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DTSTART;TZID=Europe/London:20260120T100000
DTEND;TZID=Europe/London:20260120T163000
DTSTAMP:20260416T121019
CREATED:20251216T112018Z
LAST-MODIFIED:20251216T112041Z
UID:10000575-1768903200-1768926600@www.swdtp.ac.uk
SUMMARY:International large-scale assessment analysis in R workshop
DESCRIPTION:January 20th University of Bath\, 10:00 – 16:30 \n  \nThis is a workshop for analysing data from international large-scale assessments. The workshop will focus on the International Civic and Citizenship Education Study (ICCS)\, but the content is applicable for other assessments (e.g. TIMSS\, PISA\, PIRLS). \nThe goal of the workshop is to support students from any discipline interested in analysing data from international large-scale assessments. We will cover all stages in the analysis cycle\, including downloading the data\, identifying variables of interest\, descriptive statistics\, and basic analytical statistics. \nThe workshop focuses on using R for analysis. Basic familiarity with the software would be useful but support can be given for those less familiar. R code for example analyses will be provided so that students can use it as a reference in the future. \nLunch will be provided. This workshop is supported by the SWDTP and developed in collaboration with the ICCS international study centre in the Department of Education at the University of Bath. \nTo register your interest and find out more\, please contact Adam Coates: ac3615@bath.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/international-large-scale-assessment-analysis-in-r-workshop/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260113T091500
DTEND;TZID=Europe/London:20260115T160000
DTSTAMP:20260416T121019
CREATED:20251017T114807Z
LAST-MODIFIED:20251017T114807Z
UID:10000553-1768295700-1768492800@www.swdtp.ac.uk
SUMMARY:Introduction to Multilevel Modelling\, Using MLwiN\, R\, or Stata
DESCRIPTION:Introduction to Multilevel Modelling\, Using MLwiN\, R\, or Stata  \n13-15 January 2026\, Online via Zoom \nClosing date for applications is 23rd November 2025. \nThis workshop is run by the University of Bristol School of Education. Further information and the booking form can be found at the following link: \nhttp://www.bris.ac.uk/cmm/software/support/workshops/
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-multilevel-modelling-using-mlwin-r-or-stata-2/
LOCATION:Online
CATEGORIES:Higher Level Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260113T091500
DTEND;TZID=Europe/London:20260115T160000
DTSTAMP:20260416T121019
CREATED:20250930T125839Z
LAST-MODIFIED:20250930T125839Z
UID:10000550-1768295700-1768492800@www.swdtp.ac.uk
SUMMARY:Introduction to Multilevel Modelling Using MLwiN\, R\, or Stata
DESCRIPTION:Run in partnership with NCRM\n\nPlease note this event is not run by the SWDTP. Please direct enquiries to Lucy Haslam at the University of Bristol Centre for Multilevel Modelling: lucy.haslam@bristol.ac.uk\nPlease note the closing date for applications is 23rd November 2025\n \nGo to booking form >>\n\nInstructors\nProfessor George Leckie and Professor William Browne\n \nSummary\nThis 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.\n\nTestimonials\n\n“The course was really excellent – clearly structured and in a logical order. Speakers were fantastic.”\n\n“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.”\n\n“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.”\n\n“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.”\n\n“Both George and Bill have excellent presentation styles. I really liked that they ‘riffed’ off of each other with gentle humour.”\n\nTopics\n\nOverview of multilevel modelling\nVariance-components models\nRandom-intercept models with covariates\nBetween- and within-effects of level-1 covariates\nRandom-coefficient models\nGrowth-curve models\nThree-level models\nReview of single-level logistic regression\nTwo-level logistic regression\n\n\nFormat\nThe course will consist of a 2:1 mix of lectures and hands-on practical sessions applying the taught methods to real datasets. The instructors alternate the lecturing. The lectures are software independent. Each lecture is immediately followed by a software practical giving participants the chance to replicate the presented analyses and to consolidate their knowledge. The practicals are offered in participants’ choice of MLwiN\, R\, or Stata and are self-directed: participants complete the practicals at their own pace. At the end of each practical session the instructors demo the different software. In both the lectures and practicals\, participants have opportunities to interact with the instructors.\n\nZoom\nThe course will be delivered online via the freely accessible Zoom platform. The lectures will be delivered live. 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.\n\nParticipants are encouraged to join the lectures live\, 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. After twelve weeks\, video access will end and will not be extended.\n\nDuring the practicals\, participants can also speak with the instructors. Participants can use these opportunities to ask specific questions about the course material or about multilevel modelling related to their own research. Each software package will be demonstrated in a different breakout room.\n\nMaterials\nParticipants will be emailed in advance with comprehensive PDF copies of the lecture slides together with point-and-click instructions and datasets for MLwiN\, and annotated syntax files and datasets for R and Stata. During the practicals\, participants are encouraged to view the lecture slides on a second screen (or tablet etc.)\, else print copies out to have in front of them. Those choosing to use MLwiN may also want to view the point-and-click instructions on a second screen\, else print them out.\n\nSoftware\nFor those choosing to use MLwiN\, we will provide instructions as to how to download and install the free teaching version of this software. For those wishing to use R or Stata we assume you are already users of these software so have them installed.\n\nPre-requisites\nWe assume no prior knowledge of multilevel modelling. However\, participants should be familiar with estimating and interpreting linear regression models\, including the writing and interpretation of model equations\, hypothesis testing and model selection\, and the use and interpretation of dummy variables and interaction terms.\n\nWe will email in advance a pre-recorded lecture\, to be completed at the participant’s leisure\, which provides a review of linear regression accompanied with software instructions and datasets to replicate the analyses in MLwiN\, R\, and Stata.\n\nFor those choosing to use MLwiN\, we assume no prior knowledge of using this software and so we provide step-by-step instructions to allow you to replicate all presented analyses in MLwiN. For those choosing R or Stata\, we assume you are already users of these software and so know the basics.\n\nTimings\nThe course starts and ends each day at 09:15 and 16:00 with a 30-minute morning break and a one-hour break for lunch from 13:00 to 14:00.\n \nFees\n\nFor UK-registered MSc and PhD students – £180\nFor UK university academics\, UK public sector staff\, and staff at UK registered charity organisations – £360\nFor all other participants – £660\n\n\nPlease note\, in order to be eligible for the reduced pricing brackets please submit your application using your UK academic/organisational email address.\n \nCancellation/refunds\nA full refund will be given if cancellation occurs two weeks prior to the event. No refund is given after this date. By completing the application form\, you are accepting these cancellation terms.\n \nApplications\nIf 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 23rd November 2025.\n\nApplications 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.\n\nIf you have any queries\, please email info-cmm@bristol.ac.uk.\n \nGo to booking form >>\n\nTerms and conditions\nPlease click here to read the booking terms and conditions before completing the booking form. Note that it is the participant’s responsibility to ensure that Zoom and their choice of MLwiN\, R\, or Stata software is up-to-date and works on their computer in advance of the course\, as the Centre for Multilevel Modelling is unable to provide technical support.\n\nMLwiN\nMLwiN is dedicated multilevel modelling software developed by our research team for more than 30 years. On this course we will be using the free teaching version of MLwiN. This version works with all the datasets used on the course and a wide range of other teaching datasets which come with the software. We will email you the teaching version prior to the start of the course.\n\nShould you wish to use MLwiN after the course with your own data\, you will need to use the regular version of MLwiN. This is free to UK academics (but without user support) reflecting long periods of funding from the UK’s Economic and Social science Research Council (ESRC). For all other users\, there is a 30-day trial version\, but after that you will have to purchase MLwiN if you wish to continue using it to analyse your own data. There are various price options available. http://www.bristol.ac.uk/cmm/software/mlwin/\n \nMLwiN is Windows software\, but can be run on Mac via the Wine software or through a virtual machine such as Parallels\, depending on the Mac model and version of MacOS on your machine.
