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The SGP package is a collection of classes, functions and data used to calculate student growth percentiles and projected/trajectories using large scale longitudinal education assessment data. As an implementation of quantile regression it offers various statistical techniques for estimating achievement conditional densities, covariance matrix calculations for given students as well as making projections of future scores (percentile growth projections) to show progress a student must make towards reaching their achievement target.

SGP models differ from VAMs in that they provide direct estimates of individual differences in learning processes that lead to student achievement, making them more sensitive to changes in instructional practices than VAMs and thus suitable for future accountability systems that prioritize growth measures.

To conduct SGP analyses, you will require longitudinal assessment data containing raw scores and growth percentiles from Early Literacy and Math content areas – specifically 8 windows (3 annually). A minimum dataset required is the sgptData_LONG set which provides 8 windows of annual assessment data in long format from Early Literacy and Math content areas; state level student aggregates can also be found within the sgpstateData meta-data set.

SGP analyses require significant work in terms of data preparation. Once prepared correctly, calculating student growth percentiles and projections becomes relatively straightforward – however, in practice the preparation phase may take significantly more time than anticipated.

To maximize results from SGP analysis, it is crucial to recognize its limitations. More specifically, the SGP model only accurately estimates student score distribution within a limited range. For instance, if student scores are highly skewed then this method may only accurately represent some points within that distribution – this limitation of SGP analysis must be addressed through additional statistical methods that correct for its effect; however these additional methods require expert knowledge in order to properly implement.