Data sgp is a powerful analysis tool for longitudinal student assessment data that generates statistical growth plots (SGP) depicting students’ academic progress relative to their peers. The SGP is a more accurate method of assessing student achievement compared to traditional percentile scores. However, using the SGP requires careful data preparation and formatting. Failure to prepare the data correctly can lead to inaccurate or misleading results.
In order to use the SGP effectively, educators must first understand the method’s methodologies and assumptions. Then they can make more informed decisions about how to best utilize their data and any limitations that may limit further analysis techniques being applied to their dataset.
Teachers seeking SGP data can register with their state’s website and then download reports tailored to their schools or districts. These reports provide educators with an analysis of student performance by subject, grade level and time; including which percentage fell outside or within the target range, as well as how that percentage changed over the course of the year. These reports can be useful for helping educators identify areas for improvement and highlighting areas of excellence.
The SGP can also be used to compare student growth across groups of schools. This allows administrators to assess the overall effectiveness of their schools and programs by identifying which schools are growing the fastest, as well as which ones need the most assistance. This information can help them determine which resources and strategies to apply to their own school or district.
While the SGP is working to assemble unprecedented amounts of data for this type of research, it remains a relatively small effort in comparison to other efforts in large-scale data management and analysis such as Facebook analytics. In fact, we like to think of it as a ‘medium data’ project, where the scale is far greater than previous efforts but the size is still manageable for simple, straightforward application and interpretation.
SGP research has many benefits, but it is important to keep in mind that these analyses are limited by the quality of the underlying data. SGP analyses are only as good as the quality of the underlying data set, which means that educators should take steps to ensure that their data sets are prepared and cleaned properly before attempting any SGP analyses.
In the case of SGP, it is generally preferable to format the data in LONG format rather than WIDE. This is especially true if you plan to run SGP analyses operationally year after year, as the higher level wrapper functions that use the LONG data format have numerous preparation and storage benefits over the lower level WIDE data formats. The sgptData_LONG data set is an example of an anonymized, panel data set consisting of 8 windows (3 windows annually) of assessment data in LONG format for 3 content areas (Early Literacy, Mathematics, and Reading). There are 7 required variables when using the LONG data with SGP analyses: VALID_CASE, CONTENT_AREA, YEAR, ID, STUDENT_CATEGORIZATION, SCALE_SCORE and GRADE.