What Are Student Growth Percentiles (SGPs)?
Student Growth Percentiles (SGPs) provide an insightful way to evaluate student assessment data. SGPs measure students’ growth relative to academic peers and past performance on MCAS tests over time.
An SGP score ranges from 1-99 and measures the percentage of similar students who experienced more or less growth than one student did. For instance, an SGP of 65 indicates that Simon outperformed roughly half of her academic peers across all subject matter test administrations administered during grades 4, 5, 6, 7, 8 and 10.
SGP reports for individual students aren’t the only reports available – there are also district-level SGP estimates available that can help educators and leaders assess trends in student growth across schools or districts, and pinpoint where improvement efforts must focus.
SGPs are calculated utilizing up to two years of historical MCAS data. A student’s SGP is determined by comparing their performance during spring 2024 against that of their academic peers who share similar MCAS score histories statewide in their grade, including all demographic groups and educational programs (such as sheltered English immersion or special education).
SGPs calculated for students are reported on the Badger Exam Dashboard along with other assessment data for each student. SGPs will no longer be included as educator evaluation scores since 2015/16 due to state transitioning towards value-added assessment models. To learn more about measuring student growth and its use for teacher evaluations, visit Academic Growth webpage.
To conduct SGP analyses, users require access to the open source software environment R. While numerous packages exist that can help perform statistical analysis, SGP analyses typically utilize R package data sgp for conducting these tasks. CRAN offers this package for any operating system and requires at least 64-bit processor and enough memory space to accommodate large datasets. Additionally, users should read the documentation provided by the authors of data sgp in order to familiarise themselves with its data structures and input/output formats used by the function sgptdata(). While analyses using SGP can be run on both WIDE and LONG format data sets, users who plan on running them on an ongoing basis may prefer LONG data because it offers greater preparation and storage benefits.