A student growth percentile (SGP) score compares a student’s current performance to the performance of their academic peers across the country, using data from large scale longitudinal education assessments. This is accomplished through the use of quantile regression (aka, linear model) and produces a set of matrices that can be used to project/model future achievement levels using a variety of metrics including, but not limited to, percentile rank and mean absolute deviation.

The SGP software package contains classes, functions and data sets that enable users to perform these analyses. It also provides an interface that allows a simple workflow for preparing, loading and running SGP analyses. Almost all errors that occur during this process revert back to data preparation so it is critical to prepare data properly before beginning analysis.

There is an ever growing amount of information that needs to be collected and analyzed by the modern organization. Often this data is stored in multiple locations and on a multitude of devices making it difficult for teams to find, organize and share the information they need. This is known as data sprawl and it can be a huge problem for organizations. Data sprawl takes smart people away from impactful work to manage these numerous sources of data. It can also result in security issues and a lack of consistency when sharing the data.

Originally the term “big data” was used to describe datasets that were too large for traditional data management applications. However, the SGP research we do is relatively modest in scope compared to other big data efforts. The SGP team is working to assemble an unprecedented amount of information for the scientific questions at hand but in comparison to, say, a global analysis of Facebook interactions it is still fairly small potatoes.

The SGP observatory is located on 160 acres of cattle pasture and wheat fields southeast of Lamont, Oklahoma and consists of in situ and remote-sensing instrument clusters that cover an area of approximately 9,000 square miles in north central and south central Oklahoma and southern Kansas. The observatory offers scientists high-quality observations of atmospheric processes that are then used to improve models of the Earth’s climate system.

The SGPdata package provides 4 examplar data sets for use with SGP analyses. The first, sgpData, specifies data in the WIDE format that is used by the lower level SGP functions studentGrowthPercentiles and studentGrowthProjections. The other two data sets, sgpData_LONG and sgptData_LONG specify data in the LONG format that is used by higher level functions like abcSGP, prepareSGP and analyzeSGP. We recommend that most analyses are run with the LONG data format as it has many preparation and storage benefits over the WIDE format.