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BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260318T100000
DTEND;TZID=Europe/London:20260319T160000
DTSTAMP:20260416T170203
CREATED:20260310T094917Z
LAST-MODIFIED:20260310T094917Z
UID:10000581-1773828000-1773936000@www.swdtp.ac.uk
SUMMARY:Social Network Analysis Workshop
DESCRIPTION:Networks shape nearly every aspect of our lives\, from the spread of ideas and diseases to the dynamics of friendship\, crime\, and power. This two-day workshop introduces the theory and practice of Social Network Analysis (SNA) using R\, combining insights from sociology\, data science\, and computer science. \nParticipants will learn how to map\, measure\, and model networks using R. Through lectures\, interactive analysis walk-throughs\, and hands-on exercises\, we will cover the fundamentals of graph theory\, key network concepts\, and the principles of visualising\, modelling\, and interpreting network structures. \nBy the end of the workshop\, participants will understand how to collect and prepare network data\, analyse patterns of connection\, and design their own network-based research project. The course provides both the conceptual foundation and the practical skills to think critically about how networks shape our social world. \n  \nTo find out more\, please follow the link below.
URL:https://www.swdtp.ac.uk/event-calendar/social-network-analysis-workshop/
CATEGORIES:Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260312T130000
DTEND;TZID=Europe/London:20260312T140000
DTSTAMP:20260416T170203
CREATED:20251205T142902Z
LAST-MODIFIED:20260113T142348Z
UID:10000565-1773320400-1773324000@www.swdtp.ac.uk
SUMMARY:Negotiating positionality in data analysis
DESCRIPTION:Reflexive Thematic Analysis on researcher’s position as an “in-betweener”\nClaire Hadfield\, Senior Lecturer and PhD researcher in Education at Plymouth Marjon University \nReflecting on my journey using Reflexive Thematic Analysis (RTA) in a qualitative longitudinal study of early career secondary teachers’ professional identities\, I draw on my position as an “in-betweener”—moving from school teaching into initial teacher education. This role placed me close enough to share aspects of participants’ experiences while also able to view them from a different perspective. Working with interviews\, viva reflections\, and journals\, I returned to the data repeatedly in an iterative process of theme development. RTA supported deep engagement through analytic journaling and participant discussion. I will discuss how insider knowledge both enriched and complicated the analysis\, how slowing the process helped avoid premature conclusions\, and how reflexivity was essential in managing bias. The session will share practical strategies and honest reflections on the challenges and insights that came from applying RTA in a long-term\, identity-focused study. \n  \nHow to prioritise participant voice in data analysis when a third voice is present – the use of advocates in research\nKim Collett\, Lecturer in Education at The Open University \nFor my PhD I conducted interpretivist research\, using research driven photo elicitation interviews and thematic analysis\, comparing experiences of inclusion in the classroom. For some of the participants adjustments were needed to ensure the research was accessible. This included having advocates present during data collection. One of the under explored\, issues with advocacy in research is how to deal with advocate voices in the data. The words of advocates will appear in verbatim transcripts and removing them can change meaning/context. However\, keeping them means they become part of the analysis and introduce a third voice. \nI kept the voices of advocates and analysed these along with the words of the participants. However\, careful consideration was needed to determine if the data was really reflecting the experiences/thoughts of the participant when the advocate was speaking. Sometimes it was clear as the participant would verbally or non-verbally agree/disagree\, or the content would reflect other parts of the conversation. However\, sometimes there was no confirmation. Reflexivity was key to assessing this and the findings chapter had to carefully cover the use of advocates when a finding was based on or informed by the advocate. \nThis session is part of the SWDTP Data Analysis Webinar Series. Visit the following link for further information and registration: https://www.tickettailor.com/events/swdtp/1956811
URL:https://www.swdtp.ac.uk/event-calendar/negotiating-positionality-in-data-analysis/
CATEGORIES:Higher Level Training,Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260303T130000
DTEND;TZID=Europe/London:20260303T140000
DTSTAMP:20260416T170203
CREATED:20251205T142720Z
LAST-MODIFIED:20260113T142353Z
