Data SGP utilizes longitudinal student assessment data to produce statistical growth plots (SGP) which measure students’ relative progress compared to their academic peers. SGPs help educators and districts determine whether or not a student has met an agreed upon growth target (e.g. 75% of peers).
SGPs report growth more clearly to all stakeholders by reporting percentage increases within each academic peer group and making interpretation easier; they allow schools/districts to more clearly communicate to teachers, parents, and educators the level of student growth expected of them and serve as motivation tools by linking performance against measurable goals (something not possible with standard growth models alone).
SGPs stand out from other methods for measuring student achievement because they allow schools and districts to measure the rate of students’ improvement over time, rather than simply comparing current results against an artificial benchmark. This feature allows schools and districts to more effectively inform instructional practice, provide feedback to students, educators/administrators, support classroom research initiatives and evaluate overall effectiveness/performance as schools/districts.
SGPs are produced for each student in a district using an innovative methodology that takes into account the assessment’s content complexity, grade-level differences or subgroup distinctions, as well as differences in ability levels among academically similar peers. As a result, SGPs have outshone seven state-of-the-art methods of creating comparable longitudinal growth estimates.
For more details on the methodology behind SGPs, please consult our SGP Technical Vignette.
SGP analyses require a computer capable of running R, an open-source, free programming environment available on Windows, OSX and Linux operating systems. While SGP analysis functions within the sgp package require some knowledge of R for optimal operation, CRAN offers many resources that can assist newcomers in becoming acquainted with its basics.
SGP analyses typically require software/hardware as well as an understanding of its methodology; once data preparation has been completed correctly, SGP analyses can often be relatively straightforward and quick to run. Most errors during analysis usually stem from improper preparation; we advise schools/districts to spend most of their time here before diving into operational use of the tool.