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-multilevel-modelling-using-mlwin-r-or-stata/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251210T100000
DTEND;TZID=Europe/London:20251211T160000
DTSTAMP:20260416T121019
CREATED:20250930T145538Z
LAST-MODIFIED:20250930T145538Z
UID:10000551-1765360800-1765468800@www.swdtp.ac.uk
SUMMARY:From Theory to Practice: Participatory Methods for Doctoral Students
DESCRIPTION:Are you interested in making your research more inclusive\, impactful\, and grounded in lived experience? This two-day interactive workshop delivered by Dr Ben Scher\, introduces doctoral students to the theory and practice of participatory research methods. \nWhere: In-person |6 Worcester St\, Oxford OX1 2BX \nWhen: 10.12.2025 & 11.12.2025|10:00-16:00 \nAdvert & Registration:  https://granduniondtp.web.ox.ac.uk/event/from-theory-to-practice-participatory-methods-for-doctoral-students-2  \nThis event is not organised by the SWDTP. Please direct enquiries to the Grand Union DTP: paula.sheppard@anthro.ox.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/from-theory-to-practice-participatory-methods-for-doctoral-students-2/
LOCATION:6 Worcester St\, Oxford OX1 2BX
CATEGORIES:Higher Level Training,Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251205T093000
DTEND;TZID=Europe/London:20251205T123000
DTSTAMP:20260416T121019
CREATED:20250829T091457Z
LAST-MODIFIED:20250829T091457Z
UID:10000541-1764927000-1764937800@www.swdtp.ac.uk
SUMMARY:Forecasting: Methods\, Evaluation\, and Applications
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \n  \nSENSS Specialist Training: Forecasting: Methods\, Evaluation\, and Applications   \nInstructor: Prof. Giovanni Urga (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nThe course will provide an introduction to time series (and panel) methods for modelling and fore­casting economic and financial variables.  The course covers several theoretical and empirical topics in economics and financial econometrics providing a comprehensive presentation of the econometric methods applied to finance. Topics include: forecasting and forecast evaluation\, estimation methods such as GMM and MLE\, univariate and multivariate GARCH models\, realised and stochastic volatility models\, measurement techniques and tests for contagion\, principal components and factor analysis\, the use of OxMetrics/Autometrics in model selection in presence of a large number of regressors. The theory is illustrated in practice modelling of interest rates\, asset prices and forex time series at several temporal frequencies. \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \nSoftware \nParticipants will use OxMetrics during the session. Instructions for installation will be provided in advance. \nContent Outline \n\nModelling and forecasting the conditional mean of financial time series.\nModelling and forecasting the volatility of financial time series.\nModelling and forecasting correlations and contagion.\nHands-on modelling with real-world big data using OxMetrics/Autometrics.\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nYou will master statistical analysis tools to model and forecast economic and financial time series\, volatility and correlations.\nYou will develop expertise in identifying and measuring contagion between markets.\nYou will gain practical experience with high-frequency data analysis and assessing the impact of market announcements.\nYou will apply advanced econometric techniques to real-world financial problems through case studies and simulations.\n\nMain References \n\nBrockwell\, P.J and R. A. Davis (2016)\, Introduction to time series and forecasting\, Springer.\nCastle\, J. L.\, Clements\, M. P.\, and D. F. Hendry (2019)\, Forecasting an essential introduction\, Yale University Press\nDiebold X. Francis (2024)\, Forecasting in economics\, business\, finance and beyond. Department of Economics\, University of Pennsylvania\, http://www.ssc.upenn.edu/~fdiebold/Textbooks.html\nElliott\, G. and A. Timmermann (2016) Forecasting in Economics and Finance. Annual Review of Financial Economics\, 8\, 81-110.\nGhysels\, E. and M. Marcellino (2018) Applied Economic Forecasting using Time Series Methods\, Oxford University Press.\nTimmermann\, A. (2018)\, “Forecasting Methods in Finance\, Annual Review of Financial Economics\, 10\, 449-479.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_7VAr6DaaGpubDNA \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk \n 
URL:https://www.swdtp.ac.uk/event-calendar/forecasting-methods-evaluation-and-applications/2025-12-05/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251204T093000
DTEND;TZID=Europe/London:20251204T123000
DTSTAMP:20260416T121019
CREATED:20250829T091457Z
LAST-MODIFIED:20250829T091457Z
UID:10000540-1764840600-1764851400@www.swdtp.ac.uk
SUMMARY:Forecasting: Methods\, Evaluation\, and Applications
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \n  \nSENSS Specialist Training: Forecasting: Methods\, Evaluation\, and Applications   \nInstructor: Prof. Giovanni Urga (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nThe course will provide an introduction to time series (and panel) methods for modelling and fore­casting economic and financial variables.  The course covers several theoretical and empirical topics in economics and financial econometrics providing a comprehensive presentation of the econometric methods applied to finance. Topics include: forecasting and forecast evaluation\, estimation methods such as GMM and MLE\, univariate and multivariate GARCH models\, realised and stochastic volatility models\, measurement techniques and tests for contagion\, principal components and factor analysis\, the use of OxMetrics/Autometrics in model selection in presence of a large number of regressors. The theory is illustrated in practice modelling of interest rates\, asset prices and forex time series at several temporal frequencies. \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \nSoftware \nParticipants will use OxMetrics during the session. Instructions for installation will be provided in advance. \nContent Outline \n\nModelling and forecasting the conditional mean of financial time series.\nModelling and forecasting the volatility of financial time series.\nModelling and forecasting correlations and contagion.\nHands-on modelling with real-world big data using OxMetrics/Autometrics.\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nYou will master statistical analysis tools to model and forecast economic and financial time series\, volatility and correlations.\nYou will develop expertise in identifying and measuring contagion between markets.\nYou will gain practical experience with high-frequency data analysis and assessing the impact of market announcements.\nYou will apply advanced econometric techniques to real-world financial problems through case studies and simulations.\n\nMain References \n\nBrockwell\, P.J and R. A. Davis (2016)\, Introduction to time series and forecasting\, Springer.\nCastle\, J. L.\, Clements\, M. P.\, and D. F. Hendry (2019)\, Forecasting an essential introduction\, Yale University Press\nDiebold X. Francis (2024)\, Forecasting in economics\, business\, finance and beyond. Department of Economics\, University of Pennsylvania\, http://www.ssc.upenn.edu/~fdiebold/Textbooks.html\nElliott\, G. and A. Timmermann (2016) Forecasting in Economics and Finance. Annual Review of Financial Economics\, 8\, 81-110.\nGhysels\, E. and M. Marcellino (2018) Applied Economic Forecasting using Time Series Methods\, Oxford University Press.\nTimmermann\, A. (2018)\, “Forecasting Methods in Finance\, Annual Review of Financial Economics\, 10\, 449-479.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_7VAr6DaaGpubDNA \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk \n 
URL:https://www.swdtp.ac.uk/event-calendar/forecasting-methods-evaluation-and-applications/2025-12-04/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251203T143000
DTEND;TZID=Europe/London:20251203T173000
DTSTAMP:20260416T121019
CREATED:20250829T091457Z
LAST-MODIFIED:20250829T091457Z
UID:10000539-1764772200-1764783000@www.swdtp.ac.uk
SUMMARY:Forecasting: Methods\, Evaluation\, and Applications
