examine how the mean of a numerical outcome, like final year mark, might be associated with different characteristics. If the outcome is binary, such as drop-out, logistic regression is used to investigate how the chance of failing to continue to the second year is associated with different characteristics.  Logistic regression is a popular modelling technique, for example it is advocated by the Office for Students in their Financial support evaluation toolkit.The course also illustrates how these modelling techniques may be used for one-step-ahead forecasting into next year.Presentations, demonstrations and hands-on computer practicals are based around the free statistical software R; see https://www.r-project.org/. Formulae are kept to a minimum; instead, we concentrate on results, their interpretation and reporting in plain language.PresentersSandro Leidi and James Gallagher Cost£516 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 10:00 and 16:30 (GMT+1). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Administrators in educational establishments working in Policy, Planning and Strategy units; Data and Insight units; Business Intelligence units; those involved in learning analytics or extracting actionable insights from student records and in reporting to policy makers or committees. Anyone in these positions needing to answer questions around how student outcomes may be associated with different factors will benefit greatly from this course.It is assumed that participants will, prior to the course, have:An understanding of mathematical functions and equations. In particular, the natural logarithmic and exponential functions (loge() and exp() respectively), the equation of a straight line and its geometrical representationAttended the one-day course Statistics for University Administrators, or Statistics for University Administrators using R, or have equivalent knowledge.No previous experience of the R software is required; a brief introduction for the purpose of the course will be given.How You Will BenefitBy the end of the course you will be familiar with two common statistical modelling methods for investigating associations and extracting actionable insights, be able to report the results in plain language, and be able to perform analyses using free statistical software. You will also be able to follow official guidance on the use of such models, e.g. the Office for Students’ guidance on the use of binary logistic regression for investigating the effectiveness of financial support with respect to student outcomes.What Do We Cover?Introduction to the R software·Simple linear regression for relating a numerical outcome to a numerical explanatory variableExtending the linear regression model to incorporate categorical explanatory variables and interactions to allow for effect modificationUsing binary logistic regression in place of linear regression when modelling binary outcomesOne-step-ahead forecasting.SoftwarePractical work will be done in R.Note:For practical work, participants must download and install the R software prior to the start of the coursePractical work is based on the Windows operating system.Extra InformationThe R software is used on the course for two reasons:It is a free dedicated statistics package and can be used for other analysesIt is a widely used software which will be maintained by the R Foundation for many years to come. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14668 https://www.ncrm.ac.uk/training/show.php?article=14791 Overview of 1-day courseIn survival analysis in medical research, the proportional hazards assumption and the hazard ratio effect measure have been popular for decades, fuelled by extensive application of the log-rank test and the Cox regression model.  However, the hazard ratio can be clinically awkward to interpret, or the proportional hazards assumption may not hold, rendering the use of a hazard ratio effect measure questionable at best.In this course we introduce the restricted mean survival time (RMST), which is a well established, but under-used summary of the survival experience.  In recent years there has been a surge of interest in the RMST, particularly in oncology, but also in many other areas. We begin with a review of the definitions of the RMST, approaches to estimation and different RMST-based effect measures which are clinically meaningful alternatives to the hazard ratio and are not based on a proportional hazards assumption.For the practical analysis of survival data, which includes right-censoring, the course focuses on a non-parametric analysis for comparing treatments and a generalised linear model (GLM) -type modelling approach based on the use of pseudo-values.  The latter provides a flexible method for directly modelling the RMST in a regression framework, where a treatment effect may be adjusted for covariates.  Model checking is also considered. The course concludes with a brief consideration of an extension to the RMST known as the window mean survival time (WMST) or the long-term RMST (LT-RMST).The course is a practical introduction to analysing survival data using a RMST-based effect measure.  Only essential theoretical aspects of the methodology will be summarised.  Examples used will be drawn from applications in medicine and health, particularly clinical trials.Practical work will be based around the statistical software R; see https://www.r-project.org/.PresentersSandro Leidi and James GallagherCost£312 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 17:30 (GMT). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked verbally or using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter.​  We also use Zoom meetings rather than webinars to encourage further interaction during an online course.Who Should Attend?Statisticians and data analysts working with survival data in medical research. Participants will be assumed to have a working knowledge of:Survival analysis techniques applicable to right censoringRegression modellingThe R statistics software.How You Will BenefitYou will acquire practical experience in the use of RMST-based effect measures as an alternative to the hazard ratio.  You will also be able carry out adjusted as well as unadjusted analyses.What Do We Cover?Problems with proportional hazards and hazard ratio effect measure.  Introduction to the RMST: definition, approaches to estimation, RMST-based effect measures as an alternative to the hazard ratio.  The restricted mean time lost (RMTL)Non-parametric analysis for comparing two groups. Statistical inference: estimation, confidence intervals and hypothesis testing for different effect measures. Advantage of RMST-based effect measures over the hazard ratioAdjusting a treatment effect for covariates. Modelling the RMST using a GLM-type model based on pseudo-values; choice of link functions and effect measures; model fitting and comparison of modelsModel checking and the use of pseudo-residualsExtending the RMST: window mean survival time (WMST)CRAN packages, including geepack for modelling, emmeans for post-processing and survRM2 for non-parametric analysis.The course does not cover time dependent covariates.SoftwarePractical work will be done in R.Note: For practical work, participants must download and install a number of CRAN packages in R.  This must be done prior to the start of the course. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 30 Mar 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14791 https://www.ncrm.ac.uk/training/show.php?article=14410 Level: Intermediate (I)This course will be delivered over 4 afternoon sessions, running from 1:00pm to 5:00pm each day.The intensive course on programming principles in R. This course covers the fundamental techniques such as functions, for loops and conditional expressions. It also covers the {tidyverse} package, {purrr}. {purrr} is a very powerful package that gives great flexibility to analysts, by enhancing R’s functional programming toolkit. By the end of this course, you will understand what these techniques are and when to use them. The course will also demonstrate how to use functions such as map(), map2() and pmap(), to iteratively map functions over multi-element objects like vectors and lists. Emphasis will also be placed on how to manipulate list outputs and how this can be applied to data..Learning OutcomesBy the end of the course, delegates will: Understand basic functions, multiple arguments and variable scopes.Have a thorough understanding for loops.Be able to apply basic functions.Have a thorough understanding of conditionals such as if, else and else if statements.Be familiar with possible R workflows such as directory structure and working with directories.Understand how the aforementioned techniques can be applied to their own data.Understand how these techniques will improve efficiency and results.Understand where to find help in R using resources and the help() function.Understand lists in R and know how to use {purrr} to map functions.Know what nested loops are and use {magrittr} to extract elements from them.Be able to create list columns and know how to access the data in them.Iteratively loop two or more objects to a function of choice using functions such as map2(), pmap() and imap().Recognize the advantages of using {purrr}.Understand how to extract elements from nested lists to achieve a desired output object class.Be able to effectively debug their code using multiple {purrr} functions for the debugging process.Save precious debugging time using e.g. safely() Topics CoveredConditionals: using if and else statements in RFunctions: what a function is, how are they used, and how can we construct our own functions.Looping in R: an introduction to the concept of looping in R. In particular for and while loops.Help: The help system in R can at first glance appear daunting, however, after the initial shock, R’s documentation is second to none.Project structure: Practical tips on how to structure a project.Data manipulation and aggregation using dplyrIntroduction to {purrr} and Lists: Introduction to lists in R and using {purrr} to map a function across a list.List-Columns and Nesting: Exploring nested data in list columns and using the mapping functions to manipulate them.Parallel Mapping: Using {purrr} functions to map over multiple lists in parallel.Manipulating {purrr} Output: Using {purrr} to efficiently extract elements from lists into vector and dataframe format, and change the hierarchy within nested lists.Best Practices in {purrr}: Showcase of functions from {purrr} which aid in the debugging process.  Target AudienceThis course is idea for anyone who would like to extend their basic familiarity with using R, and using R to write their own bespoke functions or optimizing their code. Assumed KnowledgeBasic prior experience with the R programming language is assumed. Namely that participants have some experience of R data structures, such as vectors, data frames, and experience in using pre-made functions from R packages.The course is aimed as a follow up the 'Introduction to R and Regression Modelling in R' training course.Whilst no statistical knowledge will be assumed, some of the examples will be statistical in nature.For this online course, participants are not required to have R installed on their own laptops. A virtual environment, which can be accessed through a web browser, will be used to run R and view course materials. