Comparing Curriculum-Based Measures and Extant Datasets for Universal Screening in Middle School Reading

Autor/in	Stevenson, Nathan A.
Titel	Comparing Curriculum-Based Measures and Extant Datasets for Universal Screening in Middle School Reading
Quelle	In: Assessment for Effective Intervention, 42 (2017) 4, S.195-208 (14 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1534-5084
DOI	10.1177/1534508417690399
Schlagwörter	Curriculum Based Assessment; Screening Tests; Comparative Analysis; Middle School Students; Grade 7; Grade 8; Reading Tests; At Risk Students; Data Analysis; Accuracy; Reliability; Identification; Regression (Statistics); Achievement Tests; Michigan + Suchen Sie Ihr Suchwort? Screening-Verfahren; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; School year 07; 7. Schuljahr; Schuljahr 07; School year 08; 8. Schuljahr; Schuljahr 08; Lesetest; Auswertung; Reliabilität; Identifikation; Identifizierung; Regression; Regressionsanalyse; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen
Abstract	As a school-wide framework, Multi-Tiered Systems of Support (MTSS) relies on the prevention and early identification of students at risk of academic failure. Approaches to early identification of students in need of support include the administration of universal screening assessments and the analysis of existing student data such as attendance, grades, office discipline referrals, and prior performance on statewide assessments. However, there is little research that directly compares the accuracy and reliability of these approaches, particularly in middle grades. This investigation provides a direct comparison of curriculum-based measures in reading and the examination of archival data at the middle school level for the identification of students at risk for academic failure. Data were collected for students in Grades 7 (n = 197) and 8 (n = 237). Data were analyzed through hierarchical logistic regression using statewide reading achievement tests as the dependent variable. Results inform how data from universal screening assessments and existing sources can be used to accurately and efficiently identify students in need of academic support. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01