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An Evaluation of the Validity of Growth on Two Computer Adaptive Tests to Predict Performance on End-of-Year Achievement Tests using Quantile Regression

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This study explored the validity of growth on two computer adaptive tests, Star Reading and Star Math, in explaining performance on an end-of-year achievement test for a sample of students… Click to show full abstract

This study explored the validity of growth on two computer adaptive tests, Star Reading and Star Math, in explaining performance on an end-of-year achievement test for a sample of students in Grades 3 through 6. Results from quantile regression analyses indicate that growth on Star Reading explained a statistically significant amount of variance in performance on end-of-year tests after controlling for baseline performance in all grades. In Grades 3 through 5, the relationship between growth on Star Reading and the end-of-year test was stronger among students who scored higher on the end-of-year test. In math, Star Math explained a statistically significant amount of variance in end-of-year scores after statistically controlling for baseline performance in all grades. The strength of the relationship did not differ among students who scored lower or higher on the end-of-year test across grades.

Keywords: growth; year; end year; performance end

Journal Title: Assessment for Effective Intervention
Year Published: 2022

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