Skip to 0 minutes and 0 seconds Hi there! I’m Professor Ted Miguel and I’ll be your Lead Instructor for this course. Welcome to the course! I’m a Professor of Economics at the University of California, Berkeley in the U.S. I’m also the Faculty Director of the Berkeley Initiative for Transparency in the Social Sciences, or BITSS, as well as the Center for Effective Global Action. My own research focuses on international development economics, with a regional emphasis on Africa. In my work in Kenya and Sierra Leone, I’ve seen first-hand how important it is to get the research right. The stakes are high with economics and other social science research.
Skip to 0 minutes and 39 seconds The evidence we generate is used to shape public policy, public health decisions, and the funding choices of non-profit organizations, NGOs, and households. But with all of the claims that are out there, how can we know whether a body of scientific evidence is credible? That’s where research transparency, and this course, come in. Researchers across many different fields and academic disciplines have recently developed ways to keep science scientific. To make sure that results are generated in ways that are largely free of bias, to make them more reproducible, and ultimately more credible. We’ll start by learning about some of the major problems and pitfalls facing social science research today, problems including publication bias and selective reporting of results.
Skip to 1 minute and 29 seconds Then we’ll move on to the solutions to those problems, through tools including pre-analysis plans, study registries, open data, and more. When it comes to social science research, the future is open. It needs to be open. That’s why I decided to teach this course. To share with you what I and others scholars working in this area have learned about how to improve the quality of our research. So we can make our research live up to our scientific ideals and to the growing needs of our societies. Thank you, and welcome to the course!
Let's get started!
Hi, I’m Professor Ted Miguel, your lead educator for this course. Over the next five weeks, you will learn about the major transparency and reproducibility issues involved in doing social science research, as well as useful tools that have emerged from a variety of social science disciplines.
I’m joined by BITSS Postdoc Fernando Hoces de la Guardia who will guide you through the quizzes and interactive steps in each week of the course so that you can get comfortable with some of the tools and platforms we introduce. You can find out more about Fernando by clicking on his profile, or follow him to see his comments throughout the course.
In Week 1, we’ll start by discussing the scientific ethos - widely held values about how science should be done and how they contribute to what we hope is an accurate understanding of our world. We’ll also examine the extent to which scientists agree with this ethos and how well they actually uphold it.
In Week 2, we’ll identify major issues related to transparency and try to understand how they’ve come to be. We’ll focus especially on publication bias and the “file drawer problem.”
In Week 3, we’ll begin exploring different tools that researchers can use to make their research more transparent and reproducible, including pre-registration and pre-analysis plans the Open Science Framework, and meta-analysis.
In Week 4, we’ll discuss replication - the foundation of evidence - and how sharing data makes replication possible.
Finally, in Week 5, we’ll discuss the benefits of transparent data visualization and how we, as a scientific community, can move forward in changing the norms of research for the better.
Now that we’ve met, please introduce yourself to us and the other enrolled learners! Let us know what made you decide to join this course. What’s your background? What piqued your interest in research transparency and reproducibility? What are you hoping to get out of this course?
As in most courses, you may have questions regarding the material. Feel free to comment with your questions. We’d also suggest that you “Like” other posted questions similar to your own so that we can respond to them most efficiently.
We hope you enjoy the course! Happy learning!
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