A comprehensive course on conducting and presenting policy evaluations using interrupted time series analysis. Interrupted time series analysis and regression discontinuity designs are two of the most rigorous ways to evaluate policies with routinely collected data. ITSx comprehensively introduces analysts to interrupted time series analysis (ITS) and regression discontinuity designs (RD) from start to finish, including definition of an appropriate research question, selection and setup of data sources, statistical analysis, interpretation and presentation, and identification of potential pitfalls.
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At the conclusion of the course, students will have all the tools necessary to propose, conduct and correctly interpret an analysis using ITS and RD approaches. They will also develop a real-life research proposal that could form the basis of future research. This will help them position themselves as a go-to person within their company, government department, or academic department as the technical expert on this topic.
ITS and RD designs avoid many of the pitfalls associated with other techniques. As a result of their analytic strength, the use of ITS and RD approaches has been rapidly increasing over the past decade. These studies have cut across the social sciences, including:
- Studying the effect of traffic speed zones on mortality
- Quantifying the impact of incentive payments to workers on productivity
- Assessing whether alcohol policies reduce suicide
- Measuring the impact of incentive payments to physicians on quality of care
- Determining whether the use of HPV vaccination influences adolescent sexual behavior
What you'll learn:
- The strengths and drawbacks of ITS and RD studies
- Data requirements, setup, and statistical modelling
- Interpretation of results for non-technical audiences
- Production of compelling figures
- How to Produce an ITS/RD proposal for your own area of study
Course Syllabus
Week 1: Course overview
Introduction to ITS and RD designs
Assumptions and potential biases
Data sources and requirements
Example studies
An introduction to R (optional)
Week 2: Single series ITS
Data setup and adding variables
Model selection
Addressing autocorrelation
Graphical presentation
Week 3: ITS with a control group
Data setup
Adding a control to the model
Graphical presentation
Predicting policy impacts
Week 4: Extensions
Advanced modeling issues in ITS and RD
Non-linear Trends · Differencing
“Wild” Points and Transition periods
Adding a Second Intervention
Week 5: Regression Discontinuities and Wrap-up
Regression Discontinuities
Any Remaining Questions
University of British
