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Information on IES-Funded Research
Grant Open

Moderation and Non-compliance in Multi-Site Trials with Measurement Error and Missing Data

NCER
Program: Statistical and Research Methodology in Education
Program topic(s): Core
Award amount: $899,995
Principal investigator: Yongyun Shin
Awardee:
Virginia Commonwealth University
Year: 2021
Project type:
Methodological Innovation
Award number: R305D210022

Purpose

The purpose of this grant is to produce a general statistical framework and an associated set of statistical guidelines and tools for studying such impact variability. The project will address three major design issues: 1) A treatment effect may be moderated by covariates that are measured with error; 2) The covariates and outcome, whether continuous or discrete, may be only partially observed; and 3) Compliance to treatment assignment may be imperfect. The project will also extend these issues to RCTs in which a treatment effect may be random and moderated by covariates.

Project Activities

The research team will develop the statistical analysis by estimating the parameters of the theoretical model via maximum likelihood from incomplete data using adaptive Gauss-Hermite Quadrature. They will extend this approach to multi-site trials in which each can be regarded as possessing a unique set of principal strata.

People and institutions involved

IES program contact(s)

Charles Laurin

Education Research Analyst
NCER

Products and publications

Products: First, the software will be programmed in C for initial development and testing. Then, the team will render a more user-friendly interface through R and Shiny web applications. The research team will also provide a user's guide and results from the analysis of data from multisite trials, and software documentation. The team will prepare research manuscripts, present at conferences, and teach at in workshops for education researchers.

Publications:

Shin, Y., & Raudenbush, S. W. (2024). Maximum Likelihood Estimation of Hierarchical Linear Models from Incomplete Data: Random Coefficients, Statistical Interactions, and Measurement Error. Journal of Computational and Graphical Statistics, 33(1), 112-125.

Supplemental information

Co-Principal Investigator: Raudenbush, Stephen

Questions about this project?

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

 

Tags

MathematicsData and AssessmentsEducation Technology

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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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