|Title:||NYU/Columbia Postdoctoral Training Program|
|Principal Investigator:||Hill, Jennifer||Awardee:||New York University|
|Program:||Postdoctoral Research Training Program in the Education Sciences [Program Details]|
|Award Period:||5 years (9/1/2012 - 8/31/2017)||Award Amount:||$686,999|
Co-Principal Investigators: Scott, Marc; Gelman, Andrew
This training program prepared four researchers to (a) develop statistical methods required to meet future education research challenges and (b) teach other researchers how to use more advanced quantitative methods. Four fellows joined researchers for two years each at the Promotion of Research Involving Innovative Statistical Methodology (PRIISM) Center at New York University Steinhardt School of Culture, Education, and Human Development and the Applied Statistics Center at Columbia University. Fellows had the opportunity to focus on methods to address missing data, tools to address computational limitations for multilevel models, and strategies to address failures in random assignment.
In addition to hands-on, practical research experience, fellows accessed a wide selection of statistics classes and seminar series to build their knowledge base. They developed communication skills to work across fields and communicate advanced statistical ideas to researchers with less formal experience. In addition, fellows received mentoring in career development topics such as preparing high-quality education research grant proposals, teaching, ethics, writing, and applying for jobs.
As of 2020, Dr. Diakow was a psychometrician for the New York City Department of Education, and Dr. Dorie was a staff data scientist for Code for America.
Carpenter, B., Gelman, A., Hoffman, M.D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M.A., Guo, J., Li, P., Riddell, A. (2017). Stan: A Probabilistic Programming Language. Journal of Statistical Software, 76 (1):1-32 Full Text
Dorie, V., Harada, M., Carnegie, N., and Hill, J. (2016). A Flexible, Interpretable Framework for Assessing Sensitivity to Unmeasured Confounding. Statistics in Medicine, 35(20): 3453-70. Full Text
Dorie, V., Hill, J., Shalit, U., Scott, M. and Cervone, D. (2019). Automated or Do-It-Yourself Approaches to Causal Inference: Results from a Data Analysis Competition. Statistical Science, 34(1), 43-68.
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2019). Visualization in Bayesian Workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389-402.
Gelman, A., Simpson, D., and Betancourt, M. (2017). The Prior Can Often Only Be Understood in the Context of the Likelihood. Entropy 19:555.
Middleton, J., Scott, M., Diakow, R., and Hill, J. (2016). Bias Amplification and Bias Unmasking. Political Analysis, 24(3): 307-323. Full Text
Scott, M., Diakow, R., Hill, J., and Middleton, J. (2018). Potential for Bias Inflation with Grouped Data: A Comparison of Estimators and a Sensitivity Analysis Strategy. Observational Studies, 4: 111-149.
Torres Irribarra, D., Diakow, R., Freund, R., and Wilson, M. (2015). Modeling for Directly Setting Theory-Based Performance Levels. Psychological Test and Assessment Modeling, 57(3): 396. Full Text
Weber, S., Gelman, A., Lee, D., Betancourt, M., Vehtari, A. and Racine-Poon, A. (2018). Bayesian Aggregation of Average Data: An Application in Drug Development. Annals of Applied Statistics, 12:1583-1604.