Data SGP

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Student Growth Percentiles (SGPs) measure how a student’s progress compares to academically similar students with similar assessment histories. Comparing performance to peers can provide educators with important insight into students’ performance that might otherwise go undetected, making SGPs an integral component of teacher evaluation systems.

SGPs are calculated from large scale longitudinal education assessment data and used to estimate latent achievement trait models that are then compared against growth standards established through various means such as least squares regression modeling or Bayesian inference (Akram, Erickson & Meyer 2013; Lockwood & Castellano 2015).

For SGP analyses to be carried out successfully, accessing longitudinal (time dependent) student assessment data is needed. Often this data is stored in WIDE format with each case/row representing an individual student while each column represents variables associated with them at various points in time. With such access one can then calculate SGPs along with other measures of academic progress like percentile rank and achievement trajectories for each student.

These Student Growth Progression (SGP) estimates are then compared against growth standards established via teacher evaluation criteria and student covariates to generate error-free estimates of individual student growth. Estimation errors will always exist when comparing an individual student against an average value from similar performers in order to establish error-free estimates of growth.

The SGP package contains classes, functions and data structures designed to conduct SGP analyses. These analyses include Student Growth Projection/Trajectory analyses; quantile regression estimation/percentile rank calculations; as well as state specific meta data that will be embedded within sgpData class.

To conduct SGP analyses, a computer with R installed is necessary. The SGP package uses advanced R functions that require familiarity with R; however, there are plenty of resources on CRAN that provide assistance to beginners in using it. In addition, having an understanding of its concepts behind its methodology would be advantageous; additional guidance on these topics is provided through its documentation.