UID:10000564-1772542800-1772546400@www.swdtp.ac.uk
SUMMARY:Validating constructs through quantitative sampling
DESCRIPTION:Using multi-dimensional experience sampling via smartphones to map thought-emotion interactions in daily life\nAnqi Lei\, PhD researcher at the University of Plymouth \nPatterns of on-going thought have crucial implications for emotional health. In the present study\, we used multi-dimensional experience sampling (MDES) via smartphones to examine how daily-life thought patterns relate to concurrent affective states (valence\, arousal\, stress) as well as how alexithymia traits (reflecting atypical emotional awareness) modulate these thought patterns across a range of affective and social situations. Principal Component Analysis of the MDES data identified four latent thought dimensions: future-self orientation\, intrusive distraction\, sensory engagement\, and task-focus. Linear Mixed Models revealed different associations between thought dimensions and affective states\, which may reflect distinct adaptive and maladaptive cognitive processes\, particularly in relation to alexithymia. High overall alexithymia predicted fewer future-self-oriented thoughts as well as more different sensory engagement across affective and social contexts. Regarding specific facets of alexithymia\, difficulty identifying feelings selectively reduced future-self orientation during intense sadness\, and externally oriented thinking rendered thought patterns less sensitive to affective contexts. By mapping affective experiences onto thought dimensions in daily life\, these findings uncover cognitive pathways that support emotional well-being\, providing a scalable framework for understanding variability in human affective experience. \n  \nMeasuring sensitive constructs in conservative contexts\nSara Yadollahi\, PhD researcher at the University of Bath \nAs a psychometrician\, I was responsible for data gathering and analysis in the project: “Development and Validation of an Iranian Scale for Problematic Online Pornography Use\,” which used a descriptive-correlational design and online non-random snowball sampling. A total of 1\,921 adults (813 women\, 1\,108 men) completed the scales: the Iranian Scale for Problematic Online Pornography Use (developed by the research team following multiple steps\, including a thorough literature review) and Difficulties in Emotion Regulation Scale (DERS). \nData were analysed using AMOS and SPSS. Confirmatory factor analysis supported a seven-factor model—Salience\, Mood Modification\, Tolerance and Escalation\, Withdrawal\, Relapse\, Conflict and Problems\, and Guilt (RMSEA=0.07\, CFI=0.91\, df=228). Construct validity was confirmed through intercorrelations among subscales and the total score; discriminant validity by the Fornell-Larcker criterion; convergent validity by Average Variance Extracted (AVE); and criterion validity by correlations with pornography use frequency (r=0.56)\, duration per session (r=0.35)\, and frequency of masturbation with (r=0.56) and without pornography (r=0.25). Reliability was strong (CR=0.98\, α=0.93). \nIn this webinar\, I will focus on the data analysis process\, including designing the scale with the research team\, conducting the pilot study\, being creative in measuring sensitive constructs in conservative contexts\, working with a large dataset\, and ensuring participant anonymity during data collection and analysis. \nThis session is part of the SWDTP Data Analysis Webinar Series. Visit the following link for further information and registration: https://www.tickettailor.com/events/swdtp/1956811
URL:https://www.swdtp.ac.uk/event-calendar/validating-constructs-through-quantitative-sampling/
CATEGORIES:Higher Level Training,Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260303T000000
DTEND;TZID=Europe/London:20260306T235959
DTSTAMP:20260416T170203
CREATED:20260217T095411Z
LAST-MODIFIED:20260217T095411Z
UID:10000580-1772496000-1772841599@www.swdtp.ac.uk
SUMMARY:Digital Research Skills for Social Scientists
DESCRIPTION:Date: 3rd – 6th March 2026 (four consecutive mornings) \nLocation: online \nThe National Centre for Research Methods are offering the opportunity to attend this introductory short course for the reduced price of £25 for all participants. Improve the efficiency and reliability of your research. Learn foundational computational skills including automating tasks using the command line on your computer\, tracking changes to your work using version control and building simple programs using the programming language python. These skills are the foundation of many powerful data analysis techniques including using Artificial Intelligence or High Performance Computing. \nPlaces are limited so don’t delay. Book your place here: https://www.ncrm.ac.uk/training/show.php?article=14333 [ncrm.ac.uk]
