Curriculum-Based Measurement of Oral Reading: Quality of Progress Monitoring Outcomes

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.

The effects of baseline estimation on the reliability, validity, and precision of CBM-R growth estimates.

“…Justification for using simulation methodology as an initial low risk way of investigating applied problems in fields as diverse as agriculture, meteorology, medicine, and economics follows a similar cost-benefit argument (Robinson, 2004). Christ et al (2012) used simulation methods to evaluate how slope calculation methods, quality of datasets, and number of observations affected estimates of growth. Siniilar to this study, the researchers first analyzed a large high quality extant CBM-R database (n = 3,078 AIMSweb progress monitoring cases).…”

Robust Regression for Slope Estimation in Curriculum-Based Measurement Progress Monitoring

“…Of these estimation methods, OLS regression is considered to be the most accurate (Christ, Zopluoglu, Long, & Monaghen, 2012;Good & Shinn, 1990;Parker, Tindal, & Stein, 1992;Shinn, Good, & Stein, 1989), most likely because it involves statistical estimation of the best-fitting trend line rather than a series of procedures to draw trend lines on graphed CBM data, as in the Tukey, split middle, and quarter-intersect methods. However, OLS regression is sensitive to the presence of extreme values, and methods to detect and account for extreme values are receiving increasing attention in single-case and CBM research (Brossart, Parker, & Castillo, 2011;.…”

Curriculum-Based Measurement of Oral Reading

“…Such results provide impetus to evaluate less dense and burdensome schedules of progress monitoring. Christ et al (2012) used regression to evaluate the relative impact of the progress-monitoring duration, quality of the data set (i.e., residual/error), and the density of the data collection schedule (i.e., CBMs-R per week). Results indicate that the 67% of the variance in the precision of growth estimates was associated with the progress-monitoring durations, which spanned 2 to 20 weeks.…”