MIT
Edx
date_range Starts on May 21, 2019
event_note End date August 7, 2019
list 12 sequences
assignment Level : Introductive
chat_bubble_outline Language : English
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Key informations

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timer 144 hours in total

About the content

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.

Course Previews:

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.

Please use the this link to access the course preview.

  • Designing a Randomized Evaluation
  • Selecting a sample
  • Measurement of outcomes
  • Collecting and managing your data
  • Research Integrity, Transparency, and Reproducibility

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Prerequisite

Although not required, prior familiarity with basic statistical concepts is recommended.

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Syllabus

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
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Intructors

Rachel Glennerster
Chief Economist, DFID; Executive Director on leave, J-PAL
Massachusetts Institute of Technology

Anja Sautmann
Director of Research, Education, & Training, J-PAL
Massachusetts Institute of Technology

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Content designer

MIT is a world-class educational institution where teaching and research — with relevance to the practical world as a guiding principle — continue to be its primary purpose.

MIT is independent, coeducational, and privately endowed. Its five schools and one college encompass numerous academic departments, divisions and degree-granting programs, as well as interdisciplinary centers, laboratories and programs whose work cuts across traditional departmental boundaries.

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Platform

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