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Koon, Sharon; Petscher, Yaacov – Regional Educational Laboratory Southeast, 2015
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…
Descriptors: Classification, Regression (Statistics), Models, At Risk Students
Koon, Sharon; Petscher, Yaacov; Foorman, Barbara R. – Regional Educational Laboratory Southeast, 2014
This study examines whether the classification and regression tree (CART) model improves the early identification of students at risk for reading comprehension difficulties compared with the more difficult to interpret logistic regression model. CART is a type of predictive modeling that relies on nonparametric techniques. It presents results in…
Descriptors: At Risk Students, Reading Difficulties, Identification, Reading Comprehension
Johnson, Evelyn S.; Jenkins, Joseph R.; Petscher, Yaacov – Assessment for Effective Intervention, 2010
In a response-to-intervention framework, schools typically employ a direct route approach to screening, in which students identified as at risk by a screening process are directly placed into intervention. Direct route approaches require screening decisions to be highly accurate, but few studies examining the predictive validity of reading…
Descriptors: Screening Tests, Reading Tests, At Risk Students, Predictive Validity