Modeling of Nonlinear Growth to Improve the Accuracy of Identification Decision Rules

Abstract

Progress monitoring using curriculum-based measures administered to a student at multiple points in time is common in educational settings. Recent research has demonstrated that common approaches to identifying individuals in need of special services, such as the trend line or median techniques, can be negatively impacted by the nonlinear change in scores over time. The purpose of this study was to test and demonstrate a nonlinear regression model for adjusting the linear trend line for the presence of such nonlinearities, thereby improving the accuracy of common methods for identifying students in need of special services. Results demonstrated that use of this nonlinear model improved the accuracy of common methods for identifying students in need of special services.