Advanced Topics in Quantitative Social Research

• Credit value: 15 credits at Level 7
• Convenor: Chao-Yo Cheng
• Tutors: Chao-Yo Cheng, Laszlo Horvath
• Assessment: two 1000-word problem sets (20% each) and a 3000-word final research note (60%)

This module introduces you to a selection of advanced statistical methods that are commonly used in quantitative social research. The module is designed for students who have prior exposure to basic descriptive statistics, hypothesis testing and multiple linear regression, as well as some working knowledge of the R programming language. We will cover several topics, such as generalised linear models, multilevel/hierarchical models, panel data analysis, and basic tools of causal inference.

By the end of this module, you will become familiar with the key issues in crafting careful and thoughtful quantitative research. You will also perform hands-on statistical programming in R for a variety of data analytical tasks. Altogether, this module provides you with a theoretical understanding of key techniques, as well as the practical experience to apply these techniques independently and critically to advance our understanding of political and socioeconomic issues.

This module is highly recommended if you intend to incorporate quantitative methods into your dissertation research.

Indicative module syllabus

• Refresher: statistical programming in R
• Generalised linear models
• Multilevel/hierarchical models
• Panel data analysis
• Basic tools of causal inference
• Refresher: multiple linear regression

By the end of this module, you will be able to:

• perform advanced statistical methods on a variety of data
• demonstrate hands-on experience with R statistical programming
• engage critically with advanced published quantitative research
• write up and present the findings of advanced quantitative analysis.