Basic familiarity with different types of statistical software allows for more flexibility in course selection during university studies. More importantly, basic familiarity with different types of statistical software is an asset to have beyond university in the labor market. The course aims to foster this important skill by providing an applied introduction to the most often used software in social science research. Furthermore, the course aims to provide students with a sure ground for data preparation and to give an overview of basic application of OLS regression. The course also aims to equip students with basic tools to design analysis and interpret quantitative data stemming from surveys and public opinion polls.

The course is built around the replication of a recent study on attitudes toward income inequality. The first session focuses on the design of the study, the issues related to operationalization, and the theoretical underpinnings of OLS regression. The subsequent three sessions will replicate the paper in question each time using different software. The final session is devoted to discussing data visualization techniques (with the R package ggplot2). During the course the merits and weaknesses of the software is also touched upon.

Basic knowledge of one of the software dealt within the course and a basic understanding of linear regression are requisites. Please make yourself familiar with the following text prior to the first session, so we can commence the semester right away: Larsen, Christian A. 2016. “How Three Narratives of Modernity Justify Economic Inequality.” Acta Sociologica 59(2):93–111.

You can access the detailed syllabus here: