Developing Local Oral Reading Fluency Cut Scores for Predicting High-Stakes Test Performance

This study evaluated the classification accuracy of a second grade oral reading fluency curriculum-based measure (R-CBM) in predicting third grade state test performance. It also compared the long-term classification accuracy of local and publisher-recommended R-CBM cut scores. Participants were 266 students who were divided into a calibration sample (n = 170) and two cross-validation samples (n = 46; n = 50), respectively. Using calibration sample data, local fall, winter, and spring R-CBM cut scores for predicting students’ state test performance were developed using three methods: discriminant analysis (DA), logistic regression (LR), and receiver operating characteristic curve analysis (ROC). The classification accuracy of local and publisher-recommended cut scores was evaluated across subsamples. Only DA and ROC produced cut scores that maintained adequate sensitivity (≥.70) across cohorts; however, LR and publisher-recommended scores had higher levels of specificity and overall correct classification. Implications for developing local cut scores are discussed