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \n  \nSENSS Specialist Training: Forecasting: Methods\, Evaluation\, and Applications   \nInstructor: Prof. Giovanni Urga (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nThe course will provide an introduction to time series (and panel) methods for modelling and fore­casting economic and financial variables.  The course covers several theoretical and empirical topics in economics and financial econometrics providing a comprehensive presentation of the econometric methods applied to finance. Topics include: forecasting and forecast evaluation\, estimation methods such as GMM and MLE\, univariate and multivariate GARCH models\, realised and stochastic volatility models\, measurement techniques and tests for contagion\, principal components and factor analysis\, the use of OxMetrics/Autometrics in model selection in presence of a large number of regressors. The theory is illustrated in practice modelling of interest rates\, asset prices and forex time series at several temporal frequencies. \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \nSoftware \nParticipants will use OxMetrics during the session. Instructions for installation will be provided in advance. \nContent Outline \n\nModelling and forecasting the conditional mean of financial time series.\nModelling and forecasting the volatility of financial time series.\nModelling and forecasting correlations and contagion.\nHands-on modelling with real-world big data using OxMetrics/Autometrics.\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nYou will master statistical analysis tools to model and forecast economic and financial time series\, volatility and correlations.\nYou will develop expertise in identifying and measuring contagion between markets.\nYou will gain practical experience with high-frequency data analysis and assessing the impact of market announcements.\nYou will apply advanced econometric techniques to real-world financial problems through case studies and simulations.\n\nMain References \n\nBrockwell\, P.J and R. A. Davis (2016)\, Introduction to time series and forecasting\, Springer.\nCastle\, J. L.\, Clements\, M. P.\, and D. F. Hendry (2019)\, Forecasting an essential introduction\, Yale University Press\nDiebold X. Francis (2024)\, Forecasting in economics\, business\, finance and beyond. Department of Economics\, University of Pennsylvania\, http://www.ssc.upenn.edu/~fdiebold/Textbooks.html\nElliott\, G. and A. Timmermann (2016) Forecasting in Economics and Finance. Annual Review of Financial Economics\, 8\, 81-110.\nGhysels\, E. and M. Marcellino (2018) Applied Economic Forecasting using Time Series Methods\, Oxford University Press.\nTimmermann\, A. (2018)\, “Forecasting Methods in Finance\, Annual Review of Financial Economics\, 10\, 449-479.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_7VAr6DaaGpubDNA \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk \n 
URL:https://www.swdtp.ac.uk/event-calendar/forecasting-methods-evaluation-and-applications/2025-12-03/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251203T090000
DTEND;TZID=Europe/London:20251203T163000
DTSTAMP:20260416T121019
CREATED:20251126T093338Z
LAST-MODIFIED:20251126T093338Z
UID:10000558-1764752400-1764779400@www.swdtp.ac.uk
SUMMARY:17th Annual Postgraduate Research Conference
DESCRIPTION:This conference is a celebration of the incredible work being carried out by Bournemouth University postgraduate researchers\, and they are proud to provide this platform for sharing knowledge\, fostering collaboration and building connections. Whether you are presenting\, exhibiting\, or attending\, this is a wonderful opportunity to network with fellow PGRs\, colleagues from across the university and external visitors. \n  \nFor more information on the conference\, click here\, or see below to register.
URL:https://www.swdtp.ac.uk/event-calendar/17th-annual-postgraduate-research-conference/
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251128T093000
DTEND;TZID=Europe/London:20251128T123000
DTSTAMP:20260416T121019
CREATED:20250829T091053Z
LAST-MODIFIED:20250829T091053Z
UID:10000538-1764322200-1764333000@www.swdtp.ac.uk
SUMMARY:Panel Data and Factor Model for Social and Economic Research
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Panel Data and Factor Model for Social and Economic Research  \nInstructor: Prof. Giovanni Urga (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nThere is huge body of literature applying panel data techniques using firm-level\, consumer\,  stock market and banking data. In this course\, we will present most important panel data techniques for stationary and nonstationary panels. We will discuss the importance of modelling heterogeneity and we will discuss static and dynamic models\, introducing the crucial distinction between fixed and random effects. The course will also provide a short introduction to both factor models and principal components. Practical applications using economic and financial (stocks\, interest rates) and banking (accounting) datasets will be delivered using Stata\, which is the most comprehensive econometric software for dealing with panel data analysis. \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \n  \nSoftware \nParticipants will use Stata19 during the session. Participants are required to be familiar with the software and have it installed . \n  \nContent Outline \n\nStatic Panel Data Models\nDynamic Panel Data Models.\nNonstationary Panel Data Models.\nCross-sectional dependence in Panel Data.\nIntroduction of factor models\n\nLearning Objectives \n\nYou will learn how to handle and summarise panel datasets.\nYou will learn a large number of panel data techniques for stationary and nonstationary variables.\nYou will learn how to implement panel data analysis using econometric software.\n\nMain References \nA list of relevant papers will be provided at the beginning of the course. The following textbooks are recommended: \n\nBaltagi\, B. H. (2008)\, Econometric analysis of panel data\, Forth Edition\, John Wiley & Sons.\nBaltagi\, B. H. (2009)\, A companion to econometric analysis of panel data\, John Wiley & Sons.\n\n\nBoffelli\, S.\, and G. Urga (2016). Financial Econometrics Using Stata. Stata Press Publication\nBrooks\, C.\, (2019). Introductory Econometrics for Finance\, Cambridge University Press\, 4th edition.\n\n\nPesaran\, M. H. (2015)\, Time series and panel data econometrics. Oxford University Press.\nWooldridge\, J. (2010)\, Econometric analysis of cross section and panel data\, MIT Press.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_1KQfEPArrCX6fCm \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk \nhttps://essex.eu.qualtrics.com/jfe/form/SV_1KQfEPArrCX6fCm
URL:https://www.swdtp.ac.uk/event-calendar/panel-data-and-factor-model-for-social-and-economic-research/2025-11-28/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251127T103000
DTEND;TZID=Europe/London:20251127T124500
DTSTAMP:20260416T121019
CREATED:20251017T132614Z
LAST-MODIFIED:20251118T142748Z
UID:10000555-1764239400-1764247500@www.swdtp.ac.uk
SUMMARY:Channel or Challenge Perfectionism?
DESCRIPTION:In this participative\, graphically-facilitated workshop\, we will look at perfectionism that channels continuous improvement and optimistic approaches. We’ll share ways you can identify realistic standards and goals\, reframe mistakes as learning\, how to approach planning and preparation\, and how to counter risk-aversion and procrastination. Perfection can impact productivity. So in terms of sought-after transferable skills such as time management\, we’ll look at how to identify ‘good enough’\, redirecting time and energy to other priorities. Perfectionism is a common trait in academia. In this workshop we’ll consider where this can be best directed as it does not need to be applied across all aspects of your work. We’ll look at different strategies to channel improvement in areas that will have a positive effect on your research and research experience – such as how to manage expectations\, setting realistic goals\, dealing with feedback\, developing a more flexible approach\, and unlocking your creativity – helping open up new opportunities. Sabina will illustrate concepts\, share her own experiences and demonstrate tools by sharing live visualisations and respond to your particular questions and objectives.
