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/ Fri, 09 Oct 2026 03:04:16 +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=14396 Level: Professional (P)This course introduces the Bayesian approach to meta-analysis. Attendees will learn practical ways in which they can combine multiple sources of published evidence while accounting for uncertainties such as response bias, publication bias, confounding, and missing information, using either BUGS, JAGS or Stan as software. With Bayesian models, this can be transparent and reproducible.This two-day course begins by reviewing classic meta-analysis methods and expressing them as statistical models. Once attendees understand meta-analysis in this larger context, they are able to extend the model flexibly to account for common problems such as papers that report only change from baseline. A series of problems will be tackled in this course, and attendees will leave with model code that they can immediately start using with their own projects. Learning OutcomesAfter attending, participants will be able to:Write out standard meta-analyses as statistical modelsUse BUGS, JAGS or Stan to fit such models to dataRecognise several common problems in meta-analysisExtend these models to account for these problemsUnderstand and communicate their findings Topics CoveredDay 1:A review of statistical models of meta-analysis​Introduction to Bayesian analysis problems in meta-analysis, and sources of uncertaintyModels for basic DerSimonian-Laird and Biggerstaff-Tweedie meta-analysesIntroduction to Bayesian software options: BUGS, JAGS and StanDay 2:Models for network meta-analysisModels for missing statisticsModels for reporting biasModels for publication biasModels for a mixture of statisticsModels for a mixture of study typesReporting Bayesian meta-analyses Target AudienceThis course will be of interest to evidence-based healthcare researchers, including those writing guidelines and evaluating policies. Attendees should be comfortable conducting simple meta-analyses in some software but do not have to have experience of Bayesian methods. Assumed KnowledgeThis course assumes that all participants have a basic grounding in Bayesian statistics, to the level covered by the RSS courses "Introduction to Bayesian Statistics" or "Introduction to Bayesian Analysis using Stan". There is no specific software expertise required, but examples will be written in BUGS and Stan, using R as an interface. We also assume that participants are familiar with the principles of systematic reviews, for example from reading relevant parts of the Cochrane Collaboration Handbook online. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14396 https://www.ncrm.ac.uk/training/show.php?article=14410 Level: Intermediate (I)This course will be delivered over 4 afternoon sessions, running from 1:00pm to 5:00pm each day.The intensive course on programming principles in R. This course covers the fundamental techniques such as functions, for loops and conditional expressions. It also covers the {tidyverse} package, {purrr}. {purrr} is a very powerful package that gives great flexibility to analysts, by enhancing R’s functional programming toolkit. By the end of this course, you will understand what these techniques are and when to use them. The course will also demonstrate how to use functions such as map(), map2() and pmap(), to iteratively map functions over multi-element objects like vectors and lists. Emphasis will also be placed on how to manipulate list outputs and how this can be applied to data..Learning OutcomesBy the end of the course, delegates will: Understand basic functions, multiple arguments and variable scopes.Have a thorough understanding for loops.Be able to apply basic functions.Have a thorough understanding of conditionals such as if, else and else if statements.Be familiar with possible R workflows such as directory structure and working with directories.Understand how the aforementioned techniques can be applied to their own data.Understand how these techniques will improve efficiency and results.Understand where to find help in R using resources and the help() function.Understand lists in R and know how to use {purrr} to map functions.Know what nested loops are and use {magrittr} to extract elements from them.Be able to create list columns and know how to access the data in them.Iteratively loop two or more objects to a function of choice using functions such as map2(), pmap() and imap().Recognize the advantages of using {purrr}.Understand how to extract elements from nested lists to achieve a desired output object class.Be able to effectively debug their code using multiple {purrr} functions for the debugging process.Save precious debugging time using e.g. safely() Topics CoveredConditionals: using if and else statements in RFunctions: what a function is, how are they used, and how can we construct our own functions.Looping in R: an introduction to the concept of looping in R. In particular for and while loops.Help: The help system in R can at first glance appear daunting, however, after the initial shock, R’s documentation is second to none.Project structure: Practical tips on how to structure a project.Data manipulation and aggregation using dplyrIntroduction to {purrr} and Lists: Introduction to lists in R and using {purrr} to map a function across a list.List-Columns and Nesting: Exploring nested data in list columns and using the mapping functions to manipulate them.Parallel Mapping: Using {purrr} functions to map over multiple lists in parallel.Manipulating {purrr} Output: Using {purrr} to efficiently extract elements from lists into vector and dataframe format, and change the hierarchy within nested lists.Best Practices in {purrr}: Showcase of functions from {purrr} which aid in the debugging process.  Target AudienceThis course is idea for anyone who would like to extend their basic familiarity with using R, and using R to write their own bespoke functions or optimizing their code. Assumed KnowledgeBasic prior experience with the R programming language is assumed. Namely that participants have some experience of R data structures, such as vectors, data frames, and experience in using pre-made functions from R packages.The course is aimed as a follow up the 'Introduction to R and Regression Modelling in R' training course.Whilst no statistical knowledge will be assumed, some of the examples will be statistical in nature.For this online course, participants are not required to have R installed on their own laptops. A virtual environment, which can be accessed through a web browser, will be used to run R and view course materials. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14410 https://www.ncrm.ac.uk/training/show.php?article=14791 Overview of 1-day courseIn survival analysis in medical research, the proportional hazards assumption and the hazard ratio effect measure have been popular for decades, fuelled by extensive application of the log-rank test and the Cox regression model.  However, the hazard ratio can be clinically awkward to interpret, or the proportional hazards assumption may not hold, rendering the use of a hazard ratio effect measure questionable at best.In this course we introduce the restricted mean survival time (RMST), which is a well established, but under-used summary of the survival experience.  In recent years there has been a surge of interest in the RMST, particularly in oncology, but also in many other areas. We begin with a review of the definitions of the RMST, approaches to estimation and different RMST-based effect measures which are clinically meaningful alternatives to the hazard ratio and are not based on a proportional hazards assumption.For the practical analysis of survival data, which includes right-censoring, the course focuses on a non-parametric analysis for comparing treatments and a generalised linear model (GLM) -type modelling approach based on the use of pseudo-values.  The latter provides a flexible method for directly modelling the RMST in a regression framework, where a treatment effect may be adjusted for covariates.  Model checking is also considered. The course concludes with a brief consideration of an extension to the RMST known as the window mean survival time (WMST) or the long-term RMST (LT-RMST).The course is a practical introduction to analysing survival data using a RMST-based effect measure.  Only essential theoretical aspects of the methodology will be summarised.  