Strategies for Improving Precision in Group-Randomized Experiments

By Stephen Raudenbush, Andres Torres Martinez, Jessaca K. Spybrook. 2007

227 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fcbefc41cb04af16205a66a8a553b2673ff3e002

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Citations in the corpus

The essential role of pair matching in cluster-randomized experiments, with application to the Mexican Universal Health Insurance Evaluation (2009, background): “For political, ethical, or administrative reasons, researchers conducting field experiments are often unable to randomize treatment assignment to individuals and so instead randomize treatments to clusters of individuals (Murray, 1998; Donner and Klar, 2000; Raudenbush et al., 2007).”
The Impact of an Instructional Intervention on the Science and Language Learning of Middle Grade English Language Learners (2009, background): “This variation also implies a potential limitation on power for teacher-level randomized trials: many more teachers are needed ( Konstantopoulos, 2008 ; Raudenbush, Martinez, & Spybrook, 2007 ).”
Advancing Educational Policy by Advancing Research on Instruction (2008, methodology): “This approach will produce power that typically approximates that in the case of analysis of covariance (Raudenbush et al., 2007).”
Using Covariates to Improve Precision for Studies That Randomize Schools to Evaluate Educational Interventions (2007, methodology): “…in the literature for decades (beginning with Fisher, 1937/1947) and is widely used, has important strengths and weaknesses, which are discussed by Raudenbush et al. (2007) and a number of other authors.8 Another approach, which is the basis for the present article, is to control statistically…”
An Examination of the Precision and Technical Accuracy of the First Wave of Group-Randomized Trials Funded by the Institute of Education Sciences (2009, methodology): “The advantages of blocking in terms of increasing the precision of the estimate and improving the face validity of the study support the greater number of blocked trials compared to nonblocked trials (Raudenbush et al., 2007).”
A Multistate District-Level Cluster Randomized Trial of the Impact of Data-Driven Reform on Reading and Mathematics Achievement (2011, methodology): “We used a baseline achievement measure to improve the precision, and corresponding power, of the estimated impacts (Bloom, 2005; Raudenbush et al., 2007).”
The Implications of “Contamination” for Experimental Design in Education (2011, background): “…of treatment effects is larger in experimental designs that randomize clusters than it is in designs that randomize units within clusters (Blitstein, Hannan, Murray, & Shadish, 2005; Bloom et al., 2007; Cornfield, 1978; Konstantopoulos, 2008a, 2008b; Raudenbush et al., 2007; Schochet, 2008).”
Causal Inference and Omitted Variable Bias in Financial Aid Research: Assessing Solutions (2008, background): “Raudenbush, and Ball (2003), Cook (2002), Cronbach and Associates (1980), Raudenbush (1997), Raudenbush, Martinez, and Spybrook (2007) , and Shadish, Cook, and Campbell (2002).”
PowerUp!: A Tool for Calculating Minimum Detectable Effect Sizes and Minimum Required Sample Sizes for Experimental and Quasi-Experimental Design Studies (2013, unknown): “…in impacts across blocks or effect size heterogeneity also affects the MDES and MRSS (Hedges & Rhoads, 2010; Konstantopoulos, 2008a, 2009; Raudenbush et al., 2007).1 For RD design studies, an inherent factor in determining MDES or MRSS is the ratio of the asymptotic variances of impact…”
The Effect of Teacher–Family Communication on Student Engagement: Evidence From a Randomized Field Experiment (2013, methodology): “We adopt a parsimonious multilevel modeling framework across all three outcomes following Raudenbush (1997, 2007) and Bloom, Bos, and Lee (1999) to account for the clustered nature of our data.”