(University of Oregon, 2012) Lai, Cheng-Fei; Lai, Cheng-Fei; Kamata, Akihito
Piecewise latent class growth analysis (LCGA) was used to examine growth patterns in reading comprehension and passage reading fluency on easyCBM, a popular formative assessment system. Unlike conventional growth modeling, LCGA takes into account the heterogeneity of growth and may provide reliable predictions for later development. Because current methods for classifying students are still questionable, this modeling technique could be a viable alternative classification method to identifying students at risk for reading difficulty. Results from this study suggested heterogeneity in reading development. The latent classes and growth trajectories from the LCGA models were found to align closely with easyCBM's risk rating system. However, results from one school district did not fully generalize across another. The implications for future research on examining growth in reading are discussed.