URL:https://www.swdtp.ac.uk/event-calendar/digital-research-skills-for-social-scientists/
CATEGORIES:Higher Level Training,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260226T130000
DTEND;TZID=Europe/London:20260226T140000
DTSTAMP:20260416T170203
CREATED:20251205T142459Z
LAST-MODIFIED:20260113T142359Z
UID:10000563-1772110800-1772114400@www.swdtp.ac.uk
SUMMARY:Messy and mixed: working with quant and qual data
DESCRIPTION:Making Sense of Messy Legal Data: Analysing Climate Litigation in Latin America and the Caribbean\nCristian Heredia Ligorria\, PhD researcher in Socio-legal Studies at UWE Bristol \nMy doctoral research investigates rights-based climate litigation (RBCL) in Latin America and the Caribbean applying a socio-legal methodology and from a decolonial perspective. Chapter 3 of my thesis is grounded in the construction and analysis of a working dataset of 51 RBCL cases (as of November 2024). This process combined qualitative and quantitative methods to identify high-level trends\, map actors (who litigates\, against whom\, and in what contexts)\, and develop a typology of cases. \nThe analysis presented several methodological challenges: the diversity of legal systems across the region\, inconsistencies in reporting\, language barriers\, and the evolving nature of climate litigation. Data were cleaned and verified manually\, drawing on databases such as the Sabin Center and supplemented by direct regional expertise. Supervisory feedback\, peer-reviewed collaborations\, and external expert input were essential in refining the methodology and ensuring rigour. \nThis experience highlights both successes and the practical challenges of working with heterogeneous legal data\, and offers lessons for socio-legal researchers conducting comparative data analysis in underexplored regions. \n  \nUsing a structured case review tool to understand police investigation of rape cases\nAneela Khan\, Postdoctoral Research Assistant at Bournemouth University \nOperation Soteria Bluestone aimed to improve understanding of how police investigate rape cases in the UK. As part of this work\, we developed a structured case review tool to collect detailed information on individual investigations. In its initial format\, the tool required a junior officer to document case details and evaluate investigative strengths and weaknesses\, followed by a senior officer who repeated the review and provided oversight on the junior officer’s assessment. Due to several challenges\, the tool was adapted for the second year to allow researchers to directly access the cases and then populate the template. The dataset comprised qualitative and structured quantitative information\, including case characteristics\, investigative actions\, and assessments of investigative quality. Data analysis combined descriptive statistics to summarise trends and thematic coding to identify recurring strengths\, weaknesses\, and procedural patterns across cases. This methodology provided a systematic approach to evaluating investigative practices in rape cases and supports evidence-based recommendations for improving police investigations. \nThis session is part of the SWDTP Data Analysis Webinar Series. Visit the following link for further information and registration: https://www.tickettailor.com/events/swdtp/1956811
URL:https://www.swdtp.ac.uk/event-calendar/messy-and-mixed-working-with-quant-and-qual-data/
CATEGORIES:Higher Level Training,Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260223T100000
DTEND;TZID=Europe/London:20260223T163000
DTSTAMP:20260416T170203
CREATED:20251205T150732Z
LAST-MODIFIED:20251205T150732Z
UID:10000574-1771840800-1771864200@www.swdtp.ac.uk
SUMMARY:GW4 Neurodivergent PGR Community Festival 2026
DESCRIPTION:Join us at the GW4 Neurodivergent Postgraduate Community Festival 2026 for a day of celebration\, connection\, and support! \n  \n Registration now open for the GW4 Neurodivergent PGR Community Festival 2026 We’re excited to invite neurodivergent postgraduate researchers from across Cardiff\, Bath\, Bristol and Exeter to join us on 23 February 2026 in Bristol for a day of connection\, celebration and community. This festival creates space for ND PGRs to: share experiences build supportive networks explore research identity meet others navigating academia as neurodivergent researchers ️ 23 February 2026 St Michael’s Centre\, Bristol\, BS34 8PD (5 minute walk from Bristol Parkway)  Register via Eventbrite: https://www.eventbrite.co.uk/e/gw4-neurodivergent-pgr-community-festival-2026-tickets-1964553969808…
URL:https://www.swdtp.ac.uk/event-calendar/gw4-neurodivergent-pgr-community-festival-2026/
CATEGORIES:Conference
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260217T100000
DTEND;TZID=Europe/London:20260217T170000
DTSTAMP:20260416T170203