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14410 https://www.ncrm.ac.uk/training/show.php?article=14396 Level: Professional (P)This course introduces the Bayesian approach to meta-analysis. Attendees will learn practical ways in which they can combine multiple sources of published evidence while accounting for uncertainties such as response bias, publication bias, confounding, and missing information, using either BUGS, JAGS or Stan as software. With Bayesian models, this can be transparent and reproducible.This two-day course begins by reviewing classic meta-analysis methods and expressing them as statistical models. Once attendees understand meta-analysis in this larger context, they are able to extend the model flexibly to account for common problems such as papers that report only change from baseline. A series of problems will be tackled in this course, and attendees will leave with model code that they can immediately start using with their own projects. Learning OutcomesAfter attending, participants will be able to:Write out standard meta-analyses as statistical modelsUse BUGS, JAGS or Stan to fit such models to dataRecognise several common problems in meta-analysisExtend these models to account for these problemsUnderstand and communicate their findings Topics CoveredDay 1:A review of statistical models of meta-analysis​Introduction to Bayesian analysis problems in meta-analysis, and sources of uncertaintyModels for basic DerSimonian-Laird and Biggerstaff-Tweedie meta-analysesIntroduction to Bayesian software options: BUGS, JAGS and StanDay 2:Models for network meta-analysisModels for missing statisticsModels for reporting biasModels for publication biasModels for a mixture of statisticsModels for a mixture of study typesReporting Bayesian meta-analyses Target AudienceThis course will be of interest to evidence-based healthcare researchers, including those writing guidelines and evaluating policies. Attendees should be comfortable conducting simple meta-analyses in some software but do not have to have experience of Bayesian methods. Assumed KnowledgeThis course assumes that all participants have a basic grounding in Bayesian statistics, to the level covered by the RSS courses "Introduction to Bayesian Statistics" or "Introduction to Bayesian Analysis using Stan". There is no specific software expertise required, but examples will be written in BUGS and Stan, using R as an interface. We also assume that participants are familiar with the principles of systematic reviews, for example from reading relevant parts of the Cochrane Collaboration Handbook online. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14396 https://www.ncrm.ac.uk/training/show.php?article=14871 Narrative inquiry is a valuable investigative technique in qualitative research. Narrative inquiry and storytelling offer us a different way of knowing, of investigating the lived experiences of individuals, and of exploring subjectivity. Narrative knowledge is created and constructed through the stories of lived experience and sense-making, the meanings people afford to them, and therefore offers valuable insight into the complexity of human lives, cultures, and behaviours. It allows us to capture the rich data within stories, including for example shedding insight into feelings, beliefs, images and time. It also takes account of the relationship between individual experience and the wider social and cultural contexts. Crucially, it also involves collaborative inquiry and co-construction of meaning between participants and the researcher. Examples of narrative inquiry in qualitative research include for instance: stories, interviews, life histories, journals, photographs and other artefacts.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Thu, 04 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14871 https://www.ncrm.ac.uk/training/show.php?article=14666 Overview of 2-day courseBayesian statistics have become very popular in recent years. Modern software has made this possible and Bayesian methods are now applied in a wide range of scientific application areas from medicine to ecology. This course provides an introduction to the motivation, methods and applications of Bayesian statistics. The analysis tool is R (https://www.r-project.org/); prior knowledge of this software is assumed.The course is a mixture of presentations and hands-on computer exercises. It begins with an overview of the rationale and methodology underpinning Bayesian analysis, and the Markov chain Monte Carlo (MCMC) computational tools behind the methodology are outlined. An introduction to the JAGS engine within the R software is then provided, followed by data analysis applications, including linear models and generalised linear models. The advantages of Bayesian approach applied to the latter are emphasised and considered in detail. For example, the question “What is the chance that method A better than method B?” can be easily addressed in a Bayesian framework, but not in classical statistics.The emphasis in this course is on practical data analysis, although the essential theory will be outlined. Examples are drawn from a range of scientific disciplines.PresentersSandro Leidi and James Gallagher Cost£582 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 09:00 and 17:30 (GMT+1). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Data analysts and statisticians who want an introduction to Bayesian methods for statistical analysis. No prior knowledge of Bayesian statistics is required. Participants are expected to have:An A-level mathematics qualification or equivalent, including knowledge of probability density functions and probability mass functions for describing distributionsA working knowledge of linear models and generalised linear modelsA working knowledge of the R statistics software.How You Will BenefitBy the end of the course you will have a firm understanding of Bayesian methods and their flexibility. You will also have acquired a working knowledge of specialised software for Bayesian data analysis and will be able to fit and interpret linear and generalised linear models in a Bayesian framework. You will also appreciate the practical benefits of Bayesian methods.What Do We Cover?Bayesian versus classical frequentist statisticsLikelihood, prior and posterior distributions and the use of Bayes' theoremBayesian analysis of single-parameter models and multi-parameter modelsConjugate, vague and informative priorsSimulation of posterior distributions; posterior summariesMarkov chain Monte Carlo (MCMC) methods and MCMC diagnosticsLinear models, generalised linear models and model selectionQuestions that classical statistics find difficult to answer or cannot answerUse of the JAGS software via R and the CRAN packages rjags, runjags and coda.SoftwarePractical work will be done in R.Note: For practical work, participants must download and install (i) the JAGS software and (ii) a number of CRAN packages in R.  This must be done prior to the start of the course. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14666 https://www.ncrm.ac.uk/training/show.php?article=14870 Qualitative data analysis reveals patterns and themes from the large volume of data generated by qualitative research. It is useful for gaining detailed understanding of social phenomena and individual experiences, perceptions and behaviours. However, it is often seen as a mysterious and complex stage of the research process. There are also challenges in terms of how researchers conduct analysis and the steps that they need to follow.This advanced course provides participants with the skills to conduct qualitative data analysis. While providing an overview of different analytical approaches, the focus in our activities will be on thematic analysis. It provides an introduction to qualitative data analysis. It explores ways of organising and analysing qualitative data, and the practicalities of doing so. Through a practical exercise where we analyse qualitative interview data provided by the trainer, participants will be able to gain experience of conducting their qualitative data analysis by focusing on thematic analysis.By the end of the course, participants will have knowledge of various methods and theories of qualitative data analysis and how it differs from quantitative analysis. They will be able to choose an appropriate data analysis technique for different forms of qualitative data. They will also be able to conduct their own thematic analysis, code, and organise data for analysis.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Thu, 04 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14870 https://www.ncrm.ac.uk/training/show.php?article=14869 This course on creative and inclusive workshop designs will give you practical tips and tools for designing and delivering workshops for a range of stakeholders, participants and communities. Why focus on workshops? As we move into a place of embedding equitable, trauma informed and inclusive research practice, we need to look for better ways to reduce power imbalances, empowering more people to be involved in research, including research and project design. This often means stepping away from traditional models of research, and being open to more safe, creative and collaborative methods for engaging and interacting with people. Workshops foster inclusive practice by creating accessible, engaging spaces where diverse voices are heard and valued. However, they need to be designed well.This one day practical focused course will:Increase your awareness and understanding on how workshop designs can support empowerment, inclusive practice and trauma informed practice - and why these approaches matter.Provide you with tangible approaches and tools for designing inclusive and engaging workshops, in a trauma informed way. This includes approaches for co-design, co-production, creative methods and group decision making.Give you ideas and confidence in creating your own innovative approaches for research, research and co-design workshops.Looking to book for six or more people from your organisation?  Contact training@the-sra.org.uk to ask about our in-house courses training@the-sra.org.uk (Social Research Association) Thu, 04 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14869 https://www.ncrm.ac.uk/training/show.php?article=14395 Level: Professional (P)Standard methods of survival analysis based on the Kaplan-Meier estimate of a survivor function, the log rank test and Cox regression modelling are widely used in many different areas of application.   But often, the assumptions that underlie these techniques may not be valid, or the data structure may be more complex.  Extensions of these basic methods allow particular features of data that occur in practice to be handled appropriately.  This course will begin with an overview of standard methods and then move on to some of the more advanced techniques.   Their practical application will be illustrated using the R software, with an emphasis on interpreting output rather than on writing R code.  The course will consist of a series of presentations and practical sessions. Learning OutcomesAn appreciation of how the methods of survival analysis can be used in a variety of situations.Topics CoveredOverview of standard methods for summarising survival data and the Cox regression model.  Types of censoring in survival data, including interval and dependent censoring.  Time dependent variables and the counting process formulation of survival data.  Parametric models for survival data, including flexible models based on splines.  Incorporating random effects into a survival analysis; frailty models.  Analysis of data where there is more than one type of event; models for competing risks.  