Data sgp is a software tool that provides a variety of growth projection analyses for teachers and students. Its documentation, vignettes and examples provide thorough explanations of its calculations and processes for conducting SGP analyses; users are strongly encouraged to familiarize themselves with these resources prior to conducting any analyses. In addition to its basic and advanced growth projection functions, the SGP package also includes higher level function wrapper packages such as abcSGP and updateSGP that combine lower level functions into a single function call, simplifying source code associated with operational analyses.
The sgpData exemplar data set contains five years of annual, vertically scaled assessment data. Its identifier columns contain a unique student identifier, grade level/time associated with the students assessment occurrence and numeric score associated with each occurrence. This exemplar data set models the format required by the lower level SGP functions studentGrowthPercentiles and studentGrowthProjections. In addition, the sgpData exemplar dataset provides examples of how to use the higher level wrapper functions to perform growth projection analyses with WIDE or LONG format data sets. For most analyses, you will likely be better off formatting your data in the LONG format, particularly if you plan on running these analyses operationally year after year.
The SGP software tool produces two types of models, a cohort-referenced and a baseline-referenced model. Cohort-referenced models compare students’ current year growth to a prior year’s scale score. Baseline-referenced models use the students’ baseline or initial scale scores as their comparison value, and may require multiple years of stable assessment data to produce a reliable model. These models are less popular in educator evaluation systems because they require more time and effort from students, instructors, and data sources than their cohort-referenced counterparts. In addition, correlations between baseline and prior year scale scores are unlikely to be exactly zero, which can introduce substantial bias into interpretations of the resulting SGPs.
SGPs are also prone to error due to the fact that they may be correlated with other factors such as classroom demographics, teacher qualifications, and student characteristics. As such, they should only be used in conjunction with a variety of other measures when making educator evaluation decisions.
The SGP site is home to a number of scientific observatories that monitor various atmospheric conditions. These observatories collect and store continuous observational data. This data is available to researchers for a wide range of purposes, from single-observation process studies to assimilation into Earth system models. These observations, combined with the SGP software tools, are critical to understanding the complexity of our planet and how it functions. They are also essential in identifying the key factors responsible for planetary change. For example, the SGP software is used by NASA scientists to examine the impact of climate change on the global ozone layer and its constituent species. This information is then used to develop effective mitigation strategies and policies for addressing environmental problems in the future. SGP data also play a vital role in the development of new technologies to reduce greenhouse gas emissions.