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Webinar Details

Non-experimental study designs: The basics and recent advances


Thursday, April 21, 2022
1:00 - 2:00 PM Eastern Time

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Speaker: Elizabeth Stuart, Vice Dean for Education and Bloomberg Professor of American Health in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health

About Elizabeth: ElizabethStuart-Photo-SocialMedia.jpg
Elizabeth A. Stuart, Ph.D. is Vice Dean for Education and Bloomberg Professor of American Health in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health, with joint appointments in the Department of Biostatistics and the Department of Health Policy and Management. She received her Ph.D. in statistics in 2004 from Harvard University and is a Fellow of the American Statistical Association (ASA) and the American Association for the Advancement of Science (AAAS). Dr. Stuart has extensive experience in methods for estimating causal effects and dealing with the complications of missing data in experimental and non-experimental studies, particularly as applied to mental health, public policy, and education. She has received research funding for her work from the National Science Foundation, the Institute of Education Sciences, the WT Grant Foundation, and the National Institutes of Health.  

Description: Randomized experiments are not always feasible for assessing the effectiveness of programs and policies.  Strong and rigorous non-experimental study designs are crucial to ensure that the results obtained are accurate and reliable.   Luckily, a set of strong designs exist, and recent advances have helped researchers understand the strengths and limitations of different designs, including best practices for their use. 

This talk will give a broad overview of common non-experimental study designs, touching briefly on instrumental variables, regression discontinuity, difference-in-differences, and strong comparison group designs.   More focus will be given to propensity score methods for ensuring comparable intervention and comparison groups, including discussion of recent advances that can handle data complications such as multilevel settings, measurement error, and missing data.  A motivating example from suicide research will be used to illustrate the ideas.




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