• Create and interpret data visualizations in R.
• Demonstrate critical assessment skills regarding measurement choices for important social, political, and business concepts, such as discrimination, trust, corruption among others.
• Use R to generate and interpret basic univariate and bivariate statistical data summaries.
• Estimate and interpret multiple regression models.
• Use R to estimate and interpret advanced quantitative methods to answer important questions in the social sciences and study of business, in particular estimating causal effects
• Discuss the fundamentals of statistical inference and carry out null hypothesis testing.
• Apply bivariate regression analysis and interpret the specific results including coefficients, standard errors, p-values, etc.
• Understand the importance of and inherent difficulties in estimating causal effects.
• Understand and discuss structure and components of quality research designs.
• Evaluate different research design strategies and select adequate quantitative approaches for estimating causal effect for a given question.
• Summarize and illustrate the differences between experimental and observational studies employed within business and social sciences.

In the realms of business, public policy, and the non-governmental sector, there is an unprecedented emphasis on the need for "evidence." Consequently, the current labor market exhibits a strong demand for graduates proficient in data analysis, capable of designing inquiries, and adept at drawing conclusions from quantitative data.

This course covers both the theoretical background and application of introductory, intermediate, and more advanced statistical and quantitative methods in business and social science. Upon completion of the course, students should have a theoretical understanding of the methods introduced and the ability to apply them to specific research problems. The course will be taught over two semesters. In the first semester, we will discuss concepts related to research design, measurement, introductory data analysis (incl. data visualization and descriptive statistics). We will then transition to multiple regression and the estimation of statistical uncertainty. The second semester starts with a more thorough coverage of multiple regression analysis. Next, the course will introduce more advanced statistical methods and methods of causal inference, for example, difference-in-differences estimation and regression discontinuity designs. The course consists of both lectures and applied exercises, in which we will actively work on implementing the methods covered in lectures. 

Please note: This course uses an applied approach and thus we will use software (R) to work with data throughout the whole course and in the examination. Beyond the software examples in the applied textbooks, students will receive a refresher on the software in the early stages of the course.

In relation to Nordic Nine

Throughout the course, students will develop important competencies in several of the Nordic Nine capabilities. Understanding and working with data is becoming ever more important in our changing world. In this class, students learn important skills to empirically evaluate causal statements and analyze quantitative data (NN2). Students will practice thinking critically and analytically to answer important societal challenges (NN6). Moreover, students work collaboratively in data analytics tasks, collectively teaching and learning from each other (NN8).

• Methodology

• Data collection
• Analysis
• Discussion, critical reflection, modelling