Methodology is the cornerstone of research in Social Sciences. Social Science Data analysis aims at testing theories and allowing researchers to make correct and reliable inferences.
The Essex Summer School in Social Science Data Analysis is proud to offer an MA that allows students to acquire a broad range of relevant research methods in the social sciences and to master their own research at the highest level of proficiency. The MA currently is restricted to concentrations in content in quantitative social science data analysis.
The MA is only available to students via the Essex Summer School in Social Science Data Analysis.
Course requirements
To be eligible for the MA you must undertake exams in four courses. Two of these courses must be Advanced-level courses. The two other courses counted toward this requirement can include no more than one introductory course.
In addition, you will need to submit a Thesis of a maximum of 10, 000 words not including references. The pass mark for the Masters is 50.
The timeline for completion
We encourage students to aim to complete the Masters in three years.
For example, a student finishing their first course in Summer 2024 could submit the dissertation by August 2027 and would finish all courses by August 2026.
The Masters must be completed over a maximum period of five years from the first day of the first qualifying course until completion.
Cost of the MA
The MA cost is a total of £10,500 which covers the costs of the eligible courses, the costs of the exams and the cost of Supervision of the Thesis. When students become enrolled as MA students, their previous payments toward eligible courses are counted toward this total. This total applies to an MA enrolled in 2025 and is subject to annual review. Students who have paid their MA fees in total also have the option to take one additional ESS course free of additional charge before they submit their dissertation, separate from those counted toward the MA requirements.
Process
If you are interested in undertaking the Masters then please contact the ESS office (essexsummerschoolssda@essex.ac.uk) who can support you through the process which is detailed below:
- Complete the application form which asks for a CV, a brief description of your research interests and research proposal and some basic information.
- We will assess your application and invite you to discuss your proposed research.
- We will work with you to appoint an appropriate Supervisor which may be an instructor from the Summer School.
- You will work with your Supervisor and submit your thesis for marking within the timescales.
- Once the thesis has been completed the dissertation will be double marked and sent for review by an External Examiner.
- Your overall award will be ratified by a Board of Examiners which takes place in November.
- Attend a Graduation ceremony to be awarded your Masters.
Eligible courses and categories*
Introductory
Introduction to Regression
Introduction to Quantitative Text Analysis
Introduction to Statistics for Survey Data Analysis
Introduction to Quantitative Methods in R
Game Theory
Survey Experimental Design
Designing and Analysing Surveys
Web Scraping and Data Management for Social Scientists
Intermediate
Introduction to Social Network Analysis
Multilevel Statistical Models for The Social Sciences Using Stata
Introduction to Applied Bayesian Statistics
Causal Inference and Experiments in the Social Sciences
Longitudinal Data Analysis
Categorical Data Analysis
Quantitative Data Analysis and Statistical Graphics with R
How To Communicate and Engage Using Data Analysis in R
Applied Social Statistics using Stata
Multilevel Models: Practical Applications
Longitudinal and Panel Data Analysis
Mixed Methods Research
Quantitative Text Analysis
Quantitative Methods for Causal Inference and Policy Evaluation
Introduction to Programming in Python for Social Scientists
Advanced
Machine Learning for Social Scientists
Advanced Methods for Time Series and Panel Data
Deep Learning for Text and Vision
Machine Learning for Estimating Treatment Effects from Observational Data
Machine Learning for Tabular Data
Analysing Multimodal Language Data for Quantitative Social Science
Bayesian Analysis for the Social and Behavioural Sciences
Generative AI for Social Science Research
Programming and Simulation Methods for Computational Social Science
Data Visualisation with R: Explore, Model and Communicate Social Data Analysis
Spatial Econometrics
Confirmatory Factor Analysis and Structural Equation Modelling
Advanced Methods for Text As Data: Natural Language Processing
Ideal Point Estimation, Item Response Theory, and Scaling Methods for Surveys and Behaviour
*These courses are subject to change, and availability may vary by entry year. Some courses listed here are from previous years and may no longer be on offer. The eligibility of courses for these requirements is assessed each year by the ESS staff.
