What is Data SGP?
Data sgp is the software package that provides classes, functions and data for calculating student growth percentiles (SGP) and percentile growth projections/trajectories using large scale, longitudinal education assessment data. It is built on top of the open source statistical programming environment R. Using it requires proficiency with that software. It is recommended that you familiarize yourself with the software before diving into running SGP analyses. The package is available for Windows, OSX and Linux and, being open source, can be compiled on almost any computer.
SGP is a score that describes how much a student has grown in comparison to other students with the same initial test score. It provides a more accurate measure of growth than a standard percentile because it takes into account the initial test scores and growth over time instead of just the latter. This means that a student with a lower raw score can have high SGP, while a student with a higher initial score can have low SGP.
A growing number of school districts and states are using SGP to evaluate the progress of their students. They are doing this because the data is more accurate than traditional test results and it allows them to see if their students are making progress toward their goals. The data used for SGP calculations is collected through assessments taken by students over the course of a school year. The data is analyzed using a mathematical algorithm to calculate a student’s current SGP. In addition, SGP can also be used to predict a student’s future SGP based on their current performance and their previous achievement history.
ARM transmits all of its measured data to the ARM Data Center and makes it freely available via Data Discovery. This includes large-eddy simulation model outputs, a self-consistent representation of the atmospheric system simulated by the models and their instrument data. This is important because it gives context to the measurement data and can help explain why a certain result may be observed.
In a broad sense, the term “big data” refers to datasets that are too massive for traditional data analysis tools to manage efficiently. The data that SGP is assembling for its research is far larger than what is commonly referred to as big data, but it still doesn’t quite fit the definition of the phrase.
ARM’s instruments produce a vast amount of data that must be managed and stored. To make this data useful to researchers and to enable us to answer scientific questions, ARM has developed an architecture that integrates modeling capabilities with the instrumentation data sets. In other words, the large-eddy simulation (LES) models can use the same instrumentation data as the ARM physics models to perform their own calculations of atmospheric air currents and clouds. This allows the two to work together seamlessly and to provide a self-consistent, integrated representation of the atmosphere. The resulting models and their underlying data can then be made available for other researchers to use, so that they can understand the results of these studies and improve their own.