Examples used will be drawn from applications in medicine and health, particularly clinical trials.Practical work will be based around the statistical software R; see https://www.r-project.org/.PresentersSandro Leidi and James GallagherCost£312 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 17:30 (GMT). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked verbally or using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter.​  We also use Zoom meetings rather than webinars to encourage further interaction during an online course.Who Should Attend?Statisticians and data analysts working with survival data in medical research. Participants will be assumed to have a working knowledge of:Survival analysis techniques applicable to right censoringRegression modellingThe R statistics software.How You Will BenefitYou will acquire practical experience in the use of RMST-based effect measures as an alternative to the hazard ratio.  You will also be able carry out adjusted as well as unadjusted analyses.What Do We Cover?Problems with proportional hazards and hazard ratio effect measure.  Introduction to the RMST: definition, approaches to estimation, RMST-based effect measures as an alternative to the hazard ratio.  The restricted mean time lost (RMTL)Non-parametric analysis for comparing two groups. Statistical inference: estimation, confidence intervals and hypothesis testing for different effect measures. Advantage of RMST-based effect measures over the hazard ratioAdjusting a treatment effect for covariates. Modelling the RMST using a GLM-type model based on pseudo-values; choice of link functions and effect measures; model fitting and comparison of modelsModel checking and the use of pseudo-residualsExtending the RMST: window mean survival time (WMST)CRAN packages, including geepack for modelling, emmeans for post-processing and survRM2 for non-parametric analysis.The course does not cover time dependent covariates.SoftwarePractical work will be done in R.Note: For practical work, participants must download and install a number of CRAN packages in R.  This must be done prior to the start of the course. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 30 Mar 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14791 https://www.ncrm.ac.uk/training/show.php?article=14668 Overview of 2-day courseAre you working in Learning Analytics or Student Analytics?Ever been asked if the average mark is changing significantly over academic years, or if the difference between the rate of change for females and males is statistically significant? Or which factors are associated with non-continuation?Or which factors are associated with accepting an offer?Or if the chance of achieving a first class honours degree is associated with tariff points on entry? This two-day course provides participants with hands-on experience of analysing their own type of records for data-driven planning and confidently interpreting numerical results for reports to policy makers and committees. The focus of the course is on the use of two statistical modelling techniques:Linear regressionLogistic regressionLinear regression is used to examine how the mean of a numerical outcome, like final year mark, might be associated with different characteristics. If the outcome is binary, such as drop-out, logistic regression is used to investigate how the chance of failing to continue to the second year is associated with different characteristics.  Logistic regression is a popular modelling technique, for example it is advocated by the Office for Students in their Financial support evaluation toolkit.The course also illustrates how these modelling techniques may be used for one-step-ahead forecasting into next year.Presentations, demonstrations and hands-on computer practicals are based around the free statistical software R; see https://www.r-project.org/. Formulae are kept to a minimum; instead, we concentrate on results, their interpretation and reporting in plain language.PresentersSandro Leidi and James Gallagher Cost£516 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 10:00 and 16:30 (GMT+1). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Administrators in educational establishments working in Policy, Planning and Strategy units; Data and Insight units; Business Intelligence units; those involved in learning analytics or extracting actionable insights from student records and in reporting to policy makers or committees. Anyone in these positions needing to answer questions around how student outcomes may be associated with different factors will benefit greatly from this course.It is assumed that participants will, prior to the course, have:An understanding of mathematical functions and equations. In particular, the natural logarithmic and exponential functions (loge() and exp() respectively), the equation of a straight line and its geometrical representationAttended the one-day course Statistics for University Administrators, or Statistics for University Administrators using R, or have equivalent knowledge.No previous experience of the R software is required; a brief introduction for the purpose of the course will be given.How You Will BenefitBy the end of the course you will be familiar with two common statistical modelling methods for investigating associations and extracting actionable insights, be able to report the results in plain language, and be able to perform analyses using free statistical software. You will also be able to follow official guidance on the use of such models, e.g. the Office for Students’ guidance on the use of binary logistic regression for investigating the effectiveness of financial support with respect to student outcomes.What Do We Cover?Introduction to the R software·Simple linear regression for relating a numerical outcome to a numerical explanatory variableExtending the linear regression model to incorporate categorical explanatory variables and interactions to allow for effect modificationUsing binary logistic regression in place of linear regression when modelling binary outcomesOne-step-ahead forecasting.SoftwarePractical work will be done in R.Note:For practical work, participants must download and install the R software prior to the start of the coursePractical work is based on the Windows operating system.Extra InformationThe R software is used on the course for two reasons:It is a free dedicated statistics package and can be used for other analysesIt is a widely used software which will be maintained by the R Foundation for many years to come. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14668 https://www.ncrm.ac.uk/training/show.php?article=14888 Working with quantitative data entails being proficient with data management and exploration in order to do analysis and get insights. And while there are multiple ways of doing this, R has emerged as one of the most popular tools for these tasks. R is a free and open source software for statistics and data science that is increasingly used with social survey data.Important: you will need some familiarity with quantitative data, and an understanding of variables, datasets, and re-coding. A basic knowledge of R is also a highly recommended.In this course you will be introduced to the use of R and will learn how to prepare and visualize data. We will focus on the use of the ‘Tidyverse’ package. Inspired by the concept of “tidy data” this package enables users to import, merge, recode, restructure and visualize data very efficiently.Half of the course will focus on how to efficiently transform variables and prepare them for analysis while the other half will focus on visualization. The course will combine presentations of the key concepts, hands on practical sessions and discussions of the solutions. In the practical part we will be using real world data to prepare the participants for working with their own data.Looking to book for four or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house courses. training@the-sra.org.uk (Social Research Association) Tue, 23 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14888 https://www.ncrm.ac.uk/training/show.php?article=14972 Want to turn evidence into real-world policy change? Northumbria University's Common Sense Policy Group is running a five-day online course, between 19 - 23 October 2026, for postgraduates, early career researchers and policy practitioners.Northumbria has a strong track record of delivering impactful CPD to make a difference across a variety of industries, and this course: 