About MDRC
Htet is a statistical programmer with experience in using the R programming language for interactive dashboard development, statistical package development, data preprocessing, data deduplication, and predictive modeling. Since joining MDRC in the fall of 2018, he has worked primarily on a multiple hypothesis project, creating a user-friendly R Shiny Dashboard and package for researchers. He is also working on a predictive analytics tool for the Center for Data Insights using Github, R Markdowns, and Wrapper functions. This tool will be available to analysts at MDRC and at partner organizations. Before joining MDRC, he was a Data Science for Public Good Fellow at the Social Decision Analytics Lab in Ballston, MD. There, he worked on data deduplication and synthetic data creation, among other topics.
-
MDRC Publications
MethodologyJune, 2022Multiple testing procedures reduce the likelihood of false positive findings, but can also reduce the probability of detecting true effects. This post introduces two open-source software tools from the Power Under Multiplicity Project that can help researchers plan analyses for randomized controlled trials using multiple testing procedures.
BriefEvidence from Child First
May, 2022This brief presents results from a proof-of-concept exercise that examined the potential benefits of using predictive analytics to improve service delivery by Child First, a program that provides therapeutic support to families with young children. The information may be useful for other organizations interested in implementing these cutting-edge tools.
-
Other Publications
-
Projects
Richard Hendra, Kristin Porter, Stephanie Rubino, Colleen Sommo, Mark van Dok, Zarni Htet, Camille Préel-Dumas, Jonathan BigelowAcross the social sector, government agencies, educational institutions, and nonprofit organizations are all benefiting from greater access both to more detailed and frequent data and to a variety of options for increased computing power. With data-science tools and guidance in applying them, practitioners can harness multiple sources of data to gain new insights about the individuals they serve, the contexts in which they operate, their staff members, and their program features. When such tools are incorporated into daily operations in a responsible way, they can help practitioners improve their programs and the lives of those they serve.