URL:https://www.swdtp.ac.uk/event-calendar/channel-or-challenge-perfectionism-2/
LOCATION:Online
CATEGORIES:Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251127T093000
DTEND;TZID=Europe/London:20251127T123000
DTSTAMP:20260416T121019
CREATED:20250829T091053Z
LAST-MODIFIED:20250829T091053Z
UID:10000537-1764235800-1764246600@www.swdtp.ac.uk
SUMMARY:Panel Data and Factor Model for Social and Economic Research
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Panel Data and Factor Model for Social and Economic Research  \nInstructor: Prof. Giovanni Urga (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nThere is huge body of literature applying panel data techniques using firm-level\, consumer\,  stock market and banking data. In this course\, we will present most important panel data techniques for stationary and nonstationary panels. We will discuss the importance of modelling heterogeneity and we will discuss static and dynamic models\, introducing the crucial distinction between fixed and random effects. The course will also provide a short introduction to both factor models and principal components. Practical applications using economic and financial (stocks\, interest rates) and banking (accounting) datasets will be delivered using Stata\, which is the most comprehensive econometric software for dealing with panel data analysis. \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \n  \nSoftware \nParticipants will use Stata19 during the session. Participants are required to be familiar with the software and have it installed . \n  \nContent Outline \n\nStatic Panel Data Models\nDynamic Panel Data Models.\nNonstationary Panel Data Models.\nCross-sectional dependence in Panel Data.\nIntroduction of factor models\n\nLearning Objectives \n\nYou will learn how to handle and summarise panel datasets.\nYou will learn a large number of panel data techniques for stationary and nonstationary variables.\nYou will learn how to implement panel data analysis using econometric software.\n\nMain References \nA list of relevant papers will be provided at the beginning of the course. The following textbooks are recommended: \n\nBaltagi\, B. H. (2008)\, Econometric analysis of panel data\, Forth Edition\, John Wiley & Sons.\nBaltagi\, B. H. (2009)\, A companion to econometric analysis of panel data\, John Wiley & Sons.\n\n\nBoffelli\, S.\, and G. Urga (2016). Financial Econometrics Using Stata. Stata Press Publication\nBrooks\, C.\, (2019). Introductory Econometrics for Finance\, Cambridge University Press\, 4th edition.\n\n\nPesaran\, M. H. (2015)\, Time series and panel data econometrics. Oxford University Press.\nWooldridge\, J. (2010)\, Econometric analysis of cross section and panel data\, MIT Press.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_1KQfEPArrCX6fCm \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk \nhttps://essex.eu.qualtrics.com/jfe/form/SV_1KQfEPArrCX6fCm
URL:https://www.swdtp.ac.uk/event-calendar/panel-data-and-factor-model-for-social-and-economic-research/2025-11-27/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251121T093000
DTEND;TZID=Europe/London:20251121T123000
DTSTAMP:20260416T121019
CREATED:20250829T090641Z
LAST-MODIFIED:20250829T090641Z
UID:10000536-1763717400-1763728200@www.swdtp.ac.uk
SUMMARY:Quantum Machine Learning
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Quantum Machine Learning\nInstructor: Dr Jan Novotny (Centre for Econometric Analysis\, Bayes Business School and Nomura International)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \n  \nRecent breakthroughs in quantum computing have ushered in a transformative era in computational science\, redefining the boundaries of what is algorithmically possible. This course explores the intersection of quantum mechanics and machine learning\, offering participants a gateway into a radically new paradigm where quantum principles enable novel approaches to data processing\, optimization\, and pattern recognition. As quantum hardware continues to evolve\, understanding its theoretical foundations becomes essential for researchers poised to shape the future of intelligent systems. \nThis course introduces the principles of quantum computing\, including qubits\, entanglement\, superposition\, and quantum gates. Through a blend of theoretical lectures and hands-on exercises\, participants will gain fluency in quantum algorithms and their applications to machine learning tasks. The curriculum bridges classical and quantum machine learning frameworks\, highlighting both the conceptual shifts and practical implications of transitioning to quantum-enhanced models. \nA distinctive feature of the course is its emphasis on experiential learning. Students will engage directly with quantum programming environments and\, where possible\, run experiments on real quantum hardware. By the end of the course\, participants will not only be quantum-computing literate but also equipped to apply the cutting-edge research in quantum machine learning in their respective fields. This course is ideal for those seeking to expand their computational toolkit and explore the frontier of intelligent systems in the quantum age. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics and basic working knowledge with Python and jupyter notebooks. \n  \nSoftware \nParticipants will use Python within jupyter notebooks along the standard machine learning libraries (sci-kit) and Qiskit. \n  \nContent Outline \n\nFundamental principles of the quantum mechanics and quantum computing\nThe NISQ computers and the basic operations using quantum gates\nKey quantum algorithms\nHands-on calculations using quantum computer simulators as well as real hardware\nUsing quantum computing as an extension to the traditional machine learning toolkit\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nUnderstand the principles of quantum computing.\nRun the quantum machine learning algorithms as a part of their machine learning workflow\nBe aware of the strengths of the quantum computing and assess the applicability for the real world problems.\n\nMain References \n\nJacquier\, Antoine\, et al. “Quantum Machine Learning and Optimisation in Finance.” Birmingham: Packt Publishing Ltd (2022).\nHastie\, Trevor\, Robert Tibshirani\, and Jerome Friedman. “The elements of statistical learning.” (2009). (online edition)\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_0VVlyLJs53pL7XE \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/quantum-machine-learning/2025-11-21/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251120T100000
DTEND;TZID=Europe/London:20251120T120000
DTSTAMP:20260416T121019
CREATED:20251017T131723Z
LAST-MODIFIED:20251017T131733Z
UID:10000554-1763632800-1763640000@www.swdtp.ac.uk
SUMMARY:UK Global Talent Visa Deep Dive Webinar- From PhD student to UK Global Talent
DESCRIPTION:In this session\, I will help PhD students and recent graduates understand the UK Global Talent Visa\, focusing on the Academic & Research route. At the end of the webinar they will; \n  \n1. Understand the purpose and structure of the Global Talent Visa – who it’s for\, what makes someone eligible\, and how it differs from other UK visa routes \n2. Identify the core requirements and documents needed \n3. Break down the Academic and Research endorsement pathway – from preparing your application to receiving your decision. \n4. Review key documents including how to write a standout personal statement\, structure your CV\, and secure letters of recommendation 5. Learn how to evidence your research contributions and potential \n6. Get clear on next steps and how to start preparing even if you’re still completing your PhD.
URL:https://www.swdtp.ac.uk/event-calendar/global-talent-visa-training/
LOCATION:Online
CATEGORIES:Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251114T093000
DTEND;TZID=Europe/London:20251114T123000
DTSTAMP:20260416T121019
CREATED:20250829T090641Z
LAST-MODIFIED:20250829T090641Z
UID:10000535-1763112600-1763123400@www.swdtp.ac.uk
SUMMARY:Quantum Machine Learning
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Quantum Machine Learning\nInstructor: Dr Jan Novotny (Centre for Econometric Analysis\, Bayes Business School and Nomura International)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \n  \nRecent breakthroughs in quantum computing have ushered in a transformative era in computational science\, redefining the boundaries of what is algorithmically possible. This course explores the intersection of quantum mechanics and machine learning\, offering participants a gateway into a radically new paradigm where quantum principles enable novel approaches to data processing\, optimization\, and pattern recognition. As quantum hardware continues to evolve\, understanding its theoretical foundations becomes essential for researchers poised to shape the future of intelligent systems. \nThis course introduces the principles of quantum computing\, including qubits\, entanglement\, superposition\, and quantum gates. Through a blend of theoretical lectures and hands-on exercises\, participants will gain fluency in quantum algorithms and their applications to machine learning tasks. The curriculum bridges classical and quantum machine learning frameworks\, highlighting both the conceptual shifts and practical implications of transitioning to quantum-enhanced models. \nA distinctive feature of the course is its emphasis on experiential learning. Students will engage directly with quantum programming environments and\, where possible\, run experiments on real quantum hardware. By the end of the course\, participants will not only be quantum-computing literate but also equipped to apply the cutting-edge research in quantum machine learning in their respective fields. This course is ideal for those seeking to expand their computational toolkit and explore the frontier of intelligent systems in the quantum age. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics and basic working knowledge with Python and jupyter notebooks. \n  \nSoftware \nParticipants will use Python within jupyter notebooks along the standard machine learning libraries (sci-kit) and Qiskit. \n  \nContent Outline \n\nFundamental principles of the quantum mechanics and quantum computing\nThe NISQ computers and the basic operations using quantum gates\nKey quantum algorithms\nHands-on calculations using quantum computer simulators as well as real hardware\nUsing quantum computing as an extension to the traditional machine learning toolkit\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nUnderstand the principles of quantum computing.\nRun the quantum machine learning algorithms as a part of their machine learning workflow\nBe aware of the strengths of the quantum computing and assess the applicability for the real world problems.\n\nMain References \n\nJacquier\, Antoine\, et al. “Quantum Machine Learning and Optimisation in Finance.” Birmingham: Packt Publishing Ltd (2022).\nHastie\, Trevor\, Robert Tibshirani\, and Jerome Friedman. “The elements of statistical learning.” (2009). (online edition)\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_0VVlyLJs53pL7XE \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/quantum-machine-learning/2025-11-14/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251111T133000
