Student Growth Percentiles (SGPs) measure how much progress a student makes on a given test relative to those with similar achievement histories. SGPs can be an invaluable asset to teachers, schools, districts and states as they provide teachers a way of quickly identifying underperforming students while simultaneously celebrating high achievers. SGP data provides educators with valuable guidance for improving instruction while administrators use this measure as part of district improvement initiatives.
SGPs are constructed from large samples of students who participated in school-day testing programs and took recent ACT assessments, taking into account characteristics such as gender and race/ethnicity of test takers as well as school mean ACT score, poverty level of test takers, type of school (public or private) as well as teacher presence for each grade and subject area tested. All of these factors are taken into account during analysis by weighting the overall sample to better represent its population of test-takers in each state.
educators need to understand SGP data in order to use it effectively, in order to interpret it accurately and make sense of it. They must comprehend its measurement as well as compare results across assessments. An easy way to grasp SGPs is through an example. Let’s say a sixth grade student scored 300 on an ELA statewide assessment test and wants to calculate SGPs; compare this score against lower scores on previous statewide exams to see what percentage performed better than her on that particular assessment and calculate her SGP accordingly.
This information is then used to estimate the likelihood that each student will achieve proficiency in each content area by the end of high school. These projections and SGPs are calculated based on a statistical model using historical and projected student achievement data to predict outcomes; it also takes into account student growth trajectories which outline changes over time in performance levels of individual students.
The SGP package is a free open source software program available for Windows, OSX and Linux computers. Anyone can utilize it, although gaining knowledge of R software environment as well as basic statistical principles before conducting analyses may prove beneficial.
SGP analysis follows a two step process, starting with gathering and organizing the data before moving onto analysis itself. Most of the time spent performing an analysis is spent in this initial preparation phase.
Therefore, the SGP package was created to make preparing and analyzing SGP data as simple as possible.
To prepare data, the SGP package provides functions for importing, transforming, and preparing meta-data that form the basis for all SGP analyses. Once data preparation has been completed, analyzeSGP can be run to generate SGPs and projections.