An Evaluation of the Validity of Growth on Two Computer Adaptive Tests to Predict Performance on End-of-Year Achievement Tests using Quantile Regression. (March 2023)

• Record Type: Journal Article
• Authors: Van Norman, Ethan R.; Forcht, Emily R.
• Is Part Of: Assessment for effective intervention. Volume 48:Number 2(2023)
• Page Start: 80
• Page End: 89
• Publication Date: 2023-03
• Subjects: computer adaptive tests -- progress monitoring -- response to intervention; Educational tests and measurements -- Periodicals; Special education -- Periodicals; 371.2605
• DOI: 10.1177/15345084221100421
• Languages: English
• ISSNs: 1534-5084

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.