'Evidence for transformative policymaking: Statistics, microsimulation and public opinion to achieve change' brings that same hands-on, applied approach to evidence-based policymaking.You'll leave with:A tax-benefit microsimulation model, coded in Python from scratch.Hands-on experience with the Landman Tax-Transfer Model - used in, for example, Hansard debates and EHRC research.Insight into what actually changes minds on tough policy issues.A 3,000-word policy report and code portfolio, hosted on OSF - ready for your CV.There are no formal entry requirements though some prior knowledge of quantitative methods and interest in policy analysis is assumed.Find out more HERE.Registrations close 30 September 2026.Course places are limited. If there are no places available, please do register your interest via the link on the course page. elliott.johnson@northumbria.ac.uk (Northumbria University and Common Sense Policy Group) Fri, 11 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14972 https://www.ncrm.ac.uk/training/show.php?article=14957 A table of numbers is not always the best way to present census data, often we want to visualise the information as a map to help people see patterns quickly.This free online workshop will focus on the use of the free and open-source cross-platform QGIS GIS application to create maps and other geographical visualisations from 2021/22 census data provided by the UK Data Service.The session will include:An introduction to UK census data, the UK Data Service, and the different types of UK census data provided by the service.An exploration of the help resources available within the UK Data Service to assist you with using census data.Demonstrations of how to download census data from the UK Data Service.An introduction to maps and other forms of geographical data visualisation and a consideration of the common issues that need to be considered when applying geographical data visualisation to UK census data.An introduction to preparing data for mapping, an introduction to the QGIS GIS application, and demonstrations of using QGIS to create geographical visualisations from UK census data.. bethan.jones-4@manchester.ac.uk (UKDS) Wed, 19 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14957 https://www.ncrm.ac.uk/training/show.php?article=14971 Longitudinal research frequently seeks to understand not only whether people or other units change over time, but also whether they differ in their starting points, rates of change, and the factors associated with these differences. Growth modelling provides a family of statistical approaches for addressing such questions.This masterclass introduces longitudinal growth modelling with a particular focus on Structural Equation Modelling (SEM). The first part of the session will introduce the concepts underlying growth models, contrast multilevel and SEM approaches, and consider the distinctive possibilities offered by latent growth models. The second part will provide a worked demonstration in R, using lavaan to specify, estimate, interpret and extend a basic latent growth model.The session is intended for researchers who already have some familiarity with quantitative analysis. Previous exposure to regression and/or SEM will be helpful, but specialist knowledge of longitudinal modelling is not assumed.The session is free to attend and will take place online via Microsoft Teams. J.E.Hall@Soton.ac.uk (University of Southampton) Wed, 09 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14971 https://www.ncrm.ac.uk/training/show.php?article=15024 Training for PhD and MSc students in the design of social research, quantitative and qualitative analysis.Design-based causal inference in observational and experimental settings by Dr Zach Dickson - LSEThis course will introduce students to the principles and methods of causal inference. Causal inference is the process of drawing conclusions about the causal relationships between variables based on observational data. In this course, we will start with an overview of the potential outcomes framework and will cover topics such as confounding, selection bias, and causal identification. We will also discuss directed acyclic graphs (DAGs), and methods for estimating causal effects in observational settings, including difference-in-differences, instrumental variables, and regression discontinuity designs. We will use real-world examples to illustrate the concepts and methods covered in the course, and students will have the opportunity to apply these methods to their own research projects.By the end of the course, students will have a solid understanding of the principles and methods of causal inference, and will be able to apply these methods to a wide range of research questions. A basic knowledge of programming in a scripting language, such as R or Python, is recommended for this course. methodology.admin@lse.ac.uk (LSE) Mon, 05 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15024 https://www.ncrm.ac.uk/training/show.php?article=14390 Level: Intermediate (I)If you’re looking to improve the way you plan your work and improve efficiency by introducing statistical forecasting, then this course is ideal. By the end of the session you will have a firm grasp of how to summarise and measure trends, as well as how to extrapolate trends into a forecast. You will also have a good understanding of how to perform relevant calculations in Excel. This course is being delivered over two morning sessions, which will run from 9:00am to 1:00pm on both days.This course looks at one of the big questions in businesses -- finding out what is going to happen next. It would be so much easier to plan sales, purchases, production, staff and logistics if we knew the answer to this question! Many businesses know how important it is to forecast for the future, yet many fail to apply the fundamental concepts of statistical forecasting. Those that don’t use statistical forecasting face higher costs and uncertainty when reality diverges from their plans. Those that do use statistical forecasting are able plan for the future much more effectively and efficiently. Learning OutcomesUnderstand the distinction between sober & drunk time series (seriously!)Learn how to use hypothesis testing to confirm that a trend is a genuine trend.Learn how to use moving averages correctly to identify potential turning points.Discover the simplest method of identifying seasonality in your time series and to confirm it with hypothesis test.Uncover the basic principles of statistical process control and how you can use it to confirm deviations from an expected trend.Learn 4 different ways of extrapolating an existing trend to produce forecast. Topics CoveredTime series analysis, forecasting, trend identification, seasonality, moving averages, trend extrapolation, statistical process control, forecasting using extrapolations. Target AudienceAnyone involved in business planning, performance analysis and other similar roles that require analyses of historical trends and extrapolation of those trends to create forecasts. Course PrerequisitesThis course would be suitable for anyone who has completed our two day "Basic Statistics" course.Other participants should ideally have an understanding of the following:Basic statistical concepts including expectation, variance, distribution and correlation.Probability and risk including knowing what false positives and false negatives areKnow how to calculate of confidence intervals for the mean of 1 sample and the difference in means between 2 samples.Know how to calculate the slope and intercept of a simple regression models with 1 variableUse of Microsoft Excel including the use of formulae such as IF, VLOOKUP, OFFSET, etc.Produce and interpret line charts, column charts and scatter plots in Excel. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14390 https://www.ncrm.ac.uk/training/show.php?article=14407 Level: Intermediate (I)This tutor-lead virtual course will introduce how the tidyverse and ggplot2 can be used to reproducibly create publication quality charts from R. Learning OutcomesReproducibly import and wrangle data with the tidyverse in preparation for charting with ggplot2. Confidently choose the appropriate geoms for visualising data with ggplot2. Understand how to use factors using the forcats package to control the display (or order) of chart elements. Effectively control the use of colours and themes in ggplot2 charts. Understand how to augment GIS data using sf and the tidyverse to be visualised with ggplot2. Reproducibly export publication quality charts for papers, posters and other printed media.Delegates are expected to have a laptop with the R software installed. Topics CoveredR, Data Visualisation, ggplot2, Data Presentation, Exploratory Data Analysis. Target AudienceThis course is designed for both novice and experienced R users who want to create publication quality printed charts with ggplot2. