Learning more from social experiments: evolving analytic approaches

By Howard S. Bloom. 2006

228 citations recorded by the source corpus.

Subfields: Management of Technology and Innovation, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a880f3f036392aa93595c65a094c5b3a3ebdf18d

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In Management of Technology and Innovation

In Management Science and Operations Research

Citations in the corpus

How neighborhoods influence child maltreatment: a review of the literature and alternative pathways. (2007, background): “Evaluators could randomly assign geographic areas to become targeted prevention zones or controls (Bloom, 2005; Cook, 2005).”
Modern Regression Discontinuity Analysis (2012, background): “The degree of complexity is parallel to that for clustered randomized trials (see, e.g., Bloom, 2005; Bloom, Richburg-Hayes, & Black, 2007).”
PowerUp!: A Tool for Calculating Minimum Detectable Effect Sizes and Minimum Required Sample Sizes for Experimental and Quasi-Experimental Design Studies (2013, unknown): “where Mv is the sum of two t statistics (Bloom, 1995, 2005, 2006; Murray, 1998).”
The Implications of “Contamination” for Experimental Design in Education (2011, background): “The phenomenon of contamination is also variously referred to as leakage (Plewis & Hurry, 1998), spillover effects (Bloom, 2005), or treatment diffusion (Shadish, Cook, & Campbell, 2002).”
Evaluating Kindergarten Retention Policy (2006, background): “0 (Raudenbush, Bryk, Cheong, Congdon, and du Toit 2005) for most of the analyses.”
The Impact of Sample Size and Other Factors When Estimating Multilevel Logistic Models (2016, background): “The ratio of the between-group variance to total variance is the intraclass correlation coefficient (ICC), serving as a metric for the degree of correlation among lower level units (Bloom, 2005; Kreft & De Leeuw, 1998; Snijders & Bosker, 1999).”
Final Reading Outcomes of the National Randomized Field Trial of Success for All (2007, methodology): “As Bloom (2005) noted, this parameter, which is a multiple of the impact estimator’s standard error, depends on: whether a one- or two-tailed test of statistical significance is used; the level of statistical significance to which the result of the significance test will be compared; the desired sta”
Improving Generalizations From Experiments Using Propensity Score Subclassification (2013, background): “Second, the distributions of many population variables are highly skewed themselves (e.g., income), whereas current methods for selecting samples tend to focus on the inclusion of average and modal units, not those at the full range of covariate values (e.g., Bloom, 2005; Cook, 1993).”
Intraclass Correlation Values for Planning Group-Randomized Trials in Education (2007, background): “For general discussions of the design and analyses of cluster-randomized experiments, see Murray (1998); Bloom, Bos, and Lee (1999); Donner and Klar (2000); Klar and Donner (2001); Raudenbush and Bryk (2002); Murray, Varnell, and Blitstein (2004); or Bloom (2005).”
Strategies for Improving Precision in Group-Randomized Experiments (2007, methodology): “According to Bloom (2005), correlations at the group level in the social sciences are typically higher than correlations at the individual level.”
Early-Grade Retention and Children’s Reading and Math Learning in Elementary Years (2007, background): “(6) In studies of treatment effects on student learning, once the pretest scores are controlled, other covariates typically add comparatively little to the explained variation in the outcome (Bloom, 2005).”
Teacher Study Group (2010, methodology): “…(Raudenbush, Liu, Spybrook, Martinez, & Congdon, 2006) prior to the conduct of the study, we estimated the minimum detectable effect size (MDES; Bloom, 2005), which is defined as the smallest true impact that can be detected with 80% power using a two-tailed test with alpha set at .05 (see…”
Remediation in the Community College (2008, background): “See Brock & LeBlanc (2005), Appendix A; Lesik (2006); and Calcagno (2007).”
Using Covariates to Improve Precision for Studies That Randomize Schools to Evaluate Educational Interventions (2007, background): “This finding is consistent with the fact that increasing the number of individuals per cluster often has little effect on precision (Bloom, 2005).”