Data sgp is the collection and storage of raw digital information in computer systems. It includes the bits and bytes behind applications, network protocols, documents, media, address books, user preferences, and more. A typical view into data storage is at the infrastructure level, with no direct connection between storage volumes and a lack of native ways to visualize and interact with the collection of information in its entirety.

A key feature of SGP analyses is the comparison of student growth percentiles against growth standards established by comparing a student’s performance on an assessment with that of students with similar academic histories. The goal is to identify student progress and make inferences about the effectiveness of teacher practice based on this progression. However, these comparisons can be prone to error from many sources, including differences in student or teacher characteristics and the design of baseline cohorts. Therefore, educators should always review the results of their SGP analyses and take care to account for these potential sources of error.

Student growth percentiles are calculated by comparing a student’s score on a test section with the scores of other students with similar academic histories and weighting these comparisons accordingly to derive an estimate of the percentage of other students scoring lower than a given student on that same section. The higher the rank, the more improvement is indicated. Unfortunately, these comparisons can be misleading for students who already possess superior academic abilities, and they are susceptible to spurious correlations from a variety of sources, such as teacher/student characteristics or the design of the baseline cohort.

In order to conduct SGP analyses, an administrator must have access to longitudinal (time dependent) student assessment data. These data are typically stored in a WIDE format with each case/row representing one student and columns representing variables associated with that student at different points in time. The SGP package offers a number of low level functions, such as studentGrowthPercentiles and studentGrowthProjections, along with higher-level wrapper functions, abcSGP and updateSGP, which can run these analyses on both WIDE and LONG formats.

Depending on the circumstances of each project, it may be more efficient to store these analyses in a LONG data format and utilize the higher-level wrapper functions. This is especially true if an analyst plans on running these analyses operationally year after year. The SGP package provides exemplar LONG data sets sgpData_LONG and sgpData_INSTRUCTOR_NUMBER, which include variables like VALID_CASE, CONTENT_AREA_YEAR, SCALE_SCORE, GRADE and the INSTRUCTOR-STUDENT lookup table that associates a teacher with each student’s test record.