CREATED:20260113T141335Z
LAST-MODIFIED:20260113T141335Z
UID:10000576-1771322400-1771347600@www.swdtp.ac.uk
SUMMARY:Reproducible data analysis pipelines in R
DESCRIPTION:Years ago\, you wrote a data analysis script. Hundreds of lines of R code\, all in a single file. It was not beautiful\, but it worked\, and you got a great paper out of it. But now some new version of one of the datasets you used has come out\, and there is also that new statistical technique that you have been meaning to try anyway. Assuming you still have your original code somewhere\, can you still run it? Even on your new machine? Maybe. And do you need to re-run the whole thing if you only change parts of it? It did take ages to run… \nIn this one-day course\, you will learn about packages and practices that can help you make your analyses reproducible and portable. The material is centred around the `targets` package for building computational pipelines\, but we will also talk about `renv` for package management\, `git` and GitHub for version control and remote execution\, and Quarto for the production of final research outputs. We will also\, of course\, use the `tidyverse` throughout. \nSome basic proficiency in R is required to make the most of this course. \nSpaces are limited so we cannot guarantee a place at the point of registration. Places will be allocated on a first-come\, first-served basis\, and once places are full\, we will maintain a waiting list. \n  \nPlease note this event is not delivered by the SWDTP. Please direct enquiries to granduniondtp@socsci.ox.ac.uk \nRegistration: https://www.granduniondtp.ac.uk/event/reproducible-data-analysis-pipelines-in-r
URL:https://www.swdtp.ac.uk/event-calendar/reproducible-data-analysis-pipelines-in-r/
LOCATION:6 Worcester St\, Oxford OX1 2BX
CATEGORIES:Higher Level Training,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260212T100000
DTEND;TZID=Europe/London:20260212T170000
DTSTAMP:20260416T170203
CREATED:20260113T141656Z
LAST-MODIFIED:20260113T141656Z
UID:10000577-1770890400-1770915600@www.swdtp.ac.uk
SUMMARY:Introduction to agent-based modelling in NetLogo Introduction to agent-based modelling in NetLogo
DESCRIPTION:Whether it’s ant colonies\, traffic jams\, fisheries\, predator-prey interactions\, segregation patterns in urban areas\, or viruses spreading through populations\, we are surrounded by complex systems. Those have lots of different parts that interact in non-linear ways\, giving rise to patterns that are difficult to predict by looking at individual components in isolation. And when these components are agents that can adapt and learn\, it gets even harder. \nAgent-based models (ABMs) are one way of looking at these systems. By explicitly representing agents\, their behaviours and interactions\, and using simulations to work out the consequences of these mechanisms\, ABMs can provide candidate explanations for the observed patterns. \nIn this introductory course\, we will look at where ABMs come from\, how they work\, and what they are good for. We will learn how to build a simple model using NetLogo\, a programming environment specialised in agent-based modelling. We will also see how to estimate the parameters of a model using empirical data and\, once we have a calibrated model\, how to use it for policy optimisation. \nSome familiarity with computer programming is desirable\, but no prior experience with NetLogo is expected. \n  \nPlease note this event is not delivered by the SWDTP. Please direct enquiries to: granduniondtp@socsci.ox.ac.uk \nRegistration: https://www.granduniondtp.ac.uk/event/introduction-to-agent-based-modelling-in-netlogo
URL:https://www.swdtp.ac.uk/event-calendar/introduction-to-agent-based-modelling-in-netlogo-introduction-to-agent-based-modelling-in-netlogo/
LOCATION:6 Worcester St\, Oxford OX1 2BX
CATEGORIES:Higher Level Training,Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260210T130000
DTEND;TZID=Europe/London:20260210T140000
DTSTAMP:20260416T170203
CREATED:20251205T142338Z
LAST-MODIFIED:20260113T142401Z
UID:10000562-1770728400-1770732000@www.swdtp.ac.uk
SUMMARY:Philosophy as method for data analysis in research
