- Value-added measures partially level the playing field by controlling for many student characteristics. But if they don't fully adjust for all the factors that influence achievement and that consistently differ among classrooms, they may be distorted, or confounded (An estimate of a teacher’s effect is said to be confounded when her contribution cannot be separated from other factors outside of her control, namely the the students in her classroom.)
- Simple value-added models that control for just a few tests scores (or only one score) and no other variables produce measures that underestimate teachers with low-achieving students and overestimate teachers with high-achieving students.
- The evidence, while inconclusive, generally suggests that confounding is weak. But it would not be prudent to conclude that confounding is not a problem for all teachers. In particular, the evidence on comparing teachers across schools is limited.
- Studies assess general patterns of confounding. They do not examine confounding for individual teachers, and they can't rule out the possibility that some teachers consistently teach students who are distinct enough to cause confounding.
- Value-added models often control for variables such as average prior achievement for a classroom or school, but this practice could introduce errors into value-added estimates.
- Confounding might lead school systems to draw erroneous conclusions about their teachers – conclusions that carry heavy costs to both teachers and society.
Value-added models have caught the interest of policymakers because, unlike using student tests scores for other means of accountability, they purport to "level the playing field." That is, they supposedly reflect only a teacher's effectiveness, not whether she teaches high- or low-income students, for instance, or students in accelerated or standard classes. Yet many people are concerned that teacher effects from value-added measures will be sensitive to the characteristics of her students. More specifically, they believe that teachers of low-income, minority, or special education students will have lower value-added scores than equally effective teachers who are teaching students outside these populations. Other people worry that the opposite might be true - that some value-added models might cause teachers of low-income, minority, or special education students to have higher value-added scores than equally effective teachers who work with higher-achieving, less risky populations.
In this brief, we discuss what is and is not known about how well value-added measures level the playing field for teachers by controlling for student characteristics. We first discuss the results of empirical explorations. We then address outstanding questions and the challenges to answering them with empirical data. Finally, we discuss the implications of these findings for teacher evaluations and the actions that may be based on them.
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