PhD Courses in Denmark

Welcome to Explanatory modeling of observational quantitative data: Causal graphs, interactions, and research design choices

Description:

"Explanatory modeling of observational quantitative data " is an applied course designed for PhD students in quantitative social sciences who wish to deepen their understanding and skills in working with observational quantitative data (e.g., non-experimental data). Causality is hard to establish with observational data, yet a theory-testing approach still forces researchers to formulate causal theoretical models to motivate their statistical choices. This course works with this tension in quantitative social science research and engages with applied recommendations for best practice.

The first day of the course introduces directed acyclic graphs (DAGs), discusses the need for causal reasoning for regression-based modeling, and offers training for developing DAGs.

The second day is dedicated to further complexities emerging from interactive and non-linear hypotheses, as well as mediation relationships, introducing statistical packages to ensure valid inferences for these statistical scenarios.

The third day introduces approaches for ‘causal’ research designs to observational data, focusing on matching estimators and different regression-based approaches (e.g., instrumental variables). We compare these to regression-based approaches and discuss strengths and weaknesses. We end the third day with a Lab session that provides the chance to apply some of the content to your own research.

The course expects a basic familiarity with quantitative methods (e.g., linear regression). The applied statistical teaching is done with R, and students are recommended to have basic knowledge of R programming. Most of the course content, however, can also be followed with STATA (e.g., similar/same packages in STATA). Students are encouraged to bring their own research questions to the course and engage in potential inferential/modeling challenges in their field during the practical parts of the course.

Through a combination of lectures, practical exercises, and case studies, students will engage with current best practices in observational quantitative social science research. You will learn about the use of directed acyclic graphs. You will explore various statistical tools, such as kernel or bin plots of linear interactive relationships, as well as approaches for causal mediation analysis. Finally, you will be enabled to make informed choices of whether matching or regression-based approaches might be useful tools for your analysis.

By the end of the course, you will have a comprehensive toolkit of advanced statistical approaches to tackle complex research questions in quantitative social sciences. You will also gain the ability to critically evaluate existing literature and design rigorous empirical studies using observational data.

Teaching methods:

• Lectures, practical exercises, and case studies
• Applied programming with R (Lab Sessions)
• Illustration of the statistical approaches with real world cases and data
• Opportunities to work with your own data during the course

For additional information, updates, and registration, please refer to AAU PhD Moodle via the link provided on the right side of this page.