Data SGP is an extensive information database designed to assist educators with pinpointing issues of student development and improving teaching methods. However, conducting analyses on such an enormous volume of data can be daunting and require significant time and effort – it is vitally important that educators fully comprehend how Data SGP operates as well as its limitations.
“Big data” has become a buzzword in both science and society. While SGP research may include large volumes of information, it still falls below other forms of analysis; for instance, an analysis of global Facebook interactions would require much greater computing power. Therefore, we refer to data sgp as medium data: it fits within an established relational database yet still produces millions of analytical results.
Data SGP was founded with the primary objective of providing researchers with a dataset that will enable them to address particular aspects of Earth history. As such, its primary use is not as a permanent repository, but as a platform upon which other databases may be established.
As opposed to other student performance data, SGP offers unique capabilities in showing growth over time and how students compare with their academic peers. This is made possible because it utilizes student-level covariates derived from prior testing histories in determining relative progress – providing more accurate evidence of academic growth than traditional percentile scores do.
SGP allows schools to set achievement targets/goals using state growth standards, making this tool useful in communicating to stakeholders that proficiency must be reached within a specified timeframe, as well as serving as an effective motivational tool for teachers.
To use SGP, you will require a computer running the free software R, available for Windows, OSX and Linux platforms. Before beginning an SGP analysis it is highly advised that you familiarise yourself with its basics – there are various resources on CRAN to assist with that goal.
The SGPdata package includes an example WIDE format data set to allow users to experiment with creating SGPs, the formulae for SGPs, an illustrated vignette on how to construct one and other utilities for working with this type of data.
SGPdata’s primary limitation lies in its incompatibility with other statistical programs other than R. Though you could try using it with other statistical programs like Python or Stata, significant manual conversions will likely be required – so we advise only using it on machines running R for best results. If this proves inapplicable for your needs, expect future versions of the package to accommodate other statistical programs; otherwise please open an issue on GitHub so we can discuss this and implement future changes into it as quickly as possible.