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Gerald Tindal; Joseph F. T. Nese – Behavioral Research and Teaching, 2024
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…
Descriptors: Classification, Accuracy, Validity, Criteria
Alonzo, Julie; Anderson, Daniel – Behavioral Research and Teaching, 2018
This technical report is an addendum to a study we initially reported on in 2014 (Wray, Lai, Saez, Alonzo, & Tindal, 2014). In response to a request for additional analyses, in particular reporting confidence intervals around the results, we re-analyzed the data from the Wray et al study. This supplementary report presents the results of the…
Descriptors: Curriculum Based Assessment, Response to Intervention, Kindergarten, Grade 1
Alonzo, Julie; Anderson, Daniel – Behavioral Research and Teaching, 2018
In response to a request for additional analyses, in particular reporting confidence intervals around the results, we re-analyzed the data from prior studies. This supplementary report presents the results of the additional analyses addressing classification accuracy, reliability, and criterion-related validity evidence. For ease of reference, we…
Descriptors: Curriculum Based Assessment, Computation, Statistical Analysis, Accuracy
Alonzo, Julie; Anderson, Daniel – Behavioral Research and Teaching, 2018
In response to a request for additional analyses, in particular reporting confidence intervals around the results, we re-analyzed the data from prior studies. This supplementary report presents the results of the additional analyses addressing classification accuracy, reliability, and criterion-related validity evidence. For ease of reference, we…
Descriptors: Curriculum Based Assessment, Computation, Statistical Analysis, Classification
Anderson, Daniel; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
In this technical report, we document the results of a cross-validation study designed to identify optimal cut-scores for the use of the easyCBM[R] mathematics test in the state of Washington. A large sample, randomly split into two groups of roughly equal size, was used for this study. Students' performance classification on the Washington state…
Descriptors: Testing Programs, Mathematics Tests, Prediction, Measurement Techniques
Park, Bitnara Jasmine; Anderson, Daniel; Irvin, P. Shawn; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
Within a response to intervention (RTI) framework, students are typically identified as "academically at-risk" if they score below a specified cut-point on a benchmark screener. Students identified as at-risk are provided with an intervention intended to increase achievement. In the following technical report, we describe a process for…
Descriptors: Intervention, Diagnostic Tests, Response to Intervention, At Risk Students
Irvin, P. Shawn; Park, Bitnara Jasmine; Anderson, Daniel; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
This technical report presents results from a cross-validation study designed to identify optimal cut scores when using easyCBM[R] reading tests in Washington state. The cross-validation study analyzes data from the 2009-2010 academic year for easyCBM[R] reading measures. A sample of approximately 900 students per grade, randomly split into two…
Descriptors: Intervention, Diagnostic Tests, Response to Intervention, At Risk Students
Park, Bitnara Jasmine; Irvin, P. Shawn; Anderson, Daniel; Alonzo, Julie; Tindal, Gerald – Behavioral Research and Teaching, 2011
This technical report presents results from a cross-validation study designed to identify optimal cut scores when using easyCBM[R] reading tests in Oregon. The cross-validation study analyzes data from the 2009-2010 academic year for easyCBM[R] reading measures. A sample of approximately 2,000 students per grade, randomly split into two groups of…
Descriptors: Testing Programs, Reading Tests, Prediction, Measurement Techniques