Data Sgp is a powerful tool that empowers educators to gain insight into a student’s performance relative to his or her peers. By comparing assessment scores against those of academically similar students, Data Sgp provides educators with insights into areas in which a student excels as well as areas for improvement. Furthermore, this gives educators an opportunity to identify those students who are experiencing difficulty and create plans to help them overcome any difficulties that might be hindering success.
Student Growth Percentiles (SGP) provide estimates of student progress over time. Their aim is to provide a meaningful measure that does not fluctuate over time and can help identify students at risk of falling behind their peers. Unfortunately, producing accurate and valid SGP estimates requires extensive statistical and methodological expertise, and therefore schools and districts must have trained personnel available for conducting SGP analyses.
A standard SGP analysis involves applying latent achievement trait models to estimate a student’s performance level and compare this estimate against covariates and teacher evaluation criteria. For best results, longitudinal datasets for each student should be collected over several years and used in this manner; this approach allows for the most precise estimates of SGPs; however, as resources may limit its collection this type of analysis cannot always be employed; hence SGP analysts must have experience handling both longitudinal and non-longitudinal data types simultaneously.
While most school districts possess the ability to generate SGP estimates, teachers and administrators may not always prioritize it over other competing initiatives. Furthermore, SGPs require extensive training and analysis time; so educational leaders must understand both their potential value and limitations before making their decision to implement SGPs in their district.
SGP estimates may contain some error, which can be reduced by comparing one student’s estimated SGP against that of an identical cohort at a similar grade level. Furthermore, each year of measurement should compare SGPs against an identical baseline cohort to help minimize differences in norms, assignment of Highest Obtainable Scale Score (HOSS) students and baseline methods of calculating SGPs that might impact results.
SGPdata includes two example WIDE and LONG formatted data sets (sgpData and sgpData_LONG) that demonstrate how to prepare data for use with studentGrowthPercentiles and studentGrowthProjections functions of lower level studentGrowth models.
For a comprehensive discussion on how to use and calculate SGPs, visit the School Growth Plans for Schools and Districts webpage. In addition, instructional videos and presentations covering everything from how SGPs work fundamentally through interpretation and application can also be found here; please visit this page to watch these examples from across Washington State school districts!