Detecting and handling non proportional hazards. Target AudienceStatisticians and epidemiologists in public sector research organisations, pharmaceutical companies and related organisations.  University research students and fellows. Assumed KnowledgeSome familiarity with basic methods for summarising survival data, including estimates of the survivor function and the log rank test.  Some experience in using the Cox regression model would be advantageous.  While knowledge of the R software is not essential, participants generally find it useful to be able to undertake the practical work using R on their laptop.   training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14395 https://www.ncrm.ac.uk/training/show.php?article=14831 Date: October 5th through to October 9th, 2026Registration Closes: 23:59 BST, 25th September 2026By: Jakub Bijak and GuestsLocation: Nuffield College, University of OxfordCourse description: This comprehensive, interdisciplinary short course provides foundations in applied Bayesian Statistics with a focus on methods for Population Data Science – a dynamically-growing area of research, connecting statistical rigour with fascinating real-life applications. Led by one of the pioneers of Bayesian thinking for Demographic Research, and illustrated by examples of Bayesian methods for estimation and prediction, the course will help the participants understand the main concepts and methods unlocking the study of uncertain processes. In addition, the participants will be able to appreciate the breadth of possible applications, such as local planning, national policy setting or human rights monitoring. The course is primarily intended for Masters students, doctoral and post-doctoral researchers, and other academics, but it is open to anyone with interest in population topics, who would like to enter into the world of population uncertainty equipped with state-of-the-art tools for dealing with unknowns – known and unknown alike.Objectives: To familiarise the participants with the philosophy and methodology of Bayesian Statistics, and to enable them to use selected software tools and packages to application Bayesian methods in practice.Learning outcomes: By the end of the course, the participants will be able to:1. Understand the philosophical and statistical basics of Bayesian statistics.2. Appreciate the different sources of uncertainty for various applications and the way in which they are reflected in models.3. Specify a Bayesian model for a given problem, estimate it, and assess its quality and sensitivity to various assumptions.4. Carry out an applied piece of Bayesian analysis, by using freely-available dedicated statistical software, and interpret the results.Timetable (Subject to change): AM session 10:00-13:00, Lunch 13:00-14:00, PM Session 14:00-17:00Lectures in the morning, practicals/computer workshops in the afternoon.Day 1. AM: History and philosophy of Bayesian statistics. Bayes Theorem: prior, posterior, likelihood. Applications of Bayesian methods in demography. PM: Pen-and-paper exercises: deriving posterior probabilities for a few simple discrete distributions. Point-and-click Bayesian estimation in JASP. Day 2. AM: Sources of uncertainty. Hierarchical models. Prior selection and elicitation. Brief introduction to numerical methods (MCMC, HMC). PM: Getting started: introduction to Stan. Coding simple models on pre-prepared data. Guided choice of individual mini-project topics; collection of data.Day 3. AM: Model comparison, selection and averaging, Bayesian model critique. Sensitivity analysis: not only priors. PM: Guided work on mini-projects – Part 1: Data preparation, model design, coding, estimation and troubleshooting. Day 4. AM: Guest lectures: real-life examples of Bayesian demographic models (details tbc). PM: Guided work on mini-projects – Part 2: Model critique and sensitivity analysis. Informative versus non-informative priors.Day 5. AM: Building theory: complex models and uncertainty quantification. New directions of Bayesian demography. Finalisation of mini-projects and individual presentations (five minutes, five slides). Conclusions to the course.Prerequisites: Undergraduate-level knowledge of statistics (any paradigm). Knowledge of calculus (integration) is beneficial, but not necessarily required.Teaser: A short video featuring Jakub Bijak and John Bryant discussing Bayesian demography back in 2016. Preliminary Reading Gelman A et al. (2013/2025) Bayesian Data Analysis: Third edition. CRC/Chapman & Hall.Bijak J and Bryant J (2016) Bayesian demography 250 years after Bayes. Population Studies, 70(1), 1–19.Bryant J and Zhang J (2018) Bayesian Demographic Estimation and Forecasting. CRC/Chapman & Hall.Attendance will be recognised through Accredible badges. Please kindly see the registration link onm the LCDS homepage to sign up for this couse. The course currently costs £600 for the entire 4.5 days of sessions. A 50% fee-waiver is available for internal Oxford students and participants from low and middle income countries upon request: please email teaching@demography.ox.ac.uk for more details.For any other or additional queries, please kindly contact teaching@demography.ox.ac.uk. charle.rahal@demgraphy.ox.ac.uk (Demographic Science Unit, Universty of Oxford) Sun, 03 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14831 https://www.ncrm.ac.uk/training/show.php?article=14764 This online course, taught over four mornings, aims to teach participants how to conduct qualitative field research, particularly participant observation and ordinary language interviewing. The course provides an understanding of the distinctiveness of ethnographic fieldwork compared to other data collection methods. By the end of the course, students should be able to understand how to conduct ethnography rigorously and the skills needed to produce high-quality ethnographic research. Students will be able to practice data collection methods associated with ethnography, such as participant observation, field notes, and ordinary language interviews. Finally, the course will discuss how to use fieldwork data to produce new and general theoretical insights.The course covers:Introduction to EthnographyOrdinary Language InterviewParticipant ObservationDigital EthnographyTheory building with qualitative dataBy the end of the course participants will:Explain the distinctive features of ethnographic fieldwork, particularly how participant observation and ordinary language interviewing differ from other qualitative research methods.Apply core ethnographic methods such as participant observation, field notes, digital ethnography, and interviews in their own research projectsCritically assess the methodological and ethical considerations involved in designing and conducting ethnographic research.Analyse fieldwork data to generate theoretical insightsTarget AudiencePostgraduate students (Master’s and PhD) in political science, sociology, anthropology, international relations, cultural studies, linguistics, arts, geography, archaeology, anthropology, and development studies, and related fields who are interested in incorporating ethnographic methods into their research;Early-career researchers and practitioners studying political or social dynamics who wish to strengthen their qualitative fieldwork skills—especially in participant observation and interviewing;Students planning or currently conducting fieldwork, particularly those working on topics like political parties, social movements, state institutions, or the everyday practices of politics.Preparatory ReadingBorges Martins da Silva, Mariana, 2025. "Notes from the Classroom: Lessons and Best Practices for Teaching Digital Ethnography", Qualitative and Multi-Method Research.Schatz, Edward. 2009. “Ethnography Immersion and the Study of Politics.” In Political Ethnography: What Immersion Contributes to the Study of Power. University of Chicago Press.Hammersley, G., M. Hammersley, and P. Atkinson. 1995. Ethnography: Principles in Practice. Research Methods, Sociological Theory, Ethnography. Routledge. (Chapter 1)Jerolmack, Colin, and Shamus Khan. 2014. ‘Talk Is Cheap: Ethnography and the Attitudinal Fallacy’. Sociological Methods & Research 43 (2): 178–209.Schaffer, F.C. 2014. Elucidating Social Science Concepts: An Interpretivist Guide. Routledge Series on Interpretive Methods. Routledge. (Chapter 1, 2)Schaffer, Frederic Charles. 2006. ‘Ordinary Language Interviewing’. In Interpretation and Method: Empirical Research Methods and the Interpretive Turn, edited by Dvora Yanow and Peregrine Schwartz-Shea, 150–60. Armonk, London: M.E. Sharpe.Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up. Chicago Guides to Writing, Editing, and Publishing. Chicago ; London: The University of Chicago Press. (chapter 4 and 5)Walt, Kathleen M., and Billie R. DeWalt. 2011. Participant Observation: A Guide for Fieldworkers. Rowman Altamira. (chapter 2-5)Emerson, R.M., R.I. Fretz, and L.L. Shaw. 2011. Writing Ethnographic Fieldnotes, Second Edition. Chicago Guides to Writing, Editing, and Publishing. University of Chicago Press. (Chapters 1-3)Fujii, Lee Ann. 2012. “Research Ethics 101: Dilemmas and Responsibilities.” PS: Political Science & Politics 45 (4): 717–23. https://doi.org/10.1017/S1049096512000819.Fu, Diana. 2017. “Disguised Collective Action in China.” Comparative Political Studies 50 (4): 499–527. (Please also read the methodological appendix)Borges Martins da Silva, Mariana. 2023. “Weapons of Clients: Why Do Voters Support Bad       Patrons? Ethnographic Evidence from Rural Brazil.” Latin American Politics and Society 65 (1): 22–46.Timmermans, Stefan, and Iddo Tavory. 2012. ‘Theory Construction in Qualitative Research: From Grounded Theory to Abductive Analysis’. Sociological Theory 30 (3): 167–86There are no prerequisites. The course is designed to be accessible to those new to ethnographic research, though some familiarity with qualitative methods may enhance your experience.PLEASE NOTE THIS COURSE EQUATES TO 1.5 DAYS FOR PAYMENT PURPOSES.Programme2 October – 10AM-12PMIntroduction to Ethnography and Ordinary Language Interview9 October - 10AM-12PMParticipant Observation16 October 10AM-12PMWriting Fieldnotes; Digital Ethnography23 October - 10AM-12PMConstructing Theory with Ethnographic Data   jmh6@soton.ac.uk (NCRM, University of Southampton) Wed, 11 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14764 https://www.ncrm.ac.uk/training/show.php?article=14868 The aim of this interactive workshop is to consider data analysis in qualitative research with a specific focus on how to treat and deal with data that is not textual, but comes out of the use of creative methods (drawings, paintings, pick-a-card, LEGO models, etc.). Using real data from research using creative methods for data collection we explore how analysis of "messy data" can be approached.We consider the principles and process of analysis within qualitative research in general when we discuss if analysis is ever an objective process and if there is a difference between analysing data from linear texts or visual/sensory data, such as that from building LEGO models, song lists, photographs, videos and the like. Delegates have opportunities to practise analysing visual data on its own, in connection with textual data employing the "Systematic Visuo-Textual Analysis" or by employing creative forms of expression.In line with the pedagogical principles of social constructivism the course is delivered as a mixture of interactive group tasks, discussions and lectures to enable active and experiential learning. This workshop can be taken on its own or following on from the workshop "Creative methods in qualitative data