Data SGP
Data sgp refers to any set that is analyzed using Student Growth Percentiles and Student Growth Projection functions from the SGP package. There are multiple ways this data can be managed and utilized during analyses; lower level functions like studentGrowthPercentiles and studentGrowthProjections may be utilized with either WIDE formatted data sets or LONG formatted ones – for operational analyses using LONG data formats is highly recommended as it offers easier data management as new years of data become available and updates.
The Data Sgp (anonymized panel dataset encompassing five years of annual, vertically scaled assessment data) is organized into tables where each row represents an individual student while columns represent different test occurrences for that student, each associated with one or more teachers. Each column in each table provides an unique student identifier followed by grade level information and numeric scores for that assessment occurrence.
An important concept to keep in mind when understanding student growth percentile data is that assessments are measured relative to all other students in their academic grade. Therefore, even two students who perform differently on state exams could have identical student growth percentile scores because this metric measures ratios of individual scores against scores across an entire grade group.
Student Growth Perspectives provides the basis for understanding student achievement in schools and districts, giving administrators and educators an effective tool for understanding student growth relative to other school populations or factors – for instance instructional practices, demographic changes or other variables. By tracking individual student progress over time in relation to others in a class or district population, this approach helps educators identify strengths and weaknesses of their schools or districts while simultaneously tracking trends related to teaching practices, student demographics or any other factor affecting student development in real time.
Though the term “big data” has become trendy, analyzing student growth does not require massive amounts of data. Although Data SGP provides a significant step up from previous studies, its scale still falls far short of a full community database such as Genbank or EarthChem.
Research consortia and full community databases often aggregate information from various sources to address specific scientific questions (see Why this is Not Big Data). The SGP Working Group seeks to assist community-based efforts that tackle such queries while providing infrastructure for sharing standardized SGP data.