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Policy Analysis Using Interrupted Time Series

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
Статус:
Курс по выбору
Когда читается:
2-й курс, 1 модуль

Course Syllabus

Abstract

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. The course is a Massive Open Online Course delivered at Edx platform (https://www.edx.org/course/policy-analysis-using-interrupted-time-ubcx-itsx-2). Students are required to attend the course and take an oral examination at HSE for completing the course. The examination is taken after completion of the course during examination weeks. The full syllabus is published at the course website. (https://www.edx.org/course/policy-analysis-using-interrupted-time-ubcx-itsx-2). 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. Only for students of Comparative Social Research programme
Learning Objectives

Learning Objectives

  • Provide students 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
Expected Learning Outcomes

Expected Learning Outcomes

  • the strengths and drawbacks of ITS and RD studies
  • Data requirements, setup, and statistical modeling
  • Interpretation of results for non-technical audiences
  • Production of compelling figures
Course Contents

Course Contents

  • Week 2: Course overview
    • 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
  • 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)
Assessment Elements

Assessment Elements

  • Partially blocks (final) grade/grade calculation after attending the MOOC it is required to present the final results (certificate/another document)
  • non-blocking Oral exam
  • Partially blocks (final) grade/grade calculation after attending the MOOC it is required to present the final results (certificate/another document)
  • non-blocking Oral exam
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    After attending the MOOC it is required to present the final results (certificate or another document - C). The document has to be submitted to the study office immediately after completion of the course. After successful completion of the course an examination is undertaken. Prerequisite for attending the examination is submission of the certificate to the study office. The examination grade (E) is the final grade for the course. Final control: oral group exam. The overall course grade (G) (10-point scale) is calculated as a sum of G = C*0.7+ E*0.3
Bibliography

Bibliography

Recommended Core Bibliography

  • Chatfield, C., & Xing, H. (2019). The Analysis of Time Series : An Introduction with R (Vol. Seventh edition). Boca Raton, Florida: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2110461
  • Derryberry, D. R. (2014). Basic Data Analysis for Time Series with R. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=817454

Recommended Additional Bibliography

  • Linden, A. (2018). Using permutation tests to enhance causal inference in interrupted time series analysis. Journal Of Evaluation In Clinical Practice, 24(3), 496–501. https://doi.org/10.1111/jep.12899
  • Linden, A., & Yarnold, P. R. (2018). Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysis. Journal Of Evaluation In Clinical Practice, 24(4), 740–744. https://doi.org/10.1111/jep.12966
  • Palma, W. (2016). Time Series Analysis. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1229817