Expansion of the use of student test score data to measure teacher performance has fueled recent policy interest in using those data to measure the effects of school administrators as well. However, little research has considered the capacity of student performance data to uncover principal effects. Filling this gap, this article identifies multiple conceptual approaches for capturing the contributions of principals to student test score growth, develops empirical models to reflect these approaches, examines the properties of these models, and compares the results of the models empirically using data from a large urban school district. The article then assesses the degree to which the estimates from each model are consistent with measures of principal performance that come from sources other than student test scores, such as school district evaluations. The results show that choice of model is substantively important for assessment. While some models identify principal effects as large as 0.18 standard deviations in math and 0.12 in reading, others find effects as low as 0.0.05 (math) or 0.03 (reading) for the same principals. We also find that the most conceptually unappealing models, which over-attribute school effects to principals, align more closely with nontest measures than do approaches that more convincingly separate the effect of the principal from the effects of other school inputs.