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SGP is a method for comparing student performance in a content area to the performance of students who have similar prior test scores (their academic peers). Although data sgp are complex, information can be shared in percentile terms that are familiar to teachers and parents. The sgp package provides tools to help users produce SGP analyses.
The sgpdata package includes exemplar WIDE and LONG formatted data sets as a reference to assist with the SGP analysis process. In general, the lower level SGP functions, such as studentGrowthPercentiles and studentGrowthProjections, utilize WIDE data sets. However, for operational analysis of longitudinal data it is generally better to use the LONG formatted data sets.
Data sets sgpData_INSTRUCTOR_NUMBER and sgpData_LONG are provided with the SGPdata package as exemplary WIDE and LONG formatted data sets. These exemplar data sets provide examples of how to organize assessment data into a SGP compatible format for time dependent analyses. These data sets require a unique student identifier, grade level/time associated with the students assessment occurrences and scale scores from 5 years of assessments.
The SGP stateData object provides an array of baseline coefficient matrices and lagged projections for the SGP model. These can be produced for a single content area or multiple content areas using a variety of regression models. For example, a model could include linear and quadratic models as well as a variety of different trend models. The SGPstateData object also contains a set of pre-built confidence intervals for the lagged projections and the SGP trajectories. These confidence intervals can be used to estimate the confidence interval for the SGP trajectories, projections and quartiles.
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