9th Jun 2025 - 13th Jun 2025
Greenwich Campus
Hamilton House
15 Park Vista, Greenwich, London SE10 9LZ
Each course runs 10:00-16:00 for full day courses, 10:00-13:00 and 13:00-16:00 for half-day courses: Instructor: Bruce Cronin About: The goal of the course is to provide participants with an overview of how to use R for research – including data processing and visualisation. It also provides a foundation for the course on Organisational Network Analysis with xUCINET for those that haven't experience in R. By the end of the course participants will be able to: Requirements: Instructor: Instructor: Mohit Kumar Singh About: By the end of the course participants will be able to: Requirements: Instructor: Instructor: Francisca Da Gama About: By the end of the course participants will be able to: Requirements: Instructor: Instructor: Martina Testori About: The course provides a step-by-step introduction to oTree, covering everything from installation to launching an experiment. Participants will learn key experimental design principles, including causal inference, randomization, and validity assessment, before moving on to practical applications of oTree. At the end of the course participants will be able to: Instructor: General References: Instructors: Srinidhi Vasudevan, Anna Piazza, Balint Diószegi About: At the end of the course participants will be able to: Requirements Instructor General references Instructor: James Duong (Quang Huy) About: With the proliferation of large corpora of text data, manual thematic/content analysis is no longer effective to extract common topics and key themes. Furthermore, text data is multifaceted, and it is challenging to derive the sentiment of the authors in an accurate way. To cope with that issue, machine learning-based topic modelling and sentiment analysis are well-known techniques to explore prominent topics and their sentiment from a big collection of texts. This course aims to provide a basic knowledge about text pre-processing, sentiment extraction using HuggingFace and an introduction of the most common topic model – Latent Dirichlet Allocation (LDA) using the Python-programming language. The participants will have an opportunity to practise on real customer review dataset from Amazon. At the end of the course participants will be able to: Requirements: Instructor Instructor: Mohit Kumar Singh About: By the end of the course participants will be able to: Requirements: Participants should have a basic understanding of Python programming; course 2 in the Summer School is sufficient grounding, Prior experience with NLP is beneficial but not required. Participants should bring their own laptops with Python installed. Instructor: Instructor: Guido Conaldi About: Participants will discover how to leverage AI assistants to process relational data, calculate network metrics, identify structural patterns, and create compelling visualisations—all through natural language instructions. The session covers fundamental SNA concepts including centrality measures, community detection, and network visualisation through practical examples relevant to contemporary social science research. This hands-on workshop provides a foundation for researchers interested in incorporating network perspectives into their work without requiring extensive technical training. Participants will gain practical skills for analysing various forms of relational data, from interpersonal connections to organisational networks and digital interactions. By the end of the course participants will be able to: Requirements: Instructor Instructor: Guido Conaldi About: During this intensive one-day session, participants will discover how to leverage GenAI tools to translate prompts into code for functional statistical analyses. The workshop takes a practical approach, demonstrating how researchers can focus on research design and interpretation while AI handles the technical implementation of analyses. This hands-on session is designed to equip social scientists with a principled framework to conduct quantitative analysis independently regardless of their coding background. Participants will learn to inspect, modify and understand AI-generated code, developing essential skills for creating well-documented and replicable research. By the end of the course participants will be able to: Requirements: Instructor Instructor: Bruce Cronin About: By the end of this course participants will be able to: Requirements Instructor General references Book Now. Early Bird offer ends on Friday 16 May at 5pm. Half-day courses (Courses 1- 3): Full-day courses (Courses 4, 6-10): Doing Research with SNA: Tools, Theories, and Applications (Course 5): If you are unsure about which ticket you are to purchase, please contact us.