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14407 https://www.ncrm.ac.uk/training/show.php?article=14915 Artificial intelligence and large language models (LLMs) are becoming part of everyday social research, being used in everything from literature reviews to survey question development, analysis and reporting. This course will equip participants with the tools to understand the current capabilities of these models and how to use them effectively in the quantitative research workflow.The course starts by introducing key concepts such as AI, machine learning, and Large Language Models (LLMs) in non-technical language. It then discusses the stages of quantitative research and how LLMs can be used effectively at each stage. Participants will learn how to choose between different types of tools, such as chatbots, in-editor assistants, and coding agents, and why instructions, context, tools, and human review shape what LLM systems can do.The course is a mix of lectures covering key concepts in non-technical terms, workflows, and examples, as well as hands-on practicals where researchers can apply key concepts to real-world problems to better understand the strengths and limitations of these tools.Looking to book for four or more people from your organisation? Please let us know before booking by emailing: training@the-sra.org.uk training@the-sra.org.uk (Social Research Association) Wed, 29 Jul 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14915 https://www.ncrm.ac.uk/training/show.php?article=14404 Level: Intermediate (I)This course will be delivering over 4 afternoon sessions, running from 1:00pm to 5:00pm each day.This course covers the fundamentals of machine learning and the methodology for applying these to real-world analytics problems. The course outlines the stages involved in a machine learning analysis, and walks through how to perform them using the R programming language and the tidymodels suite of packages. Participants will be provided with exercises to complete through the course in order to gain hands-on experience in using the methods presented.The individual stages of: problem formulation, data preparation, feature engineering, model selection and model refinement will be walked through in detail giving participants a solid process to follow for any machine-learning analysis. This includes methods for evaluating machine-learning models in terms of a performance metric as well as assessing bias and variance.  Learning OutcomesFollowing this course the attendees will:Be familiar with the overall process of how to apply machine-learning methods in an analysis projectUnderstand the differences and similarities between statistical modelling and machine-learning theoriesHave gained hands-on experience in working with the tidymodels suite of packages in RGain an intuitive understanding of how several specific machine-learning methods solve the problems of prediction and classification Topics CoveredIntroduction to machine-learning: parsnip package; basic train and testStages of machine-learning: problem formulation; data preparation; feature engineering; model selectionHighlighted Models: Decision trees and random forests; K-nearest neighbours, linear regression and logistic regression. Target AudienceMachine Learning can be applied to data in a whole range of fields from Finance to Pharmaceutical, Retail to Marketing, Sports to Travel and many, many more! This course is aimed at anyone interested in applying machine learning methods to their data in order to: gain deeper insight, make better decisions or build data products Assumed KnowledgeThis course assumes participants are comfortable with the basic syntax and data structures in the R languageFor this online course, participants are not required to have R installed on their own laptops. A virtual environment, which can be accessed through a web browser, will be used to run R and view course materials. training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14404 https://www.ncrm.ac.uk/training/show.php?article=14876 Overview of 1-day courseAre you working in Learning Analytics or Student Analytics?Ever been asked for the margin of error on a percentage?Or if the difference between your percentage and a benchmark is statistically significant?Or if the change in student retention rate from last year is statistically significant?This one-day course provides participants with hands-on experience of analysing their own type of records for data-driven planning and confidently interpreting numerical results for reports to policy makers and committees, or for returns to the Higher Education Statistics Agency.Presentations, demonstrations and hands-on computer practicals will make use of the free statistical software R; see https://www.r-project.org/.  Note the course does not focus on teaching R, but on the use of supplied R functions to perform statistical analyses of interest.  Formulae are kept to a minimum; instead, we concentrate on results, their interpretation and reporting in plain language.​PresentersSandro Leidi and James GallagherCost£282 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live between 09:00 and 17:30 (GMT).  The delivery platform is Zoom, which may be freely accessed.  Questions may be asked verbally or using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Administrators in educational establishments working in Policy, Planning and Strategy units; Data and Insight units; Business Intelligence units; those involved in learning analytics or extracting actionable insights from student records and in reporting to policy makers or committees; those involved in producing returns to the HESA.  Anyone in these positions will benefit greatly from this course.No previous experience of the R software is required.How You Will BenefitBy the end of the course you will be familiar with basic statistical methods for making sound assessments, and extracting actionable insights.  What Do We Cover?Estimation: standard errors and confidence intervals for percentagesComparing a percentage to a benchmark: one-sample z-testComparing two percentages: two-sample z-testComparing more than two percentages: chi-square test.The course will make use of our R functions which will be made freely available. Note the course does not focus on teaching R, but on the use of the supplied R functions to perform statistical analyses covering the above syllabus.SoftwarePractical work will be done in R.Note:For practical work, participants must download and install the R software.  This must be done prior to the start of the coursePractical work is based on the Windows operating system.Extra InformationThe content of this course is identical to that of Statistics for University Administrators. The only difference is the R statistical software is used instead of Excel.  R has two advantages:It is a free dedicated statistics package and can be used for other analysesIt is a widely used software which will be maintained by the R Foundation for many years to come. