SGP-package: SGP: Student Growth Percentiles & Percentile Growth...

In SGP: Student Growth Percentiles & Percentile Growth Trajectories

SGP: Student Growth Percentiles & Percentile Growth Trajectories

Description

SGP contains classes and functions to calculate student growth percentiles and percentile growth projections/trajectories following methodology found in Betebenner (2008, 2009). The package contains two primary functions, studentGrowthPercentiles and studentGrowthProjections, and numerous higher level functions that make use of them including: prepareSGP, analyzeSGP, combineSGP, summarizeSGP, visualizeSGP and outputSGP. These functions are used to calculate and visualize student growth percentiles and percentile growth projections/trajectories for students using large scale, longitudinal assessment data. These norm- and criterion-referenced growth values are currently used in a number of states for many purposes including diagnostic and accountability. The functions employ quantile regression (using the quantreg package) to estimate the conditional density for current achievement using each student's achievement history. Percentile growth projections/trajectories are calculated using the coefficient matrices derived from the student growth percentile analyses. These quantities are summarized in a variety of ways to describe student growth. Beginning with version 1.4-0.0, the SGP package also calculate time dependent SGPs (SGPt) and allows for student growth projections to be calculated across assessment transitions by equating the two tests.

Details

Package:	SGP
Type:	Package
Version:	2.1-0.0
Date:	2023-10-23
License:	GPL-3
LazyLoad:	yes

Calculation of student growth percentiles and percentile growth trajectories/projections is typically performed by grade and subject. Data for growth percentile calculation must be specifically formatted. See sgpData for an example data set. Batch R syntax for performing analyses across all grades and years is provided in the examples of the studentGrowthPercentiles and studentGrowthProjections using the higher level functions prepareSGP, analyzeSGP, combineSGP, summarizeSGP, and visualizeSGP.

Author(s)

Damian W. Betebenner [e-mail], Adam Van Iwaarden [e-mail], Ben Domingue [e-mail] and Yi Shang [e-mail]

References

Betebenner, D. W. (2008). Toward a normative understanding of student growth. In K. E. Ryan & L. A. Shepard (Eds.), The Future of Test Based Accountability (pp. 155-170). New York: Routledge.

Betebenner, D. W. (2009). Norm- and criterion-referenced student growth. Educational Measurement: Issues and Practice, 28(4):42-51.

Betebenner, D. W. (2012). Growth, standards, and accountability. In G. J. Cizek, Setting Performance Standards: Foundations, Methods & Innovations. 2nd Edition (pp. 439-450). New York: Routledge.

Castellano, K. E. & McCaffrey, D. F. (2017). The Accuracy of Aggregate Student Growth Percentiles as Indicators of Educator Performance. Educational Measurement: Issues and Practice, 36(1):14-27.

Koenker, R. (2005). Quantile regression. Cambridge: Cambridge University Press.

Shang, Y., VanIwaarden, A., & Betebenner, D. W. (2015). Covariate measurement error correction for Student Growth Percentiles using the SIMEX method. Educational Measurement: Issues and Practice, 34(1):4-14.

SGP documentation built on Oct. 23, 2023, 5:08 p.m.