Daniel Anderson is a Research Assistant Professor in the College of Education at the University of Oregon. His research lies at the intersection of measurement and large-scale policy, with a specific focus on educational inequities. He is particularly interested in computational approaches to educational and social science problems, including leveraging data from a variety of different sources and building predictive models. Much of his work also focuses on large-scale longitudinal data, including descriptive evaluations of students’ learning trajectories over time, and evaluations of factors influencing these trajectories. Daniel is currently leading the design, development, and teaching of a data science specialization for the college of education, with a sequence of three courses being piloted during the 2018-19 academic school year.

Terminal Project of Distinction 2009: Awarded for outstanding Masters Terminal Project in Educational Leadership: Graduating class of 2009. 

Tindal, G., and Anderson, D. (2019). Changes in status and performance over time for students with specific learning disabilities. Learning Disabilities Quarterly, 42, 3-16.doi: 10.1188/0731948718806660

Fien, H., Anderson, D., Nelson, N. J., Kennedy, P., Baker, S. K., & Stoolmiller, M. (2018). Examining the impact and school‐level predictors of impact variability of an 8th grade reading intervention on at‐risk students’ reading achievement. Learning Disabilities Research & Practice, 33, 37-50. doi: 10.1111/ldrp.12161

Anderson, D., Kahn, J, and Tindal, G. (2017). Exploring the robustness of a unidimensional item response theory model with empirically multidimensional data. Applied Measurement in Education. 30, 163-177. doi: 10.1080/08957347.2017.1316277

Farley, D., Anderson, D., Irvin, P. S.,  & Tindal, G. (2016). Modeling reading growth in Grades 3-5 with an alternate assessment. Remedial and Special Education, 38, 195-206. doi: 10.1177/0741932516678661 

Anderson, D., Farley, D., & Tindal, G. (2015). Test design considerations for students with significant cognitive disabilities. The Journal of Special Education, 49, 3-15. doi: 10.1177/0022466913491834

Anderson, D., Irvin, P. S., Alonzo, J., & Tindal, G. A. (2015). Gauging item alignment through online systems while controlling for rater effects. Educational Measurement: Issues and Practice, 34, 22-33. doi: 10.1111/emip.12038

Achievement gaps have been a near constant in the American educational system dating back to at least the 1960’s, when large disparities were documented in the well-publicized Coleman report. Yet, surprisingly little research has investigated variability in achievement gaps. In a recent conference paper, we documented considerable between-school variance in achievement gaps. Further, the magnitude of the estimated achievement gaps appeared to be geographically correlated – i.e., schools were generally clustered by the magnitude of the estimated achievement gaps. Daniel’s current work focuses on the factors that relate to this between-school and geographic variation in achievement gap magnitude.