POL50050 Quantitative Methods II

Academic Year 2022/2023

This course seeks to extend the analytical and theoretical understanding developed in Quantitative Methods I. It covers the fundamentals of regression analysis, starting with extensions to the basic linear model and then introducing the generalized linear model as applied to limited (categorical and count) dependent variables. While the course mostly considers modelling as a general tool for prediction, several sessions in the second part focus on methods for inferring causal effects from observational data.

This course is applied. A certain degree of mathematical and theoretical discussion is indispensable to gain a sufficiently deep insight into the methods covered. However, the emphasis is on understanding, implementation, and interpretation of the various techniques in the context of actual research. This approach is reflected in the choice of texts and in the focus on practical coursework. Consequently, the learning method combines lectures and reading with statistical programming exercises using real datasets.

The statistical package being used is R.

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Curricular information is subject to change

Learning Outcomes:

At the end of this course, students will be able to:
--- Use and interpret interaction effects in the linear model;
--- Apply generalizations of the linear model to limited dependent variables (categorical and count data) and interpret the results;
--- Explain and (where possible) test assumptions of causal inference techniques and use these with real data;
--- Select, run, and interpret a fairly complex regression model to answer a practical research question.

Indicative Module Content:

- Interaction effects in the linear model
- Logit models for binary and polytomous dependent variables
- Simulation
- Count data models
- Causal inference from observational data
- Multilevel data modeling

Student Effort Hours: 
Student Effort Type Hours
Autonomous Student Learning




Computer Aided Lab




Approaches to Teaching and Learning:
The class time will be a mixture of lectures and guided hands-on work with actual data. Students need to work on regular homeworks conducting similar applied analyses. 
Requirements, Exclusions and Recommendations
Learning Requirements:

This course assumes prior training in basic statistics, including:
- descriptive statistics
- hypothesis tests, p-values, sampling distribution
- linear regression with multiple (categorical and continuous) predictors
- basic data management (e.g., recoding variables)

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Homework 3 Week 8 n/a Alternative linear conversion grade scale 40% No


Assignment: Homework 2 Week 6 n/a Alternative linear conversion grade scale 40% No


Essay: Course paper Coursework (End of Trimester) n/a Graded No


Assignment: Homework 1 Week 3 n/a Alternative linear conversion grade scale 40% No


Assignment: Homework 4 Week 10 n/a Alternative linear conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Summer 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 on homework