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Benchmark reading measures support predictions for Smarter Balanced (SB) performance made months in advance or near administration, and benchmarks support screening students for risk in learning to read. 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T. Nese, PhD  \nUniversity of Oregon  \neasyCBM: Criterion Validity and Classification Accuracy Page 2  \nPublished by  \nBehavioral Research and Teaching University of Oregon • 175 Education  \n5262 University of Oregon • Eugene, OR 97403-5262 Phone: 541-346-3535 • Fax: 541-346-5689  \n[http://brt.uoregon.edu](http://brt.uoregon.edu)  \nNote: This technical report was supported in part by Riverside Insight, the exclusive distributor of easyCBM. The report does not reflect any endorsement by any of these organizations.  \nCopyright © 2024. Behavioral Research and Teaching. All rights reserved. This publication or parts thereof, may not be used or reproduced in any manner without written permission.  \nAPA Reference: Tindal, G. & Nese, J. F. T., & (2024) . Criterion validity and classification accuracy of easyCBM: Grades 3-8. (Technical Report \\# 2401) . Eugene, OR: Behavioral Research and Teaching, University of Oregon.  \nThe University of Oregon is committed to the policy that all persons shall have equal access to its programs, facilities, and employment without regard to race, color, creed, religion, national origin, sex, age, marital status, disability, public assistance status, veteran status, or sexual orientation. This document is available in alternate formats upon request.  \neasyCBM: Criterion Validity and Classification Accuracy Page 3  \nAbstract  \nWe present two types of validity evidence to support inferences and decisions about use of easyCBMs in relation to state testing programs. The first type involves the use of Benchmarks in reading to use in making predictions of performance on the Smarter Balanced (SB) test. These predictions can be made both well in advance (several months) or nearly concurrent with administration of the SB test. The second type of validity evidence supports the use of Benchmarks in screening students for risk of problems in learning to read. With data from two states and four school districts, we analyzed well over 8,000 anonymized records, with students reflecting a broad range of demographics. In the first section of the report, we present both the grade level demographics of this sample and their performances on the following easyCBMs in the Fall, Winter, and Spring: Proficient Reading, Vocabulary, Oral (Passage) Reading Fluency, and Math. Criterion validity data (both predictive and concurrent) are first presented for grade level easyCBM raw scores for each measure correlated with the SB test. However, because different CBMs were used within and across grades, we also computed a composite that reflected a standard score (with z-scores computed for each grade level in reading and math) . This score transformation allowed both correlationsand regression analyses to be presented between easyCBMs and SB using this composite. In the second section of the paper, we present the results of a classification analysis and report a range of statistics: cut points for the 20th percentile rank (PR), classification data (true and false positives and negatives, area under the curve (with 95% confidence intervals for the lower and upper bounds), base rates, overall classification rates, sensitivity and specificity, false positive and negative rates, and finally positive and negative predictive power. The results from these classification analyses support the use easyCBMs as screeners with high AUCs, high values of sensitivity and specificity, and across the board high values of positive predictive power.  \neasyCBM: Criterion Validity and Classification Accuracy Page 4  \nIntroduction  \nThis report complements previous BRT technical reports on the technical adequacy of easyCBMs in Grades 3 through 8 (Anderson et al., 2014), where various forms of reliability and validity information were summarized. In this report, however, we focus only on validity. According to the Standards for Educat","cbCaik0z2Ut454an","https://ap.wps.com/l/cbCaik0z2Ut454an","pdf",1069206,48,"English","# Abstract\n# Introduction\n# Criterion Validity and Classification Accuracy\n## Predictive and Concurrent Validity\n## Classification Analysis and Cut Points\n## Performance Measures and Composite Scores","[{\"question\":\"What kinds of validity evidence does the report focus on?\",\"answer\":\"The report presents two types of validity evidence: benchmark-based predictions for Smarter Balanced (SB) performance and benchmark-based screening for students at risk of reading learning problems.\"},{\"question\":\"How was the study data constructed and analyzed?\",\"answer\":\"Data came from two states and four school districts, using well over 8,000 anonymized records. Analyses include both grade-level descriptive statistics and criterion validity relationships between easyCBM measures and the SB test.\"},{\"question\":\"What evaluation methods are used for classification accuracy?\",\"answer\":\"The report includes classification analysis with cut points (e.g., 20th percentile rank), true/false positives and negatives, area under the curve with confidence intervals, base rates, overall classification rates, sensitivity and specificity, and predictive power for positive and negative classifications.\"}]","Criterion Validity and Classification Accuracy of easyCBM - Grades 3-8 - Technical Report | PDF",1792045361,121]