- A teacher’s value-added score in one year is partially but not fully predictive of her performance in the next.
- Value-added is unstable because true teacher performance varies and because value-added measures are subject to error.
- Two years of data does a meaningfully better job at predicting value added than does just one. A teacher’s value added in one subject is only partially predictive of her value added in another, and a teacher’s value added for one group of students is only partially predictive of her valued added for others.
- The variation of a teacher’s value added across time, subject, and student population depends in part on the model with which it is measured and the source of the data that is used.
- Year-to-year instability suggests caution when using value-added measures to make decisions for which there are no mechanisms for re-evaluation and no other sources of information.
Value-added models measure teacher performance by the test score gains of their students, adjusted for a variety factors such as the performance of students when they enter the class. The measures are based on desired student outcomes such as math and reading scores, but they have a number of potential drawbacks. One of them is the inconsistency in estimates for the same teacher when value added is measured in a different year, or for different subjects, or for different groups of students.
Some of the differences in value added from year to year result from true differences in a teacher’s performance. Differences can also arise from classroom peer effects; the students themselves contribute to the quality of classroom life, and this contribution changes from year to year. Other differences come from the tests on which the value-added measures are based; because test scores are not perfectly accurate measures of student knowledge, it follows that they are not perfectly accurate gauges of teacher performance.
In this brief, we describe how value-added measures for individual teachers vary across time, subject, and student populations. We discuss how additional research could help educators use these measures more effectively, and we pose new questions, the answers to which depend not on empirical investigation but on human judgment. Finally, we consider how the current body of knowledge, and the gaps in that knowledge, can guide decisions about how to use value-added measures in evaluations of teacher effectiveness.