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SGPs compare the performance of students on a single test section against other students who have taken the same assessment in the past. The goal of SGPs is to provide educators with a new metric to better understand student progress and growth, while also minimizing concerns about the reliance on one metric alone for educator evaluations. The tool has been the subject of much academic debate and has received some criticism from researchers.

While SGPs can be useful, educators should be careful when interpreting them and apply them in ways that are consistent with established practices. It is particularly important to understand that SGPs do not measure achievement directly; rather, they represent a comparison of student performance on a single test section against the performances of other students with similar background conditions who have also taken the same assessment in the past. This means that even students who have very high raw scores on previous test sections may not be considered to be making great progress if they haven’t surpassed the performance of other students who took the same assessment in the past.

In addition, educators should ensure that they understand how SGPs are calculated and the limitations of the metric before using it in their practice. They should be particularly cautious when comparing students across grades or schools and when assessing teachers based on SGPs. While SGPs can be used to identify students in need of assistance, they should not be used as a substitute for more comprehensive and rigorous evaluations of teacher practice.

Data sgp is available in two formats, WIDE and LONG. The higher level wrapper functions studentGrowthPercentiles and studentGrowthProjections require the LONG format, which has numerous preparation and storage benefits over WIDE data.

When using the sgp_percentiles and sgp_projections functions, it is recommended that the user have at least 4GB of memory per core. This is because the analysis requires both a large amount of RAM and many cores to run in parallel. In addition, the sgp_percentiles function uses all four cores available, while the sgp_projections only requires 2 cores to run. Using these settings will result in faster, more stable analyses for state data that would otherwise take days to complete on a typical desktop machine. It is also recommended that users test their SGP analyses on a smaller dataset to verify that the results are valid before applying them to their operational SGP processes.