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Information on IES-Funded Research
Grant Closed

NYU/Columbia Postdoctoral Training Program

NCER
Program: Research Training Programs in the Education Sciences
Program topic(s): Postdoctoral Research Training Program in the Education Sciences
Award amount: $686,999
Principal investigator: Jennifer Hill
Awardee:
New York University
Year: 2012
Award period: 5 years (09/01/2012 - 08/31/2017)
Project type:
Training
Award number: R305B120017

Purpose

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.

Project Activities

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.

People and institutions involved

IES program contact(s)

Meredith Larson

Project contributors

Marc Scott

Co-principal investigator

Andrew Gelman

Co-principal investigator

Completed fellows

Ronli Diakow

Vincent Dorie

Lauren Fellers

Products and publications

Publications:

ERIC Citations: Find available citations in ERIC for this award here.

Selected Publications by the Fellows:

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.

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

Questions about this project?

To answer additional questions about this project or provide feedback, please contact the program officer.

 

Tags

Data and AssessmentsMathematics

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Questions about this project?

To answer additional questions about this project or provide feedback, please contact the program officer.

 

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