Quantitative Methods for Policy Research and Evaluation
The purpose of this course is to help student gain a deeper understanding and appreciation
of quantitative methods, expand their knowledge of quantitative analysis, apply knowledge
to policy-relevant questions, and critically evaluate the claims of those who use quantitative
research to promote specific policies.
Through handling empirical problems when working with real-world data, this course will
cover a wide range of techniques useful to policy research and evaluation, including:
tabular analysis, regression analysis in its various forms (multiple linear regression,
multilevel methods, etc.), regression diagnostics and robust regression, etc.
Apart from learning different techniques, this course will also have a substantial component
detailing the rationale of crucial pillars of statistical wisdom, as well as contemporary
debates over standards and good practice of quantitative analysis. The pillars
aforementioned include: aggregation, information, likelihood, intercomparison, regression,
design, and residual.