Early Prediction of Reading Risk in Fourth Grade: A Combined Latent Class Analysis and Classification Tree Approach

Abstract

ABSTRACT Purpose Fourth grade typically involves shifting the instruction from learning to read to reading to learn, which can cause students to struggle. However, early reading intervention guided by assessment has demonstrated effectiveness in preventing later reading difficulties (RD). This study presents a classification and regression tree (CART) model predicting fourth-grade reading groups using first-grade measures. Method Students were assessed in first and fourth grade (N = 452). Fourth-grade groups were determined using latent class analysis based on word reading and reading comprehension measures with a cut-point at the 15th percentile. A CART model was trained to determine the best decision rules to classify students at risk of developing later RD and compared to a logistic regression model. Results Important first-grade predictors included a mix of oral language and foundational word-reading skills with final classification accuracy estimates of .90 AUC, .91 sensitivity, and .75 specificity. Conclusion While the CART and logistic regression models’ classification accuracy was similar, CART has the advantage of offering a more intuitive way for practitioners to determine risk. Multivariate screening can be time-consuming, but CART models offer the potential to reduce false positives and guide targeted interventions, leading to better use of school resources.