SGPs provide a powerful tool for educators to identify students that are struggling academically. Educators can then focus their efforts on these students to ensure they are making the academic progress needed to meet achievement targets and to help them identify areas where more work is required to accelerate student learning. In addition, SGPs help educators better understand how much growth is necessary for students to reach grade level so they can articulate these expectations to their students.
However, there are many barriers that must be overcome before SGPs can be used effectively by educators and the general public. One of the most significant barriers is the amount of data SGP analyses require, which can be difficult to manage. This is why the data sgp team has worked hard to develop tools that simplify this process and reduce the time it takes to conduct an analysis.
In order to run SGP analyses on any data set, it is necessary to perform several preprocessing steps. These include filtering, sorting and aggregation. These tasks can be time consuming and require the expertise of skilled data analysts. However, the resulting SGPs are valuable and worth the effort. In fact, the SGPs provide the most accurate representation of a student’s achievement level over time.
While the SGPs are complex and require careful preparation, the underlying algorithms behind them are straightforward. Most errors that occur during the SGP analysis process revert back to data preparation issues. This is why we recommend that users spend time preparing their data before running any SGP analysis.
The prepareSGP function is a tool that helps users prepare their data for SGP analyses. It creates additional variables often used in SGP analyses, such as HIGH_NEED_STATUS which identifies the top and bottom quartile of students by year and content area. It also creates a longitudinal data set for each student that can be used to generate SGPs and growth projections/trajectories.
A key piece of this function is the long formatted data it returns in the @Data slot of an SGP object (created by prepareSGP). This format is more manageable than wide data and all higher level functions in the SGP package are designed to work with this format. In addition, prepareSGP uses the embedded SGPstateData meta-data to provide state specific information such as coefficient matrices, cut scores and CSEMs when available.
The data sgp team has developed a number of user friendly tools that streamline this process and make it more efficient for educators to use SGPs. In the future, we plan to continue expanding this toolkit to further simplify the SGP analysis process. We will keep our community updated as these new features are released.