REESE Empirical Research on Emerging Topics in STEM Education: Statistical Methods for Assessing Teaching and Program Effectiveness

Arizona State University, Scottsdale AZ

Investigators

Abstract

This basic research project will develop new statistical techniques that will provide more robust estimates of the Value-Added Models (VAM). Multivariate response value-added models will be developed to include continuous and categorical responses and nested data structures, and address missing data problems. These models will employ latent-class mixture models, and will use classification trees and random forest methods for data analyses. The new techniques will allow the models to be used not only with continuous response data, such as test scores, but also categorical response data such as completion of a STEM degree. The techniques will also allow researchers to investigate the effects of missing data on value added models, as can occur when students drop out of STEM degree programs during college. The models will improve upon the current VAM models in three aspects: 1) incorporating the various missing data structures, 2) considering both continuous and categorical outcomes, and 3) taking into account complex relationships among subgroups of students and program characteristics. The potential benefits of developing such value added statistical models will be for informing educational policy and practice. These benefits will include better decisions based on more precise estimates of teacher effects and the effects of other inputs on student outcomes in STEM. The researchers propose to address limitations of current value-added models to provide stronger models for assessing STEM program effectiveness and measure teacher or school effects on student achievement.