DESCRIPTION:Educational researchers are often encouraged to reflect on their ‘philosophical positioning’\, i.e. the ontological\, epistemological and axiological (ethical) assumptions that underpin their research design. Meanwhile\, in recent years in anglophone educational research departments\, using philosophy as a ‘method’ in its own right\, as opposed to a tool supporting empirical research\, has tended to go out of fashion. A group identifying as philosophers of education\, including current doctoral researchers and their supervisors\, seek to demonstrate the benefits and attractions of continuing to work philosophically\, sometimes treating the existing literature as priori data to be analysed\, at other times working in partnership with empirical research. They showcase a range of distinctive philosophical perspectives\, including examples from hermeneutical/analytical (Janet Orchard + 1) and critical/post-structuralist (Naomi Hodgson + 1) traditions.  This event is aimed at doctoral researchers at any stage who would like to hear more from enthusiasts of the theoretical on how to think more abstractly about data analysis in research. \nThis session is part of the SWDTP Data Analysis Webinar Series. Visit the following link for further information and registration: https://www.tickettailor.com/events/swdtp/1956811
URL:https://www.swdtp.ac.uk/event-calendar/philosophy-as-method-for-data-analysis-in-research/
CATEGORIES:Higher Level Training,Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260205T100000
DTEND;TZID=Europe/London:20260206T170000
DTSTAMP:20260416T170203
CREATED:20260202T144541Z
LAST-MODIFIED:20260202T144541Z
UID:10000579-1770285600-1770397200@www.swdtp.ac.uk
SUMMARY:Bayesian Statistics for Applied Research
DESCRIPTION:This two-day course provides a practical and accessible introduction to Bayesian statistics for applied research in any field. Students will benefit from a combination of lectures and discussion to explore fundamental concepts unlocking the potential to design bespoke statistical analyses based on your data and hypotheses as well as practical exercises to gain hands-on experience implementing Bayesian models using free and open-source software. The course is designed as a springboard to overcome the steepest part of the Bayesian learning curve with an immersive two-day deep-dive.
URL:https://www.swdtp.ac.uk/event-calendar/bayesian-statistics-for-applied-research-2/
CATEGORIES:Training
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260205T100000
DTEND;TZID=Europe/London:20260206T170000
DTSTAMP:20260416T170203
CREATED:20250819T142729Z
LAST-MODIFIED:20250819T142729Z
UID:10000528-1770285600-1770397200@www.swdtp.ac.uk
SUMMARY:Bayesian Statistics for Applied Research
DESCRIPTION:This two-day course provides a practical and accessible introduction to Bayesian statistics for applied research in any field. Students will benefit from a combination of lectures and discussion to explore fundamental concepts unlocking the potential to design bespoke statistical analyses based on your data and hypotheses as well as practical exercises to gain hands-on experience implementing Bayesian models using free and open-source software. The course is designed as a springboard to overcome the steepest part of the Bayesian learning curve with an immersive two-day deep-dive. \nStudents will have the opportunity to schedule a follow-up Bayesian surgery appointment (30 mins in-person or remote) for one-to-one engagement (or small groups\, as preferred) with the course instructor to answer burning questions that remain and/or to troubleshoot technical challenges related to their own research applications. \nPre-requisites: Students will need good programming skills in R and a basic understanding of linear regression to be successful in this course. \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/bayesian-statistics-for-applied-research/
LOCATION:London School of Economics and Political Science
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260127T103000
DTEND;TZID=Europe/London:20260127T120000
DTSTAMP:20260416T170203
CREATED:20250930T145717Z
LAST-MODIFIED:20250930T145804Z
UID:10000552-1769509800-1769515200@www.swdtp.ac.uk
SUMMARY:Philosophy of Social Science
DESCRIPTION:This talk will draw upon Alexander Betts’ recent book Social Science: A Very Short Introduction to offer a contemporary take on the philosophy of social science. It will focus in particular on the underpinnings of interdisciplinary social science\, arguing that across disciplines\, the social sciences have more in common than that which divides them.  \nWhere: Hybrid | GUDTP Hub \nWhen: 27.01.2026|10:30-12:00 \nAdvert & Registration: https://granduniondtp.web.ox.ac.uk/event/philospophy-of-social-science \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/philosophy-of-social-science/
LOCATION:Online
CATEGORIES:Higher Level Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260127T090000
DTEND;TZID=Europe/London:20260128T170000
DTSTAMP:20260416T170203
CREATED:20251104T160046Z
LAST-MODIFIED:20251104T160046Z
UID:10000557-1769504400-1769619600@www.swdtp.ac.uk
SUMMARY:Qualitative Research Symposium - Applications & Call for Papers
DESCRIPTION:The Centre for Qualitative Research looks forward to inviting you all to the University of Bath to ponder important questions around participation\, access and inclusion in qualitative research. \n  \nIf you would like to attend or apply for the call\, check out the link below.
