Data sgp is an analysis tool for longitudinal student assessment data. It creates statistical growth plots (SGPs) that show students’ progress in comparison to their academic peers. SGPs use standardized test scores with covariate information to estimate latent achievement traits. This allows the comparison of students’ performance over time, even if they enter school with different ability levels. It also provides more accurate measurements of student growth than traditional percentile scores do.
SGP analyses compare a student’s current assessment score with the score of an identical cohort of students with similar initial MCAS performance to establish whether a student has met or exceeded a teacher evaluation criteria or other student-specific growth standards. The methodology utilized to construct SGPs is based on least squares regression and Bayesian inference. These methodologies attempt to minimize error by estimating models of latent achievement traits and comparing the estimates against the observed data using the maximum likelihood method. These error estimates are then used to establish the students’ relative growth – a measure that is comparable across time, teachers, and schools.
Located on 160 acres of cattle pasture and wheat fields southeast of Lamont, the SGP observatory is home to many instrumented facilities. The central facility hosts continuous observational and simulation capabilities to support atmospheric scientists. The observatory is staffed by technicians who monitor observations from the Central Facility and numerous smaller instruments at other locations throughout the site.
The SGP website contains a variety of data sets and tools to facilitate scientific research. The ARM site team is constantly working to enhance the user experience and add new data sets and analytical capabilities. These data sets range from single observation analyses, to multi-observation process studies, and assimilation into earth system models.
The SGP website provides access to a growing collection of high quality scientific data for public use. The site features a wide variety of instrumentation, from large-eddy simulation (LES) modeling frameworks to single observation analyses. All of the ARM data collected at the SGP are available for download from the ARM Data Discovery service and can be incorporated into earth system models through the LES ARM Symbiotic Simulation and Observation (LASSO) project. These models are designed to enable scientists to perform a variety of tasks, from simple single-observation analysis to complex simulation and assimilation of the SGP data into global Earth systems models. They can help to understand the dynamic processes that govern our atmosphere and the connections between climate change and human health. They can help to predict future conditions, develop policies to mitigate climate change impacts, and provide important inputs into other models of the atmosphere, such as weather forecasting. This is critical to achieving a more sustainable future for the planet.
