The Essex Summer School will be returning in 2026 for our 59th year!
2026 Programme Dates
Pre-sessional 1: Monday 29 June – Friday 3 July 2026
Session 1: Monday 6 July – Friday 17 July 2026
Pre-sessional 2: Monday 13 July – Friday 17 July 2026
Session 2: Monday 20 July – Friday 31 July 2026
Session 3: Monday 3 August – Friday 14 August 2026
Essex Summer School Courses for 2026
Confirmed courses are listed below and more courses are being added. Courses are offered hybrid, online, or in person.
- Hybrid: Includes live sessions where on-campus and online students learn together.
- Online: Fully online with no in-person option.
- In Person: Taught at the Colchester campus with no online option.
The delivery mode for each course is listed below. Students must choose their mode by which they will study at the time of application. Courses take place in the morning or afternoon, British Summer Time (BST). Recordings of class sessions will, in most cases, be provided to support participants’ learning during the course period. If a course does not offer recordings, this is clearly indicated in the course description.
For enquiries, please contact us at essexsummerschoolssda@essex.ac.uk
To stay informed about ESS updates, subscribe to our mailing list
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PRE-SESSIONAL 1: Monday 29 June – Friday 3 July 2026
0A Introduction to R Programming for the Social Sciences (17.5hrs, one week, Online) – AM
0D Introduction to Python for Social Scientists (17.5hrs, one week, Online) – AM
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SESSION ONE: Monday 6 July – Friday 17 July 2026
Morning
1B Introduction to Quantitative Methods in R (Hybrid)
1F Longitudinal and Panel Data Analysis (Hybrid)
1G Categorical Data Analysis (Hybrid)
1H Designing and Analysing Surveys (Hybrid)
1K Introduction to Quantitative Text Analysis (Online)
1M Doing Discourse Analysis: Populism, Neoliberalism and Radical Democratic Politics (Online)
1P Mixed Methods Research (Hybrid)
1Q Preparing for Fieldwork in the Social Sciences: Methods, Logistics, Ethics (Hybrid)
Afternoon
1C Introduction to Data Science and Programming in R (Hybrid)
1E Applied Social Statistics using Stata (Hybrid)
1I Applied Causal Inference with Observational Data (17.5hrs, one week, 13-17 July) (Hybrid)
1N Qualitative Data Analysis: Methodologies for Analysing Text and Talk (In person)
Full day
1R Qualitative Interviewing (35hrs, One week, 6-10 July) (In person only)
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PRE-SESSIONAL 2: Monday 13 July – Friday 17 July 2026
00A Introduction to R Programming for the Social Sciences (17.5hrs, one week, Online) – AM
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SESSION TWO: Monday 20 July – Friday 31 July 2026
Morning
2A AI-Enhanced Research Design (17.5hrs, one week, 27-31 July) (Online)
2B Generative AI for Social Science Research (Hybrid)
2C Introduction to Programming in Python for Social Scientists (Hybrid)
2D Quantitative Data Analysis and Statistical Graphics with R (In person only)
2L Geospatial Data and Spatial Analysis (17.5hrs, one week, 20 – 24 July) (Online)
2P Machine Learning For Estimating Treatment Effects From Observational Data (Hybrid)
2T Applied Discourse Analysis for Social Sciences: From Theory to Practice (In person)
2U Applying Discourse Theory – Politics, Ideology, Populism (In person only)
Afternoon
2G Bayesian Analysis for the Social and Behavioural Sciences (Hybrid)
2H Web Scraping and Data Management for Social Scientists (Hybrid)
2I Longitudinal Data Analysis (Online)
2J Advanced Methods for Time Series and Panel Data (Hybrid)
2M Scaling, Ideal Point Estimation, and IRT Methods for Surveys and Behaviour (Hybrid)
2N Causal Inference and Experiments in the Social Sciences (Hybrid)
2V Qualitative Methods for Empirical Social Science (Online)
Full day
2W Ethnographic Methods for the 21st Century (35hr course, one week 20-24 July) (In person only)
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SESSION THREE: Monday 3 August – Friday 14 August 2025
Morning
3A Introduction to Regression (Online)
3B Multilevel Models: Practical Applications (Online)
3C Bayesian Latent Variable and Measurement Models (Online)
3G Text Analysis for Social Scientists: Utilizing LLM-Assisted Coding (Online)
3J Deconstruction and Discourse Theory as Method (Online)
Afternoon
3E Casual Inference for Evaluation in Social Sciences (Online)
3K Tree-based Methods and Machine Learning for Tabular Data (Online)
Full day
3H Deep Learning For Text and Vision (35hrs, one week, 3-7 Aug) (Online)
