The University is required to collect the following information by the Higher Education Statistical Agency.
The University of Essex is registered under the terms of the Data Protection Act to enable it to hold and process personal data about its participants. These data comprise details about admission, academic background, course registration and academic progress while at the University. They also include information collected by the University for the purposes of equal opportunities monitoring. The University will process these data for various administrative, academic and health and safety purposes. The data will be kept secure and accurate and will only be disclosed to people who have a need to know in accordance with the University’s registration under the Act.
Ethnic Origin * White-British White-Irish Other White Background Black or Black British- Carribbean Black or Black British- African Other Black Background Asian or Asian British- Indian Asian or Asian British- Pakistani Asian or Asian British- Bangladeshi Chinese or Other Ethnics Background- Chinese Other Asian Background Mixed- White and Black Carribbean Mixed- White and Black African Mixed- White and Black Asian Other Mixed Background Other Ethnic Background Not Known Information Refused
Disabilities * Dyslexia Blind/ Partially Sighted Deaf/ Hearing Impaired Weelchair User/ Mobility Problems Personal Care Support Mental Health Difficulties An Unseen Disability (E.G Asthma or Diabetes) Multiple Disabilities A Disability not mentioned above Prefer not to say None
Special Requirements / Medical Notes
Please indicate your background knowledge in the following areas.
Note: if you are part of a group enrolment, please indicate this in the ‘research interests’ box below. Maths
Calculus None Elementary Moderate Strong
Linear Algebra None Elementary Moderate Strong Statistics
OLS None Elementary Moderate Strong
Maximum Likelihood None Elementary Moderate Strong Computing
STATA None Elementary Moderate Strong
R None Elementary Moderate Strong
MPLUS None Elementary Moderate Strong
Please give details of your primary subfield and research interests using a few keywords
The course schedules for each session can be found here and fees are described here. Session 1 – Monday 13 to Friday 24 July 2020 Session 2 – Monday 27 July to Friday 7 August 2020 Session 3 – Monday 10 to Friday 21 August 2020 Morning courses are held weekdays 10:00-13:30. Afternoon courses are held weekdays 14:15-17:45.
no more than one afternoon and one morning course for each session you will attend.
– Note that full-day courses 1R, 2Q, and 2S include morning and afternoon sessions and
cannot be taken in conjunction with an afternoon or morning course that occurs simultaneously.
– Maths classes begin at 8:15 am every morning and is offered free of charge with any paid course. Participants are encouraged to attend these lectures if they feel they need supplementary maths instruction.
– Intro to R (4 hours) takes place the Sunday before each session and can be taken in conjunction with any course.
– If you wish to enrol in
1P and 1Q simultaneously, 2R and 2T simultaneously, or 2Q and 2S simultaneously, this is allowed and encouraged, but please leave blank your course selection (otherwise completing the application) and contact the summer school office by email with this request. Session 1
Introduction to R
Morning Course 1B Introduction to Quantitative Text Analysis 1E Introduction to Multilevel Models 1G Longitudinal Data Analysis 1H Qualitative Data Analysis: Methodologies for Analysing Text and Talk 1I Introduction to Qualitative Interviewing 1M Data Analysis and Programming in Stata 1N Methods for Fieldwork in Social Science 1P Introduction to GIS, Geospatial Data and Spatial Analysis (17.5 hours - week 1 only) 1Q Introduction to Causal Inference, Qualitative Research Design, and Measurement (17.5 hours - week 2 only)
Afternoon Course 1C Applying Regression 1D Introduction to Social Network Analysis 1F Introduction to Survey Data Analysis 1J Beyond OLS: Categorical, Choice and Count Models 1K Introduction to Quantitative Methods in R 1L Introduction to Statistics for Social Science Research with SPSS
Full Day Course 1R Introduction to Big Data for the social sciences (presented by UK Data Archive - week 2 only, full day) Session 2
Introduction to R
Morning Course 2C Advanced Survey Data Analysis and Survey Experiments 2F Advanced Methods for Social Media & Textual Data 2G Panel Data Analysis for Comparative Research 2H Mixed Methods Research 2I Applying Discourse Theory - Politics, Ideology, Populism 2L Generalized Linear Models 2N Factor Analysis & Structural Equation Modelling with MPLUS
Afternoon Course 2B Quantitative Data Analysis with R 2D Case Study Methods 2E Multilevel Models: Practical Applications 2J Machine Learning for Social Scientists 2K Causal Inference and Experiments in the Social Sciences 2M Introduction to Bayesian Analysis 2R Advanced Social Network Analysis (17.5 hours - week 1 only) 2T Survival Analysis and Event History Modelling (17.5 hours - week 2 only)
Full Day Course 2Q Ethnography and Ethnographic Methods (35 hours - week 1 only) 2S Quantitative methods for surveying hard-to-reach populations (35 hours - week 2 only) Session 3
Introduction to R
Morning Course 3B Quantitative Text Analysis 3C Doing Discourse Analysis: Populism, Neoliberalism and Radical Democratic Politics 3I Web Scraping and Data Management for Social Scientists 3J Advanced Quantitative Data Analysis 3K Time Series and Panel Models for Dynamic and Heterogeneous Processes 3N Advanced Machine Learning for Social Scientists
Afternoon Course 3D Computational methods for Social Data Science 3E Maximum Likelihood Estimation 3F Bayesian Analysis for the Social and Behavioural Sciences 3G Quantitative Methods for Causal Inference and Policy Evaluation 3H Spatial Econometrics 3L Scaling Methods and Ideal Point Estimation 3P Agent Based Models (17.5 hours - week 1 only)