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ABSTRACT
Within multitiered systems of support, assessment practices that limit the amount of time students miss instruction should be prioritized. At the same time, decisions about student response to intervention need to be based upon technically adequate data. We evaluated the impact of data collection frequency and trend estimation method on the magnitude of average rates of growth as well as the stability of individual estimates of growth from a computer-adaptive test. Students in Grades 2 through 5 (n > 2,000) were progress monitored once a month across the 2015-2016 school year with Star Reading or Star Math assessments. Results suggest that using ordinary least squares regression to estimate growth from triannual screening periods is generally sufficient to make program evaluation decisions about response to instruction across a school year. To make decisions about individual student progress, data should be collected at a minimum bimonthly but preferably once a month.
ARTICLE HISTORY
Received April 10, 2018
Accepted September 24, 2019
KEYWORDS
computer-adaptive tests, progress monitoring, reading, math
ASSOCIATE EDITOR
Kelli Cummings
10.1080/2372966X.2020.1716634
(ProQuest: ... denotes formula omitted.)
Historically, students who experience academic difficulties had to wait to receive help until their needs became so severe that special education services were warranted (Ysseldyke & Reschly, 2014). Within contemporary prevention-based frameworks, such as multitiered systems of support, students receive supplemental interventions when they first experience academic difficulties. A substantial research base has emerged regarding how to identify at-risk students through universal screening (Albers, Glover, & Kratochwill, 2007; Clemens, KellerMargulis, Scholten, & Yoon, 2016). Data must also be collected and evaluated periodically to ensure that students are benefiting from supplemental interventions or determine whether changes need to be made (Jenkins, Schiller, Blackorby, Thayer, & Tilly, 2013). Relative to universal screening practices, fewer empirical recommendations have been identified regarding how best to monitor and evaluate student progress (Ball & Christ, 2012). Providing high-quality evidence-based interventions, with fidelity, should be a key priority in any tiered system of support. At the same time, a sufficient number of data points must be collected to ensure that decisions to maintain or modify instructional programs are informed by reliable and valid estimates of student improvement (Fuchs & Fuchs, 2005; Salvia, Ysseldyke, & Witmer, 2017). Identifying data collection practices in which estimated growth...