This course is part of the new MITx MicroMasters program in Data, Economics, and Development Policy (DEDP). To enroll in the MicroMasters track or to learn more about this program and how it integrates with MIT’s new blended Master’s degree, please visit the MicroMasters portal.
A randomized evaluation, also known as a field experiment or randomized controlled trial (RCT), is an impact evaluation that uses random assignment to minimize bias, and strengthen our ability to draw causal inferences.
This course will provide step-by-step training on how to design and conduct an RCT. You will learn how to build a well-designed, policy relevant study, including why and when to conduct RCTs.
Additionally, this course will provide insights on how to implement your RCT in the field, including questionnaire design, piloting, quality control, data collection and management. The course will also introduce common research transparency practices.
No previous economics or statistics background is needed.
Our course previews are meant to give prospective learners the opportunity to get a taste of the content and exercises that will be covered in each course. If you are new to these subjects, or eager to refresh your memory, each course preview also includes some available resources. These resources may also be useful to refer to over the course of the semester.
A score of 60% or above in the course previews indicates that you are ready to take the course, while a score below 60% indicates that you should further review the concepts covered before beginning the course.
JPAL 102x – Designing and Running Randomized Evaluations
Week One: Introduction & Randomized Evaluation Design I Week Two: Randomized Evaluation Design II Week Three: Sampling and Sample Size Week Four: Measurement I (Intro, Sensitive Topics, Market Activity) Week Five: Measurement II (Welfare, Health, Networks) Week Six: Measurement III (Behavior, Education, Gender and Empowerment) Week Seven: Data Collection & Management I (Questionnaire Design) Week Eight: Data Collection & Management II (Logistics and Monitoring) Week Nine: Data Collection & Management III (Managing Data) Week Ten: Research Integrity, Transparency, and Reproducibility I Week Eleven: Research Integrity, Transparency, and Reproducibility II