The student growth percentile (SGP) describes a student’s progress compared to their academic peers. SGPs allow us to fairly compare students, even if they enter the class with a different level of proficiency. While the calculations behind SGPs are complex, percentiles are a familiar method for measuring student progress.
OSPI is partnering with district educators to provide training on how to use SGP data to inform instructional decisions for their classrooms. Training will be available in the fall, winter and spring. We will also post a series of short videos on our Student Growth website to help teachers and families understand the value of this data.
We recommend that schools and districts review the videos on our Student Growth website and attend the trainings to get started using SGP data in their classrooms. District leaders can also contact their Regional Support Center for more information and to request a training date.
To run SGP analyses, you need a computer running the open source software R. R is available for Windows, OSX and Linux and there are many resources on CRAN to help you get set up. It is important to do proper data preparation before running any SGP analyses because most errors that occur during analysis revert back to issues in data preparation.
The data sgp package includes 4 examplar data sets for use with SGP analyses. The first, sgpData, specifies the format of data used with the lower level SGP functions studentGrowthPercentiles and studentGrowthProjections. The other 3 data sets are examples of yearly average SGP ratings for reading, math and science or a combination of these three subjects. These data sets are provided in both the WIDE and LONG formats. The higher level SGP functions, prepareSGP and analyzeSGP, use the LONG format data.
For SGP analyses to run correctly, the data must be sorted in ascending order by the student identifier (eg, by ID) and in descending order by the assessment occurrence (eg, by the test name). In addition, all tests must have the same scale and be taken during the same testing window.
SGPs compare a student’s performance to the performances of their academic peers nationwide. These peers are in the same grade with a similar achievement history on Star assessments. The SGP model, which was developed by Damian Betebenner, utilizes the latest in catch-up and keep-up growth projections to provide longitudinal SGP data.