MARC SHOTLAND: Greetings from
Cambridge, Massachusetts. My name is Marc Shotland, and I lead the
training team at the Abdul Latif Jameel Poverty Action Lab. J-PAL was established in
2003 as a research center in the economics department of MIT. But more importantly, there’s a network
of social science faculty researchers at universities all over the globe. And together, they have conducted
about 1,000 randomized evaluations in 70 countries. Beyond running randomized
evaluations, we also have a team at J-PAL that
trains others on how to do so. And that’s the team I lead. Since 2005, we’ve been training others
all over the globe on how to design their own randomized evaluations. However, designing randomized
evaluations is only half of the challenge. The other half, the expensive
half, the time-consuming half, and probably the most difficult half,
is running randomized evaluations in the field. That’s why since 2008,
along with IPA, we’ve been formally training our staff
on how to implement field trials. We always had ambitions
to make our staff training available to existing
and aspiring researchers outside of our network. Now, with J-PAL 102X, we are
finally able to do so, and reach a massive audience
through the edX platform. So this course, J-PAL 102X, combines
the material from J-PAL 101X– on how to design randomized valuations–
with material we’ve been teaching thousands of our own research staff. Specifically, it is
comprised of five sections. First, designing randomized
evaluations, where we tackle the question, what is evaluation? What is the purpose of
evaluation in general? And what is the theory behind
our evaluation question? Second, selecting a sample. How do we figure out who will
participate in the study? How do we find them? And how large should our study be? Third, measurement. Determining what data
we will collect and how. Fourth, data collection and management. We’ll discuss here how to conduct high
quality data and the logistics of data collection. Will we use paper surveys or digital? How do we train our survey team? How will they travel to
households they intend to survey? How do we manage that inflow of data? And how do we ensure the
security of our data? And fifth, and finally,
research integrity, transparency and reproducibility. Here we will talk about what one must
do to make sure our research meets the highest level of ethical standards. How the scientific community
expects research to be conducted. And practical tips for
overall project management. This course will be comprised of lecture
sequences, case studies, exercises, and quizzes, and an exam at the end. After taking this course,
you should be equipped with everything you need
to know to design and run your own randomized evaluation. The rest you will
learn by doing, gaining your own experience and insights. Until then, we look forward
to seeing you in class.

Designing and Running Randomized Evaluations | MITx on edX | Course About Video
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3 thoughts on “Designing and Running Randomized Evaluations | MITx on edX | Course About Video

  • December 7, 2016 at 4:51 pm

    Great! I love Data Science and EdX platform

  • December 8, 2016 at 3:00 am

    Dear awesome people, it might be helpful to include a link in the description.

  • June 19, 2018 at 6:03 pm



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