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To use the SGP package, one needs a computer with the R software environment installed. R is open source and available for Windows, Mac OSX, and Linux. Since the SGP package is a data analysis tool, familiarity with R is helpful. Running SGP analyses also assumes some knowledge of statistics and mathematics. If these topics are not familiar, there are many resources available to help learn these concepts.
The SGP package uses a variety of statistical techniques to analyze large scale, longitudinal education assessment data. It includes several quantile regression models to estimate the conditional density associated with each student’s achievement history and generates percentile growth projections/trajectories using those estimated values. The resulting coefficient matrices are then used to calculate student growth percentiles, a measure of how far a student must grow to reach future achievement targets.
Unlike standard test scores, SGP compares a student’s performance to the performances of his or her academic peers nationwide. Thus, two students with very different scale scores can have the same SGP score. A student’s SGP may also change over time. This is because a new assessment can produce different results. The same is true if a student’s teacher changes, or if the students switch classrooms.
In addition to providing student growth percentiles, the SGP package can also produce a variety of educational trend reports and graphs. These are useful for tracking student progress over time and identifying a student’s strengths and weaknesses. Educators can then use these reports and graphs to develop individual plans for improvement.
As part of its mission, the SGP maintains a large-scale, long-term atmospheric observatory in an agricultural setting southeast of Lamont, Oklahoma. The main facility consists of a heavily instrumented central facility and an extensive network of remote sensors. The observatory provides high quality observational and modeling data for a wide range of atmospheric research. Observations are supplemented by guest instruments during field research campaigns and by the use of advanced Earth system models for data assimilation.
Although SGP has taken a major leap in scale and scope from previous research, it still remains a relatively small data set by the standards of the big data movement. In comparison, for example, to a study of global Facebook interactions, the SGP dataset is still quite manageable. Nevertheless, we have found that assembling this information presents some unique challenges and opportunities for analysis. To meet these challenges, we have developed a number of tools to make the most of our data. These include a variety of scripts to perform complex analysis tasks and web interfaces that present the results in an intuitive way. We have also created a wiki that describes the SGP and its development. This wiki is an evolving resource that will continue to be updated as new ideas and approaches are incorporated.