Module Overview

POP88162 Introduction to Quantitative Research Methods

Author

Tom Paskhalis, Department of Political Science

Syllabus Blackboard

Introduction Introduction (pdf)

The goal of this module is to introduce students to the practice of data analysis at an elementary postgraduate level. More than ever before, political science research relies upon data — information about people, firms, nations, etc. that can be compiled, compared, and analyzed en masse. Political scientists analyze data with an eye to explaining the social world. Not all political scientists perform quantitative data analysis, but as empirical quantitative studies provide so much of our knowledge about politics and society, every student of the subject must now know at least a little about how it works.

Instructors

Module Meetings

Week Date Lecture Topic Workshop Topic Tutorial Topic Assignment Due
1 21 January Introduction R Overview Getting Started with R
2 28 January Descriptive Statistics Data Structures Data & Variables
3 4 February Probability Theory Probability Distributions Distributions & Sampling 1 R Assignment
4 11 February Hypothesis Testing Data Frames Data Frames & Plotting
5 18 February Analysis of Proportions & Means Factor Variables Cross Tabulation
6 25 February Correlation Visualisations Correlation 2 R Assignment
7 4 March - - - -
8 11 March Linear Regression I RQ Presentations I Linear Regression I
9 18 March Linear Regression II RQ Presentations II Linear Regression II
10 25 March Linear Regression III RQ Presentations III Linear Regression III 3 R Assignment
11 1 April Causation RQ Presentations IV Causation
12 8 April Logistic Regression RQ Presentations V Logistic Regression Research Design

Prerequisites

This is an introductory class and no prior experience with statistics or programming is required.

Software

In this class we will use R to work with data. R is free, open-source and interactive programming language for statical analysis. RStudio is a versatile editor for working with R code and data that provides a more intuitive interface to many features of the language.

Both R and RStudio are widely available for all major operating systems (Windows, Mac OS, Linux). You should install them on your personal computer prior to attending tutorials. Use these links to download the installation files:

Materials

We will primarily be relying on the following core texts for this module:

  • Alan Agresti. 2018. Statistical Methods for the Social Sciences. 5th ed. London: Pearson
  • Ethan Bueno de Mesquita and Anthony Fowler. 2021. Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis. Princeton, NJ: Princeton University Press

Some very good additional texts:

  • Andrew Gelman, Jennifer Hill and Aki Vehtari. 2020. Regression and Other Stories. Cambridge: Cambridge University Press
  • Kosuke Imai. 2017. Quantitative Social Science: An Introduction. Princeton, NJ: Princeton University Press

For more thorough treatment of causal inference and econometric models broadly refer to:

  • Joshua D. Angrist and Jorn-Steffen Pischke. 2015. Mastering ’Metrics The Path from Cause to Effect. Princeton, NJ: Princeton University Press
  • James H. Stock and Mark W. Watson. 2019. Introduction to Econometrics. 4th ed. London: Pearson
  • Jeffrey M. Wooldridge. 2018. Introductory Econometrics: A Modern Approach. 7th ed. Boston, MA: Cengage

Additional online resources:

See syllabus for further details.

Assessment

  • Participation (10 %)

    • Tutorial attendance, RQ presentation

  • 3 R assignments (5% each)

  • Research design (15%)

    • Approximately 1-2 pages and no more than 500 words (references excluded)

  • Research project (60%)

    • Approximately 10 pages and no more than 5,000 words (all included)

The final research paper will be due by 23:59 Tuesday, 22 April 2025.

See syllabus for further details.

Plagiarism

Plagiarism - defined by the College as the act of presenting the work of others as one’s own work, without acknowledgement — is unacceptable under any circumstances. All submitted coursework must be individual and original (you should not re-use parts of a paper you wrote for another module, for example). You need to reference any literature you use in the correct manner. This is true for use of quotations as well as summarising someone else’s ideas in your own words. When in doubt, consult with the lecturer before you hand in an assignment. Plagiarism is regarded as a major offence that will have serious implications. For more information on the College policy on plagiarism, please see avoiding plagiarism guide. All students must complete the online tutorial on avoiding plagiarism which can be found on this webpage.