|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: Marc Scott, New York University, and Andrew Gelman, Columbia University
The focus of this training program is to develop researchers who are prepared to both (a) develop the new statistical methods that will be required to meet future education research challenges and (b) teach other researchers how to use more advanced quantitative methods. Four fellows will be recruited to join researchers for two years each at the PRIISM (Promotion of Research Involving Innovative Statistical Methodology) Center at New York University Steinhardt School of Culture, Education, and Human Development, and the Applied Statistics Center at Columbia University. In this program, fellows will participate in ongoing research that aligns to IES' Statistical and Research Methodology in Education grant program. Fellows will have 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 the hands-on, practical research experience the fellows will receive through participating in research projects, they will have access to a wide selection of statistics classes and seminar series to build their knowledge base. They will also develop the communication skills necessary for working across fields and communicating advanced statistical ideas effectively to researchers with less formal experience through teaching or consulting. In addition, fellows will receive mentoring in career development topics such as preparing high-quality education research grant proposals, teaching, ethics, writing, and applying for jobs.
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. (in press). Automated or Do-It-Yourself Approaches to Causal Inference: Results from a Data Analysis Competition. Statistical Science.
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.