URL:https://www.swdtp.ac.uk/event-calendar/qualitative-research-symposium-applications-call-for-papers/
CATEGORIES:Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260122T130000
DTEND;TZID=Europe/London:20260122T140000
DTSTAMP:20260416T170203
CREATED:20251205T142213Z
LAST-MODIFIED:20260113T142404Z
UID:10000561-1769086800-1769090400@www.swdtp.ac.uk
SUMMARY:Analysing large-scale assessment data
DESCRIPTION:Issues with using police data to investigate offending: A research perspective\nDr Ioana Crivatu\, Research Fellow at the University of Birmingham\nDr Ruth Spence\, Senior Research Fellow at Middlesex University \nPolice data is an important source of information for researchers about investigations\, suspects\, and victims. However\, crime records can be problematic to work with. Here we outline three key issues along with our approach in combining and quantitatively analysing police data from several police forces in England and Wales which used different crime recording systems. We discuss data quality\, which reflects missing and misclassified values; inconsistency\, which refers to the vague and at times different definitions provided; and granularity\, which reflects the lack of detailed information included in the datasets. We recommend developing a robust strategy for working with missing data\, triangulating across different sources\, creating higher-order categories where necessary\, and creating a detailed data governance plan before analysis begins. \nLink to published paper: https://journals.sagepub.com/doi/full/10.1177/0032258X251313944 \n  \nPreparation of a Large-scale Assessment in Education and its use in a Quantitative Intersectional analysis in R\nDr Natalia López-Hornickel\, Postdoctoral Research Associate at Roehampton University; SWDTP alumni\nIn this presentation\, first\, I aim to show the considerations and challenges of preparing large-scale assessment data\, using the International Civic and Citizenship Education Study (ICCS) from 2016. This includes the sources of the data and the merging process\, which is usually an overlooked but crucial step before proceeding with the analysis. Second\, I will refer to the analysis steps to obtain descriptives and models. Particularly\, I will use the case of the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to develop an intersectional analysis of students’ endorsement of the gender equality scale (Fifth paper of my thesis). This technique is a parsimonious alternative to multiplicative terms in regressions. \nAll the explanations will be conceptual and also accompanied by a description of some R syntax. \nThis session is part of the SWDTP Data Analysis Webinar Series. Visit the following link for further information and registration: https://www.tickettailor.com/events/swdtp/1956811
URL:https://www.swdtp.ac.uk/event-calendar/analysing-large-scale-assessment-data/
CATEGORIES:Higher Level Training,Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260120T130000
DTEND;TZID=Europe/London:20260120T140000
DTSTAMP:20260416T170203
CREATED:20251104T151112Z
LAST-MODIFIED:20260113T142409Z
UID:10000556-1768914000-1768917600@www.swdtp.ac.uk
SUMMARY:What is Research Data? Practical Guidance on Organising and Sharing your Files and Findings
DESCRIPTION:Every research project generates data. It’s the material that you gather\, create\, or interpret to answer your research questions; whether that is numbers\, images\, recordings\, or documents. Good research data management and sharing are essential for making your work accessible\, your methods transparent\, and your findings easy to use and build upon. Funders\, publishers\, and universities require researchers to share and cite their research data – but what does this look like in practice? This webinar offers practical tips and guidance to organise\, store and share your documents and results effectively throughout your project. This session covers: \n\nWhat is research data and why it matters\nThe expectations of funders\, publishers\, and universities for research data storage and sharing\nHow to organise and describe your files so you can easily find and understand your research data throughout your project\nHow to ethically share research data when working with human participants\nHow to find a suitable research data repository for your work\nWhat support is available beyond your supervisory team\n\nThe speaker for this session is Dr Jade Godsall who is an Assistant Research Support Librarian in Research Data Management and Digital Scholarship at The University of Bristol. \nThis session is part of the SWDTP Data Analysis Webinar Series. Visit the following link for further information and registration: https://www.tickettailor.com/events/swdtp/1956811
URL:https://www.swdtp.ac.uk/event-calendar/what-is-research-data-practical-guidance-on-organising-and-sharing-your-files-and-findings/
CATEGORIES:Higher Level Training,Training,Webinar/Seminar/Symposium
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20260120T100000
DTEND;TZID=Europe/London:20260120T163000
DTSTAMP:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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:20260416T170203
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
END:VCALENDAR