The influence of trend estimation method on forecasting curriculum-based measurement of reading performance

Abstract

Estimating a trend line through words read correct per minute scores collected across successive weeks is a preferred method to evaluate student response to instruction with curriculum-based measurement of reading (CBM-R). This is due in part, because the slope of that line of best fit is used to predict the trajectory of student performance if the current intervention is maintained. In turn, trend lines should predict future scores with a high degree of accuracy when an intervention is maintained. We evaluated the forecasting accuracy of a trend estimation method currently used in practice (i.e., ordinary least squares), and five alternate methods recently evaluated in CBM-R simulation studies, using actual student data. Results suggest that alternate trend estimation methods predicted future performance with a similar level of accuracy as ordinary least squares trend lines across most conditions, with the exception of slopes estimated via Bayesian analysis. Bayesian trend lines estimated using informed prior distributions yielded noticeably less biased and more precise predictions when applied to short data series relative to all other estimation methods across most conditions. Outcomes from the current study highlight the need to further explore the viability of Bayesian analysis to evaluate individual time series data.