Module Directory

This module offers an introduction to the theory and practice of quantitative data analysis techniques. The goals are to provide students with the skills that are necessary to: 1) read, understand, and evaluate the academic literature, and 2) design and carry out studies that employ these techniques for testing substantive theories. The module serves three principal purposes.

The first is to ground students in the language of social science research: research questions, independent and dependent variables, hypotheses, causality, etc. Students will come across these terms relentlesssly in this module, in other modules, and throughout social science. It is thus important that you are able to use them readily and correctly.
The second purpose is to familiarise yourself with the types of data and the practice of data analysis in the social sciences. Students are introduced to a range of sources from which they can access quantitative data. Students will also be introduced to the programming language R, which is widely used by academics and practitioners for the analysis of quantitative data. I will assume that students have no prior experience with any of this software, and so students will be given a full introduction to their use.
The third purpose is to introduce a series of statistical techniques for the analysis of quantitative data.

The aims of this module are:

• To demonstrate the role of quantitative methods in answering research questions


• To ground students in the language of quantitative research


• To equip students with knowledge of a range of statistical techniques


• To develop students' ability to interpret statistical information in substantive terms


• To develop students' ability to comment critically on their own and others' analyses


• To provide training in the use of the R program


• To show students how to build their own and to locate existing datasets

By the end of the module, students will be expected to be able to:

1. Read, understand, and evaluate quantitative analyses published in the leading journals.


2. Assess quantitative measurement in terms of reliability and validity.


3. Understand the correct statistical method for particular research questions and variables.


4. Use a range of statistical methods, from calculating means to various regression models.


5. Analyse quantitative data with R.


6. Build their own datasets, and to download and use existing datasets.

Students will require, use, and develop the following key skills:

1. Critical thinking: students will consider the pros and cons of quantitative analysis and be able to evaluate their own and others'; research designs, measures and analyses.


2. Transfer of ideas: students will be helped to follow and assess quantitative research in other modules: political behaviour, comparative politics, international relations, etc.


3. Use of statistical programs to analyse data: students will learn in lab sessions how to use the statistical programs R to analyse quantitative data.


4. Improving independent learning and performance: Students will learn how they might think about and to address their own research topics in a quantitative framework.


5. Communication and interaction: Both the lectures and lab sessions involve not only questions from the lecturer but also group discussions.


6. Writing: Students learn how to report on and assess results of their quantitative analyses.

Most weeks, there are also practice exercises with answer keys (sometimes these are referred to as Formative Assessments). These exercises are not required (and are not part of your final grade). The practice exercises (and the answer keys) provide examples of how the HWs should be completed and are very useful guides for helping successfully complete the homework assignments.

This module will be delivered via:

• 2 hours per week