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Day Course Instructor 9 June 2025 Morning 1. Introduction to coding for quantitative and qualitative research with R Bruce Cronin 9 June 2025 Afternoon 2. Introduction to coding for quantitative and qualitative research Python Mohit Kumar Singh 9 June 2025 Afternoon 3. Introduction to Discourse Network Analysis Francisca Da Gama 9 June 2025 All day 4. Experimental methods and programming in oTree Martina Testori 10-12 June 2025 All day 5. Doing Research with Social Network Analysis: Tools, theories, and applications Srinidhi Vasudevan, Anna Piazza, Balint Diószegi 10 June 2025 All day 6. Programmatic approaches to thematic analysis for text data James Duong (Quang Huy) 11 June 2025 All day 7. Textual analysis with Generative AI Mohit Kumar Singh 12 June 2025 All day 8. Generative AI for Social Network Analysis without coding Guido Conaldi 13 June 2025 All day 9. Generative AI for statistical analysis without coding Guido Conaldi 13 June 2025 All day 10. Organisational Network Analysis with xUCINET in R Bruce Cronin Course Descriptions
1. Introduction to coding for quantitative and qualitative research with R
This half-day workshop provides an introduction to the R programming language for those without any previous experience with this or as a refresher if you haven’t used it for a while.
No prior knowledge of R is required. Ideally, participants should bring their own laptops with RStudio installed.
Bruce Cronin is Professor of Economic Sociology at the University of Greenwich, where he is co-director of the Networks and Urban Systems Centre.2. Introduction to coding for quantitative and qualitative research with Python
This half-day course introduces coding with Python, tailored for those interested in quantitative and qualitative research. Participants will learn the basics of Python programming and how to apply it to various research methodologies. The course will cover fundamental coding concepts, data manipulation, and basic analysis techniques. It also provides a foundation for the course on programmatic approaches to thematic analysis for text data.
No prior programming experience is required. Ideally, participants should bring their own laptops with Python and Jupyter Notebook installed.
Dr Mohit Kumar Singh is a lecturer in transport and logistics management at the University of Greenwich. A graduate of IIT Delhi and Visiting Research Fellow in AI at Loughborough University, he pursues leveraging technology for the development of efficient and sustainable transportation systems. He has extensive experience in applying Python to research projects and has taught several coding and related modules.3. Introduction to Network Discourse Analysis
The workshop provides an introduction to Discourse Network Analysis, a software-supported set of methods for analysing the development of social relationships in discourse such as policy debates. As with other content analysis tools, discourse is manually but additionally coded with actor attributes highlighting sentiment and belief structures. The network data generated can be used to identify narrative or advocacy coalitions, key players and strategic discourse shifts.
No prior knowledge of SNA is required, though some exposure to this would be helpful. Ideally, participants should bring their own laptops with Discourse Network Analyser and Visone installed (both are java-based multi-platform executables)
Dr Francisca Da Gama is a senior lecturer in International Business at the University of Greenwich. A graduate of the University of Auckland, her research focuses on indigenous responses to extractivism in Latin America, and the ways in which business narratives and political networks engage with non-Western cultures.4. Experimental methods and programming in oTree
This course provides an introduction to causal inference, equipping participants with the skills to critique methods used in contemporary academic work and apply these methods in their research. It begins with an overview of causality in experimental designs, covering differences between observational and experimental data, randomised experiments, and random sampling. In the second part of the course, participants will gain a hand-on experience with oTree, a flexible framework based on Python. oTree is a powerful and simple tool for developing social science experiments, enabling researchers to conduct studies both online and in laboratory settings.
Dr Martina Testori is a computational social scientist studying how different means can be used to sustain cooperative and sustainable behaviours. I look at how information, including gossip, and reputation impacts cooperation in groups and communities. I am especially interested in how different interventions can promote more pro-environmental behaviours and the achievement of sustainable development. I use experimental methods and agent-based modelling to investigate cooperative and socially sustainable dynamics at the individual and collective level.
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. https://doi.org/10.2307/j.ctv1c29t27
Llaudet, E., & Imai, K. (2022). Data analysis for social science: a friendly and practical introduction. Princeton University Press.
Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC
Chen, D. L., Schonger, M., & Wickens, C. (2016). oTree—An open-source platform for laboratory, online, and field experiments. Journal of Behavioral and Experimental Finance, 9, 88-97.
