Annie Murphy Karabell, MA and Sarah Irvine Belson, PhD
University of Maryland and American University
ABSTRACT:
Despite renewed attention to universal screening, too many students remain in the “red” or “high-risk” band throughout their elementary years. However, extant research suggests that there is high predictive validity and diagnostic accuracy in early fluency measures, such as the subtests of the DIBELS measure (Edwards et al., 2022; Goffreda et al., 2009; Landry et al., 2022). Given the widely available data on early reading given the growth of universal screeners, there is an opportunity to intervene much earlier. In this quantitative analysis of universal screening data (DIBELS) of kindergarten students in a historically resilient mid-Atlantic school district, linear regression is employed to analyze the contributions of sub-tests to end-of-year reading proficiency and to identify persistently “red” readers. To determine how to better support persistently "red" readers in kindergarten, we generated two models: The first examined the contributions of growth in PSF and LNF to decoding growth while the second examined the contributions of static measures of LNF and PSF to end-of-year composite levels.
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