The purpose of this study is to develop statistical models to create a coherent model of the effects of teacher professional development in mathematics on student learning. The project works closely with two partnerships of the NSF Math and Science Partnership (MSP) program, the Math in the Middle Institute Partnership and NebraskaMATH, and with the Lincoln Public Schools. Utilizing data already collected through these MSP partnerships, the study builds newly developed models to help these and other MSP partnerships and their evaluators interpret student and teacher data in statistically productive and meaningful ways. Specifically, the project focuses on layered value-added models to estimate teacher effects, and how those models can be adapted for use with messy data, for example when different tests are administered in different grade levels at different times of year. The project is investigating the use of Z-scores, parallel processing, and binning by quantile to address issues arising with available student achievement data.
