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Petscher, Yaacov; Koon, Sharon – Assessment for Effective Intervention, 2020
The assessment of screening accuracy and setting of cut points for a universal screener have traditionally been evaluated using logistic regression analysis. This analytic technique has been frequently used to evaluate the trade-offs in correct classification with misidentification of individuals who are at risk of performing poorly on a later…
Descriptors: Screening Tests, Accuracy, Regression (Statistics), Classification
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Erbeli, Florina; He, Kai; Cheek, Connor; Rice, Marianne; Qian, Xiaoning – Scientific Studies of Reading, 2023
Purpose: Researchers have developed a constellation model of decodingrelated reading disabilities (RD) to improve the RD risk determination. The model's hallmark is its inclusion of various RD indicators to determine RD risk. Classification methods such as logistic regression (LR) might be one way to determine RD risk within the constellation…
Descriptors: At Risk Students, Reading Difficulties, Classification, Comparative Analysis
Kent, Shawn C.; Wanzek, Jeanne; Yun, Joonmo – Assessment for Effective Intervention, 2019
This study examined the predictive validity and classification accuracy of individual- and group-administered screening measures relative to student performance on a year-end state reading assessment in two states. A sample of 321 students was assessed in the areas of word-level and text fluency, as well as reading comprehension in the fall of…
Descriptors: Screening Tests, Grade 4, Elementary School Students, At Risk Students
Kent, Shawn C.; Wanzek, Jeanne; Yun, Joonmo – Grantee Submission, 2019
This study examined the predictive validity and classification accuracy of individual and group-administered screening measures relative to student performance on a year-end state reading assessment in two states. A sample of 321 students were assessed in the areas of word-level and text fluency, as well as reading comprehension in the fall of…
Descriptors: Screening Tests, Grade 4, Elementary School Students, At Risk Students
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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