Developing Local Oral Reading Fluency Cut Scores for Predicting High-Stakes Test Performance

Autor/inn/en	Grapin, Sally L.; Kranzler, John H.; Waldron, Nancy; Joyce-Beaulieu, Diana; Algina, James
Titel	Developing Local Oral Reading Fluency Cut Scores for Predicting High-Stakes Test Performance
Quelle	In: Psychology in the Schools, 54 (2017) 9, S.932-946 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Grapin, Sally L.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3085
DOI	10.1002/pits.22035
Schlagwörter	Oral Reading; Reading Fluency; Cutting Scores; Classification; Accuracy; Regression (Statistics); Prediction; Curriculum Based Assessment; Discriminant Analysis; Grade 2; Elementary School Students; Achievement Tests + Suchen Sie Ihr Suchwort? Oral work; Reading; Mündliche Übung; Leseprozess; Lesen; Classification system; Klassifikation; Klassifikationssystem; Regression; Regressionsanalyse; Vorhersage; Diskriminanzanalyse; School year 02; 2. Schuljahr; Schuljahr 02; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen
Abstract	This study evaluated the classification accuracy of a second grade oral reading fluency curriculum-based measure (R-CBM) in predicting third grade state test performance. It also compared the long-term classification accuracy of local and publisher-recommended R-CBM cut scores. Participants were 266 students who were divided into a calibration sample (n = 170) and two cross-validation samples (n = 46; n = 50), respectively. Using calibration sample data, local fall, winter, and spring R-CBM cut scores for predicting students' state test performance were developed using three methods: discriminant analysis (DA), logistic regression (LR), and receiver operating characteristic curve analysis (ROC). The classification accuracy of local and publisher-recommended cut scores was evaluated across subsamples. Only DA and ROC produced cut scores that maintained adequate sensitivity (=0.70) across cohorts; however, LR and publisher-recommended scores had higher levels of specificity and overall correct classification. Implications for developing local cut scores are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01