SGP measures student performance on standardized tests and is an invaluable resource for districts. However, collecting and analyzing SGP data can be complex; errors can arise and it’s crucial that we recognize how any variations from actual achievement affect SGP estimates.

One strategy for minimizing errors in SGP analyses is using baseline-referenced SGPs, which compare student scores against those from comparable students in their grade level at the beginning of measurement period. This reduces estimation error caused by spurious correlations between measured students and other variables such as teacher or school characteristics or baseline cohort design.

Another way to reduce estimation error is using a statistical model that compares each student’s estimated score with what would be expected based on performance on all standardized tests administered within their content area. This approach uses latent achievement trait models and Bayesian inference to enable multiple estimates of student growth to identify which offers the most accurate result.

Districts that collect and analyze their own SGP data have the power to make more informed decisions regarding instruction, assessment, and student learning. Furthermore, they can create leagues against each other in an attempt to win prizes; those paying an entry fee have an assurance that standings information won’t be altered by “schleps” who join only for a couple weeks and then abandon it before signing their team sheets – making premium leagues the ideal way to ensure accurate SGP data collection and analysis.