Data sgp leverages longitudinal student assessment data to create statistical growth plots (SGPs) that provide visual evidence of students’ relative progress compared to academic peers. SGPs are calculated using students’ standardized test scores with covariate information and a “growth standard” established through prior testing history. Unlike traditional percentile ranks, which report an absolute value, SGPs represent relative performance, making them more meaningful to educators and providing more accurate measurements of student achievement.

Educators use SGPs to identify students in need of extra support, differentiate classroom instruction for high-performing students and monitor student progress over time. Educators can also utilize growth information when designing accountability systems. The ability to compare students to official state achievement targets/goals (unlike standard growth models or other methods) is particularly powerful as it enables schools and districts to communicate to educators that student proficiency needs to be reached within a specified timeframe – an objective that cannot be achieved with standard growth models alone.

SGPs place a student’s MCAS score growth in context with other students who have similar score histories. For example, Simon’s sixth grade growth is compared with the growth of other sixth graders who started the year with MCAS scores that were similar to his. SGPs are reported as a percentage of the group of students with similar score histories. A student who grows at the 90th percentile, for example, has scored better on the MCAS this year than 90% of the students with comparable MCAS score histories.

In order to calculate SGPs, educators must have access to a state’s MCAS data and the ability to process this data. A significant amount of time and resources are spent on the processing, cleaning and organizing this data. Once these steps have been completed, the data sgp software can be used to quickly generate and analyze SGPs.

Using the software, it is possible to generate growth/achievement plots for all students in a school or district, for specific subgroups (e.g., gender, race/ethnicity), for a selected cohort and for the entire state. These plots can then be used for a variety of purposes including informing instructional practice, supporting classroom research initiatives, and evaluating schools/districts.

The software is based on the open-source R programming language. R is available free-of-charge for Windows, Mac OSX and Linux computers. The software can be downloaded from CRAN. There are numerous tutorials and other resources on the CRAN website to help users learn how to use R. In addition, the sgp package includes a vignette that guides users through the SGP analysis process.

To perform SGP analyses, users must have access to a state’s longitudinal MCAS data in WIDE or LONG format. The lower level functions, such as studentGrowthPercentiles and studentGrowthProjections require the use of WIDE formatted data whereas the higher level wrapper functions utilize LONG formatted data. In general, it is best to format the data in LONG format if the intent is to run SGP analyses operationally year after year as this has many preparation and storage advantages.