Advanced Quantitative Methods

This is a postgraduate module covering the basics of research design and research methods through state of the art scholarship. We cover a range of statistical methods, from descriptive statistics and multivariate regression analysis, to experimental and observational methods for making causal inferences. The module emphasises conceptual understanding and clear thinking over mathematical and technical issues, and does not assume any prior knowledge of maths or statistics. Through the computer labs and assignments, students gain not only a theoretical but also a practical understanding of quantitative methods and coding using the R language and tidyverse.

Week 1: Introduction, description, variation, correlation

This week introduces the course and look at the basics of two of the fundamental tasks of statistics – describing and summarising data.

Week 2: Linear regression I

This week we introduce linear regression – the workhorse model for quantitative research. 

Week 3: Linear regression II

We continue our introduction to regression, looking at polynomials, covariate adjustment, and interactions. 

Week 4: Samples, Uncertainty and inference

We look at the fundamentals of sampling, uncertainty, confidence intervals, and statistical significance testing.

Week 5: Regression beyond straight lines

This week we look at regression when the relationship between variables is not linear. 

Week 6: Thinking causally, not casually

This week we introduce the fundamentals of causal inference, particularly Directed Acyclic Graphs (DAGs).

Week 7: Randomized Experiments

This week we look at the Potential Outcomes framework examine questions of causal inference using randomised experiments.

Week 8: Natural experiments I: Regression Discontinuity Designs

We return to causal inference with observational data, introducing the ideal of a ’natural experiment’ and introducing the Regression Discontinuity Design.

Week 9: Natural experiments II: Difference-in-Differences Designs

We continue our examination of natural experiments, looking at another common method – the difference-in-difference design.

Week 10: Thinking clearly about quantitative methods

In the final week of the course we examine some of the common pitfalls that trip up quantitative researchers, problems with the academic publishing process, and some avenues for further quantitative methods.