POL30430 Data Analytics for Social Sciences

Academic Year 2019/2020

This module provides an overview of common statistical methods applied to the social sciences, with particular focus on political science, sociology, public policy and development. It starts with a brief recap of the basic principals of statistical analysis, then discusses how to access, manipulate, and summarize data, and then moves on to a range of different methods - regression analysis, logistic regression, dimension reduction techniques, quantitative text analysis, etc. - that are commonly used in social science empirical research or in contemporary data science applications. It reviews both long established and cutting-edge techniques.

All material is discussed using real world examples of data analysis, with both micro- and macro-level data, and the lab exercises form the basis for the continuous assessments. Rather than delving deeply into the mathematical properties of various techniques, this module focuses on the application and the types of problems where particular techniques can be applied.

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Curricular information is subject to change

Learning Outcomes:

- Basic understanding of statistical analysis in the social science
- Ability to manipulate data sets to prepare for statistical analysis
- Ability to select the appropriate statistical technique for a range of different types of empirical questions
- Ability to execute a range of standard techniques
- Ability to describe, interpret, and present statistical analysis to a wider audience
- Ability to translate statistical results to substantive relevance
- Introductory level skills in data analysis in R
- Ability to organise data analysis and results

Indicative Module Content:

Project 1: Survey data analysis

Data inspection and visualisation
Descriptive statistics

Project 2: Analysis in international political economy / international relations

Linear regression
Logistic regression
Regression trees
Networks and spatial econometrics

Project 3: Quantitative analysis of political text

Cluster analysis
Principal component analysis
Topic models

Student Effort Hours: 
Student Effort Type Hours
Lectures

12
Computer Aided Lab

12
Autonomous Student Learning

100
Total

124
Approaches to Teaching and Learning:
This course is entirely lab based, with relatively short introductory lectures to each topic, avoiding technical detail and focusing on the basic idea and interpretation of the statistical tool of that week.

Students are provided with detailed instructions on how to perform analyses during the lab, and homework assignments are based on the interpretation of the lab analyses. This allows for students to learn about a wide range of techniques, without prior training in statistics or programming, and to think about how this can be relevant to provide insights into social science questions. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Essay: Interpretation of analysis in Block 3 Week 12 n/a Graded No

40
Essay: Interpretation of analysis in Block 2 Week 8 n/a Graded No

40
Essay: Interpretation of analysis in Block 1 Week 4 n/a Graded No

20

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Feedback will be provided within 20 days from submission, as per university guidelines.

James, Gareth, Daniela Witten, Trevor Hastie and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With applications in R. Springer.