Data sgp is the collection of aggregated student performance data collected over time that teachers and administrators use to better understand their students’ growth and development. It is comprised of individual-level measures such as test scores and growth percentiles, as well as school/district level measures such as class size and attendance rates. Data sgp can be used to inform instructional practices, evaluate schools/districts, and support broader research initiatives.
Currently, the majority of available SGP data is reported in a spreadsheet format. The sgpData spreadsheet makes it easy for educators to compare their students’ growth to other students in the same grade or subject, as well as across the school or district. The spreadsheet also provides additional information about each student, such as gender and socioeconomic status.
The spreadsheet is updated each year when new assessments are released, and contains both state and local data. It is important to note that this spreadsheet requires the use of long format SGP data (see our vignette on working with sgpData in long format for more details). The spreadsheet also assumes the existence of an embedded data set called sgpData_INSTRUCTOR_NUMBER, which provides teacher-student lookup tables for each content area. Using this data, teachers can easily see the instructor for each student and how much growth she/he had in a particular course.
It is important to note that SGP estimates based on standardized test scores are error-prone. This is due to the fact that the prior and current test scores used in SGP calculations are error-prone measures of their corresponding latent achievement traits, and the estimation process itself introduces further errors into the estimate (Akram, Erickson, & Meyer, 2013; McCaffrey, Castellano, & Lockwood, 2015). These errors, when combined, can make estimated SGP noisy and may not be accurate enough to support instructional decision making for individual students.
SGP analysis can help us identify patterns in student growth and develop a more complete understanding of why some students grow faster than others. It can also highlight areas where student growth is less than expected and provide guidance on how to improve instruction.
This article describes a method for estimating SGPs that overcomes these limitations and provides evidence of its utility for supporting student learning and educator accountability decisions. The approach uses multi-year SGPs derived from official state achievement targets/goals, and specifies both what future achievement standard must be attained and how much the student has grown relative to that standard. The results demonstrate that the methodology can be extended to multi-year growth standards and that SGPs can be used to inform student trajectories and evaluate schools/districts. This is an important step forward in the field of SGP evaluation and is one of many steps toward achieving the vision of an educational system that is grounded in research, evidence, and equity.