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Advanced Quantitative Research Methods



[BSc], GPH, EES, S, GED, ID, Psc

Admission Requirements

Numeracy, Quantitative Research Methods.


The course introduces students to more advanced issues in quantitative research in economics, political science, environmental science and other and provides the students with hands-on opportunity to apply these approaches to real policy problems. The course builds on students’ understanding of basic inferential theory and linear regression and familiarizes them with statistical techniques, such as limited dependent variable models, time-series models, simultaneous equations models and panel data models. Students will be asked to develop a project of their choosing in their area of interest that incorporates some of these statistical techniques.

Course Objectives

  • Provide basic understanding of more advanced statistical techniques.

  • Understand how these techniques can be applied in the context of substantive research topics.

  • Further develop the skills of conducting quantitative research.

  • Further develop statistical programming skills in R package, including graphical display of data.

Mode of Instruction

The course consists of Interactive lectures, where students are required to participate and demonstrate their familiarity with readings and lab seminars dedicated to improving the conceptual understanding of these methods in a series of mini research projects conducted in R.


To be confirmed in course syllabus:

In-class participation: 10%
Project presentation: 10%
Take-home exam: 25%
Final research project and project updates: 40%


Christopher Dougherty: Introduction to Econometrics. Third Edition, 2007. Oxford University Press.

The Chicago Guide to Writing About Numbers (2004), Haack, Dennis, University of Chicago Press – 1st edition.
The Chicago Guide to Writing About Multivariate Analysis. Haack, Dennis, University of Chicago Press.

Weekly Overview

Week 1 – Assumptions for linear models
Week 2 – Log and semi-log models
Week 3 – Limited dependent variable models
Week 4 – Time-series and panel-data models
Week 5 – Simultaneous equation models
Week 6 – Modeling Issues in biological and environmental sciences
Week 7 – Project presentations