Establishing predictive validity for oral passage reading fluency and vocabulary curriculum-based measures (CBMs) for sixth grade students
Megert, Brian R.
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Megert, Brian R.
In recent years, state and national policy created the need for higher accountability standards for student academic performance. This increased accountability creates an imperative to have a formative assessment system reflecting validity in inferences about the effectiveness of instruction and performance on statewide large-scale assessments. Curriculum-based measurement (CBM) satisfies both functions. However, research shows the predictive power of oral passage reading fluency (PRF) diminishes in middle and high school. Because of the decreased predictive validity of PRF in the upper grade levels, additional reading CBMs should be explored. This study compares PRF and Vocabulary CBM data for all sixth grade students in a school district using two statistical procedures: correlation and regression. The correlation coefficients were moderately high among PRF, Vocabulary CBM, and the Reading test in Oregon Assessment of Knowledge and Skills (OAKS). A regression analysis indicated that the Vocabulary CBM explained more variance than PRF in predicting reading performance on OAKS. A second multiple regression analysis introduced three non-performance indicators (Gender, Attendance, and NCLB At-Risk), along with the two CBMs (Vocabulary and PRF). The second regression results revealed that Vocabulary again was more predictive than PRF, Gender, Attendance, or NCLB At-Risk. At-Risk status was the only non-performance indicator that was significant. All the findings have been discussed within the context of understanding reading skills using CBMs and their relation to performance on a large-scale test used for accountability. The findings have been framed as part of an information system that allows schools and districts to better tailor staffing, instruction, and schedules to student needs. Suggestions for future research also have been discussed, particularly in enhancing the predictions on large-scale test outcomes using a variety of CBMs.