Introduction to Quantitative Research Methods (MSc)

This is for the MSc programme in Comparative European Politics 2008-2009 (Spring 2010 semester), Module 6: Introduction to Quantitative Research Methods.

Version: March 31, 2010
Trinity College Dublin, Spring 2010
Wednesdays 14:00-16:00, IIIS Seminar Room

IMPORTANT NOTICES (updated March 31):

  • Week 10 will meet from 10-12 (on Mar 31), instead of the normal time of 14:00-16:00.

Detailed Schedule

  1. Introduction and Fundamentals
    R code from the class is here, along with the data file dail2002.Rdata.
    LFF Ch. 1Crawley Ch. 1.
    Suggested: Benoit, Kenneth. 2005. “How Qualitative Research Really Counts.” Qualitative Methods Newsletter (Spring).

    1. Download and install R on your computer. A very simple set of instructions can be found at
    2. Do the following problems from LFF pp24-29: 6, 7, 8, 14, 15
  2. An introduction to data
    R Code for the examples from the slides.
    Readings: LFF Ch. 2Crawley Ch. 2
    Exercises: Homework 2.
  3. Central Tendency
    R Code for the examples from the slides.
    Readings: LFF Ch. 3, Crawley Ch. 3
    Exercises: Homework 3.
  4. Variability
    R Code for the examples from the slides.
    Readings: LFF Ch. 4, Crawley Ch. 4
    Exercises: Homework 4.
  5. Probability and the normal curve
    R Code for the examples from the slides.
    Readings: LFF Ch. 5, Crawley Ch. 5
    Exercises: None this week.
  6. Samples and populations
    R Code for the examples from the slides.
    Readings: LFF Ch. 6
    Exercises: Homework 5 consists of doing the following problems from LF&F Chapter 6, using R (including for estimating probabilities and critical values from z and t distributions) and showing your work: 13, 19, 22, 25.
  7. Comparing groups
    R Code for the examples from the slides.
    Readings: LFF Ch. 7, Crawley Ch. 6
    Exercises: Homework 6.
  8. Correlation
    R Code for the examples from the slides.
    Readings: LFF Ch. 10.
    Exercises: See week 9.
  9. Regression analysis I
    R Code for the examples from the slides.
    ReadingsLFF Ch. 11, Crawley Ch. 8.
    Exercises: Homework 7.
  10. Regression analysis II
    Readings: Same as previous week.
    Exercises: None this week.


This module provides students with an introduction to working with quantitative data in comparative political research. It assumes no prior knowledge of statistics, and covers descriptive and inferential statistics up to the level of multivariate data analysis. The main topics covered are: least-squares regression; logistic regression; among others. Class exercises and homework will be carried out using STATA or the freely available R statistical package.

The objectives and learning outcomes from this course are:

  • To introduce students to a range of descriptive and inferential quantitative methods for comparative political research;
  • For each method, to identify its suitability and its purpose;
  • To conduct and interpret simple analysis using the techniques discussed;
  • To be able to interpret basic quantitative results from written or published work.

The course assumes no prerequisites, although the more background maths you remember, the better. All of the techniques may be implemented in any statistical package, and you are welcome to use your package of choice for the exercises, although the class will be taught using the freely available R package. This will be done at a very basic level however and no prior knowledge of R is required for this course.


This course uses two texts that you should purchase. Both have been ordered at Hodges and Figgis bookstore, but also I have provided links below to the pages for purchasing them. The first is a general text for introductory statistics for the social sciences, the second is another text for introductory statistics but also aimed specifically for users of the R statistical package. These are:

The Crawley text should contain all you will need to know about R for this course, but if you would like to delve deeper or simply have an alternative perspective on this incredibly powerful (and free!) statistical package, consider the following resources:


Meetings. Classes will meet nine weeks for one session per week, on Wednesdays from 14-16:00. The class will be mostly lectures and presentations by me, with some at the end devoted to practical data analysis relating to weekly problem sets. For this reason I encourage students to bring their laptop computers to class, although this is not an integral requirement. (Since electrical outlets are limited in the classroom, please have your batteries charged ahead of time!)

Computer Software. The statistical package R will be used for all exercises, although if you prefer to use other software that should be fine as well.

Grading. Grading will be based on:

  1. Problem sets: 50%. Problem sets will be handed out each Wednesday and must be submitted to the class page at before class the following Wednesday. Each problem set will consist of a number of problems combining computer analysis with interpretation and analytical problems. Computer output, when supplied, should include both the commands used as well as results. Computer results should be indicated clearly. Problem set answers must be neatly organized and clearly presented, and must be submitted as a single file (this can be Word or pdf). If you have any problems that you wrote by hand, then you can use our department’s excellent scanner to convert them easily to pdf.
  2. Final exam: 50%. The exam will take place in the week following the course. It will most likely be a take-home exam.

Because of St. Patrick’s Day, there will be no class on March 17. Instead we will hold class that week on March 18, same time and place.