CenterForAssessment/Albuquerque
SGP analysis source code & documentation
Discovered public repositories for CenterForAssessment in the GitHub catalog.
SGP analysis source code & documentation
A collection of resources (journal articles, white papers, presentations, tech manuals, etc.) for the Student Growth Percentiles methodology and the SGP package for R.
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
Repository for spatial data associated with SGP analyses
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP analysis source code & documentation
SGP & sgpFlow source code & documentation
SGP analysis source code & documentation
State/Nation specific meta data used within SGP analyses and the SGP package with the object SGPstateData
SGP analysis source code & documentation
SGP analysis source code & documentation
The R package SGPdata contains three data sets utilized by the SGP Package as exemplars for users to set up their own data for SGP analyses.
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.
Function to generate random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.