oTree Documentation: https://otree.readthedocs.io/en/latest/install.html5. Doing Research with SNA: Tools, Theories, and Applications
The goal of the course is to provide attendees with a general overview of the field of social network analysis, confidence in using its key analytical tools in practice, and insight into how it can be used in scholarly practice in the social, economic, managerial and political disciplines. The focus is on research design and how SNA elements can be successfully integrated into a research project, paper, or dissertation. Participants will be introduced to UCINET and Netdraw software via practical exercises
All social science backgrounds are welcome, and participants are assumed not to have any previous knowledge of SNA, or of any analytical or statistical software. No previous experience with the software is expected. Ideally, participants should bring their own laptops with Ucinet installed (Ucinet is windows-based so Mac users need a windows compatibility layer such as Wine or dual boot)
Dr Srinidhi Vasudevan is a senior lecturer in Business Management and Programme Leader for the MSc Business Analytics at the University of Greenwich. Dr Anna Piazza is a senior lecturer in Economic Sociology at the University of Greenwich. Both are graduates and alumni of the Networks and Urban Systems Centre. Dr Balint Diószegi is a lecturer in Network Science at the University of Greenwich. A graduate of ETH Zurich and a Visiting Research Fellow at Imperial college, his research focuses on the cognitive and behavioural foundations of social networks, using sociometric badge technology and experimental approaches.
Borgatti, SP, Everett, MG and Johnson, JC (2018) Analysing Social Networks, 2nd Edition. London: Sage.6. Programmatic approaches to thematic analysis for text data
Participants should have an elementary knowledge of the Python-programming language; course 2 in the Summer School is sufficient grounding,
Dr Quang (James) Duong is a senior lecturer in Business Operations at the University of Greenwich. He is a graduate and alumnus of the Networks and Urban Systems Centre.7. Textual Analysis with Generative AI
This full-day course covers the use of Generative AI for text analysis. Participants will explore advanced techniques for analysing and generating text using AI models. The course will cover topics such as natural language processing (NLP) and sentiment analysis with state-of-the-art AI tools.
Dr Mohit Kumar Singh is a lecturer in transport and logistics management at the University of Greenwich. A graduate of IIT Delhi and Visiting Research Fellow in AI at Loughborough University, he pursues leveraging technology for the development of efficient and sustainable transportation systems. He has extensive experience in applying Python to research projects and has taught several coding and related modules.8. Generative AI for Social Network Analysis Without Coding
This workshop introduces social scientists to the application of Generative AI (GenAI) for exploring, analysing and visualising social networks. Traditionally, social network analysis (SNA) has required specialised programming skills or dedicated software packages that present a steep learning curve. This session demonstrates how GenAI tools can transform the accessibility of network analysis techniques, allowing researchers to focus on substantive research questions rather than technical implementation.
Some familiarity with social network analysis concepts is not required but useful. Participants should bring a laptop with internet access. The session is designed specifically for social scientists new to network analysis who wish to incorporate relational perspectives into their research. While the focus is on accessibility, the workshop will provide sufficient methodological grounding for participants to critically engage with network concepts and findings.
Dr Guido Conaldi is Associate Professor in Organisational Sociology at the University of Greenwich, where he is deputy director of the Networks and Urban Systems Centre.9. Generative AI for Statistical Analysis Without Coding
Generative Artificial Intelligence (GenAI) tools have transformed how researchers approach statistical analysis, making sophisticated quantitative methods accessible without extensive programming knowledge. This workshop introduces social scientists to the capabilities of GenAI coding assistants for conducting statistical analyses using natural language prompts rather than writing code themselves.
No prior programming experience is required, though familiarity with basic statistical concepts is helpful. Participants should bring a laptop with internet access. The workshop is designed specifically for social scientists seeking to enhance their quantitative research capabilities without investing substantial time in learning programming languages.
Dr Guido Conaldi is Associate Professor in Organisational Sociology at the University of Greenwich, where he is deputy director of the Networks and Urban Systems Centre.10. Organisational Network Analysis with xUCINET in R
This course provides an introduction to social network analysis applied to the study of organisational networks. These social networks are shaped and influenced by organisational tasks and structures and various methods of accounting for these effects are considered in the course. The course also builds on elementary understanding of the UCINET software package by examining how many repetitive analytical tasks, common in organisational network analysis, can be automated using the new R-based version of the software, xUCINET.
Participants should have an elementary understanding of Social Network Analysis and R; course 1 in the Summer School is sufficient grounding. Participants should bring their own laptops with RStudio installed. No prior knowledge of UCINET is needed.
Bruce Cronin is Professor of Economic Sociology at the University of Greenwich, where he is co-director of the Networks and Urban Systems Centre.
Borgatti, SP, Everett, MG, Johnson, JC, and Agneessens, F. (2022) Analysing Social Networks Using R. London: Sage.Fees
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