Value-added models (VAMs) are increasingly used in research and policy applications to evaluate schools and teachers. VAMs are based on student scores on standardized tests, and the properties of the testing instruments can influence model performance. This is an understudied area in the value-added literature. The papers in this panel focus on two key issues related to the properties of standardized testing instruments and their implications for value-added modeling. First is the issue of test measurement error. While it is widely acknowledged that standardized tests are noisy measures of student learning, most VAMs do not directly account for imprecision in the tests. Furthermore, in instances where VAMs are adjusted to account for test measurement error, the adjustments are made using information from a limited research base. This panel covers methods by which currently available test-measurement-error metrics can be used to immediately improve VAM performance, as well as methods to better measure and model test measurement error moving forward. A second issue covered in the panel involves the interaction between testing-instrument properties and student tracking in higher grades. Researchers and policymakers are still trying to determine how VAMs can and should be used beyond the elementary level, and an important practical consideration in later grades is that students are more-strictly tracked by ability. The influence of testing-instrument properties on student scores (e.g., test-score ceilings), and the alignment between exam content and coursework, differ across tracks of students. This has important implications for the application of VAMs in later grades.