collection".n.b. This course runs over two consecutive afternoons:Part 1 - 1 October - 1.00 pm to 4.30 pmPart 2 - 2 October - 1.00 pm to 4.30 pmLooking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses training@the-sra.org.uk (Social Research Association) Thu, 04 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14868 https://www.ncrm.ac.uk/training/show.php?article=14846 Focus groups are a popular qualitative research method which allow us to explore a variety of views and experiences from participants. They produce a particular type of qualitative data via the interaction which participations have with each other and the activities they engage in. As a qualitative method, focus groups also rely on effective moderation and facilitation skills. This online interactive course helps participants to improve the quality of their focus groups and moderation skills, and provides strategies to help them generate a participative and effective focus group discussion.The course aims to give participants a clear understanding of when and how to use focus groups as a qualitative method and to provide first-hand experience of one of the key roles: moderator and group member. We also consider research ethics, how to modify the style and approach depending on the sensitivity of the topic, and the nature of the participants. Although the focus is primarily on in person focus groups, participants will consider strategies for conducting focus groups in both in-person and online settings, and the different challenges these focus group styles present for moderators. Through practical hands-on activities on designing focus group schedules and moderation, participants will gain skills in focus group design, questioning, moderation, and facilitation.By the end of the course, participants will have knowledge of focus groups as a qualitative method and the type of data they generate. They will have knowledge of the role of the moderator and how to effectively design, plan and conduct a focus group. They will also have an awareness of the different ways in which focus groups can be facilitated (i.e. in-person, online, text) although the focus of this course will be on designing and facilitating in-person focus groups.Looking to book for four or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Wed, 06 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14846 https://www.ncrm.ac.uk/training/show.php?article=14867 Qualitative interviewing is a popular method in social research and it is often described as a conversation between interviewer and interviewee. It allows us to collect detailed and rich information about individuals’ lives, their experiences, behaviours, and how they understand and make sense of the world. The rich insight it provides into people’s lives is one of the benefits which the method offers over standardised surveys or questionnaires.This introductory level course introduces participants to the method of qualitative interviewing. This includes its benefits, examples of effective interviewing, and the key ethical and practical issues to be considered. We look at types of qualitative interview which include structured, unstructured and semi-structured interviews. In particular, we explore the benefits of semi-structured interviewing which involves a combination of pre-set open ended questions with room for the exploration of other (sometimes unanticipated) topics. Participants gain experience of designing their own interview schedule and of conducting a semi-structured interview.By the end of the workshop, participants will have knowledge of various forms of qualitative interview and theories of interviewing. They will be able to distinguish between various types of interviews and questioning. They will be aware of practical and ethical issues which must be considered prior to interviews. They will also be able to design their own semi-structured interview schedule and conduct a semi-structured interview.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Thu, 04 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14867 https://www.ncrm.ac.uk/training/show.php?article=14866 This one-day live online course will provide training on depth interviews. The course is designed to provide early-career and mid-career researchers, and other policy and practice professionals with the skills required to plan and conduct depth interviews.The morning session introduces participants to the foundational principles of depth interview design, including defining the sample, recruiting participants, designing research questions and topic guides, building rapport, audio and video recording, and the importance of listening.The afternoon session provides the opportunity for more in-depth small-group practice exercises and discussion, and for the practical application of the material covered to existing and future research projects in which participants are engaging.Looking to book for six or more people from your organisation? Get in touch to ask about our in-house training: Training@the-sra.org.uk training@the-sra.org.uk (Social Research Association) Thu, 04 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14866 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=14387 Level: Intermediate (I)This course will be delivered over 4 morning sessions, running from 9:30am to 12:30pm each day. This course is ideal for beginners or intermediate users of Bayesian modelling, who want to learn how to use Stan software within R (the material we cover can easily be applied to other Stan interfaces, such as Python or Julia). We will learn about constructing a Bayesian model in a flexible and transparent way, and the benefits of using a probabilistic programming language for this. The language in question, Stan, provides the fastest and most stable algorithms available today for fitting your model to your data. Participants will get lots of hands-on practice with real-life data, and lots of discussion time. We will also look at ways of validating, critiquing and improving your models. Learning OutcomeUse Stan to fit various models to dataCheck outputs for computational problems, and know what to do to fix themCompare and critique competing modelsJustify their modelling choices, including prior probability distributionsUnderstand what Stan can and cannot do Topics CoveredA quick overview of Bayesian analysisSimulation is useful for statistical inferenceWhat is a probabilistic programming language?Parts of a Stan modelUnivariate models; exploring priors and likelihoodsPrior predictive checkingBivariate regression modelsPredictions and posterior predictive checkingHierarchical modelsLatent variable models including item-response theoryWorking with missing and coarse dataGaussian processesLimitations of Stan Target AudienceAnyone with some statistics training who is aware of the advantages of Bayesian modelling could benefit from attending. Fields where this may be most popular are: insurance, political pollsters, finance, marketing, healthcare, education research, psychology, econometrics.Assumed KnowledgeAttendees should be comfortable with using R, Python, Julia or Stata. They should understand probability distributions and basic regression models, though this can be intuitive and doesn’t have to be mathematically rigorous. They do not need to have used Stan before. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14387 https://www.ncrm.ac.uk/training/show.php?article=14669 Overview of 1-day courseChoosing an appropriate sample size is a common problem and should be given due consideration in any research proposal, as an inadequate sample size invariably leads to wasted resources. Hence objective sample size determination is increasingly requested by regulatory authorities in a number of disciplines. This course aims to give a practical introduction to sample size determination, or power calculations, in the context of some commonly used hypothesis tests. Examples from a scientific background will be used to highlight the problems associated with sample size determination, and suggest potential solutions. Practical work will be based around the free statistical software R; see https://www.r-project.org/. Formulae and algebraic notation will be kept to a minimum.PresentersSandro Leidi and James Gallagher Cost£282 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 17:30 (GMT+1). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Scientists and related who need to address the problem of sample size determination or power calculations in planning a study. Participants will be assumed to have a working knowledge of sampling distributions, confidence intervals and hypothesis tests for both means and proportions.  No previous experience of the R software is required.How You Will BenefitThis course will give you a sound introduction to sample size determination, and be able to conduct common power calculations using the free R software. What Do We Cover?Concepts of significance and power in relation to hypothesis testsSample size determination (power calculations) with one sample, two samples and paired samples for comparing means with a t-testSample size determination (power calculations) with one sample, two samples and paired samples for comparing proportions with a z-testPractical problems associated with sample size determination and possible solutionsOther issues such as the role of confidence intervals and why it can be difficult to determine sample size.The course will make use of our R functions which will be made freely available.SoftwarePractical work will be done in R.Note:For practical work, participants must download and install the R software.  This must be done prior to the start of the coursePractical work is based on the Windows operating system. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14669 https://www.ncrm.ac.uk/training/show.php?article=14845 We will begin with an introduction to the concept of trauma itself. We'll discuss the prevalence of trauma in society, including vicarious trauma and the impact this can have on researchers. We then introduce the principles of trauma-informed practice (developed in healthcare) and how this aligns with inclusive research design, particularly considering how experiences of discrimination and oppression can lead to trauma.At the end of the first day, we will begin to focus on the How. We will provide advice, guidance and case study examples on how to design and carry out trauma informed research.n.b. This course runs over two separate full days which must be booked individually. Day 2 can be booked here Applying Equity-Based Trauma-Informed Research in Practice.Day 1 - 23 September - 9.30 am to 4.00 pmDay 2 - 30 September - 9.30 am to 4.00 pmLooking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses! training@the-sra.org.uk (Social Research Association) Wed, 06 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14845 https://www.ncrm.ac.uk/training/show.php?article=14739 The problem: Your team collected thousands of words of data. You try a traditional thematic analysis of the text. Soon, colour coding, close reading, writing ad hoc reflections about the text become too onerous a task. You doubt the validity of your observations. You wish there was another way to streamline the process, that would extract key themes in data in a faster and empirically-valid way.Solution: Join us for a session in which we showcase empirical methods for the extraction and analysis of meaning, concepts, and themes in texts. The session will provide training in corpus linguistics and mixed-method tools that enable the analysis of texts in an empirical, bottom-up