DTEND;TZID=Europe/London:20251111T140000
DTSTAMP:20260416T121019
CREATED:20250908T105018Z
LAST-MODIFIED:20250908T105018Z
UID:10000544-1762867800-1762869600@www.swdtp.ac.uk
SUMMARY:Code Anxiety Club
DESCRIPTION:Why attend? \n\nFeeling overwhelmed by the command line? Confused by file pathways? Want to navigate the world of coding with confidence? Join the Code Anxiety Club! \n\nViewers 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. \n\nThere is no need to book a place\, please follow the livestream link to join the session. \n\nWorkshop date and topic: \n  \nProject organisation: Best practices for coding projects \n\nContent: \n\nGet to grips with naming conventions and why consistency is key.\nUnderstand how to structure your directory.\nLearn how to ‘set your directory’ so that you can easily read-in files in Python (Visual Studio Code) or RStudio.\n\n  \nTo join this session\, please follow the link to our livestream – 11 November 2025
URL:https://www.swdtp.ac.uk/event-calendar/code-anxiety-club/
LOCATION:Online
CATEGORIES:Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251111T100000
DTEND;TZID=Europe/London:20251111T130000
DTSTAMP:20260416T121019
CREATED:20250916T154547Z
LAST-MODIFIED:20250916T154547Z
UID:10000546-1762855200-1762866000@www.swdtp.ac.uk
SUMMARY:Viva Survivor Training with Nathan Ryder
DESCRIPTION:The viva is almost the end of the PhD. A lot of work leads to this point\, but often anxiety can mix with the sense of achievement at completing the thesis. For many candidates the time before the viva is filled with uncertainty about the day\, uncertainty about how to prepare well – and stress for what the viva might be like. \nViva Survivor is for postgraduate researchers who want to know how to be ready for their viva. In this three-hour live webinar\, you will:\n• learn realistic expectations for the PhD viva;\n• identify key practical steps to take before submission;\n• explore practical strategies for preparation and the day of the viva. \nTime will be spent exploring expectations for both in-person and video vivas\, and there will be plenty of time for Q&A over text chat. Viva Survivor will be delivered live by Dr Nathan Ryder over Zoom. Registration is limited to 25 places.
URL:https://www.swdtp.ac.uk/event-calendar/viva-survivor-training-with-nathan-ryder/
CATEGORIES:Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251107T093000
DTEND;TZID=Europe/London:20251107T123000
DTSTAMP:20260416T121019
CREATED:20250829T090641Z
LAST-MODIFIED:20250829T090641Z
UID:10000534-1762507800-1762518600@www.swdtp.ac.uk
SUMMARY:Quantum Machine Learning
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Quantum Machine Learning\nInstructor: Dr Jan Novotny (Centre for Econometric Analysis\, Bayes Business School and Nomura International)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \n  \nRecent breakthroughs in quantum computing have ushered in a transformative era in computational science\, redefining the boundaries of what is algorithmically possible. This course explores the intersection of quantum mechanics and machine learning\, offering participants a gateway into a radically new paradigm where quantum principles enable novel approaches to data processing\, optimization\, and pattern recognition. As quantum hardware continues to evolve\, understanding its theoretical foundations becomes essential for researchers poised to shape the future of intelligent systems. \nThis course introduces the principles of quantum computing\, including qubits\, entanglement\, superposition\, and quantum gates. Through a blend of theoretical lectures and hands-on exercises\, participants will gain fluency in quantum algorithms and their applications to machine learning tasks. The curriculum bridges classical and quantum machine learning frameworks\, highlighting both the conceptual shifts and practical implications of transitioning to quantum-enhanced models. \nA distinctive feature of the course is its emphasis on experiential learning. Students will engage directly with quantum programming environments and\, where possible\, run experiments on real quantum hardware. By the end of the course\, participants will not only be quantum-computing literate but also equipped to apply the cutting-edge research in quantum machine learning in their respective fields. This course is ideal for those seeking to expand their computational toolkit and explore the frontier of intelligent systems in the quantum age. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics and basic working knowledge with Python and jupyter notebooks. \n  \nSoftware \nParticipants will use Python within jupyter notebooks along the standard machine learning libraries (sci-kit) and Qiskit. \n  \nContent Outline \n\nFundamental principles of the quantum mechanics and quantum computing\nThe NISQ computers and the basic operations using quantum gates\nKey quantum algorithms\nHands-on calculations using quantum computer simulators as well as real hardware\nUsing quantum computing as an extension to the traditional machine learning toolkit\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nUnderstand the principles of quantum computing.\nRun the quantum machine learning algorithms as a part of their machine learning workflow\nBe aware of the strengths of the quantum computing and assess the applicability for the real world problems.\n\nMain References \n\nJacquier\, Antoine\, et al. “Quantum Machine Learning and Optimisation in Finance.” Birmingham: Packt Publishing Ltd (2022).\nHastie\, Trevor\, Robert Tibshirani\, and Jerome Friedman. “The elements of statistical learning.” (2009). (online edition)\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_0VVlyLJs53pL7XE \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/quantum-machine-learning/2025-11-07/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251104T110000
DTEND;TZID=Europe/London:20251104T120000
DTSTAMP:20260416T121019
CREATED:20250919T162406Z
LAST-MODIFIED:20250919T162406Z
UID:10000549-1762254000-1762257600@www.swdtp.ac.uk
SUMMARY:Demystifying the publishing process and Open Access
DESCRIPTION:Publishing is central to being a researcher but we seldom talk about how you publish and what you need to do to look after yourself in the process. This session will talk through common routes to publishing and how to deal with\, the infamous\, Reviewer 2. Publishing your work is not just about what you say\, but how you say it. Open Access is a key part of the ‘how’ of research communication\, particularly if you’re work is supported by a funder who requires you to make your research freely available. In this webinar\, you’ll find out about the different free and paid routes to Open Access and start to think about Open Access publishing as part of broader ethical research practices. \nThe speaker for this session is Dr Kate Holmes who is an Open Access Advocacy Research Support Librarian at The University of Bristol. \nThis session is open to all interested attendees so please feel free to share widely. Attendance is free but please register to secure a place. Register on Ticket Taylor: https://buytickets.at/swdtp/1870887
URL:https://www.swdtp.ac.uk/event-calendar/demystifying-the-publishing-process-and-open-access/
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251031T093000
DTEND;TZID=Europe/London:20251031T123000
DTSTAMP:20260416T121019
CREATED:20250829T090215Z
LAST-MODIFIED:20250829T090736Z
UID:10000533-1761903000-1761913800@www.swdtp.ac.uk
SUMMARY:Modelling Big Data using Autometrics
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \n  \nSENSS Specialist Training: Modelling Big Data using Autometrics\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn the age of big data\, researchers and analysts increasingly face challenges of model selection\, overfitting\, and interpreting complex high-dimensional datasets. This session introduces Autometrics\, a powerful econometric modelling tool designed to automate model selection in large datasets while maintaining strong statistical foundations. \nAutometrics is part of the OxMetrics suite and builds on the general-to-specific (Gets) modelling philosophy. It enables users to extract robust models from large sets of candidate variables\, making it especially suitable for empirical work in economics\, finance\, and the social sciences where theory may not fully specify the data-generating process. \nThis practical session will combine core theoretical principles with hands-on demonstrations\, using real-world data examples to illustrate how Autometrics addresses the challenges of modelling with many variables. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics. \n  \nSoftware \nParticipants will use OxMetrics during the session. Instructions for installation will be provided in advance. \n  \nContent Outline \n\nGeneral-to-specific (Gets) modelling and the problem of model selection\nAutometrics algorithm: overview and logic\nDiagnostic testing and model evaluation\nHands-on modelling with real-world big data using Autometrics\n\n  \nLearning Objectives \nBy the end of the session\, participants will be able to: \n\nUnderstand the principles behind general-to-specific model selection and how Autometrics operationalises these.\nRecognise the challenges of modelling large datasets\, including overfitting\, multicollinearity\, and irrelevant variables.\nCritically evaluate the strengths and limitations of automated model selection tools in empirical research.\n\nMain References \n\nDoornik\, J.A.\, 2009a. Autometrics\, in: J.L. Castle\, N. Shephard (Eds.)\, The Methodology and Practice of Econometrics\, Oxford University Press\, Oxford (2009)\, pp. 88–121.\nHendry\, D.F.\, and J.A. Doornik (2014). Empirical Model Discovery and Theory Evaluation. MIT Press.\nHendry\, D.F.\, and J.A. Doornik(2018). Empirical Econometric Modelling – PcGive 16: Volume I. Timberlake Consultants Press\, London.\n\nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_9Zit5afuGNbuIfk \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/97349/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251030T100000
DTEND;TZID=Europe/London:20251030T170000
DTSTAMP:20260416T121019
CREATED:20250916T150754Z
LAST-MODIFIED:20250916T150754Z
UID:10000545-1761818400-1761843600@www.swdtp.ac.uk
SUMMARY:SWDTP Conference - Everyday and Beyond: Encountering\, Sharing\, Caring
DESCRIPTION:The SWDTP Annual Student Conference is an event created for students\, by students. It presents a fantastic opportunity to engage in a dynamic and inclusive platform that promotes innovative ideas and critical reflections on the future of social sciences. \n  \nThis year’s conference theme is: \nEveryday and Beyond- Encountering\, Sharing\, Caring. \n  \nThis conference involves plenary and breakout sessions. Please navigate to the Conference Agenda to read more about the sessions\, as you will have to indicate your preferences as part of the registration form. \n  \nAgenda: https://www.swdtp.ac.uk/swdtp-student-conference-agenda-2025/ \n  \nWe are pleased to invite all social science research students (whether funded by the SWDTP or not) to register to this free event\, being held in the Commons Building of Bath Spa University on Thursday October 30th 2025.
