Courses for the fifty-seventh Essex Summer School in Social Science Data Analysis
Courses are delivered either online, in person or in hybrid mode. The mode of delivery for each course is stated below.
Please note the following:
In person study: These course will be delivered in person at the Colchester campus. Online study is not available for these courses.
Online study: These courses will be taught online only. In person study is not available for these courses.
Hybrid: Hybrid delivery of courses will include synchronous live sessions during which on campus and online students will be taught simultaneously. Students need to choose the mode by which they will study at the point of application.
Please note: Courses take place in mornings or afternoons, British Summer Time (BST).
The schedule for each session is described in the menu on the left.
For enquiries, please contact us at esumsda@essex.ac.uk
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PRE-SESSIONAL: Monday 1 July – Friday 5 July 2024
0A Introduction to R Programming for the Social Sciences (Online only, one week course) – AM
SESSION ONE: Tuesday 9 July – Friday 19 July 2024
1A Mathematics for Social Scientists (Online only)
Morning course:
1B Introduction to Quantitative Text Analysis (Online only)
1E Multilevel Statistical Models For The Social Sciences Using Stata (In-person only)
1G Longitudinal and Panel Data Analysis (Hybrid)
1H Qualitative Data Analysis: Methodologies for Analysing Text and Talk (In-person only)
1J Categorical Data Analysis (In-person only)
1M Introduction to Quantitative Methods in R (Hybrid)
1N Methods For Field Work in Social Science (Online only)
1Q Doing Discourse Analysis: Populism, Neoliberalism and Radical Democratic Politics (Online only)
Afternoon course:
1C Introduction to Regression (Online only)
1D Introduction to Social Network Analysis (Online only)
1F Designing and Analysing Surveys (Hybrid)
1K Introduction to Applied Bayesian Statistics (Hybrid)
1R Introduction to Game Theory (Hybrid)
Full day course:
1I Qualitative Interviewing (35 hours, week two only, in-person only)
SESSION TWO: Monday 22 July – Friday 2 August 2024
2A Mathematics for Social Scientists (Online only)
Morning course:
2D Introduction to Programming in Python for Social Scientists (Hybrid)
2E Multilevel Models: Practical Applications (Online only)
2F Quantitative Text Analysis (Online only)
2I Applying Discourse Theory – Politics, Ideology, Populism (In-person only)
2L Spatial Econometrics (Hybrid)
2N Confirmatory Factor Analysis and Structural Equation Modelling (Online only)
2T Introduction to GIS, Geospatial Data and Spatial Analysis (17.5 hours, week one only, Hybrid)
Afternoon course:
2B Quantitative Data Analysis and Statistical Graphics with R (In-person only)
2C Survey Experimental Design (Online only)
2G Longitudinal Data Analysis (Online only)
2H Mixed Methods Research (Online only)
2J Machine Learning for Social Scientists (Hybrid)
2K Causal Inference and Experiments in the Social Sciences (Hybrid)
2P Quantitative Methods for Surveying Hard to Reach Populations (17.5 hours, week two only, Hybrid)
2V Web Scraping and Data Management for Social Scientists (Hybrid)
2W Programming and Simulation Methods for Computational Social Science (Hybrid)
Full day course:
2Q Ethnography and Ethnographic Methods (35 hours, week one only, in-person only)
SESSION THREE: Monday 5 August – Friday 16 August 2024
3A Mathematics for Social Scientists (Online only)
Morning course:
3B Machine Learning For Estimating Treatment Effects From Observational Data (Hybrid)
3C How To Communicate and Engage Using Data Analysis in R (Hybrid)
3D Data Visualization with R: Explore, Model and Communicate Social Data Analysis (Hybrid)
3F Bayesian Analysis for the Social and Behavioural Sciences (Hybrid)
Afternoon course:
3E Machine Learning For Tabular Data (Hybrid)
3G Quantitative Methods for Causal Inference and Policy Evaluation (Online only)
3H Advanced Methods for Text as Data: Natural Language Processing (Online only)
3L Ideal Point Estimation, Item Response Theory, and Scaling Methods for Surveys and Behaviour (Hybrid)
3P Agent-Based Models (17.5 hours, week one only, Hybrid)
Full day course:
3N Deep Learning For Text and Vision (35 hours, week one only, Hybrid)
