SGPA is a dataset containing tens of thousands of geochemical observations and associated metadata. This project is designed to support the scientific community in understanding environmental changes on Earth through time. The initial focus has been on assembling a well-vetted data set that is tractable to multivariate statistical analyses, accounting for multiple geological and methodological biases. This first phase of the project focused on the Neoproterozoic and Paleozoic, with future phases planned to capture a broader range of geologic time, data types, and geography.
The SGPA database contains a unique set of chemical data for sedimentary rock samples collected from global locations and across many geological conditions, representing the broadest and most diverse collection of sedimentary geochemical data ever assembled. The SGPA dataset is complemented by additional data from other projects and sources, including NGDB and the OZCHEM whole-rock database. A primary goal of SGP has been to compile a high-quality dataset that is readily available for the research community, and to develop methods for making best use of this data.
The SGP dataset is an important tool in the development of new computational models of sedimentary formations and lithologic processes. The data are provided free of charge, and are used to develop statistical and probabilistic methods for analyzing and understanding geological problems. This will help scientists better understand the relationships between the environment and natural resources, and improve the ability to predict future trends.
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In addition to providing long formatted data in the @Data slot (from prepareSGP), this function provides a wrapper for producing student growth percentiles and projections/trajectories. These values are based upon priorly established coefficient matrices, derived from a stratified random sample. If the argument is NULL, no stratified SGPs are calculated.
SGPs are a measure of the amount of progress a student has made relative to academically-similar students with similar latent achievement traits. However, studies have shown that SGPs estimated from standardized test scores suffer from large estimation errors. This results in noisy measures of true SGPs, defined as the average of a student’s current latent SGP over all students with that same previous latent SGP. The figure below shows SGPs by subject and cohort. Group mean differences are reported for gender, race/ethnicity, and home language. Note that the groups are ordered from most negative to most positive, although the averages of the math and ELA group mean differences tend to be quite similar. The EXCLUDE statements specify individual terminal IDs or record IDs to exclude from the set of X-SGP records that this X-SGP record cross-references.