POL50070 Quantitative Methods I (CORE) (TCD)

Academic Year 2019/2020

Introduction to the use of data for statistical analysis in political science and related disciplines (sociology, public policy, international relations, etc.). The module will introduce concepts such as measurement, variables, statistical data, and provide an introduction to basic descriptive statistics summarizing numerical data, both graphically and numerically. The core of the module will be an introduction to applied multiple regression analysis, discussing the purpose, implementation, and interpretation of standard regression models, for both continuous and dichotomous variables. It will introduce the basics of statistical inference, drawing conclusions about populations on the basis of sample data, and apply this to the regression context. Foundational knowledge of frequentist and Bayesian statistical inference will be provided and the end result will be basic ability to perform, interpret, and report on multiple regression analysis.

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Curricular information is subject to change

Learning Outcomes:

- basic understanding of working with R and RStudio
- being able to summarize and describe statistical data
- solid understanding of frequentist statistical inference
- basic understanding of Bayesian statistical inference
- basic understanding of executing and interpreting multiple regression
- preliminary understanding of logistic regression

Indicative Module Content:

Accessing and visualising data
Simple regression
Descriptive statistics
Multiple regression
Categorical independent variables
Writing up regression results
Interaction models
Sampling distribution & Central Limit Theorem
Hypothesis tests & confidence intervals in regression
Model specification and fit / statistical vs causal inference
Logic of Bayesian inference
Logistic regression

Student Effort Hours: 
Student Effort Type Hours
Lectures

15

Computer Aided Lab

12

Autonomous Student Learning

200

Total

227

Approaches to Teaching and Learning:
The sessions consists of lectures and labs each week. The lectures are complemented with online video lectures on the fundamental aspects of statistical inference, in order to focus the lectures more on interpretation and examples. The lectures will make use of small group exercises to allow students to work directly with example material. In the lab, students will be provided with clear instructions and additional teaching assistance will be available to assist students struggling with the software and tasks. The homework assignments are structured so that they gradually lead up to an overall regression analysis and associated social science paper, putting the technical material of the class in practice. The content of the paper will align with the subject of the overall research project of the student in the PhD. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.  
Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade
Assignment: Homework assignment - preparation for course paper Week 10 Graded No

20

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

30

Assignment: Homework assignment Week 3 Graded No

25

Assignment: Homework assignment Week 7 Graded No

25


Carry forward of passed components
Yes
 
Resit In Terminal Exam
Spring 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 will be provided within 20 days from submission, as per university guidelines. Feedback on Homework 3 in particular will also count as formative assessment in preparation of the course paper.