Data SGP is an R package (software) which offers classes, functions and data for calculating student growth percentiles and projections/trajectories from longitudinal education assessment data. Utilizing quantile regression and matrix representation of conditional density density curves as growth indicators. Comprehensive documentation and help can be found on the Data SGP website.
SGPs measure students relative to academic peers with comparable MCAS test histories and grades; academic peers do not need to belong to the same subgroup, but must share a testing grade/subject/subject area.
Each year, the state calculates SGPs based on trends in student performance in their entire school district and across the state. Thus, depending on these statewide performance trends and individual student factors (for instance: high sick student numbers during Covid-19 pandemic which could have caused them to experience less-than-typical growth), their SGP could vary between years.
Interpreting a student’s SGP should follow the same pattern as interpreting Renaissance Star achievement scores or rankings. SGP can be extremely helpful to teachers when combined with classroom assessments; teachers can use it to determine if a child is making adequate growth towards meeting state standards, or whether additional progress needs to be made.
Utilizing SGP data to identify students who require additional support is also critical. Beyond helping identify those not meeting grade-level expectations, the information can also help teachers plan instruction and assess student learning by targeting interventions or resources to address any gaps they identify in performance of their students.
Teachers eligible for modified SGP should submit district course roster submission data along with relevant SGP scores from students to calculate their mSGP score. Teachers should review this data with their supervisors in order to ensure all applicable students have been included on their rosters.
SGP analyses utilize two data sets – long formatted data for window specific SGPs, and current SGPs that represent students’ current progress. Long formatted data makes managing and updating analyses much simpler than working with wide formatted data; most higher level SGP analyses are designed around longer formats as a result. When working with LONG data analysis sets, these variables must be present: VALID_CASE, CONTENT_AREA, YEAR, ID, SCALE_SCORE and GRADE are mandatory when dealing with long formatted analyses while LAST_NAME and FIRST_NAME are optional when creating individual student level student growth and achievement plots.