fashion. Through a range of case-studies, you will be guided to extract meaning and other thematic patterns from texts to gain insight into thoughts and behaviours of authors of those texts. We will share best practises on the thematic analysis of various data types, such as diaries, interview transcripts, data scraped from the web, and outputs of both new and traditional media. We also demonstrate ways of building the results of such analyses into answering research questions, developing business strategy, or a public policy.This session will be run by researchers from the University of Sussex’s Concept Analytics Lab (https://conceptanalytics.org.uk/) using texts from Mass Observation Archive to showcase approaches to thematic analysis. We will demonstrate solutions developed for a variety of problems and text types coming from our work with medical sciences, psychology, economics, and the energy industry. We will also show how linguistic patterns within or between texts (e.g. those that differ demographically or diachronically) can be explored, particularly through the use of new visualisation techniques. The workshop will conclude with a showcase of next-generation textual analysis tools that have been developed at Concept Analytics Lab.This will be a practical session, enabling attendees to develop hands-on experience with using corpus analysis tools. The course will consist of six hours of training over the course of one day [9.30am - 5pm] and will be delivered online. The course covers: How to extract meaning from large textual dataHow to build a corpus using textual data How to engage with existing corpora, such as multi-billion word corpora scraped from the webHow to use corpus methods for bottom-up and top-down researchTechniques for the visualisation of unstructured language dataAn introduction to discourse analysis and its application to corpora (corpus-assisted discourse analysis)By the end of the course participants will:Know how to engage a suite of mixed-method corpus linguistic tools to extract meaning from a corpusBe able to use corpora to answer a variety of research questionsBe able to build their own corporaConduct comparative corpus analysis (e.g. between texts that differ demographically or diachronically)Programme:9:30: Welcome and introduction to corpus linguistics10:00: Interrogating existing corpora - quantitative analysis12:00: Lunch13:00: Interrogating existing corpora - qualitative analysis15:00: Break15:15: Building your own corpus16:15: The Concept Cruncher: The next generation of text analysis16:45: Final remarksSpeakers:Dr Justyna Robinson is a Director of Concept Analytics Lab at the University of Sussex. She researches meaning in language and is interested in methods of analysing meaning empirically. Her publications focus on ways of researching meaning from historical perspectives (2012), from cognitive angles (2014), using socio-demographic information and other text metadata (2012, 2022), using corpus and statistical methods (2014, 2022). She researches meaning represented by words (2010), concepts and themes (2017, 2023). With the research team at Concept Analytics Lab, she delivered a range of projects investigating current meanings of loneliness, aging, UK trade deals post Brexit, political manifestos, recycling practises, or post-covid behaviour changes. Dr Rhys Sandow is a Senior Research Associate at Concept Analytics Lab, University of Sussex. He specialises in applying corpus methods to answer applied research questions, such as in collaborative work with economists, psychologists, historians, and medical humanities researchers, as well as organisations in the private sector. He also specialises in sociolinguistic variation and change, including its intersection with corpus linguistics, where he has worked as an expert witness in a legal context. He has published academic articles and book chapters on corpus linguistics and sociolinguistics and has a forthcoming co-edited book on Sociolinguistic Approaches to Lexical Variation in English to be published by Routledge. p.c.white@southampton.ac.uk (University of Southampton) Thu, 19 Feb 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14739 https://www.ncrm.ac.uk/training/show.php?article=14843 Have you ever discovered too late that your survey questions did not deliver useful or useable data? This course highlights ways to avoid pitfalls in the wording of individual survey questions as well as for the questionnaire whole. It also points out questionnaire design differences between face-to-face and telephone interviews, web and mobile web surveys and paper self-completion. Drawing on 30 years of the instructor’s experience and research findings from questionnaire design experiments, this course is full of practical advice.Part 1 - 22 September - 9.30 am to 3.30 pmPart 2 - 23 September - 9.30 am to 3.30 pmLooking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Wed, 06 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14843 https://www.ncrm.ac.uk/training/show.php?article=14842 This one day workshop will give social researchers practical insight into management and process consultancy tools and techniques. The aim is to equip participants with a further dimension to their research skills, recognising that many social research projects either include a consulting element or would benefit from consulting capability – at inception, execution or dissemination stages.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, 06 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14842 https://www.ncrm.ac.uk/training/show.php?article=14768 This workshop will introduce Institutional Ethnography (IE), an interdisciplinary feminist approach to social research that focuses on how texts and language organise our everyday lives. IE is not just a methodology, but an entire approach to research with a specific ontology of how the social world works and the organising role of texts and language. In IE, the researcher ‘takes sides’ using a specific version of standpoint to explore how institutions work in practice rooted in peoples’ experiences. This often involves researching as, with, or alongside marginalised groups and making visible how institutions exclude or make invisible certain groups of people and experiences.The overall aim of the workshop is to provide attendees with a comprehensive overview of institutional ethnography as an approach and the opportunity to translate their own research ideas and projects into an IE research proposal and do a small piece of text-focused analysis. This hands-on workshop is suitable for students, academics, and anyone else interested in feminist methodologies, text and discourse analysis, and institutional or organisational ethnographies. No prior training in, or knowledge of, IE is required.The course covers:· An overview of Institutional Ethnography and the work of feminist sociologist, Dorothy Smith, who developed Institutional Ethnography· Case studies of Institutional Ethnography research projects to show how it works in practice in different disciplines· How to translate your research into an Institutional Ethnography project using a research proposal framework· Practical explanation of how to do text and discourse analysis within Institutional Ethnography through a short text analysis activityBy the end of the course participants will:· understand of the origin and development of Institutional Ethnography· know how to use Institutional Ethnography to analyse texts, processes, and discourses· have an outline of how their research ideas could become an Institutional Ethnography projectThe course is aimed at Academics, students, any other qualitative researchers, including policymakers, organisers, and activists interested in analysing organisational processes.Participants must have at least some experience in qualitative research methods, but no experience of Institutional Ethnography is required. Preparatory ReadingRequired:· 1 hour lecture by Dorothy Smith summarising Institutional Ethnography -https://www.youtube.com/watch?v=1RI2KEy9NDw · Murray, Ó.M., 2020. Text, Process, Discourse: Doing feminist text analysis in institutional ethnography, Available at: https://doi.org/10.1080/13645579.2020.1839162  Desirable: · Earles, J., & Crawley, S. L. 2020. Institutional ethnography. In P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug, & R. A. Williams (Eds.), Foundation: SAGE research methods. Retrieved July 17, 2020, from: http://dx.doi.org/10.4135/9781526421036759274  · Smith, D.E. & Griffith, A.I., 2022. Simply Institutional Ethnography: Creating a Sociology for People. Toronto: University of Toronto Press.ProgrammeDay One: 21 September 202610:00 - 10:15 Introductions10:15 - 11:30 Series of short introductory video lectures + 1 case study11:30 - 11:45 Short break11:45 - 12:45 Q&A on the videos and institutional ethnography in general12:45 - 13:00 Explain afternoon task and split everyone into small groups based on research interests 13:00 - 14:00 Lunch break 14:00 - 15:00 Small group discussions divided up by discipline/area of interest; participants collectively discuss how their research projects would translate into Institutional Ethnographies, aided by a research proposal template and guiding questions - each group is facilitated by one of the three organisers 15:00 - 15:15 Short break 15:15 - 16:00 Three groups come back together to highlight key points of discussions and any final questions before explaining what will happen on Day 2 - participants will have to choose a 'text' related to their research to bring to Day 2 to analyse. Day 2: 22 September 2026 10:00 - 11:30 Brief introductions and 2 short case studies with Q&A 11:30 - 11:45 Short break 11:45 - 13:00 Any further questions and introduction to the text analysis methods we will use in the afternoon 13:00 - 14:00 Lunch break 14:00 - 15:00 Small groups work facilitated by three organisers in which participants using text analysis methods on their research-related 'text' (in groups or individually) 15:00 - 15:15 Short break 15:00 - 16:00 Everyone comes back together to discuss their text analysis and ask any final questions about how to do Institutional Ethnography text analysis, the overall approach, and distribution of follow-up resources. Completion of online evaluation survey. