Using Subject Test Scores Efficiently to Predict Teacher Value-Added

Abstract

This article develops a simple model of teacher value-added to show how efficient use of information across subjects can improve the predictive ability of value-added models. Using matched student–teacher data from North Carolina, we show that the optimal use of math and reading scores improves the fit of prediction models of overall future teacher value-added by up to a third for reading and a tenth for a composite measure (math and reading combined). Efficiency gains are greatest when value-added must be calculated on only 1 or 2 years of data. The methods employed are flexible and can be expanded to incorporate information from other subject or subitem test metrics.