Week 1: Introduction

POP88162 Introduction to Quantitative Research Methods

Tom Paskhalis

Department of Political Science, Trinity College Dublin

Overview

Assistant Professor in Political Science and Data Science, Trinity College Dublin
My research:
- Political communication, social media
- Computational methods, text analysis
Contact
- tom.paskhalis@tcd.ie
- tom.paskhal.is

Elena Karagianni

PhD student, Trinity College Dublin
Research interests:
- Environmental politics, democratic accountability, voting behaviour
- Computational text analysis, quasi-experiments
Contact
- karagiae@tcd.ie

Juliane Wesselmann

PhD student, Trinity College Dublin
Research interests:
- Political gender stereotypes, voting and candidate preferences, parental socialisation
- Quasi-experiments, mixed-methods
Contact
- wesselmj@tcd.ie

Introductory module - no formal prerequisites
Laptop with Windows/Mac/Linux OS
Software:
- R (version 4+) - statistical programming language
- RStudio - feature-rich R editor

Lectures:
- Tuesday 09:00-11:00 in 2043 Arts Building
Workshops:
- Tuesday 13:00-14:00 in M20 Museum Building
Tutorials:
- Group 1 - Thursday 09:00-11:00 in 1014 Arts Building
- Group 2 - Thursday 16:00-18:00 in AP0.12 Aras an Phiarsaigh
Office Hours:
- Friday 11:00-13:00 in-person or online
+ Readings
+ Assignments

Core books:
- Alan Agresti. 2018. Statistical Methods for the Social Sciences. 5th ed
- Ethan Bueno de Mesquita and Anthony Fowler. 2021. Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis
Very good additional texts:
- Andrew Gelman, Jennifer Hill and Aki Vehtari. 2020. Regression and Other Stories
- Kosuke Imai. 2017. Quantitative Social Science: An Introduction
More choices and week-by-week reading list in the syllabus.

Participation (10%)
- Tutorial attendance, RQ presentation
3 R assignments (5% each)
Research design (15%)
- Approximately 1-2 pages and no more than 500 words
Research paper (60%)
- Must set up, perform, and interpret at least one test of statistical significance
- Approximately 10 pages and no more than 4,000 words
- Due by 23:59 Tuesday, 21 April 2026
Submission via Blackboard

All submitted coursework must be individual and original.
Reference any literature you use in the correct manner
- E.g. check Chicago Manual of Style or APSA Style Guide (or their derivatives) for citing correctly.
Check College’s Academic Integrity Policy:
https://libguides.tcd.ie/academic-integrity/

Permitted uses: Explaining unclear concepts and bits of R code, brainstorming, and proofreading.
No private, personal and copyrighted data (including any part of this module’s materials) should be submitted to any generative AI service.
Prohibited uses: Using generative AI to create solutions to assignments is strictly prohibited.
Prohibited uses constitute academic misconduct and will be dealt with under the College’s Academic Integrity Policy.

If you are carrying out research on human subjects (interviews, surveys, experiments, etc.),
You must first receive an ethical approval from TCD.
More information is available here:
https://www.tcd.ie/ssp/research/ethics/

Week	Date	Lecture Topic	Workshop Topic	Tutorial Topic	Assignment Due
1	20 January	Introduction	R Overview	Getting Started with R
2	27 January	Descriptive Statistics	Data Structures	Data & Variables
3	3 February	Probability Theory	Probability Distributions	Distributions & Sampling	1 R Assignment
4	10 February	Hypothesis Testing	Data Frames	Data Frames & Plotting
5	17 February	Analysis of Proportions & Means	Factor Variables	Cross Tabulation
6	24 February	Correlation	Visualisations	Correlation	2 R Assignment
7	3 March	-	-	-	-
8	10 March	Linear Regression I	RQ Presentations I	Linear Regression I
9	17 March	Linear Regression II	RQ Presentations II	Linear Regression II
10	24 March	Linear Regression III	RQ Presentations III	Linear Regression III	3 R Assignment
11	31 March	Causation	RQ Presentations IV	Causation
12	7 April	Logistic Regression	RQ Presentations V	Logistic Regression	Research Design