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 13 Mar 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14768 https://www.ncrm.ac.uk/training/show.php?article=14383 Level: Foundation (F)The course will be delivered over 5 morning sessions, running fro 9:00am to 1:00pm each day. The purpose of this course is to help participants understand some basic statistical concepts and develop a strategy for approaching simple data analysis. The course will introduce basic concepts such as hypothesis testing and confidence interval estimation. It will provide the tools to undertake simple analysis of a dataset and will include some helpful hints and tips for reading and understanding reported statistics.Learning OutcomesBy the end of this course, participants will understand basic approaches to statistical inference, including hypothesis testing and confidence interval estimation. They will be equipped with the skills necessary to undertake simple analysis and to understand some of the basic terms often used to report statistical results. The course will include some calculations by hand to aid understanding. Topics CoveredData Summary; The normal distribution; Confidence intervals; Introduction to hypothesis tests; Analysis of contingency tables – chi-squared test;  T-tests; Non-parametric tests, (Wilcoxon signed rank test, Mann-Whitney U test); Introduction to correlation and regression; Basic presentation of data and results. Target AudienceThis course is aimed at those who have either never undertaken a formal statistics course, or who have studied some statistics in the past but wish to undertake a refresher. It is ideal for statistical novices who have never had any formal training but are starting to encounter statistics in their work and wish to gain some insight.Delegate Feedback"Ellen and Jenny were extremely knowledgeable. They were also approachable and happy to give further explanations when necessary""Excellent course. Hard to fault. I can see why it's popular""This was a fantastic course, the presenters had a very high knowledge but were able to 'dumb it down' for me as I am new to this"  training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14383 https://www.ncrm.ac.uk/training/show.php?article=14841 Making inferences about a population of interest is often essential for scientific inquiry. This can be important both in traditional social science research, for example when using probability surveys, as well as in new forms of data, for example sampling from large volumes of data. Furthermore, understanding sampling can enable researchers to evaluate the strengths and limitations of research designs and guide them towards more valid and robust ways of collecting and analysing data.This course provides an overview of sampling techniques frequently used in survey designs. In particular, it focuses on the principles of designing and selecting samples of individuals. These principles are also discussed in terms of the effects on inference to the population of interest, the key goal of survey research.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Wed, 06 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14841 https://www.ncrm.ac.uk/training/show.php?article=14815 This is a hands-on introduction to the first steps in Deep Learning, intended for researchers who are familiar with (non-deep) Machine Learning.  This introduction aims to cover the basics of Deep Learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what next steps to take to improve the model. The course covers: What is deep learning?Classification by a neural network using KerasMonitor the training progressAdvanced layer typesReal world applicationLearning Outcomes:Introduction Define deep learningDescribe how a neural network is build upExplain the operations performed by a single neuronDescribe what a loss function isRecall the sort of problems for which deep learning is a useful toolList some of the available tools for deep learningRecall the steps of a deep learning workflowTest that you have correctly installed the Keras, Seaborn and scikit-learn librariesUse the deep learning workflow to structure the notebookClassification by a neural network using KerasExplore the dataset using pandas and seabornIdentify the inputs and outputs of a deep neural network.Use one-hot encoding to prepare data for classification in KerasDescribe a fully connected layerImplement a fully connected layer with KerasUse Keras to train a small fully connected network on prepared dataInterpret the loss curve of the training processUse a confusion matrix to measure the trained networks’ performance on a test setMonitor the training processExplain the importance of keeping your test set clean, by validating on the validation set instead of the test setUse the data splits to plot the training processExplain how optimization worksDesign a neural network for a regression taskMeasure the performance of your deep neural networkInterpret the training plots to recognize overfittingUse normalization as preparation step for deep learningImplement basic strategies to prevent overfittingAdvanced layer typesUnderstand why convolutional and pooling layers are useful for image dataImplement a convolutional neural network on an image datasetUse a dropout layer to prevent overfittingBe able to tune the hyperparameters of a Keras modelTransfer learningAdapt a state-of-the-art pre-trained network to your own datasetOutlookUnderstand that what we learned in this course can be applied to real-world problemsUse best practices for organising a deep learning projectIdentify next steps to take after this coursePre-requisites:Learners are expected to have the following knowledge:Basic Python programming skills and familiarity with the Pandas package.Basic knowledge on machine learning, including the following concepts: Data cleaning, train & test split, type of problems (regression, classification), overfitting & underfitting, metrics (accuracy, recall, etc.).Setup InstructionsPlease follow the setup instructions here: https://carpentries-lab.github.io/deep-learning-intro/index.html#software-setup Note that software installation can take some time.  Please set up your python environment at least a day in advance of the workshop. If you encounter problems with the installation procedure, ask your workshop organizers via email for assistance so you are ready to go as soon as the workshop begins.ProgrammeWhat is deep learning?Classification by a neural network using KerasMonitor the training progressAdvanced layer typesReal world applicationThis course is taking place on 15-17 September from 09:00 - 17:00.  jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 23 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14815 https://www.ncrm.ac.uk/training/show.php?article=14402 Level: Foundation (F)This course will run over 4 afternoon sessions, from 1:00pm to 5:00pm each day.The purpose of this course is to introduce participants to the R environment for statistical computing. The course focuses on entering, working with and visualising data in R, and linear regression modelling in R. Learning OutcomesBy the end of the course, participants will be able to use R to:Have a clear understanding of R/RStudio IDE and its background.Be familiar with navigating the RStudio IDE.Understand the core fundamentals of R.Understand functions and arguments.Be able to create vectors and applying functions.Be exposed to the tibbles and {tidyverse} package.Be able to comfortably import, export, and store data in R.Have a basic introduction to graphics with {ggplot2}.Have a basic understanding of manipulating data manipulation with {dplyr}.Understand logical and relational data partitioning.Have a thorough understanding of popular statistical techniques.Have the skills to make appropriate assumptions about the structure of the data and check the validity of these assumptions in RBe able to fit regression models in R between a response variable and understand how to apply these techniques to their own data using R’s common interface to statistical functions.Be able to cluster data using standard clustering techniques. Topics CoveredIntroduction to R: A brief overview of the background and features of the R statistical programming system.Data entry: A description of how to import data.Data types: A summary of R’s data types.R environment: A description of the R environment including the R working directory, creating/using scripts, saving data and results.R graphics: Creating, editing and storing graphics in R.Summary statistics: Measures of location and spread.Manipulating data in R: Describing how data can be manipulated in R using logical operators.Basic hypothesis testing: Examples include the one-sample t-test, one-sample Wilcoxon signed-rank test, independent two-sample t-test, Mann-Whitney test, two-sample t-test for paired samples, Wilcoxon signed-rank test.ANOVA tables: One-way and two-way tables.Simple and multiple linear regression: Including model diagnostics.Clustering: Hierarchical clustering, k-means.Principal components analysis: Plotting and scaling data. Target AudienceThis course is ideally suited to anyone who:Is familiar with basic statistical methods (e.g. t-tests, boxplots) and who want to implement these methods using RHas used menu-driven statistical software (e.g. SPSS, Minitab) and who want to investigate the flexibility offered by a command line package such as RIs already familiar with basic statistical methods in R and would like to extend their knowledge to regression involving multiple predictor variables, binary, categorical and survival response variablesIs familiar with regression methods in menu-driven software (e.g. SPSS, Minitab) and who wish to migrate to using R for their analyses Assumed KnowledgeThe course requires familiarity with basic statistical methods (e.g. t-tests, box plots) but assumes no previous knowledge of statistical computing. For this online course, participants are not required to have R installed on their own laptops. A virtual environment, which can be accessed through a web browser, will be used to run R and view course materials. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14402 https://www.ncrm.ac.uk/training/show.php?article=14840 The aim of this interactive workshop is to explore creativity within research, to identify opportunities to use creative methods within the research process and to explore the foundations and theoretical underpinning related to these methods in qualitative research.We discuss what creativity is, why we should be creative in research and how we can introduce creativity and creative methods in our existing paradigms and methods. Subsequently, delegates actively experiment with "pick a card" and "diamond 9" activities, photo elicitation, and the process of creating representations of experiences. Delegates also have opportunities to consider creativity within diary methods and observations as data collection. Creative research methods have been found particularly helpful in yielding rich qualitative data and thus provide a deeper insight into research participants' experiences. All tasks are explored in view of 4 guiding questions allowing delegates to focus on practical, methodological and ethical considerations regarding the approaches presented.In line with the pedagogical principles of social constructivism the course is delivered as a mixture of interactive group tasks, discussions and lectures to enable active and experiential learning. This workshop can be taken on its own or in conjunction with the workshop "Creative Data Analysis".n.b. This course runs over two consecutive afternoons:Part 1 - 10 September 2026 - 1.00 pm to 4.30 pmPart 2 - 11 September 2026 - 1.00 pm to 4.30 pm training@the-sra.org.uk (Social Research Association) Wed, 06 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14840 https://www.ncrm.ac.uk/training/show.php?article=14839 Deliberative democracy, citizens’ assemblies, public dialogue… you may have heard these terms, but what are deliberative methods all about, how are they distinct from other qualitative research methods and how do you use them?Deliberative methods are an exciting and distinct set of research and engagement methods. Through these processes, participants go on a journey in which they are given the time, space and structure to learn about the topic and deliberate on it with their peers (often over a period of weeks or months). With the help of expert stimulus, their own lived experience, structured facilitation and a range of perspectives from their fellow participants, these ‘mini-publics’ can tackle some of society’s biggest and most contentious issues, delivering recommendations, priorities, conclusions or messages as a result. Deliberative methods are gaining ever more interest in government as public trust in government and other institutions falls and departments look to involve the public meaningfully in decision-making