This doctoral-level core course provides a broad overview of advanced research methods to develop students’ essential skills for conducting rigorous empirical research. This course begins with the fundamentals of developing a theory-driven, empirical analysis and advances to cover more complex methods of statistical analysis. Throughout, the course addresses the limits or threats to causal inference and internal and external validity. This course is meant to develop students’ ability to apply advanced empirical skills to public policy discussions and decision-making. Students are expected to use a statistical processing program, such as Stata or SAS.
The specific learning objectives of this course are for students to:
1. Develop and structure a solid research question that advances the extant literature in the student's chosen field and addresses a pertinent policy issue;
2. Motivate their analysis through a strong theoretical framework that draws on multiple disciplines;
3. Understand and identify issues related to sampling and research design;
4. Be proficient with a variety of research techniques (both quantitative and qualitative) and be able to determine which technique best addresses a given research question;
5. Demonstrate expertise in managing issues of selection, endogeneity, reverse causality, omitted variable bias, generalizability, missing data, and other pitfalls of quantitative and qualitative research;
6. Differentiate between significant and meaningful results and clarify the policy relevance and implications of their findings;
7. Learn the structure of a peer-reviewed publication (that applies across disciplines) and write a manuscript suitable for a peer-reviewed journal;
8. Present their findings orally in a professional manner;
9. Provide meaningful critiques to peer work;
10. Understand and succeed in the peer-reviewed publication process.