August 26, 2023

Data SGP is an open-source software package developed by Adam Van Iwaarden, Daniel Aguilar and Michael Kiesling to calculate student growth percentiles and projections/trajectories using large scale longitudinal education assessment data such as tests, portfolios, grading scales or any other source. It can be used to analyze student development trends over time using large-scale longitudinal assessments such as tests, portfolios or any other source available to them.

The sgpData_WIDE data set contains anonymized panel data from five years of annual, vertically scaled assessments in WIDE format. The first column, ID, provides unique student identifiers while subsequent columns contain assessment scores from each year of assessment; this exemplar data set meets all requirements set forth by lower level studentGrowthPercentiles and studentGrowthProjections functions.

Numerous factors compound the difficulty of interpreting estimated student growth percentages at the teacher level, including student background characteristics and latent achievement trait relationships. Such influences often explain a substantial fraction of variance in SGP estimates for teachers; moreover, it remains uncertain which students truly are high achieving and which are underachieving.

SGP covers 160 acres of cattle pasture and wheat fields southeast of Lamont. It comprises an intensively instrumented Central Facility as well as numerous unstaffed facilities dotted throughout the field, which scientists utilize for studying atmospheric and ocean processes, assimilating data into Earth system models, or other purposes.

SGP has long collaborated with other research institutions worldwide to share data and information, while working alongside private companies and entrepreneurs on innovative applications of its data. Thanks to such collaborations with scientists and industry partners, it remains at the forefront of atmospheric science and ocean observation technology.

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