URL:https://www.swdtp.ac.uk/event-calendar/swdtp-conference-everyday-and-beyond-encountering-sharing-caring/
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251029T090000
DTEND;TZID=Europe/London:20251029T170000
DTSTAMP:20260416T121019
CREATED:20250918T100518Z
LAST-MODIFIED:20250918T100518Z
UID:10000547-1761728400-1761757200@www.swdtp.ac.uk
SUMMARY:Justice beyond Criminal Justice
DESCRIPTION:A one-day\, in-person symposium bringing together academics from across the social sciences to explore the justice processes that sit outside of the formal criminal justice system\, used in response to serious harm and injury.\n\nThis event is a collaboration between the Criminology Research Group at the University of Bath\, the SWDTP\, and the British Society of Criminology (South West). The keynote speaker is Professor Richard Moorhead (University of Exeter)\, who will be sharing his research on the Post Office IT Horizon Inquiry. The event also incorporates panels for oral presentations\, as well as a parallel poster and art-work exhibition session. There will be an optional training session for attendees to learn more about the ethics and practicalities of using images in and for research\, as well as their role in engaging non-academic audiences. There is a fund available to support those who are interested in producing high-quality photographic images to display at this event.\n\nThis event is organised by colleagues at the University of Bath\, and supported in part by the SWDTP academic-led higher level training fund.\n\nKey dates:\nAbstract submission deadline: 30 September\nAttendee registration deadline: 6 October\nSymposium: 29 October\n\nFurther information\, abstract submission instructions and registration: https://www.eventbrite.co.uk/e/justice-beyond-criminal-justice-tickets-1606523477969?aff=oddtdtcreator\nPlease direct enquiries to the symposium organisers.
URL:https://www.swdtp.ac.uk/event-calendar/justice-beyond-criminal-justice/
LOCATION:University of Bath\, 8 West Room 2.23\, Bath\, BA2 7AY\, United Kingdom
CATEGORIES:Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251024T093000
DTEND;TZID=Europe/London:20251024T123000
DTSTAMP:20260416T121019
CREATED:20250829T085505Z
LAST-MODIFIED:20250829T090648Z
UID:10000532-1761298200-1761309000@www.swdtp.ac.uk
SUMMARY:Introduction to Machine Learning and Data Science
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Introduction to Machine Learning and Data Science\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn today’s global and digital economic environment\, we have unprecedented access to vast amounts of data across all types of industries. It has become crucial for researchers and professionals to possess analytical skills that enable data-driven decision-making. Data science is the practice of collecting\, analysing\, and interpreting data that empowers decision-makers to make informed choices. \nThis module provides both theoretical foundations and practical skills necessary to apply machine learning and data science methods to problems in economics and finance. Theoretical concepts will be introduced in an intuitive and accessible manner. Emphasis will be placed on selecting appropriate methods for specific problems\, implementing methods correctly and presenting and interpreting analytical results \nLectures will include practical\, computer-based exercises using real-world datasets. Students will learn to use Python to carry out these tasks. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic Python knowledge (e.g.\, libraries NumPy\, Pandas\, statsmodels). \n  \nSoftware \nParticipants will use Jupyter Notebooks during the sessions for hands-on modelling demonstrations. The easiest way to access Jupyter is by installing the Anaconda Distribution\, which includes Python\, Jupyter\, and most of the required libraries. Anaconda can be downloaded here: https://www.anaconda.com/products/distribution \n  \nContent Outline \n\nLinear Regression\n\nModel assumptions\nGoodness of fit\nPrediction\nDiagnostic analysis\n\n\nLinear Model Selection\n\nBias-variance trade-off\nCross-validation\nSubset selection\nRidge Regression\nLASSO\n\n\nDimensionality Reduction\n\nPrincipal Component Analysis (PCA)\nPCA in regression contexts\n\n\nApplications in Python\n\nHands-on implementation using Python libraries such as pandas\, scikit-learn\, and statsmodels\n\n\n\nLearning Objectives \nBy the end of this module\, you will be able to: \n\nExplain the fundamental principles of linear regression\, including model assumptions\, coefficient estimation\, and diagnostic analysis.\nCompare and apply model selection techniques\, including subset selection\, Ridge Regression\, and LASSO.\nUnderstand and implement dimensionality reduction techniques\, particularly Principal Component Analysis\, and assess their impact on regression models.\nCritically assess the suitability of different machine learning methods for solving specific problems and justify methodological choices.\nUse Python programming tools and libraries (e.g.\, pandas\, scikit-learn\, statsmodels) to perform data analysis and apply machine learning methods to real-world datasets.\n\nMain References \n\nJames\, G.\, Witten\, D.\, Hastie\, T.\, & Tibshirani\, R. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.\nMcKinney\, W. (2018). Python for Data Analysis: Data Wrangling with Pandas\, NumPy\, and IPython (2nd ed.). O’Reilly Media.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_2n2VMaHX4Ahh7wy \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-machine-learning-and-data-science/2025-10-24/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251023T100000
DTEND;TZID=Europe/London:20251023T130000
DTSTAMP:20260416T121019
CREATED:20250819T142058Z
LAST-MODIFIED:20250819T142058Z
UID:10000527-1761213600-1761224400@www.swdtp.ac.uk
SUMMARY:Logical Foundations of Critical Thinking
DESCRIPTION:At the heart of critical thinking is the ability to reason well. When we reason\, our aim is to identify beliefs or actions which are supported by current mental states of ours. This course will consider different ways in which beliefs can be supported by other beliefs as well as different ways in which actions can be supported by beliefs and other mental states. \nWe will look at two types of reasoning about what to believe: deductive reasoning and inductive reasoning. In good deductive reasoning\, the beliefs functioning as premises provide total support for the conclusion; the truth of the premises guarantees the truth of the conclusion. In contrast\, in good inductive reasoning\, the premises only provide a high degree of support for the conclusion; the conclusion could be false even if the premises are true. We will discuss different conceptions of good deductive and inductive inferences. \nIn the area of reasoning about what to do\, we will consider means-end reasoning\, decision theory and moral reasoning. We will ask what legitimate role mental states like desires or emotions can play in such reasoning. \n  \nPlaces will be allocated on a first-come\, first-served basis\, and once places are full\, we will maintain a waiting list. \nPlease only register if you are certain of your availability and commitment to attend. \n  \nThis event is not delivered by the SWDTP. For enquiries\, please contact granduniondtp@socsci.ox.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/logical-foundations-of-critical-thinking/
