POL40950 Introduction to Statistics

Academic Year 2023/2024

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. Practical R programming skills for political science research are taught, strengthening analytical robustness through addressing assumptions, estimation, and inference in linear regression.

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Curricular information is subject to change

Learning Outcomes:

Upon course completion, students will be well-prepared to:

- basic understanding of working with R and RStudio
- being able to wrangle, summarise, describe, and visualise statistical data
- basic understanding of statistical inference
- basic understanding of executing and interpreting multiple regression
- preliminary understanding of logistic regression

Indicative Module Content:

The curriculum will cover these key areas:

- Accessing and visualising data
- Simple regression
- Descriptive statistics
- Multiple regression
- Sampling distribution & Central Limit Theorem
- Hypothesis tests & confidence intervals in regression
- Categorical independent variables
- Writing up regression results
- Interaction models
- Logistic regression

Student Effort Hours: 
Student Effort Type Hours
Autonomous Student Learning

200

Lectures

12

Computer Aided Lab

12

Total

224

Approaches to Teaching and Learning:
Each week's sessions include lectures and labs. The lectures center on key aspects of statistical inference and their interpretation, supported by examples. Small group exercises are integrated into lectures for direct engagement with examples. In lab sessions, students receive clear instructions and address problems involving data manipulation, visualization, and statistical techniques using R. The homework assignments are structured so that they gradually lead up to a comprehensive regression analysis and associated social science paper, putting the technical material of the class in practice. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment: Homework assignments Throughout the Trimester n/a Graded No

40

No
Examination: In-class final examination Unspecified No Graded No

30

No
Examination: In-class examination Unspecified No Graded No

30

No

Carry forward of passed components
No
 
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.

– Kosuke Imai. 2017. Quantitative Social Science: An Introduction. Princeton: Princeton University Press.
– Kellstedt, Paul, and Guy Whitten. 2018. The Fundamentals of Political Science Research, 3rd Edition.
- Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables, Volume 7.