The Precision of Data Obtained in Large-Scale Science Assessments: An Investigation of Bootstrapping and Half-Sample Replication Methods

Powerful computational procedures are now available to better determine the accuracy of statistical estimates derived from data that have unknown distributions or do not meet parametric requirements. These techniques are generally called resampling plans and include the recently developed bootstrap. Science educators often face the problem of nonnormal distributions especially when conducting large-scale assessments or evaluating national curriculum projects that require complex sampling plans. Resampling techniques permit the researcher to make inferences without the strong distributional assumptions needed for more traditional parametric approaches. In this study, the bootstrap and a simplified version of a half-sample replication are used to examine the precision of science test scores obtained in a large-scale evaluation of Scope, Sequence, and Coordination, a national science curriculum project. The resampling plans are described in some detail and the results are compared with those obtained from parametric methods.