Improving the Design of Science Intervention Studies

Résumé du livre

The capacity of the field to conduct power analyses for group randomized trials (GRTs) of educational interventions has improved over the past decade (Authors, 2009). However, a power analysis depends on estimates of design parameters. Hence it is critical to build the empirical base of design parameters for GRTs across a variety of outcomes and contexts. This study provides a first step towards building this base of design parameters specifically for science outcomes. Unlike reading and math, science is not typically tested each year. Preliminary findings from this study suggest that although not direct comparisons, unconditional intraclass correlations (ICCs) for science outcomes are smaller than grade 3 math and reading as reported by Bloom, Richburg-Hayes, and Black (2005; 2007) for five urban districts. Similarly, Hedges and Hedberg (2007) found larger ICCs for both reading and math using a nationally representative sample of students nested in schools. R-square values for school level covariates have not been computed yet, but a one year lagged student pretest appears to be highly effective in reducing variance between and within schools, more so than in reading (R-squares less than 0.86) and math (less than 0.63) as reported by Bloom et al. (2005; 2007). The empirical estimates from this study will help improve the accuracy of the power analyses for GRTs of science interventions. Tables are appended.