processes about novel, contentious and complex issues facing society. They require distinct research skills, facilitation approaches and management.n.b. This course runs over two consecutive days:Part 1 - 10 September - 10.00 am to 1.30 pmPart 2 - 11 September - 10.00 am to 1.30 pm training@the-sra.org.uk (Social Research Association) Wed, 06 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14839 https://www.ncrm.ac.uk/training/show.php?article=14814 A practical introduction to using AI to support coding in research. This course will help researchers understand how to use AI to help them write code effectively and responsibly. This course is designed for researchers with little to no experience coding. The course provides clear, hands-on guidance for using AI to write, debug, and understand code, while addressing key ethical, security, and reliability considerations in research contexts.The course covers: An overview of the AI landscapePractical skills for AI-assisted coding Ethics, reliability and security considerationsLearning Outcomes:AI Landscape Recall key milestones in the historical development of artificial intelligenceDescribe where ChatGPT and similar large language models fit within the broader AI landscape.Explain, at a conceptual level, what generative AI and ChatGPT are.Summarize the primary functions and intended use cases of common AI coding assistants.AI-Assisted CodingExplain why delegating full software development to AI without understanding the solution introduces technical, ethical, and reliability risks.Describe appropriate roles for AI tools as assistants rather than autonomous developers.Use ChatGPT as a reference tool to locate, summarize, and clarify technical information more precisely than traditional search methods.Apply AI tools to explain unfamiliar code to support learning.Use AI-generated suggestions to debug code and resolve errors.Generate boilerplate code using AI assistance.Use AI tools to draft technical documentation.Analyse when AI assistance enhances productivity versus when it may obscure understanding or introduce errors. Ethics, Reliability and Security Considerations Describe common sources of bias, inaccuracy, and unreliability in AI-generated outputs.Explain data privacy, confidentiality, and security risks associated with using AI tools in coding and research contexts.Summarize intellectual property, authorship, and citation considerations related to AI-generated code and text.Analyse the potential long-term consequences of researchers relying on AI tools without developing foundational coding skills.Assess the appropriateness of AI tool usage in specific research or coding scenarios.Develop personal or team-level guidelines for responsible and ethical AI use in coding and data analysis workflows.This course is aimed at Researchers with little to no programming experience who are interested in using AI to help them write code for their research. Setup InstructionsPlease follow the instructions on this web page to download the data and install the required software before attending the workshop: https://southampton-rsg-training.github.io/coding-with-ai/index.html Note: If using a University of Southampton machine follow the instructions under the tab labelled ‘University of Southampton Computers’.  If using a personal machine or a machine from another university, please follow the instructions under the tab labelled ‘Personal Computers’.ProgrammeAn overview of the AI landscapePractical skills for AI-assisted coding Ethics, reliability and security considerationsThis course is taking place on 3rd September 2026 from 13:00 – 16:30. jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 23 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14814 https://www.ncrm.ac.uk/training/show.php?article=14803 Qualitative data analysis reveals patterns and themes from the large volume of data generated by qualitative research. It is useful for gaining detailed understanding of social phenomena and individual experiences, perceptions and behaviours. However, it is often seen as a mysterious and complex stage of the research process. There are also challenges in terms of how researchers conduct analysis and the steps that they need to follow.This advanced course provides participants with the skills to conduct qualitative data analysis. While providing an overview of different analytical approaches, the focus in our activities will be on thematic analysis. It provides an introduction to qualitative data analysis. It explores ways of organising and analysing qualitative data, and the practicalities of doing so. Through a practical exercise where we analyse qualitative interview data provided by the trainer, participants will be able to gain experience of conducting their qualitative data analysis by focusing on thematic analysis.By the end of the course, participants will have knowledge of various methods and theories of qualitative data analysis and how it differs from quantitative analysis. They will be able to choose an appropriate data analysis technique for different forms of qualitative data. They will also be able to conduct their own thematic analysis, code, and organise data for analysis.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Wed, 08 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14803 https://www.ncrm.ac.uk/training/show.php?article=14821 Course Overview Focusing on hard-to-reach populations, this intensive course introduces applied research methods for conducting high-quality qualitative and quantitative research with marginal, hidden, and excluded groups. Research topics include (but are not limited to): migrants, refugees and displaced populations, children and adolescents, sex workers, homeless populations,  victims of violence, conflict or trafficking, people affected by HIV/AIDS, and drug users, as well as topics proposed by participants. The course is designed to bring together academics (researchers, PhD and master’s students) and practitioners (from NGOs, UN agencies, and government institutions), creating a unique space for experience sharing and methodological cross-fertilisation. Course Objectives Participants will: • Develop practical skills to design and conduct empirical research with hidden and marginal populations • Learn strategies to address challenges such as the lack of sampling frames and difficulties in reaching target groups • Understand concepts of impact, attribution, and contribution, and the political dimensions of research findings • Strengthen capacity to combine qualitative and quantitative methods ethically and effectively Key themes include: • Estimation and sampling techniques • Participatory research approaches • Evidence-based policy vs policy-based evidence • Innovation, crowdsourcing, and the use of technology • Ethical considerations when working with vulnerable populationsCourse Structure This full-time, intensive course is organised into morning and afternoon sessions combining lectures, applied exercises, and practical case work. Participants are encouraged to present past, ongoing, or planned research projects, which will be discussed and used throughout the course. andrea.rossi@gmail.com (Oxford Nuffield College) Tue, 28 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14821 https://www.ncrm.ac.uk/training/show.php?article=14822 Course overviewThe course will give an overview and introduction to qualitative research methods, including individual interviews, focus group discussions, observation, ethical issues, thematic analysis, and rigour in qualitative research. The course will also highlight the different theoretical approaches to data collection and analysis, including the Grounded Theory Approach, Ethnography and Interpretative Phenomenological Analysis.Course featuresThis five-day course aims to provide students with an understanding of the purpose and appropriate use of qualitative research methods. The course is intended to be both practical and accessible and aims to equip students with the skills to undertake their own qualitative research. Course tutors will provide examples from their own work to illustrate the application of qualitative research in the mental health field.The Introduction to Qualitative Research Methods' Summer School is offered by the Qualitative Health Research Centre (QUAHRC) at King's College London.Learning outcomesBy the end of the summer school, students should be able to:Understand the epistemological assumptions of qualitative research and key principles and practices of different theoretical perspectives, including Grounded Theory and Interpretative Phenomenological Analysis.Understand the purpose and application of qualitative methods of data collection including interviews, focus groups and participant observation.Appreciate ethical issues surrounding the use of qualitative methods in health research.Understand and apply common skills and procedures that exist across qualitative methods of data analysis.Critically appraise qualitative research and understand how to install rigour in the research process.Entry requirementsParticipants should have an undergraduate degree to attend this course, but no prior qualitative expertise is assumed. rachel_rowan.olive@kcl.ac.uk (King's College London) Wed, 29 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14822 https://www.ncrm.ac.uk/training/show.php?article=14828 Feeling overwhelmed by the command line? Confused by file pathways? Want to navigate the world of coding with confidence? Join the Code Anxiety Club!Viewers can follow along as we work through common beginner topics while coding live for a quick half hour. No prior experience, installed software or setup required. Viewers can interact via the YouTube chat (you must have a YouTube account to comment) and we will try our best to answer your questions and comments.There is no need to book a place, please follow the livestream link to join the session.Git structure4 August, 13:30-14:00Content:Understanding the root level and top-level organisation of a Git repository.Get to grips with configuration files and why they matter.Learn why consistent naming conventions for files and folders are important.Understand the difference between versions and tagged versions, and when to use each.To join this session, please follow the link to our livestream - 4 August 2026.Presenters: Jools Kasmire, Louise Capener, Nadia Kennar and Sarwat Qureshi with interactive moderation to ensure your questions are answered in real-time. bethan.jones-4@manchester.ac.uk (UKDS) Thu, 30 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14828 https://www.ncrm.ac.uk/training/show.php?article=14859 Course overview and aimsNVivo can be harnessed to undertake qualitative and mixed-methods analysis drawing on a range of different analytic methods. Thematic analysis is a common approach which has become increasingly popular in recent years – amongst students working on qualitative dissertations and researchers working across academic and applied research sectors (e.g. government, policy, charity, market-research etc.).This course focuses on how the techniques involved in the phases of work in a thematic analysis can be accomplished using the software tools provided by NVivo. Using Braun & Clarke’s (2021) six phases of Reflexive Thematic Analysis as an example framework, we set up a thematic analysis in the software and explorea range of tools that can be used to familiarise with data, code data, generate, develop, review and refine themes, capture analytic reflections throughout the process, and share findings and process. We discuss the appropriateness of tools