Data quality. Missing or faulty data can have a negative impact on the precision and stability of value-added estimates and can also contribute to bias. While data quality is important for any evaluation system, the requirements for value-added models tend to be greater because longitudinal data are needed, often for a variety of variables.

Complexity versus transparency. More complex value-added models tend to have better technical qualities. However, there is always the point at which adding more complexity to the model results in little or no additional practical advantage while, at the same time, making it more difficult for educators and the public to understand. A challenge is to find the right balance between complexity and transparency.