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Tue, 16 Jun 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14876 https://www.ncrm.ac.uk/training/show.php?article=14979 Intersectional MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy) is an increasingly popular approach for studying inequalities in individual outcomes across intersections of social identity characteristics, such as gender, race, and socioeconomic background.In the first half of the session, Professor George Leckie will introduce the MAIHDA approach through an application examining intersectional inequalities in educational attainment using data from the Programme for International Student Assessment (PISA). He will explain the underlying multilevel modelling approach and discuss the interpretation of MAIHDA results.The session will also provide a high-level overview of recent substantive and methodological work using MAIHDA, including extensions for studying intersectional inequalities in longitudinal trajectory data and for examining heterogeneity of treatment effects in randomised controlled trials.In the second half, there will be a demonstration of how to conduct a standard MAIHDA analysis in both R and Stata.Familiarity with standard multilevel models would be beneficial, but is not essential (for a QMEG introduction to multilevel models, see here: https://qmeg.soton.ac.uk/events-and-resources/ ).The session is free to attend and will take place online via Microsoft Teams. J.E.Hall@Soton.ac.uk (University of Southampton) Wed, 23 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14979 https://www.ncrm.ac.uk/training/show.php?article=14967 Large language models are now embedded in research workflows across the social sciences, yet most researchers interact with these tools through consumer interfaces that obscure how they work, where data goes, and what decisions are being made on their behalf. This three-workshop series closes that gap. Across three standalone half-day sessions, participants build a working understanding of the AI stack: from how models generate text and where inference happens, through prompt engineering, retrieval-augmented generation, and API-based workflows, to the rapidly maturing ecosystem of agentic platforms, harness engineering, and autonomous research infrastructure. Each workshop combines conceptual exposition with live demonstrations and practical exercises grounded in survey research scenarios. No programming experience is required for Workshop 1; Workshops 2 and 3 assume familiarity with earlier concepts.The course covers: Workshop 1: How Large Language Models Work: tokens, training, alignment, data security, inference, open- vs closed-weights models, reproducibility challenges, and the limitations of chatbot interfaces for research.Workshop 2: Context Engineering: prompt design and optimisation, retrieval-augmented generation (RAG), API-based workflows and batch processing, memory and tool-calling, MCP servers, and evaluation engineering.Workshop 3: Agentic AI and Harness Engineering: the agentic AI ecosystem (IDE-native agents, extended-autonomy platforms, orchestration tools), harness engineering and SDKs, memory and token economics, MCP servers and hooks, oversight, auditability, and research transparency.By the end of the course participants will:Explain how LLMs generate text and assess the implications of model architecture, training, and alignment for research practiceDistinguish between open-weights and closed-weights models and evaluate their data governance implicationsApply prompt optimisation techniques and build evaluation pipelines to validate LLM outputsMake structured API calls, manage parameters, and use retrieval-augmented generation where appropriateMap the agentic AI ecosystem, explain harness engineering, and assess how platforms orchestrate memory, tools, and contextDesign human-in-the-loop safeguards and audit protocols appropriate for agentic research workflowsPre-requisitesNo prior programming experience or specialist software knowledge is required for Workshop 1. Workshops 2 and 3 assume familiarity with concepts from Workshop 1 (or equivalent knowledge of how LLMs work). Workshop 3 benefits from some comfort with reading code, but participants are not required to write any. Setup guidance for API access will be provided before Workshops 2 and 3.No software installation is required for Workshop 1. For Workshops 2 and 3, participants will benefit from having API access to a commercial LLM provider (e.g. Anthropic, OpenAI); setup guidance will be provided in advance. All demonstrations will be conducted live by the instructor. Participants do not need prior experience with any specific software, though basic familiarity with web browsers and text editors is assumed.Target AudienceSurvey researchers, methodologists, and quantitative social scientists across academia and government who use or are considering using large language models in their research. The series is designed to be accessible to researchers at all career stages, from doctoral students to senior investigators. Although the recommendation is to attend all three workshops, you are welcome to join them individually. PLEASE NOTE THESE WORKSHOPS WILL RUN ONLINE ON 23 NOVEMBER, 30 NOVEMBER AND 7 DECEMBER 2026 FROM 09:30-13:30. jmh6@soton.ac.uk (NCRM, University of Southampton) Thu, 03 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14967 https://www.ncrm.ac.uk/training/show.php?article=14667 Overview of 2-day courseMixed models have become increasingly popular, as they have many practical applications. However, the traditional linear mixed model with normally distributed errors is not appropriate for modelling discrete responses such as binary data and counts. Such responses are typically analysed using generalised linear models such as logistic regression and Poisson regression.Commonly-used generalised linear models will be extended to deal with multiple error structures, using a variety of scientific examples, mainly medical and health related applications, such as investigating the presence of adverse events in a clinical trial.The emphasis will be on practical understanding, although an outline of the theory will be presented. Practical examples will be used to illustrate the methods and participants will have the opportunity to fit and interpret models themselves in hands-on computer practicals.Practical work will be based on the R software; see https://www.r-project.org/.  Model fitting will mainly be done using the CRAN package GLMMadaptivePresentersSandro Leidi and James Gallagher Cost£582 (inclusive of 20% VAT)Delivery ModeAll training is online and will be delivered live each day between 09:00 and 17:30 (GMT). Delivery platform: Zoom, which may be freely accessed.  Questions may be asked using Zoom's chat box.  Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support.  During presentations, the team member who is not speaking can take questions in addition to the presenter. We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​Who Should Attend?Data analysts and statisticians working in medicine, health and related areas, who wish to have a practical introduction to Generalised Linear Mixed Models. It is assumed that participants are R users and familiar with the practical use of both generalised linear models and linear mixed models. How You Will BenefitYou will learn to formulate generalised linear models with both fixed and random effects for a range of situations, how to fit them and how to interpret their output.What Do We Cover?Review of generalised linear models and linear mixed modelsBinary and binomial outcomes: logistic regression with mixed effectsCount outcomes: Poisson and negative binomial regression with mixed effectsOrdered outcomes: proportional odds regression with mixed effectsAdaptive Gauss-Hermite Quadrature fitting method; inferential proceduresConvergence issues and solutionsInterpretation of effects in a generalised linear mixed model and predictionGLMMadaptive CRAN package for fitting generalised linear mixed models; ordinal CRAN package for fitting the proportional odds model with random effects.Notes on course content:The GLMMadaptive package can currently only fit models where the random effects part is defined by a single grouping factorThe course does not cover marginal or GEE type models for repeated measurements.SoftwarePractical work will be done in R.Note: For practical work, participants must download and install a number of CRAN packages in R.  