The simple average of student growth scores is often used in accountability systems, but it can be problematic for decision making. When computed using a small/moderate number of students, it can be sensitive to the sample, resulting in inaccurate representations of growth of the students, low year-to-year stability, and inequities for low-incidence groups. An alternative designed to address these issues is to use an Empirical Best Linear Prediction (EBLP), which is a weighted average of growth score data from other years and/or subjects. We apply both approaches to two statewide datasets to answer empirical questions about their performance. The EBLP outperforms the simple average in accuracy and cross-year stability with the exception that accuracy was not necessarily improved for very large districts in one of the states. In such exceptions, we show a beneficial alternative may be to use a hybrid approach in which very large districts receive the simple average and all others receive the EBLP. We find that adding more growth score data to the computation of the EBLP can improve accuracy, but not necessarily for larger schools/districts. We review key decision points in aggregate growth reporting and in specifying an EBLP weighted average in practice.