• Identify and select adequate quantitative approaches to analyze different social research problems.
• Summarize and illustrate the differences between experimental and observational studies employed within business and social sciences.
• Demonstrate critical assessment skills regarding measurement choices for important social, political, and business concepts, such as discrimination, trust, corruption among others.
• Identify and evaluate the assumptions behind the statistical methods introduced in the course.
• Discuss the fundamentals of statistical inference and carry out null hypothesis testing.
• Apply multiple regression analysis to social science and business datasets (country and individual level) and interpret the specific results including coefficients, standard errors, p-values, etc.
• Be able to use statistical software (R) to generate results that are used as empirical supporting information to meet the preceding objectives and offer substantive interpretation of software output.

The course provides students with theoretical and applied knowledge about statistical and quantitative methods in business and social science at an introductory and intermediate level. On completion of the course, the student should be able to understand the methods introduced in the course and apply them to specific research problems. The course introduces students to causal inference, basic statistical analysis, and multiple regression analysis. The course consists of a mix of lectures and exercises. Throughout the course we will follow an applied, hands-on approach relying on software implementation in parallel of the theoretical work.

Blended learning: A short video walk-through of the software solutions will be made available prior to some exercises. Students can review those and compare with their own solutions. In such a manner, the exercises can focus on interpretation, discussion, and additional extensions.

Please note: This course uses an applied approach and thus we rely throughout the whole course and in the examination on software use (R). Beyond the software examples in the applied textbook, students will receive a thorough introduction to the software in the early stages of the course, early stage exercise supporting materials, and have the possibility to work on parallel homework tasks.

In relation to Nordic Nine

In business, public policy and the non-governmental sector, the demand for data-based decision-making is stronger than ever. In this class, students learn to meet this demand through a number of Nordic Nine (NN) capabilities. At the most fundamental level, we will learn how to work with data (NN2) in a way that will help enable us to understand the societal context of business (NN1) and, ultimately, resolve the challenges facing societies (NN3). Students will practice to think critically and analytically about social science research (NN6). Moreover, students work collaboratively with data analytics, collectively teaching and learning from each other (NN8).

Kosuke, Imai (2017/2018). Quantitative Social Science: An Introduction. Princeton: Princeton University Press.