No abstract

Curriculum-based measurement of oral reading (CBM-R) is frequently used to set student goals and monitor student progress. This study examined the quality of growth estimates derived from CBM-R progress monitoring data. The authors used a linear mixed effects regression (LMER) model to simulate progress monitoring data for multiple levels of progress monitoring duration (i.e., 6, 8, 10 … 20 weeks) and data set quality which was operationalized as residual/error in the model (σε = 5, 10, 15, and 20). The number of data points, quality of data, and method used to estimate growth all influenced the reliability and precision of estimated growth rates. Results indicated that progress monitoring outcomes are sufficient to guide educational decisions if (a) ordinary least-squares regression is used to derive trend lines estimates, (b) a very good progress monitoring data set is used, and (c) the data set comprises a minimum of 14 CBMs-R. The article discusses implications and future directions.

Curriculum-based measurement of oral reading (CBM-R) is used to index the level and rate of student growth across the academic year. The method is frequently used to set student goals and monitor student progress. This study examined the diagnostic accuracy and quality of growth estimates derived from pre–post measurement using CBM-R data. A linear mixed effects regression model was used to simulate progress-monitoring data for multiple levels of progress-monitoring duration (6, 8, 10, . . ., 20 weeks) and data set quality, which was operationalized as residual/error in the model (σε= 5, 10, 15, and 20). Results indicate that the duration of instruction, quality of data, and method used to estimate growth influenced the reliability and precision of estimated growth rates, in addition to the diagnostic accuracy. Pre–post methods to derive CBM-R growth estimates are likely to require 14 or more weeks of instruction between pre–post occasions. Implications and future directions are discussed.