Data sgp is the data that is used by educators for instructional decisions and to evaluate the performance of schools/districts. It contains student performance and growth information, including academic and behavioral measures. These metrics are based on a number of factors, such as students’ underlying abilities, student behavior, teacher instruction, and family involvement. Using this data, educators can identify areas of improvement in their instructional practices and compare student performances with the performance of their peers.

Whether you are looking to conduct your own data sgp analyses or are seeking assistance in running them, it is important to understand the fundamental concepts and methodologies of SGP before getting started. The SGP package includes tutorials and examples that introduce these fundamental concepts. Getting familiar with the underlying mathematical models and assumptions will help you make informed decisions about how to apply SGP methodology to your own data.

In order to conduct an operational SGP analysis, you will need to prepare your data set and then run the SGP functions studentGrowthPercentiles and studentGrowthProjections. These two functions are the lowest level functions in the SGP package and they require that your data be formatted in the WIDE data format. If you plan on running your SGP analyses operationally year after year, then we recommend that you consider formatting your data in the LONG format which has numerous preparation and storage advantages over the WIDE data format.

The sgpData spreadsheet provides an easy and straightforward way for educators to access and analyze SGP data. This spreadsheet allows users to easily compare individual students’ results with those of their peers and to identify areas of strength and weakness within their school or district. It also provides a convenient way for teachers to monitor their own progress in implementing the Common Core standards, as well as for administrators to assess the performance of their schools and districts.

There are 7 required variables when using LONG data with SGP: VALID_CASE, CONTENT_AREA, YEAR, ID, SCALE_SCORE, GRADE and ACHIEVEMENT_LEVEL (required if creating student growth/achievement plots). The first 5 variables must be present in all plots generated by the function summarizeSGP. The remaining variables are demographic/student categorization variables that can be added to plots generated by the function visualizeSGP.

For more information about how to prepare your data for SGP analyses, please visit our SGP Package documentation page. There, you can find the exemplar LONG data set, sgpData_LONG and INSTRUCTOR-STUDENT lookup file, sgpData_INSTRUCTOR_NUMBER and the SGP wrapper functions abcSGP and updateSGP that simplify the source code needed to conduct these analyses. SGP analysis requires careful attention to the data set preparation and management steps, so it is helpful for beginners to work with example data sets while gaining experience with the underlying processes. As proficiency in these steps increases, it becomes easier to customize and adapt the analysis for your own data set. This is the most effective and efficient way to learn SGP. The more time you spend preparing your data before conducting SGP analyses, the more accurate and reliable your outcomes will be.