An algorithm for estimation of means and standard deviations or raw scores underlying table values of standard test scores is presented. An application of a computer program, operationalizing the suggested algorithm, is discussed within the framework of a comparison of estimated and known table values for sample Minnesota Multiphasic Personality…

Explores whether flexible nonlinear models (including neural networks and genetic algorithms) can reveal otherwise unexpected patterns of relationship in typical school-productivity data. Applying three types of algorithms alongside regression modeling to school-level data in 183 elementary schools proves the hypothesis and reveals new directions…

Many arguments have been made against allowing examinees to review and change their answers after completing a computer adaptive test (CAT). These arguments include: (1) increased bias; (2) decreased precision; and (3) susceptibility of test-taking strategies. Results of simulations suggest that the strength of these arguments is reduced or…

Applies neural network methods for forecasting 1991-95 per-pupil expenditures in U.S. public elementary and secondary schools. Forecasting models included the National Center for Education Statistics' multivariate regression model and three neural architectures. Regarding prediction accuracy, neural network results were comparable or superior to…