Ji Seung Yang
Associated IES Content
Grant
Multilevel Regression Discontinuity Design With Latent Variables
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.
Federal funding program:
Award number:
R305D220030
FY2021
FY2021 Basic Processes Peer Review Panel
FY2020
FY2020 Basic Processes Peer Review Panel
FY2022
Basic Processes FY2020 - FY2022 Peer review panel
FY2019
FY2019 Basic Processes Peer Review Panel
FY2018
FY2018 Basic Processes Peer Review Panel
FY2017
FY2017 Basic Processes Peer Review Panel
FY2016
FY2016 Basic Processes Peer Review Panel
Grant
Multilevel Item Bifactor Models with Semi-Nonparametric Latent Densities
The purpose of this project was to develop a better way to deal with measurement error in predictors and to address the impact of non-normality in latent variable distributions. Predictors that contain measurement error yield attenuated correlation or regression coefficient estimates, which results in bias in treatment effect estimates. This problem is exacerbated in multilevel models when level-1 values are simply aggregated to an upper level as group means. By introducing a multilevel and ...
Federal funding program:
Award number:
R305D150052
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