1. Criterion Validity and Classification Accuracy of easyCBM: Grades 3-8. (Technical Report # 2401)
- Author
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University of Oregon, Behavioral Research and Teaching (BRT), Gerald Tindal, and Joseph F. T. Nese
- Abstract
We 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 correlations and 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. [This technical report was supported in part by Riverside Insight.]
- Published
- 2024