Policy Analysis Using Interrupted Time Series

Course Feature
  • Cost
    Free
  • Provider
    Edx
  • Certificate
    Paid Certification
  • Language
    English
  • Start Date
    Self paced
  • Learners
    No Information
  • Duration
    10.00
  • Instructor
    /
Next Course
5.0
288 Ratings
This course, Policy Analysis Using Interrupted Time Series, provides a comprehensive introduction to the use of interrupted time series analysis and regression discontinuity designs to evaluate policies with routinely collected data. Students will gain the skills to become a go-to person in their company, government department, or academic department as the technical expert on this topic. Through the course, students will learn how to select and set up data sources, conduct statistical analysis, interpret and present results, and identify potential pitfalls. Examples from the social sciences will be used to illustrate the application of these techniques.
Show All
Course Overview

❗The content presented here is sourced directly from Edx platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [August 31st, 2023]

(Please note this course detail is from the official platform)

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


Show All
Recommended Courses
free practical-time-series-analysis-17049
Practical Time Series Analysis
1.5
Coursera 0 learners
Learn More
This course, Practical Time Series Analysis, is designed for people with some technical competencies who need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of their professional topics. It covers data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. It also looks at mathematical models and graphical representations that provide insights into the data, as well as how to make forecasts. The language used is R, a free implementation of the S language. With video lectures, supporting written materials, quizzes, and discussion with fellow learners, this course is a great way to learn Time Series Analysis.
free the-econometrics-of-time-series-data-17050
The Econometrics of Time Series Data
1.5
Coursera 127 learners
Learn More
This course will provide you with the knowledge and skills to understand and apply econometric models to time series data. You will learn about stationary and non-stationary models, and how to test for non-stationarity. You will also explore the applications of time series models, forecasting exercises, and models for changing volatility. With the help of real financial market data, you will be able to estimate and interpret the empirical autocorrelation function, cointegration equations, and ARCH(p) and GARCH(p,q) models. By the end of this course, you will be able to confidently manipulate and plot data, and perform in-sample and out-of-sample forecasting exercises.
free time-series-forecasting-17051
Time Series Forecasting
3.0
Udacity 185 learners
Learn More
This Time Series Forecasting course provides students with the essential knowledge to build and apply time series forecasting models in a variety of business contexts. Learn the key components of time series data and forecasting models, how to use ETS and ARIMA models to make forecasts, and how to apply your knowledge in Alteryx. This course is part of the Business Analyst Nanodegree Program.
free specialized-models-time-series-and-survival-analysis-17052
Specialized Models: Time Series and Survival Analysis
1.5
Coursera 0 learners
Learn More
This course provides an introduction to specialized models in Machine Learning, such as Time Series Analysis and Survival Analysis. Through hands-on activities, participants will learn best practices for analyzing data with a time component and censored data, as well as verifying assumptions derived from Statistical Learning.
Favorites (0)
Favorites
0 favorite option

You have no favorites

Name delet