LOCATION:6 Worcester St\, Oxford OX1 2BX
CATEGORIES:Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251016T143000
DTEND;TZID=Europe/London:20251016T173000
DTSTAMP:20260416T121019
CREATED:20250829T085505Z
LAST-MODIFIED:20250829T090648Z
UID:10000531-1760625000-1760635800@www.swdtp.ac.uk
SUMMARY:Introduction to Machine Learning and Data Science
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Introduction to Machine Learning and Data Science\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn today’s global and digital economic environment\, we have unprecedented access to vast amounts of data across all types of industries. It has become crucial for researchers and professionals to possess analytical skills that enable data-driven decision-making. Data science is the practice of collecting\, analysing\, and interpreting data that empowers decision-makers to make informed choices. \nThis module provides both theoretical foundations and practical skills necessary to apply machine learning and data science methods to problems in economics and finance. Theoretical concepts will be introduced in an intuitive and accessible manner. Emphasis will be placed on selecting appropriate methods for specific problems\, implementing methods correctly and presenting and interpreting analytical results \nLectures will include practical\, computer-based exercises using real-world datasets. Students will learn to use Python to carry out these tasks. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic Python knowledge (e.g.\, libraries NumPy\, Pandas\, statsmodels). \n  \nSoftware \nParticipants will use Jupyter Notebooks during the sessions for hands-on modelling demonstrations. The easiest way to access Jupyter is by installing the Anaconda Distribution\, which includes Python\, Jupyter\, and most of the required libraries. Anaconda can be downloaded here: https://www.anaconda.com/products/distribution \n  \nContent Outline \n\nLinear Regression\n\nModel assumptions\nGoodness of fit\nPrediction\nDiagnostic analysis\n\n\nLinear Model Selection\n\nBias-variance trade-off\nCross-validation\nSubset selection\nRidge Regression\nLASSO\n\n\nDimensionality Reduction\n\nPrincipal Component Analysis (PCA)\nPCA in regression contexts\n\n\nApplications in Python\n\nHands-on implementation using Python libraries such as pandas\, scikit-learn\, and statsmodels\n\n\n\nLearning Objectives \nBy the end of this module\, you will be able to: \n\nExplain the fundamental principles of linear regression\, including model assumptions\, coefficient estimation\, and diagnostic analysis.\nCompare and apply model selection techniques\, including subset selection\, Ridge Regression\, and LASSO.\nUnderstand and implement dimensionality reduction techniques\, particularly Principal Component Analysis\, and assess their impact on regression models.\nCritically assess the suitability of different machine learning methods for solving specific problems and justify methodological choices.\nUse Python programming tools and libraries (e.g.\, pandas\, scikit-learn\, statsmodels) to perform data analysis and apply machine learning methods to real-world datasets.\n\nMain References \n\nJames\, G.\, Witten\, D.\, Hastie\, T.\, & Tibshirani\, R. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.\nMcKinney\, W. (2018). Python for Data Analysis: Data Wrangling with Pandas\, NumPy\, and IPython (2nd ed.). O’Reilly Media.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_2n2VMaHX4Ahh7wy \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-machine-learning-and-data-science/2025-10-16/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251015T180000
DTEND;TZID=Europe/London:20251015T193000
DTSTAMP:20260416T121019
CREATED:20250901T172248Z
LAST-MODIFIED:20250901T172248Z
UID:10000542-1760551200-1760556600@www.swdtp.ac.uk
SUMMARY:Tackling the World-Wide Gambling Epidemic. What Can We Learn from Africa?
DESCRIPTION:The lecture is themed around the important topic: “Tackling the World-Wide Gambling Epidemic. What Can We Learn from Africa?”\nThis is a Public Lecture hosted by the Bristol Hub for Gambling Harms Research at the University of Bristol.\nGambling is a growing public health issue across the globe.\nThe global gambling industry is worth around $580bn – that’s bigger than the GDP of 86% of countries on our planet. But its human cost is vast. Millions of people worldwide experience serious harms\, from financial hardship and relationship breakdown to mental health problems and suicide. Smartphones have turned gambling into a 24/7 activity across the globe.\nJoin us for an insightful evening exploring innovative\, community-driven approaches to reducing gambling harm\, with a focus on powerful learnings from African research projects funded by the Bristol Hub.\nThe evening will start with a vibrant music video co-created and produced with young people affected by gambling harms in Uganda\, followed by a talk on the effects it has had on youth communities.\nThis will be followed by a panel discussion\, featuring researchers from Uganda\, Nigeria\, Namibia\, Kenya\, and South Africa\, alongside Professor Agnes Nairn from the University of Bristol.\nThis event will uncover:\n\n\nHow creative and digital tools can engage young people in awareness of gambling harms\n\n\nThe role of gambling in migration and university settings\n\n\nAdvertising’s influence in African sport and media\n\n\nDissemination of gambling messaging\n\n\nWhat the UK can learn from these diverse African perspectives\n\n\n\nWe’d love for you to join us for this important conversation. We anticipate high interest\, so please register as soon as possible to secure a place.\n\nMore details on the event can be found here\nFor queries\, email: gambling-harms@bristol.ac.uk\n\n\nThis event is kindly sponsored by Bristol Collegiate Research Society: The Bristol Collegiate Research Society is a charity committed to supporting an annual academic research symposium in the University of Bristol. The Society was founded in 1899 by a group of Bristol citizens who wished to assist University College Bristol gain a Royal Charter and become the University of Bristol. 
