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IES Grant

Title: Direct Adjustment in Combination With Robust or Nonlinear Regression: Software and Methods for RDDs, RCTs and Matched Observational Studies
Center: NCER Year: 2021
Principal Investigator: Hansen, Ben Awardee: University of Michigan
Program: Statistical and Research Methodology in Education      [Program Details]
Award Period: 3 years (03/01/2021 - 02/29/2024) Award Amount: $785,482
Type: Methodological Innovation Award Number: R305D210029

Co-Principal Investigators: Bowers, Jacob; Errickson, Josh; Sales, Adam

The purpose of this grant is to develop open-source software that will enable researchers to separate the two functions of classical analysis of covariance — covariance adjustment and treatment effect estimation — into distinct modules for the purpose of optimally estimating standard errors. Covariates play an essential role in education evaluations. In observational studies, regression discontinuity studies, and randomized experiments with attrition, covariates can be used to enhance interpretability and limit bias from confounding. In any quantitative research design, they may be used to increase statistical precision and power. The research team will develop the tools for three broad areas of covariate use: 1) Modeling outcome-covariate associations and estimating effects in distinct regression fits, such as when using non-linear and robust regression methods in analyses that target average treatment effects; 2) Complex research designs, including blocked or clustered observations, regression discontinuity designs, and matched observational studies; 3) Scenarios in which the number of available covariates is large compared with the number of cases, including most studies combining school-level assignment with data from a state's student longitudinal data system.

The research team will test and demonstrate the methods using Monte Carlo simulations and real datasets. They will conduct user-testing for the open-source R software package with faculty and students at the research team's universities and then in increasingly broader settings, such as conference workshops, as it develops and evolves in response to user feedback. The research team will demonstrate use with primary effectiveness analyses of state programs in Michigan, and secondary analysis of student data from a randomized trial conducted in Maine. In addition to the software, the research team will produce journal manuscripts, online user's guides for the software, and conduct methodology workshops at universities and conferences.