Introductory description

This module builds on PO11Q which is essential for taking this module. PO12Q will concentrate on the tasks of "analysis" and "interpretation" of the quantitative methods typology, and provide students with a toolbox to analyse the relationship between two or more variables. We will start the module with an introduction to causality and correlation before moving to its main focus: regression analysis. This method is one of the standard tools of political science researchers and cannot only demonstrate whether relationships between variables exist, but also quantify the magnitude and direction of such a relationship. We will progress in small steps, starting with only one independent variable to explore the intuition, assumptions, and interpretation of results. The module will close by discussing the basics of multiple regression.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1. Because… Reasons: Causal Thinking
2. Detecting Patterns with Correlation
3. Beyond Patterns with Linear Regression
4. Ordinary Least Squares: A Look under the Hood
5. OLS: Why and How Well Does It Work?
6. (Reading Week)
7. From Sample to Population in Regression
8. The Real World: Multiple Regression
9. Transformation of Variables: Unlocking Patterns
   Making Your Regression BLUE

Learning outcomes

By the end of the module, students should be able to:

• Understand the different approaches to and limitations of causality
• Apply correlation analysis
• Understand and apply the method of linear regression (ordinary least squares)
• Conduct basic bivariate and multivariate analysis in R
• Begin to critically engage with quantitative findings in political science journal articles.

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

Research element

Exploration of research questions using real-world data in seminars.

Interdisciplinary

Data sets and examples from multiple social science disciplines

International

Data from around the world is used.

Subject specific skills

• Assessment of Causality
• Analysis of bivariate relationships,
• Application of bivariate and multiple regression analysis to empirical problems,
• Increased proficiency in statistical software (in comparison to prerequisite module "Quantitative Political Analysis: From Measurement to Inference").

Transferable skills

• Written communication skills,
• Oral communication skills,
• Problem-solving skills,
• Skills in the use of information technology,
• Skills of interpretation and the critical analysis of primary and secondary sources,
• The ability to digest, retain and apply complex information and ideas,
• Ability to conduct research and reference your work appropriately,
• Time management skills and the ability to meet deadlines,
• The ability to reflect critically on the extent and limitations of how and what you have learned, discovered and understood.