This must be done prior to the start of the course. jamesgallagher1929@gmail.com (Statistical Services Centre Ltd) Mon, 12 Jan 2026 00:00:00 +0000 https://www.ncrm.ac.uk/training/show.php?article=14667 https://www.ncrm.ac.uk/training/show.php?article=14938 One of the most popular software tools for data management and analysis is the open source package R, which is often used with the Rstudio interface. These are extremely powerful and are able to handle most types of data and analyses used in social research.In this course you will learn the basics of R and Rstudio. We will cover the main types of objects in R and how to select cases and variables. We will also discuss how to import and export data and how to describe the data using tables and summary analyses. Through the practical exercises you will get accustomed to running functions in R and using the syntax.IMPORTANT: participants will need to have a working knowledge of quantitative data.Looking to book for six or more people from your organisation? Contact training@the-sra.org.uk to ask about our in-house training. training@the-sra.org.uk (Social Research Association) Tue, 11 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14938 https://www.ncrm.ac.uk/training/show.php?article=14384 Level: Foundation (F)The course will be delivered over 5 morning sessions, running fro 9:00am to 1:00pm each day. The purpose of this course is to help participants understand some basic statistical concepts and develop a strategy for approaching simple data analysis. The course will introduce basic concepts such as hypothesis testing and confidence interval estimation. It will provide the tools to undertake simple analysis of a dataset and will include some helpful hints and tips for reading and understanding reported statistics.Learning OutcomesBy the end of this course, participants will understand basic approaches to statistical inference, including hypothesis testing and confidence interval estimation. They will be equipped with the skills necessary to undertake simple analysis and to understand some of the basic terms often used to report statistical results. The course will include some calculations by hand to aid understanding. Topics CoveredData Summary; The normal distribution; Confidence intervals; Introduction to hypothesis tests; Analysis of contingency tables – chi-squared test;  T-tests; Non-parametric tests, (Wilcoxon signed rank test, Mann-Whitney U test); Introduction to correlation and regression; Basic presentation of data and results. Target AudienceThis course is aimed at those who have either never undertaken a formal statistics course, or who have studied some statistics in the past but wish to undertake a refresher. It is ideal for statistical novices who have never had any formal training but are starting to encounter statistics in their work and wish to gain some insight.Delegate Feedback"Ellen and Jenny were extremely knowledgeable. They were also approachable and happy to give further explanations when necessary""Excellent course. Hard to fault. I can see why it's popular""This was a fantastic course, the presenters had a very high knowledge but were able to 'dumb it down' for me as I am new to this"  training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14384 https://www.ncrm.ac.uk/training/show.php?article=14978 This live online course (administered via Zoom) offers an engaging introduction to Structural Equation Modelling (SEM) using R, the popular open-source software for statistical computing and graphics. The course will run over 5 consecutive days and will combine synchronous live lectures, individual exercises, group discussions and small group supervisions to simulate a live classroom environment. High contact with our expert instructors and interactive group work will further contribute to recreating the classic Cambridge experience in an online setting.This course will present the lavaan package, rapidly becoming the tool of preference for SEM in R. Participants will actively work through practical examples to gain first-hand experience in the application of factor analysis and other more advanced latent trait models. You will learn the following topics in this course:  Introduction to R and data analysisBasic concepts of factor analysis (EFA/CFA/Categorical CFA)Basic and advanced Structural Equation Models (SEM)Application of mediation & moderation techniquesMulti-level modellingLatent growth curve modellingMultiple-group analysis and evaluation of continuous and categorical data measurement invariance, as well as longitudinal measurement invarianceYou don’t need to know R to follow the course. However, if you are not familiar with R, you will need to choose the 5 day option, which includes an introduction to the R software on Day 1. On completion, participants should have a good knowledge of the topics covered and have acquired an independent use of R and latent trait analysis.We believe in active learning and developing practical skills. Thus, the necessary theoretical introduction will be illustrated with practical examples and we will be working with real data. No prior knowledge about SEM is assumed. The pace of teaching is adjusted to suit the level of the participants. Teaching will be in small groups so that delegates can make the most of the teaching. ls523@cam.ac.uk (University of Cambridge Psychometrics Centre) Tue, 22 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14978 https://www.ncrm.ac.uk/training/show.php?article=14392 Level: Intermediate (I)Multiple linear regression is one of the most commonly used techniques in statistics and allows for the impact of multiple variables to be assessed simultaneously. The analysis of variance (ANOVA) is a related technique which allows the mean values of several groups to be compared. This course will utilize Jamovi's free software, to equip participants with the skills necessary to undertake both types of analysis, understand and interpret the output, check the assumptions that underpin each type of model, and present the results coherently. This course will be delivered over two morning sessions, running from 9:30am to 1:00pm on both days.Learning OutcomesBy the end of this course the attendees will:       Understand what is meant by the term Analysis of Variance (ANOVA) and the different ANOVA models availableAssess when it is appropriate to fit an analysis of varianceInterpret the result s of an analysis of varianceAssess model fitPresent the results of an analysis of varianceUnderstand what is meant by the term multiple linear regressionAssess when it is appropriate to fit a multiple linear regression modelCarry out a regression analysis using free softwareInterpret the results of a multiple linear regression analysisAssess model fitPresent the results of a multiple linear regression analysis      Topics CoveredThe first day will start with a brief recap on the concepts of hypothesis testing and choosing the right test. This will include the basic use of Jamovi software to carry out and interpret an independent t-test before progressing to the related technique ANOVA.  Assumption checking, two-way ANOVA’s and interactions will conclude the morning. The second day starts with correlation and simple linear regression to assess the relationship between two continuous variables before concentrating on multiple regression which allows multiple variables to be tested simultaneously. Both sessions will concentrate on producing and understanding outputs rather than mathematical content with regular exercises to reinforce learning. Target AudienceThis course is aimed at individuals who have some basic statistical knowledge and who wish to undertake analyses of quantitative data and who therefore wish to gain some insight into how to undertake these. Knowledge AssumedBasic statistical knowledge as the course is designed as a follow-on from our Basic Statistics course.Delegates will need to download the latest version of Jamovi onto their laptop as this will be used during the workshop: https://www.jamovi.org/download.html training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14392 https://www.ncrm.ac.uk/training/show.php?article=14397 Level: Professional (P)This online training course will be delivered over 4 afternoon sessions, running from 1:00pm to 5:00pm on each day. This course will cover R object-oriented programming techniques. It will discuss what OOP is and the different varieties within R. Beginning with the popular S3 and S4 OOP frameworks, we’ll finish with the new {R6} package that is used extensively in Shiny applications. The course will then introduce the {rlang} package as a way of parsing variables from a data set into a function. Furthermore, it cover environments and function-evaluation in R, to help you understand how the tools in {rlang} work under the hood. This course will be delivered over 4 sessions. Course Outline This course will cover the following topics:Advanced Functions: Scoping rules (including lexical scope), The … argument, Argument matchingS3 classes: Introduction to object-oriented programming, Constructing S3 objects, DrawbacksS4 classes: Creating and using S4 classes, Differences between S3 and S4 classesR6 classes: Differences between {R6} and S3/S4, Mutable states, Creating methods, Shallow and deep copiesModifying user argument in functions callsQuoting code with quosuresUsing quasi quotation Learning outcomesBy the end of this course, delegates will be able to :Select the most appropriate form of OOP for their taskLeverage encapsulation, polymorphism and inheritance to provide a nice user interface to their codeWrite functions with rich results, user-friendly display and programmer-friendly internalsExtend the functionality of functions for new object typesWrite code that is extensible by othersUse the {rlang} operators {{}}, !!, !!! and := to pass variablesModify user functions using enquo()Parse and deparse expressions Target AudienceThis course assumes that participants are comfortable with the fundamentals of R programming. As such the course will be of interest to anyone who uses R, in particular those who want to develop their computer skills to cover more advanced topics. Delegate Feedback ““Extremely good teacher, great explanations, funny examples and very flexible in terms of content and time. I got to know a lot of things, that I did not think were possible.” “Material well presented and delivered” ”I am not scared of R anymore. It was actually fun!”