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Weiss, Michael J.; Lockwood, J. R.; McCaffrey, Daniel F. – Journal of Research on Educational Effectiveness, 2016
In the "individually randomized group treatment" (IRGT) experimental design, individuals are first randomly assigned to a treatment arm or a control arm, but then within each arm, are grouped together (e.g., within classrooms/schools, through shared case managers, in group therapy sessions, through shared doctors, etc.) to receive…
Descriptors: Randomized Controlled Trials, Error of Measurement, Control Groups, Experimental Groups
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Ziegler, Matthias; Buehner, Markus – Educational and Psychological Measurement, 2009
The impact of socially desirable responding or faking on noncognitive assessments remains an issue of strong debate. One of the main reasons for the controversy is the lack of a statistical method to model such response sets. This article introduces a new way to model faking based on the assumption that faking occurs due to an interaction between…
Descriptors: Response Style (Tests), Models, Control Groups, Structural Equation Models
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Schochet, Peter Z. – National Center for Education Evaluation and Regional Assistance, 2009
For RCTs of education interventions, it is often of interest to estimate associations between student and mediating teacher practice outcomes, to examine the extent to which the study's conceptual model is supported by the data, and to identify specific mediators that are most associated with student learning. This paper develops statistical power…
Descriptors: Statistical Analysis, Intervention, Teacher Influence, Teaching Methods
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Schochet, Peter Z. – National Center for Education Evaluation and Regional Assistance, 2009
This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…
Descriptors: Control Groups, Causal Models, Statistical Significance, Computation