POL50050 Quantitative Methods II

Academic Year 2021/2022


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:
--- Outline core assumptions of the linear regression model;
--- Diagnose problems when applying the linear model to real data and choose appropriate corrections;
--- 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:

- Ordinary Least Squares
- Logit and probit
- Bootstrap and simulation
- Count data models
- Causal inference
- Multilevel data modeling

Student Effort Hours: 
Student Effort Type Hours
Lectures

18

Computer Aided Lab

6

Autonomous Student Learning

200

Total

224

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:
- hypothesis tests, p-values, sampling distribution
- correlation and linear regression
- 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

13

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

13

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

50

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

13

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

13


Carry forward of passed components
No
 
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

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
     
Lecture Offering 1 Week(s) - 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32 Tues 11:00 - 12:50