Unveiling Complexity: Statistical Approaches to Interactive Hypothesis Testing in the Social Sciences (2024)

Welcome to Unveiling Complexity: Statistical Approaches to Interactive Hypothesis Testing in the Social Sciences

 Course description

"Unveiling Complexity: Statistical Approaches to Interactive Hypothesis Testing in the Social Sciences" is an applied course designed for PhD students in quantitative social sciences who wish to deepen their understanding and skills in testing conditional hypotheses (e.g., also called statistical moderation). Interactive hypotheses are abundant in social science research, as we often want to understand the conditions under which certain relationship exist.

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

Time:
25-26 March 2024

Place:
Aalborg University, Campus Aalborg

Number of seats:
15. We will contact you after registration deadline for letting you know whether your admission has been accepted in the course or you are on the waiting list.

Paper requirements

The final paper is a post-reflection paper that should be send to the teacher 2 weeks after the course. The paper should present the whole workflow of (1) specifying an interactive (social science) hypothesis, (2) acquiring and preparing the data to test it, (3) specifying the statistical model to test the hypotheses, and (4) assess the statistical validity of the findings on the interactive relationship.

The emphasis should lie on the last point (4), so students are encouraged to use readily available data, e.g. replicating existing studies proposing interactive relationships.

Participation fee:
The course is free of charge

Deadlines:
Participation deadline: 1st February 2024
Post paper deadline: 3rd April 2024

Important information concerning PhD courses: We have over some time experienced problems with no-show for our courses. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 2,000 DKK for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up.

If you have questions you are welcome to contact PhD programme secretary Marianne Høgsbro inst.dps.phd@dps.aau.dk