Let's See More Empirical Studies on Value-Added Modeling of Teacher Effects

The insightful discussions by Raudenbush, Rubin, Stuart and Zanutto (RSZ) and Reckase identify important challenges for interpreting the output of VAM and for its use with test-based accountability. As these authors note, VAM are statistical models for the correlations among scores from students who share common teachers or schools during the years of schooling when testing occurs. We follow the convention in this literature of using the phrase "teacher effects" to describe the source of correlation among scores from students who shared a teacher, but teachers are not the only source of this correlation, and the estimated "effects" do not necessarily correspond to any well defined causal effect or attribute of the teacher. Many factors including the causal effects of teachers and schools identified by Raudenbush and RSZ as well as context effects, noneducational inputs, and the characteristics of tests all contribute to these correlations. As Reckase points out careful consideration of testing alone suggests that these statistical models will need to be highly complex to adequately describe the likely correlation structure in longitudinal student test data.