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GitHub - drc-bsorenson/SGP: Functions to calculate student growth percentiles and percentile growth projections/trajectories for students using large scale, longitudinal assessment data. Functions use quantile regression to estimate the conditional density associated with each student's achievement history. Percentile growth projections/trajectories are calculated using the coefficient matrices derived from the quantile regression analyses and specify what percentile growth is required for students to reach future achievement targets.

The SGP Package is open source software built for the R software environment. The classes, functions and data within the SGP package are used to calculate student growth percentiles and percentile growth projections/trajectories using large scale, longitudinal assessment data. Quantile regression is used to estimate the conditional density associated with each student's achievement history. Percentile growth projections/trajectories are calculated using the derived coefficient matrices and show the percentile growth needed to reach future achievement targets.

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SGP Resources

Contributors

The SGP Package is crafted with ❤️ by:

We love feedback and are happy to answer questions.

Install the latest stable release from CRAN

install.packages("SGP")
require(SGP)

Install latest development release from Github :octocat:

install.packages("devtools")
require(devtools)
install_github("CenterForAssessment/SGP")
require(SGP)

To install from Github you might need: Windows: Rtools, OS X: xcode (from the app store), Linux: apt-get install r-base-dev (or similar).

To use the SGP Package

  • The SGP Package Wiki contains instructions on how to prepare data and run SGP analyses.
  • The SGP Package Documentation contains man pages for all functions and data sets in the the SGP Package.
  • Numerous SGP Gists provide other examples of analyses that can be performed.

References

Betebenner, D. W., VanIwaarden, A., Domingue, B., and Shang, Y. (2017). SGP: Student Growth Percentiles & Percentile Growth Trajectories.

R Development Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 3-900051-07-0.