In this course, students are introduced to state-of-the-art best practice in the statistical testing of interactive hypotheses. The applied statistical teaching is done with R and students are recommended to have basic knowledge of R programming. Students are encouraged to bring their own research questions to the course and engage with potential interactive hypotheses in their field during the practical parts of the course.

Through a combination of lectures, practical exercises, and case studies, you will learn how to develop and validate interactive hypotheses in quantitative social science research using R. You will explore various statistical tools such as kernel or bin plots to assess the validity of linear interactive models. We will also touch upon more advanced machine-learning-based models, such as the Kernel Regularized Least Squares estimator, to uncover the true interactive patterns in your data.

By the end of the course, you will have a comprehensive toolkit of advanced statistical approaches to tackle complex research questions in the social sciences. You will also gain the ability to critically evaluate existing literature and design rigorous empirical studies that effectively capture and analyze interactive phenomena.

Lecturers:

Dominik Schraff, Associate Professor, Department of Politics and Society, Aalborg University

ECTS:

2

Number of seats:

15

More information

Please find more information and registration link in PhD Moodle