Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015-077

Autor/inn/en	Koon, Sharon; Petscher, Yaacov
Institution	National Center for Education Evaluation and Regional Assistance (ED); Regional Educational Laboratory Southeast (ED); Florida State University, Florida Center for Reading Research
Titel	Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015-077
Quelle	(2015), (35 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	ORCID (Petscher, Yaacov) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Classification; Regression (Statistics); Models; At Risk Students; Reading Difficulties; Comparative Analysis; Grade 1; Grade 2; Elementary School Students; Public Schools; Accuracy; Identification; Statistical Analysis; Nonparametric Statistics; Achievement Tests; Reading Tests; Predictive Validity; Florida; Stanford Achievement Tests + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Regression; Regressionsanalyse; Analogiemodell; Reading difficulty; Leseschwierigkeit; School year 01; 1. Schuljahr; Schuljahr 01; School year 02; 2. Schuljahr; Schuljahr 02; Public school; Öffentliche Schule; Identifikation; Identifizierung; Statistische Analyse; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Lesetest
Abstract	The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by which students are identified as at-risk or not at-risk readers. Logistic regression and CART were compared using data on a sample of grades 1 and 2 Florida public school students who participated in both interim assessments and an end-of-the year summative assessment during the 2012/13 academic year. Grade-level analyses were conducted and comparisons between methods were based on traditional measures of diagnostic accuracy, including sensitivity (i.e., proportion of true positives), specificity (proportion of true negatives), positive and negative predictive power, and overall correct classification. Results indicate that CART is comparable to logistic regression, with the results of both methods yielding negative predictive power greater than the recommended standard of .90. Details of each method are provided to assist analysts interested in developing early warning systems using one of the methods. Two appendixes include: (1) Literature Review; and (2) Technical details on methods and additional results. (As Provided).
Anmerkungen	Regional Educational Laboratory Southeast. Available from: Institute of Education Sciences. 555 New Jersey Avenue NW, Washington, DC 20208. Tel: 800-872-5327; Web site: http://ies.ed.gov/ncee/edlabs/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01