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.
Here at the ESSDA we are delighted to offer our 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 covers all aspects of modern 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 Masters you must undertake the following combination of courses and pass the exams in:
Maximum of 1 introductory course
A total of 3 courses at advanced or intermediary level
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
Timings
We encourage students to aim to complete the Masters in 3 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 5 years from the first day of the first qualifying course until completion.
Cost of the MA
The MA cost is a total of £10,000* which is made up from the costs of the eligible courses, the costs of the exams and the cost of Supervision of the Thesis. The total for the MA starting in 2024 will be £10,000, * please note that this is subject to annual review.
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 Quantitative Text Analysis
Introduction to Regression
Designing and Analysing Surveys
Introduction to Game Theory
Longitudinal and Panel Data Analysis
Survey Experimental Design
Introduction to Web Scraping and Data Management for Social Scientists
Introduction to Quantitative Methods in R
Qualitative Data Analysis: Methodologies for Analysing Text and Talk
Qualitative Interviewing
Methods For Field Work in Social Science
Ethnography and Ethnographic Methods
Intermediate
Introduction to Social Network Analysis
Multilevel Statistical Models for the Social Sciences Using Stata
Categorical Data Analysis
Introduction to Applied Bayesian Statistics
Quantitative Data Analysis and Statistical Graphics with R
Introductory to Programming in Python for Social Scientists
Multilevel Models: Practical Applications
Quantitative Text Analysis
Longitudinal Data Analysis
Mixed Methods Research
Causal Inference and Experiments in the Social Sciences
Quantitative Methods for Causal Inference and Policy Evaluation
How to Communicate and Engage Using Data Analysis in R
Doing Discourse Analysis: Populism, Neoliberalism and Radical Democratic Politics
Applying Discourse Theory – Politics, Ideology, Populism
Advanced
Machine Learning for Social Scientists
Spatial Econometrics
Confirmatory Factor Analysis and Structural Equation Modelling
Programming and Simulation Methods for Computational Social Science
Machine Learning For Estimating Treatment Effects From Observational Data
Data Visualisation with R: Explore, Model and Communicate Social Data Analysis
Machine Learning for Tabular Data
Bayesian Analysis for the Social and Behavioural Sciences
Advanced Methods for Text as Data: Natural Language Processing
Ideal Point Estimation, Item Response Theory, and Scaling Methods for Surveys and Behaviour
Deep Learning For Text and Vision
*(These courses are subject to change and eligible courses in one year of entry might not be available in another)
Resits and Appeals
If you fail the exam and wish to resit, your course mark will be capped at 50. All resits count as one of your attempts at assessment in a module, please note the maximum number of attempts at assessment you are allowed in a module is two. If you are offered reassessment due to Extenuating Circumstances, this will be for uncapped marks and will not count as one of your attempts at assessment.