URL:https://www.swdtp.ac.uk/event-calendar/tackling-the-world-wide-gambling-epidemic-what-can-we-learn-from-africa/
LOCATION:Great Hall\, Wills Memorial Buildng\, University of Bristol\, Queen's Road\, Bristol\, BS8 1RJ
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251010T093000
DTEND;TZID=Europe/London:20251010T123000
DTSTAMP:20260416T121019
CREATED:20250829T085505Z
LAST-MODIFIED:20250829T090648Z
UID:10000530-1760088600-1760099400@www.swdtp.ac.uk
SUMMARY:Introduction to Machine Learning and Data Science
DESCRIPTION:This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS). \nSENSS Specialist Training: Introduction to Machine Learning and Data Science\nInstructor: Dr Elisabetta Pellini (Bayes Business School)\nTerm: Autumn 2025 \n  \nModule Outline and Aims \nIn today’s global and digital economic environment\, we have unprecedented access to vast amounts of data across all types of industries. It has become crucial for researchers and professionals to possess analytical skills that enable data-driven decision-making. Data science is the practice of collecting\, analysing\, and interpreting data that empowers decision-makers to make informed choices. \nThis module provides both theoretical foundations and practical skills necessary to apply machine learning and data science methods to problems in economics and finance. Theoretical concepts will be introduced in an intuitive and accessible manner. Emphasis will be placed on selecting appropriate methods for specific problems\, implementing methods correctly and presenting and interpreting analytical results \nLectures will include practical\, computer-based exercises using real-world datasets. Students will learn to use Python to carry out these tasks. \n  \nPrerequisites \nStudents are expected to have a knowledge of statistics (descriptive and inference) and basic Python knowledge (e.g.\, libraries NumPy\, Pandas\, statsmodels). \n  \nSoftware \nParticipants will use Jupyter Notebooks during the sessions for hands-on modelling demonstrations. The easiest way to access Jupyter is by installing the Anaconda Distribution\, which includes Python\, Jupyter\, and most of the required libraries. Anaconda can be downloaded here: https://www.anaconda.com/products/distribution \n  \nContent Outline \n\nLinear Regression\n\nModel assumptions\nGoodness of fit\nPrediction\nDiagnostic analysis\n\n\nLinear Model Selection\n\nBias-variance trade-off\nCross-validation\nSubset selection\nRidge Regression\nLASSO\n\n\nDimensionality Reduction\n\nPrincipal Component Analysis (PCA)\nPCA in regression contexts\n\n\nApplications in Python\n\nHands-on implementation using Python libraries such as pandas\, scikit-learn\, and statsmodels\n\n\n\nLearning Objectives \nBy the end of this module\, you will be able to: \n\nExplain the fundamental principles of linear regression\, including model assumptions\, coefficient estimation\, and diagnostic analysis.\nCompare and apply model selection techniques\, including subset selection\, Ridge Regression\, and LASSO.\nUnderstand and implement dimensionality reduction techniques\, particularly Principal Component Analysis\, and assess their impact on regression models.\nCritically assess the suitability of different machine learning methods for solving specific problems and justify methodological choices.\nUse Python programming tools and libraries (e.g.\, pandas\, scikit-learn\, statsmodels) to perform data analysis and apply machine learning methods to real-world datasets.\n\nMain References \n\nJames\, G.\, Witten\, D.\, Hastie\, T.\, & Tibshirani\, R. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.\nMcKinney\, W. (2018). Python for Data Analysis: Data Wrangling with Pandas\, NumPy\, and IPython (2nd ed.). O’Reilly Media.\n\n  \nPlease ensure you meet the prerequisites before registering. \nSign up form: https://essex.eu.qualtrics.com/jfe/form/SV_2n2VMaHX4Ahh7wy \nPlease direct enquiries to: trainingmanager@senss-dtp.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-machine-learning-and-data-science/2025-10-10/
LOCATION:Zoom
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251009T110000
DTEND;TZID=Europe/London:20251009T180000
DTSTAMP:20260416T121019
CREATED:20250905T163924Z
LAST-MODIFIED:20250905T163924Z
UID:10000543-1760007600-1760032800@www.swdtp.ac.uk
SUMMARY:Education-focused Quantitative Research Methods Workshops Using R
DESCRIPTION:The Centre for Multilevel Modelling (CMM) at Bristol is running two education-focused quantitative research method workshops using R at the School of Education. “R Progamming for Beginners” and “An Introduction to Rasch Modelling Using R”. \nFurther details and registration instructions can be found here:  https://www.eventbrite.co.uk/e/two-cmm-hosted-quantitative-research-methods-workshops-using-r-tickets-1656870958639?aff=oddtdtcreator&_gl=1%2A1r1e1il%2A_up%2AMQ..%2A_ga%2AMTcyMTE2NzAyMC4xNzU3MDY0OTA2%2A_ga_TQVES5V6SH%2AczE3NTcwNjQ5MDYkbzEkZzAkdDE3NTcwNjQ5MDYkajYwJGwwJGgw\n\nPlease send enquiries to: ed-events@bristol.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/education-focused-quantitative-research-methods-workshops-using-r/
LOCATION:35 Berkeley Square\, Bristol\, 35 Berkeley Square\, Bristol\, BS81JA\, United Kingdom
CATEGORIES:Higher Level Training,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251003T090000
DTEND;TZID=Europe/London:20251003T160000
DTSTAMP:20260416T121019
CREATED:20250619T084542Z
LAST-MODIFIED:20250619T084542Z
UID:10000510-1759482000-1759507200@www.swdtp.ac.uk
SUMMARY:Mediation and Moderation Analysis Using R
DESCRIPTION:This one-day course introduces the concepts and techniques of moderation and mediation analysis using R\, with a focus on social science applications. Participants will learn how to test for interaction effects (moderation) using regression models\, conduct mediation analysis using structural equation modelling and combine both approaches to explore mediated moderation. Emphasis is placed on interpretation\, visualization\, and reproducible R workflows using real-world data examples. \n\nThe course covers: \n\nA brief refresher on linear regression in R\nModeration analysis using interaction terms in regression\nProbing and visualizing interaction effects\nMediation analysis using path models\nEstimating indirect effects\nMediated moderation\nApplied examples in social sciences\nPractical coding sessions using real datasets in R\n\n\nBy the end of the course participants will: \n\nUnderstand the conceptual foundations of moderation and mediation\nConduct moderation analysis using interaction terms in R\nInterpret and visualize interaction effects\nSpecify and estimate mediation models using path analysis\nTest indirect effects and interpret mediation results\nUse SEM to explore differences in mediation across groups\nApply techniques to real-world social science datasets\n\n\nThe course includes hands-on computer workshops. Participants will use the R programming language.
URL:https://www.swdtp.ac.uk/event-calendar/mediation-and-moderation-analysis-using-r/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20251002T140000
DTEND;TZID=Europe/London:20251002T150000
DTSTAMP:20260416T121019
CREATED:20250819T141247Z
LAST-MODIFIED:20250819T141247Z
UID:10000526-1759413600-1759417200@www.swdtp.ac.uk
SUMMARY:Clean Python for Reproducible Research
DESCRIPTION:This live\, practical session introduces core techniques for improving the quality and transparency of research code.\nParticipants will learn how to: \n\nWrite clearer\, modular Python scripts\nUse Git for version tracking and collaboration\nSet up reusable Jupyter Notebooks for consistent workflows\n\nNo prior experience is required. The workshop is suitable for all doctoral students using (or planning to use) Python in their research. \nPlaces will be allocated on a first-come\, first-served basis\, and once places are full\, we will maintain a waiting list. \nPlease only register if you are certain of your availability and commitment to attend. \nThis event is not delivered by the SWDTP. For enquiries\, please contact granduniondtp@socsci.ox.ac.uk
URL:https://www.swdtp.ac.uk/event-calendar/clean-python-for-reproducible-research/
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250930T093000
DTEND;TZID=Europe/London:20250930T163000
DTSTAMP:20260416T121019
CREATED:20250619T084718Z
LAST-MODIFIED:20250619T084718Z
UID:10000511-1759224600-1759249800@www.swdtp.ac.uk
SUMMARY:Getting to Grips with Reflexive Thematic Analysis
DESCRIPTION:This one-day online course offers a comprehensive introduction to Reflexive Thematic Analysis (RTA)\, balancing theoretical discussion with practical exercises. The aim of this course is to open up thinking about the processes of qualitative analysis and enable participants to enact RTA in flexible\, iterative and reflexive ways as appropriate to the needs of their projects. This course is interactive\, combining discussion\, demonstration and practical exercises. \n\nThe course covers:\n• The characteristics of RTA\n• The six phases of analysis\n• Engaging in reflexive practice\n• Understanding quality in RTA\n• Tools to support the process\n• Resources for further development \n\nBy the end of the course participants will: \n\nUnderstand the characteristics of Reflexive Thematic Analysis (RTA) in comparison to other approaches\nBe able to describe and enact the six phases of RTA (familiarising\, coding\, generating initial themes\, develop and review themes\, refine\, define and name themes\, write-up)\nBe able to engage in and document reflexive practices throughout the analysis process\nUnderstand the importance of quality and what it looks like in RTA practice\nConsider the appropriateness of different tools to facilitate RTA\nKnow where to access further resources to develop RTA practice
URL:https://www.swdtp.ac.uk/event-calendar/getting-to-grips-with-reflexive-thematic-analysis/
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
END:VCALENDAR