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Design and Analysis of Multisite Experimental Studies to Investigate Treatment Effect Heterogeneity
Sat, April 13, 3:05 to 4:35pm, Pennsylvania Convention Center, Floor: Level 100, Room 116Session Type: Symposium
Abstract
Multisite experimental studies have been widely used in education. A critical consideration in educational research is identifying for whom and under what conditions a treatment delivers benefits. Studies that investigate such moderation effects in conjunction with main effects provide a richer and more comprehensive portrait of the effectiveness and equity of a treatment. This panel advances the design and analysis of multisite experimental studies by (1) deriving power analysis methods and tools for moderator effects in three-level multisite experiments, (2) developing principles and strategies for the design and analysis of partially nested multisite randomized trials targeting moderation, (3) developing power computation formulas for longitudinal multisite experiments, and (4) evaluating machine learning algorithms to detect heterogeneous treatment effects for multisite experiments.
Sub Unit
Chair
Papers
Statistical Power for Moderation in Three-Level Multisite Individual Randomized Trials - Nianbo Dong, University of North Carolina - Chapel Hill; Benjamin Kelcey, University of Cincinnati; Jessaca K. Spybrook, Western Michigan University; Kyle Nickodem, University of Minnesota
Power Analysis for Main and Moderator Effects in Multisite Longitudinal Experiments With Site Fixed Effects - Wei Li, University of Florida; Spyros Konstantopoulos, Michigan State University
Moderation in Partially Nested Multisite Experiments: Estimation, Inference, and Design-Randomized Trials - Yanli Xie, Florida State University; Benjamin Kelcey, University of Cincinnati; Kyle T. Cox, University of North Carolina - Charlotte; Fangxing Bai, Montana State University
Application of Machine Learning Algorithms to Detect Treatment Effect Heterogeneity for Three-Level Multisite Experiments - Wei Li, University of Florida; Walter L. Leite, University of Florida; Jia Quan, University of Kansas