Identification of Reading Problems in First Grade within a Response-to-Intervention Framework

Autor/inn/en	Speece, Deborah L.; Schatschneider, Christopher; Silverman, Rebecca; Case, Lisa Pericola; Cooper, David H.; Jacobs, Dawn M.
Titel	Identification of Reading Problems in First Grade within a Response-to-Intervention Framework
Quelle	In: Elementary School Journal, 111 (2011) 4, S.585-607 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-5984
DOI	10.1086/659032
Schlagwörter	Reading Difficulties; Early Intervention; Grade 1; Response to Intervention; Disability Identification; Screening Tests; Item Analysis; At Risk Students; Predictor Variables; Models; Item Response Theory; Outcome Measures; Achievement Gains; Parochial Schools; Educational Diagnosis; Testing Programs + Suchen Sie Ihr Suchwort? Reading difficulty; Leseschwierigkeit; School year 01; 1. Schuljahr; Schuljahr 01; Screening-Verfahren; Itemanalyse; Prädiktor; Analogiemodell; Item-Response-Theorie; Achievement gain; Leistungssteigerung; Konfessionsschule; Pedagogical diagnostics; Pädagogische Diagnostik
Abstract	Models of Response to Intervention (RTI) include parameters of assessment and instruction. This study focuses on assessment with the purpose of developing a screening battery that validly and efficiently identifies first-grade children at risk for reading problems. In an RTI model, these children would be candidates for early intervention. We examined accuracy, fluency, growth, and teacher rating measures as predictors of child status (at risk, not at risk) at the end of the school year based on an unselected sample of 243 children. The prediction model that best fit our selection criteria included 2 word-fluency measures and a teacher rating of reading problems. Word-fluency growth was an equally plausible choice statistically, but, because the measure would require an additional data point, it was not the most efficient choice. The receiver-operator characteristic curve analysis yielded an area-under-the-curve index of 0.96, which indicates the selected 3-variable model is highly accurate. (Contains 1 figure, 4 tables, and 1 note.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10