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POL40950

Academic Year 2026/2027

Introduction to Statistics (POL40950)

Subject:
Politics
College:
Social Sciences & Law
School:
Politics & Int Relations
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Dr Elisa D'Amico
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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.

About this Module

Learning Outcomes:

Upon completion, students will be able to:

- work with R and RStudio at a basic level;
- wrangle, summarise, describe, and visualise statistical data;
- demonstrate a basic understanding of statistical inference;
- execute and interpret multiple regression at a basic level;
- demonstrate a 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

UNESCO highlights a set of key competencies that support individuals in their development and support society in achieving the UN Sustainable Development Goals (SDGs). UCD has adapted these competencies and is combining them with others to form a general framework of learning competencies. This module will help you develop the competencies below to the levels specified. 
Learning Competency Additional Information Level

Collaboration

 The ability to learn from others; to understand and respect the needs, perspectives and actions of others (empathy); to understand, relate to and be sensitive to others (empathic leadership); to deal with conflicts in a group; and to facilitate collaborative and participatory problem solving. Novice

Critical Thinking

 The ability to question norms, practices and opinions; to reflect on one’s own values, perceptions and actions. Advanced Beginner

Systems Thinking

 The ability to recognize and understand relationships; to analyse complex systems; to think of how systems are embedded within different domains and different scales; and to deal with uncertainty. Novice

Strategic

 The ability to collectively develop and implement innovative actions that further sustainability at the local level and further afield. Not addressed

Integrated Problem Solving

 The overarching ability to apply different problem-solving frameworks to complex sustainability problems and develop viable, inclusive and equitable solution options that promote sustainable development, integrating the competencies in this list. Novice

Self-awareness

 The ability to reflect on one’s own role in the local community and (global) society; to continually evaluate and further motivate one’s actions; and to deal with one’s feelings and desires. Not addressed

Normative

 The ability to understand and reflect on the norms and values that underlie one’s actions; and to negotiate values, principles, goals, and targets, in a context of conflicts of interest and trade-offs, uncertain knowledge and contradictions. Not addressed

Anticipatory

 The ability to understand and evaluate multiple scenarios for the future – possible, probable and desirable; to create one’s own visions for the future; to apply the precautionary principle; to assess the consequences of actions; and to deal with risks and changes. Not addressed

Digital Literacy and Judgement

 The ability to access, evaluate, create and communicate information in digital environments; to engage critically, ethically and responsibly with digital technologies and digital information; to understand their opportunities, limitations, risks and impact on individual’s digital identities; and to exercise informed judgement in digital participation and decision-making. Advanced Beginner

AI Literacy & Agency

 The ability to understand, critically evaluate and responsibly engage with artificial intelligence; to recognise how AI systems are shaped by human values; to assess their ethical, social and environmental implications; and to exercise human judgement, agency and accountability in AI-related contexts. Advanced Beginner

Wellbeing

 Wellbeing is having the resources and skills to meet life's challenges, including attributes such as personal development skills, resilience, stress management, strengths, lifestyle skills, nutrition, physical activity, sleep, alcohol/substance use, academic skills, time management, goal setting, interpersonal skills, group work, communication. Novice

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


Goal Name Description Coverage

Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all 5

Take urgent action to combat climate change and its impacts 3

Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels 3
 

Student Effort Hours:
Student Effort Type Hours
Lectures

12
Computer Aided Lab

12
Autonomous Student Learning

200
Total

224

Approaches to Teaching and Learning:
This module is delivered through short pre-recorded video lessons that students watch before class, combined with weekly in-person workshops. This reserves class time for active, hands-on work rather than lecturing. Key teaching and learning approaches include: active and task-based learning; computer lab work in R; enquiry and problem-based learning; peer and group work (students work in small support pods and give structured peer feedback); and a student presentation. Assessment is built around a staged research project: three assignments gradually lead students to a complete multiple regression analysis and a written social science paper, putting the technical material into practice. A short pre-class check and an opening question-and-answer session each week keep teaching responsive to where students are.

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 In Module Component Repeat Offered
Individual Project: Individual ~3,000-word research paper: a quantitative analysis using regression on a chosen dataset (question, theory, data, results, conclusion). Submitted as PDF. Week 14 Graded No
45
 No
Individual Project: Five-minute recorded video presenting and interpreting the student's own analysis; used in a peer-feedback workshop. Week 12 Graded No
10
 No
Participation in Learning Activities: Ongoing engagement: weekly pre-class check quizzes on the video lessons (including a skills check), Week 1 readiness tasks, and a short asynchronous lab. Mostly auto-graded. Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Graded No
10
 No
Assignment(Including Essay): Three staged R data-analysis assignments, each building a section of the research paper: (1) question, data, descriptives; (2) regression analysis; (3) full draft. Week 5, Week 9, Week 11 Graded No
35
 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.

Imai, Kosuke. 2017. Quantitative Social Science: An Introduction. Princeton UniversityPress. (QSS)

Kellstedt, Paul, and Guy Whitten. 2018. The Fundamentals of Political Science Research, 3rd edition. Cambridge University Press. (K&W)

Ismay, Chester, and Albert Y. Kim. Statistical Inference via Data Science: A ModernDive into R and the tidyverse. Free online:https://moderndive.com/ (ModernDive, recommended for R)
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