|Title:||Multilevel Regression Discontinuity Design with Latent Variables|
|Principal Investigator:||Yang, Ji Seung||Awardee:||University of Maryland, College Park|
|Program:||Statistical and Research Methodology in Education [Program Details]|
|Award Period:||3 years (06/1/2022 – 05/31/2025)||Award Amount:||$883,198|
|Type:||Methodological Innovation||Award Number:||R305D220030|
Co-Principal Investigators: Liu, Yang; Steiner, Peter
A regression discontinuity (RD) design is often employed to provide causal evidence when a randomized control trial is practically infeasible or unethical. Conventional RD models assume that all running variables, covariates, and outcomes are observed variables. The purpose of this grant is to extend the modeling framework to augment the structural RD model with multilevel latent variable measurement models for any or all of the variables.
The research team will first develop the models and their causal estimands. They will then develop software for estimating model parameters and conducting diagnostic tests of the assumptions made by the models. After the software is ready, the researchers will run a real-data demonstration of the RD model with multilevel latent variable measurement models, using data from the Oregon English Language Learners Program and the Baltimore City Public Schools Gifted and Advanced Learning Program. The research team will also develop a user-friendly version of the software in R, with supporting instructional materials. Other products from the grant will include workshops for using the software and peer-reviewed journal manuscripts in methodological and applied journals.