Expected Classification Accuracy for Categorical Growth Models

Abstract

Categorical growth models describe examinee growth in terms of performance-level category transitions, which implies that some percentage of examinees will be misclassified. This paper introduces a new procedure for estimating the classification accuracy of categorical growth models, based on Rudner's classification accuracy index for item response theory–based assessments. Results of a simulation study are presented to provide evidence for the accuracy and validity of the approach. Also, an empirical example is presented to demonstrate the approach using data from the Indiana Student Performance Readiness and Observation of Understanding Tool growth model, which classifies examinees into growth categories used by the Office of Special Education Programs to monitor the progress of preschool children who receive special education services.