“Really great course! Useful content that will greatly benefit me in my future R projects.” training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14397 https://www.ncrm.ac.uk/training/show.php?article=14399 Level: Professional (P)This online training course will be delivered over 4 afternoon sessions, running from 1:00pm to 5:00pm on each day. This course will cover R object-oriented programming techniques. It will discuss what OOP is and the different varieties within R. Beginning with the popular S3 and S4 OOP frameworks, we’ll finish with the new {R6} package that is used extensively in Shiny applications. The course will then introduce the {rlang} package as a way of parsing variables from a data set into a function. Furthermore, it cover environments and function-evaluation in R, to help you understand how the tools in {rlang} work under the hood. This course will be delivered over 4 sessions. Course Outline This course will cover the following topics:Advanced Functions: Scoping rules (including lexical scope), The … argument, Argument matchingS3 classes: Introduction to object-oriented programming, Constructing S3 objects, DrawbacksS4 classes: Creating and using S4 classes, Differences between S3 and S4 classesR6 classes: Differences between {R6} and S3/S4, Mutable states, Creating methods, Shallow and deep copiesModifying user argument in functions callsQuoting code with quosuresUsing quasi quotation Learning outcomesBy the end of this course, delegates will be able to :Select the most appropriate form of OOP for their taskLeverage encapsulation, polymorphism and inheritance to provide a nice user interface to their codeWrite functions with rich results, user-friendly display and programmer-friendly internalsExtend the functionality of functions for new object typesWrite code that is extensible by othersUse the {rlang} operators {{}}, !!, !!! and := to pass variablesModify user functions using enquo()Parse and deparse expressions Target AudienceThis course assumes that participants are comfortable with the fundamentals of R programming. As such the course will be of interest to anyone who uses R, in particular those who want to develop their computer skills to cover more advanced topics. Delegate Feedback ““Extremely good teacher, great explanations, funny examples and very flexible in terms of content and time. I got to know a lot of things, that I did not think were possible.” “Material well presented and delivered” ”I am not scared of R anymore. It was actually fun!”“Really great course! Useful content that will greatly benefit me in my future R projects.” training@rss.org.uk (The Royal Statistical Society) Mon, 18 Aug 2025 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14399 https://www.ncrm.ac.uk/training/show.php?article=14989 To be held Online, via Zoom SummaryThis three-day course provides an introduction to multilevel modelling and includes software practicals in your choice of software: MLwiN, R, or Stata. We focus on multilevel modelling for continuous and binary responses (dependent or outcome variables) when the data are clustered (nested or hierarchical). These models can be viewed as an extension of conventional linear and logistic regression models to account for and learn from the clustering in the data. Such models are appropriate when, for example, analysing exam scores of students nested within schools, or health outcomes of patients nested within hospitals. Special interest lies in disentangling social processes operating at different levels of analysis by decomposing the within- from the between-cluster effects of covariates (explanatory or predictor variables). Longitudinal data are also clustered, with repeated measurements on individuals or multiple panel waves per survey respondent. Throughout the course we emphasize how to interpret multilevel models and the types of research question they can be used to explore. Full course information and excerpts can be viewed here: https://www.bristol.ac.uk/cmm/learning/introworkshop.html  Testimonials“The course was excellent - far exceeded expectations. The course has given me the confidence to use MLM, something I very much lacked before. I feel I understand the theory behind MLM, why each stage is so important, and the various interpretations. Without this course I would be lost. I cannot thank you all enough.”“This was a beautifully constructed course. It was clear throughout that careful thought had been given to providing a balance between lecture content, time for questions and discussion, and practical sessions. Both George and Bill delivered fantastic lectures - explanations were clear and thorough (including critiques of each approach) and content built up in complexity over time with plenty of worked examples of different kinds. The course was superb - can't rate it highly enough.”“I thought it was a really good double act between George and Bill - they are both hugely knowledgeable so having one person focused on the slides and the other manning the chat was a good approach as it meant the teaching didn't get derailed by people's questions.”Topics Overview of multilevel modellingVariance-components modelsRandom-intercept models with covariatesBetween- and within-effects of level-1 covariatesRandom-coefficient modelsGrowth-curve modelsThree-level modelsReview of single-level logistic regressionTwo-level logistic regression FormatThe course will consist of a 2:1 mix of lectures and hands-on practical sessions applying the taught methods to real datasets. The lectures are software independent and are delivered live via Zoom, but recordings of the lectures will be made available shortly afterwards for twelve weeks following the course if participants are unable to attend at the scheduled time. The instructors alternate the lecturing. Participants can ask questions via Zoom’s text-based chat facility and these will be monitored and answered by the instructor not presenting or relayed to the instructor presenting to answer live.Each lecture is immediately followed by a self-directed practical, offered in participants’ choice of MLwiN, R, or Stata, giving participants the chance to replicate the presented analyses and to consolidate their knowledge. At the end of each practical session the instructors demo the different software, each in a different breakout room.ApplicationsIf you would like to attend the workshop, please complete and submit the online booking form (see below). Please note the closing date for applications is 22nd November 2026.Applications will be processed on a rolling basis, once a week, until the application deadline. A link to the University of Bristol’s online shop will be provided and your place on the course will be confirmed upon successful payment.If you have any queries, please email info-cmm@bristol.ac.uk. info-cmm@bristol.ac.uk (Centre for Multilevel Modelling, University of Bristol) Tue, 29 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14989 https://www.ncrm.ac.uk/training/show.php?article=14991 This online course on questionnaire design, explores question wording issues and the questionnaire-as-a-whole with a focus on web surveys and mobile-friendly web surveys. The course is full of practical advice. It also provides tips for anyone moving from interviewer-administered surveys to web surveys. Mirroring in-person training, this course will be interactive. There will also be 6 small group workshops to facilitate putting the course concepts into practice.Questionnaire DesignThe popularity of web surveysGetting started with a new questionnaireTrade-offs – short and simple versus clearFour cognitive stages a respondent goes through in answering a survey questionSolutions to ambiguous term, understanding recall error and reducing question sensitivityQuestion wording guidelines - This about the do's and don'ts of writing survey questions for any context.Workshop 1 - Critiquing a survey questionSome additional issues with factual questionsHighlights from mini appendix: Demographic questions are always the most difficult to writeWorkshop 2 - Writing a survey questionMini appendix on actual versus usual behaviourHighlights from mini appendix on some additional issues with subjective questionsKnow the deeper issues with open and closed questionsProblematic question formats to be aware of or avoid (eg. agree / disagree)Mini appendix on other problematic formats (satisfaction, tick all that apply, ranking and hypothetical questions)Web surveysDon't rely on web survey software templatesWorkshop 3 - Critiquing web survey software templatesModes of quantitative data collectionModes of quantitative data collection: Mixing modesModes of quantitative data collection: Overall mode differences (the obvious ones)Mini Appendix on mode effects due to satisficingWorkshop 4 - Interpreting data from a mixed mode experimentBack to web surveysDesigning the web survey itselfDay 2 appendix - 8 question testing methods for web surveysThe special things that web surveys can do, but should we?Visual versus not visualMini appendix on tips for paper self-completionWorkshop 5 - Visual problemsWeb surveys for mobile phones - earlier evidence, current thinkingHighlights from mini appendix on data collection differences: What should you do?Mini appendix on "push to web"Back to questionnaire designExamples of question revisions based on testing resultsWorkshop 6 - Revising survey questionsHighlights from mini appendix on extra tasks on mobile phones By the end of the course participants will:Have greater questionnaire design skills in general and the ability to critique existing web survey software templatesHave the ability to create effective web survey questionnaires as well as mobile-friendly onesHave better knowledge about questionnaire-related mode differences and effectsThis course is for anyone interested in questionnaire design for web and mobile web surveys. Ideally participants need some familiarity with surveys and questionnaire design.Preparatory ReadingDesirable for participants to read:https://web.stanford.edu/dept/communication/faculty/krosnick/docs/2009/2009_handbook_krosnick.pdfhttps://www.gesis.org/fileadmin/admin/Dateikatalog/pdf/guidelines/mixed_device_mobile_web_surveys_beuthner_2021.pdfPLEASE NOTE THIS COURSE IS TAUGHT OVER THREE DAYS (10:00-15:00), AND EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES. jmh6@soton.ac.uk (NCRM, University of Southampton) Tue, 29 Sep 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14991 https://www.ncrm.ac.uk/training/show.php?article=15022 Why do our friends often know one another, even though we are only a few connections away from people in very different social circles? Why does information spread easily through some networks, while changes in behaviour require repeated encouragement? Who should be targeted in an intervention, and how does the answer depend on what we want to achieve?Social network analysis offers a theoretical framework and a methodology to answer questions like these by focusing on the relationships between and among social entities. A network is a structure composed of units and the relationships that connect them. The position of these units and the overall structure help us understand how social ties form, how information and behaviour spread, and how individuals gain access to resources and opportunities.The focus of the course is on the connection between the mathematical foundations of networks and the social phenomena we want to understand. We will introduce concepts from matrix algebra and graph theory across both days, and use empirical examples and practical exercises to examine what these concepts tell us about social life. We will also discuss how network data are collected and what evidence is needed to distinguish between different explanations for the patterns we observe.The course covers:Mathematical foundations of social networks: matrix algebra and graph theory;Network structure, cohesion and models of network formation;Centrality, betweenness and brokerage;Contagion, diffusion and network interventions;Network data, sampling and research designLearning outcomes:Develop an understanding of social phenomena through social network theory;Learn how to use and interpret graph-theoretic and matrix algebra concepts with real-world data;Understand how local patterns of connection contribute to the overall structure of a network;Examine how network position and structure affect social processes;Acquire knowledge of network data collection and the implications of sampling and missing data;Assess competing explanations for observed network patterns and identify the evidence needed to investigate them.The course is aimed researchers and analysts interested in using social network analysis to understand social phenomena, including students, academics, government researchers and researchers in third sector organisations. The course is relevant to work in sociology, survey research, public health and related disciplines.Pre-requisitesBasic familiarity with data matrices and introductory statistical concepts. No previous training in social network analysis is required. Mathematical concepts will be introduced as they are used throughout the course. Practical exercises and demonstrations will accompany the theoretical sessions. Software requirements will be circulated before the course.THIS COURSE IS BEING DELIVERED OVER TWO DAYS. jmh6@soton.ac.uk (NCRM, University of Southampton) Fri, 02 Oct 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=15022 https://www.ncrm.ac.uk/training/show.php?article=14949 This one-day course is aimed at those who need to learn about the very basics of analysing and presenting statistical data.The topics covered in this course are summarising data with numbers, tables and graphs (including the Normal distribution and confidence intervals). These topics will be demonstrated using Excel and SPSS.If you are interested in this course you may also be interested in our Survey Design & Analysis courseSee our full range of courses (and options for bespoke courses) at https://go.herts.ac.uk/sscu or contact the course organiser, Professor Neil Spencer, at statistics@herts.ac.uk.No prior knowledge of statistics is required. statistics@herts.ac.uk (University of Hertfordshire) Wed, 19 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14949 https://www.ncrm.ac.uk/training/show.php?article=14950 This two-day course is aimed at those who need to plan and design questionnaire-based surveys and those who need to analyse the data collected. The first day covers the vital activities of designing a quality survey and obtaining a good sample. It covers the following topics.Defining your sample Choosing the right sampling methods to obtain a representative sample (e.g. stratified sampling, cluster sampling, multi-stage sampling,Deciding on the best way to collect your data (e.g. online, face-to-face, postal questionnaire,Designing questions to get good quality answers Deciding on the sample size needed to get estimates that have sufficient accuracy Using effect sizes and power to decide the sample size needed for statistical testsThe software G*Power will be used to demonstrate sample size calculations.The second day will cover summarising the results of your survey, presenting the results and undertaking statistical tests. It concentrates on those analyses which are particularly appropriate for the sort of data commonly collected using questionnaires. It covers the following topics.Creating graphs and chartsCalculating summary statisticsTesting for differences between groupsInvestigating relationships between variablesFitting regression modelsThe software Excel and SPSS will be used to demonstrate sample size calculations.No prior knowledge of statistics is required.See our full range of courses (and options for bespoke courses) at https://go.herts.ac.uk/sscu or contact the course organiser, Professor Neil Spencer, at statistics@herts.ac.uk. statistics@herts.ac.uk (University of Hertfordshire) Wed, 19 Aug 2026 00:00:00 +0100 https://www.ncrm.ac.uk/training/show.php?article=14950