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 simple 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 relentlessly in this module, in other modules, and throughout social science. It is thus important that they can identify them correctly in literature they consume and can come up with their own for independent research.
The second purpose is to introduce students to 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. Student 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 its use.
The third purpose is to introduce a series of statistical techniques for the analysis of quantitative data. In this module, we will focus on describing data in various ways, both graphically and using statistical techniques. By the end of the module, we will cover the basics of linear regression.