The QuALITY lab has partnered with Renaissance Learning as part of a sponsored research agreement to evaluate the use of computer adaptive tests (CATs; STAR-Reading and STAR-Math) within tiered systems of support. In particular, the project will explore:
- The extent to which growth on STAR-Reading and STAR-Math explains performance on end of year achievement tests using quantile regression.
- The relationship between growth on CATs and Curriculum-Based Measures using multivariate multilevel modeling techniques.
- The accuracy of various decision rules as a means to evaluate student response to intervention using extant data and simulation methods.
Prior to this project, the QuALITY lab has explored the influence of duration and frequency of data collection, intervention fidelity, and trend estimation methods on growth outcomes from CATs. Qualifying projects from student lab members have evaluated the functional form of within-growth from CATs as well as the diagnostic accuracy of various gated screening frameworks that employed CATs. Below are example manuscripts from that research.
Van Norman, E. R., & Nelson, P. M. (2021). The importance of growth in oral reading fluency to predict performance on high-stakes assessments among students receiving supplemental intervention. Journal of Applied School Psychology, 37, 1-15.
Van Norman, E. R., & Ysseldyke J. (2020). The impact of data collection frequency and trend estimation method on the consistency of growth estimates from two computer adaptive tests. School Psychology Review, 49, 20-30. https://doi.org/10.1080/2372966X.2020.1716634
Nelson, P. M., Van Norman, E. R., Parker, D. C., & Cormier, D. C. (2019). An examination of interventionist implementation fidelity and content knowledge as predictors of math intervention effectiveness. Journal of Applied School Psychology, 35, 234-256. https://doi.org/10.1080/15377903.2019.1568334
Van Norman, E. R., Nelson, P. M., & Parker, D. C. (2017). Technical adequacy of growth estimates from a computer adaptive test: Implications for progress monitoring. School Psychology Quarterly, 32, 379-391. https://doi.org/10.1037/spq0000175
Nelson, P. M., Van Norman, E. R., Klingbeil, D. A., & Parker, D. C. (2017). Progress monitoring with computer adaptive assessments: The impact of data collection schedule on growth estimates. Psychology in the Schools, 54, 463-471. http://doi.org/10.1002/pits.22015