Data sgp is a set of tables that contain a student’s data for all of their MCAS testing history. It is a powerful tool for educators to use as they analyze and interpret students’ growth over time.
Data SGP consists of two main tables: sgpData and sgpProjections. SgpData contains student information including the ID number, a list of the most recent assessment scores, and a set of 5-year growth percentiles. The sgpProjections table, in turn, contains a set of projections of the student’s future performance using their current rate of growth. These projections, based on the student’s rate of growth and the catch-up, keep-up model of Betebenner, can help educators understand how the student is expected to perform in the future.
The sgpProjections table is an invaluable tool for understanding the results of a student’s SGP report. However, many teachers have questions about how to interpret the data and what the projections mean for a particular student. This article provides a guide to reading and interpreting these projections.
SGP compares a student’s current performance to that of his or her academic peers with similar achievement histories on the state’s Star assessments. For example, a student’s SGP for English Language Arts will be determined by his or her score on the most recent MCAS test and the scores of academic peers who have had similar test-taking histories to the student.
As such, differences in a student’s SGP from year to year can be attributable to changes in the percentage of his or her academic peers that have a similar score history and therefore a similar growth percentage. As a result, differences in a student’s SGP should be interpreted with caution.
The SGP package allows users to format sgpData into either WIDE or LONG data formats. In general, the lower level functions (studentGrowthPercentiles and studentGrowthProjections) require WIDE formatted data while the higher level wrapper functions, which are able to handle more complicated analyses, will need LONG formatted data. If you are planning to run any analyses that are not the simplest one-offs, we recommend using LONG formatted data as this will provide numerous preparation and storage benefits over WIDE formatted data. A more comprehensive description of the SGP package’s support for LONG formatted data is available in the SGP Data Analysis Vignette. This vignette provides a walk through of the most common SGP analyses and how to best prepare your data for those analyses. The vignette also includes links to additional resources that are available for further learning. In most cases, errors that occur in the process of analyzing SGP data will revert back to issues with data preparation so it is important to devote adequate time to data preparation before proceeding with any SGP analyses.