Data SGP is a database of geochemical data from sedimentary rock from the Neoproterozoic through Paleozoic. It is a major effort to compile and generate multi-proxy sedimentary geochemical data (iron, carbon, sulfur, major and trace metals) across geological timescales for various lithologies and regions from the global distribution of rock types. The ultimate goal is to migrate these datasets into permanent archival data repositories.
The SGP team is working to assemble this unprecedented amount of information for the scientific questions at hand. While this represents a step up in scale from previous efforts in this area, the size of the dataset is still very modest in comparison to what is considered “big data” in other areas of research (e.g., Facebook interactions).
What is data sgp?
The data sgp system allows teachers and administrators to view students’ historical test score growth trajectories in percentile terms that are familiar to most parents. This can help them better understand their student’s academic progress and identify areas for improvement.
To generate these estimates, the SGP analysis program uses a combination of students’ prior test scores, current test scores and their past growth trajectories. These are combined to create a true SGP for each student, which is a measure of the student’s relative performance in MCAS compared to other students with similar testing histories. For example, a student’s SGP for math would indicate that the student has achieved better MCAS growth than 90% of other students with comparable testing histories.
One of the purported benefits of SGPs is that they provide a more fair and relevant measure of achievement than unadjusted test score differences. This is based on the fact that ranking students by their current achievement status against those of students with similar testing histories levels the playing field. SGPs are also believed to be more useful than unadjusted test score averages in evaluating educator effectiveness.
However, if SGPs are correlated with background characteristics, they may not be as beneficial as they are advertised. Hence, it is important to understand the distributional properties of true SGPs. Fortunately, it is possible to examine the distribution of these measures and how they vary with student background variables.
This article specifies a model for latent achievement attributes, defines true SGPs under this model, and evaluates to what extent they are related to student background characteristics. Specifically, we compare conditional mean estimators of e4,2,i (the true SGP for math) to the student background variables of gender, race/ethnicity and home language.
The results demonstrate that these covariates do explain a significant percentage of the variation in the true SGPs, but the relationships are not linear. This is because the estimated SGPs for different students differ slightly, and these differences are influenced by the differences in the underlying latent achievement attributes. Thus, SGPs do not necessarily level the playing field for all students and should be used with caution. It is important to develop alternative estimation methods for measuring student achievement that can reduce undesirable correlations with student background characteristics.