The Winter School is organized in two different moments during the day.

Professors Wu and Araral will co-teach the morning course daily. During the afternoon workshop sessions, participants will be divided according to the appropriateness of their topics and the two professors' expertise. 

Morning Lectures on Advanced Analytical Methods in Public Policy Research (10:00-12:00)

This intensive short course is designed to equip PhD students and public policy scholars with a comprehensive understanding of cutting-edge analytical methods and frameworks used in public policy research. The course covers qualitative and quantitative techniques for conducting high-impact policy studies. Furthermore, this cutting-edge course offers PhD students and public policy scholars a unique opportunity to explore how advanced tools like large language models (LLMs) and artificial intelligence (AI) can be integrated into research design. Participants will learn to optimize their research methodologies and understand the trade-offs involved in the process. The course will cover the following key topics:

1. 1. 1. Grounded Theory: Learn how to derive theories from data through systematic qualitative analysis.
      2. Meta-Analysis: Gain skills in synthesizing results from multiple studies to extract policy-relevant conclusions.
      3. Q Methodology: Explore this qualitative and quantitative hybrid approach to studying subjective opinions and attitudes.
      4. Regression Discontinuity: Understand this robust quasi-experimental design for estimating causal effects in public policy.
      5. Game Theory: Delve into strategic decision-making frameworks to analyze interactions among policy actors.
      6. Experimental Methods: Acquire knowledge on designing and conducting experiments to test policy interventions.
      7. Agent-Based Modeling: Discover how to simulate interactions among individuals and institutions in policy contexts using computational models.
      8. Institutional Analysis and Development (IAD) Framework: Apply this interdisciplinary framework to analyze how institutional arrangements affect policy outcomes.
      9. Improving Research Designs with LLMs: Explore how AI-driven tools can refine and enhance research designs, offering new ways to handle complex data.
      10. Trade-offs in Research Design: Understand the strategic decisions involved in balancing various elements of research, such as precision vs. generalizability, control vs. realism, and qualitative vs. quantitative approaches.
      11. Optimizing Research Design: Learn techniques to maximize the efficiency and effectiveness of research methodologies in public policy studies.
      12. Usage of AI in Research Design: Gain hands-on experience in incorporating AI tools to streamline data analysis, hypothesis testing, and design improvement.
      13. Citation Analysis: Develop skills in citation analysis to understand research trends and the impact of scholarly work across disciplines.
      14. Validity Concerns: Dive deep into the various types of validity (internal, external, construct, etc.) and how to mitigate threats to validity in research design.
      15. Interdisciplinary Research in Research Design: Discover how interdisciplinary approaches can enhance the robustness and innovation of public policy research designs.

Afternoon Workshop (13:30-16:30)

The afternoon will be dedicated to a three-hour workshop on research design, during which participants will have the opportunity to present a paper for which they would like to receive constructive feedback from the other participants and the professor. This paper can be a doctoral thesis research project or a project of an article they would like to submit to a journal or present at a conference. The relevant size would be between 5,000 and 8,000 words, and it must include a clear research question, a presentation of methodology, and some key arguments.

In addition to the traditional elements of research design, participants will have hands-on experience using AI tools to enhance their research methods, including data analysis, literature reviews, and generating insights. The discussion will focus on methods of research specifically for public policy, integrating AI to improve policy analysis and evidence-based decision-making.

Participants who do not present a paper will discuss the papers presented with the professors. All participants must read the papers before the workshop to ensure constructive and informed discussions.