Data SGP is a new, free and open source statistical software package that allows users to model student growth using multiple models. The package offers support for both normalized student growth percentageiles and growth models with covariances and weights. It can be used to generate reports for individual students or schools, and it can also be combined with other datasets to analyze trends in student performance.
Traditional student assessment reports indicate a student’s achievement, but they do not provide information about how much the student has improved from one year to the next. Student Growth Percentiles (SGP) do just that; they provide a measurement of a student’s performance in relation to their academic peers. This information can be useful in planning for future student learning and development.
sgpData is an R package that provides access to student growth percentiles (SGP) calculated from MCAS assessments for high school students in Massachusetts. sgpData includes all the required tables to calculate SGP and its associated summary statistics. It does not include all the additional calculations needed for interpreting and reporting SGPs. In addition to providing SGPs, sgpData provides the raw student assessment scores and the scaled scores.
SGPs are computed from multiple years of test data, and they provide a measure of a student’s progress over time. They are important for accountability and for comparing student outcomes in different school districts. They are also important for assessing a student’s level of mastery of content and skills.
While SGPs can be calculated from the raw assessment scores and the scaled scores, a better measure is the cohort referenced SGP (C-SGP). C-SGPs are determined by comparing a student’s score to the average of their academic peers in each grade level. This calculation takes into account the effects of changes to the student’s educational environment as well as the effect of varying tests over time.
The C-SGP is a measure of how much a student has grown relative to their academic peers in each grade. It is a useful indicator of student success because it is not affected by changes in the underlying test, but rather by a change to the student’s educational environment and how that student compares to their academic peers.
Using sgpData to perform Student Growth Percentile analyses is fairly straightforward, but there are some nuances to understanding the syntax and format of the data. It is recommended to consult the SGP data analysis vignette and the SGP documentation before using sgpData. Additionally, a good understanding of WIDE and LONG data formats is beneficial before starting to use the higher level SGP functions.