to enact different analytic tasks, and the benefits of using NVivo for thematic analysis in comparison to manual methods.This course teaches the latest version of NVivo – currently v15. Participants do not need to purchase a license to follow the course, as the trial version is sufficient for the purposes of the course. Learning objectivesBy the end of the course, participants will be able to:Understand the range of NVivo tools than can be harnessed throughout common phases of Reflexive Thematic AnalysisSet up an NVivo project and plan its use for analysisUse NVivo tools for data familiarisation, coding, theme development and refinement, and continual reflectionOrganise qualitative data based on factual characteristics (e.g. participant socio-demographics).Be comfortable with the possibilities for interrogation and mapping in the software to identify and explore thematic patternsSave and back up projects, share findings in different forms TopicsStrategies and tactics in thematic analysis – the importance of methodology and how research objectives drive the use of software toolsApproaches to thematic analysis – similarities and differences in thematic analysis approachesPlanning an analysis – the purpose and use of Analytic Planning Worksheets for planning the tasks involved in the phases of thematic analysisData formatting – transcription protocols that maximise functionality in NVivoSetting up a project – structuring the NVivo workspace in line with your objectivesFamiliarising with data – in-depth annotation and initial high-level explorationsConceptualising data – interpretive and inductive coding compared with automated coding options and their role in thematic analysisIdentifying, developing and refining themes – using NVivo tools to construct, work with and explore themes as you develop themOrganising data – attaching socio-demographics or other meta-data to the units in your analysisInterrogating and visualising data – uncovering relationships and mapping ideas, sharing findings in a variety of ways Who is this course for?This course is designed for anyone interested in using NVivo to undertake thematic analysis of qualitative materials including transcripts from interviews, group discussions, observations etc.No prior knowledge of NVivo is required. Format and documentationThis course is delivered in a live online session that combines discussion, demonstration and hands-on exercises. This is not a 'do this to achieve that' course, but uses more interactive and demonstrative techniques to give you real, tangible practice at using the software for what you need it to do. To deliver as tailored an experience as possible, we contact you on enrolment in order to understand your research goals and analytical strategies. Additionally, our participant numbers are capped to a small group size, allowing us to cover the core topics, as well as specialist needs arising out of individual projects.Participants have the opportunity to discuss their qualitative projects with each other, and with the facilitators.Participants are provided with slide decks, reading lists and resources to further knowledge about the topics covered during the day. Feedback from attendees"The way that Christina taught using thematic analysis model short projects was really interesting. The sample projects were easier to understand. One of the best lecturers that simplified the software. I will be recommending this workshop for all""Really interactive session, completing the steps alongside Christina was very rewarding in showing personal progress but also allowing plenty of opportunities to ask questions.""Christina is so knowledgeable and a great presenter. The small classes allow her to address each participant’s needs. The classes are designed to be interactive so we can use the software right along with her instructions and examples. She spent enough time on each section that I felt I could continue on my own with confidence." FacilitatorChristina Silver, PhD is the director of Qualitative Data Analysis Services and manager of the CAQDAS Networking Project at the University of Surrey, UK. Christina’s interests relate to the relationship between technology and methodology and the effective teaching of qualitative methods and digital tools. She is co-author of Using Software in Qualitative Research: A Step-by-Step Guide (Sage publications, 2007, 2014) and Qualitative Analysis using ATLAS.ti/MAXQDA/NVivo: The Five-Level QDA® Method (Routledge 2018). Christina has trained more than 11,000 researchers around the world in qualitative methods and the use of digital tools for analysis, since 1998, and is a Fellow of the UK Academy of Social Sciences.About QDAS | Qualitative Data Analysis ServicesQDAS provide tailored and flexible training, consultancy, coaching and analysis for qualitative and mixed-methods researchers. We specialise in facilitating high-quality analysis through the powerful use of digital tools. Our website provides information about our work, including our pedagogy - the Five-Level QDA method, which underpins the way we think about, undertake and teach methods and tools. info@qdas.co.uk (QDAS | Qualitative Data Analysis Services) Tue, 09 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14859 https://www.ncrm.ac.uk/training/show.php?article=14802 By the end of the workshop, participants will:Have a critical awareness of the range of AI-assisted qualitative analysis toolsBe equipped to consider their ethical incorporation into analysis practiceHave practical, hands-on experience with using general-purpose AI Chatbots, and bespoke qualitative analysis tools for GenAI-assisted analysis that can be transferred to other applicationsParticipants do not need licenses for any qualitative analysis software to attend this course. Trial versions will be made available for the purpose of the sessions. However, you will need to install the trial version of MAXQDA so will need to leave plenty of time to arrange this, especially if you will be using an organisational computer with admin rights / security. Upon registration you will be provided with installation information.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Wed, 08 Apr 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14802 https://www.ncrm.ac.uk/training/show.php?article=14863 Event timings: 09.30 to 13.30 (BST) Overview:Thematic analysis is useful for revealing patterns and themes in your work and for gaining detailed understanding of social phenomena and individual experiences, perceptions and behaviours. However, it is often seen as a mysterious and complex stage of the research process. Qualitative researchers can be criticised for not always making their techniques of analysis transparent when they write up their research findings. There are also challenges in terms of how researchers conduct analysis and the steps that they need to follow.This popular live online course provides you with skills on how to manually conduct reflexive thematic analysis. While providing a brief overview of different thematic approaches, the focus of the day will be on Braun and Clarke's 'reflexive thematic analysis'. Through a practical exercise in which we analyse qualitative interview data, you will gain experience of conducting a thematic analysis which includes coding and moving to themes. We cover:- Principles of qualitative data analysis- Different types of thematic analysis- Braun and Clarke's 6 steps for 'reflexive thematic analysis'- Organising your data, i.e. conceptualising, coding and categorising- Selective and complete coding- Latent and semantic coding- Generating themes and a thematic map- Examples of thematic analysis- Practical activities: coding and theme creation- Ensuring rigour and reflexivity in our analysis Who should attend?This course will be useful for researchers who are new to thematic analysis or who wish to brush up on their qualitative analysis skills. This includes doctoral students and academics. Researchers using qualitative methods in government, policy, consultancy, social research organisations and charities will also find this training useful.Please note: this is an interactive live course with presentation, group activities, group discussions, and opportunities to ask questions. It is helpful if you are prepared to participate and have use of camera and mic on Zoom. What is included?- 5 hours of live training on Zoom (inclusive of breaks) with Dr Karen Lumsden who has over 20 years experience in qualitative research and analysis, and in the design and delivery of qualitative research programmes, courses and workshops.- Group discussions, practical coding exercise, and opportunities to ask the trainer questions.- Access to resources including, for example: agenda, slides, resource list, and examples.- Recording of the presentation sections of the day (accessible for 30 days post course date).- Certificate of attendance Trainer biographyDr Karen Lumsden is a qualitative trainer, consultant, and coach. She has held a number of academic posts including Senior Lecturer in Sociology at the University of Aberdeen, Associate Professor in Criminology at Leicester University, and Assistant Professor at the University of Nottingham. Over the years she has been involved in a number of research projects and evaluations in social sciences, policing and health, for a range of partners and clients.She has over 20 years experience delivering qualitative methods courses and training to academics, PhD students, social researchers, and practitioners. This includes courses at the Universities of Aberdeen, Glasgow, Essex, Auckland, Kingston, via the Social Research Association and the European Consortium for Political Research, and also for government departments, NHS, charities, police organisations, social research and market research organisations. She has written and edited a number of books and journal articles on qualitative methods including Crafting Autoethnography (Routledge, 2023) and Reflexivity: Theory, Method, Practice (2019). She was on the Editorial Board of the journal Qualitative Research until 2026.For more info visit www.qualitativetraining.com or connect on LinkedIn here. Bookings:Bookings for this course should be made via Eventbrite. If your organisation requires payment via invoice, please contact me directly to check if this will be possible. I only accept invoice payment when the payment terms are confirmed in writing prior to event date. Email: karen@qualitativetraining.com Booking and refund policy:There is a 14 day 'cooling off' period from the date of booking on this course (unless it is less than 7 days before the event date). The refund policy is 100% refund up to 7 days prior to the course date. Less than 7 days before the course date, the entire fee is payable. Please note that in all cases, the Eventbrite ticket fee is non-refundable.If the course is fully booked and has a waiting list then transfer to another course or future course date might be possible, however this is at the discretion of the trainer. You can transfer your booking to another person in your organisation.Please note that Dr Karen Lumsden delivers a range of courses for other training providers in addition to these Qualitative Training courses. She takes no responsibility or liability for individuals booking on similar courses or training with other providers which may contain similar or the same course content. Refunds will not be given under these circumstances once the training has been partly/fully consumed. karen@qualitativetraining.com (Qualitative Training) Tue, 26 May 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14863