基于中断时间序列的策略分析

Policy Analysis Using Interrupted Time Series

A comprehensive course on conducting and presenting policy evaluations using interrupted time series analysis.

1345 次查看
不列颠哥伦比亚大学
edX
  • 完成时间大约为 5
  • 中级
  • 英语
注:因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

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

课程概况

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 selection and setup of data sources, statistical analysis, interpretation and presentation, and identification of potential pitfalls.

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. 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

课程大纲

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

预备知识

Participants should have an understanding of linear regression, and familiarity with data handling in a major statistical package (R, SAS, SPSS, STATA, etc.). Course content is taught in the R statistical package, so familiarity with R / RStudio will be an asset.

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