Data SGP

Data SGP uses longitudinal student assessment data to produce statistical growth plots (SGP) which measure students’ relative progress compared to academic peers. Unfortunately, creating SGPs from standard test score histories can be notoriously challenging and error-prone; any mistakes that arise as part of its calculation have an outsized influence on its interpretability and usage for measurement purposes.

Luckily, the data sgp has been designed to alleviate this burden and make creating SGPs simpler and less error-prone. It uses longitudinal records of student assessments to generate SGPs quickly allowing educators and administrators to gain insight into student performance quickly.

SGPs provide projections that show whether students will achieve proficiency by the end of their current school year, as well as what their scores might look like in future years. This information helps teachers accelerate student growth and identify high-performing and underachieving students as well as providing an overview of each student’s performance in any subject area.

SGPs use within-grade and cross-grade correlations of latent achievement trait estimates derived from prior test scores to create SGPs, providing educators with insight into which covariates in their model explain differences in student performance as well as which might be more important in different subjects and grades. Typically, more years are studied which will result in stronger within-year and cross-year correlations; but this does not always hold true.

In the Star Growth Report, historical SGPs are calculated for every student whose prior test scores were included in the model and who has been assigned an educator. These historical SGPs are then compared with an estimated average of projected SGPs among similar prior achievers in each grade and subject area; this allows us to gain insight into just how far any given student must grow before reaching proficiency levels.

The SGP package provides a series of functions designed to streamline and automate the process of gathering, analyzing, and compiling student assessment data for SGP analyses. The prepareSGP function uses LONG format data sets (sgpData_LONG), INSTRUCTOR-STUDENT lookup files (sgpData_INSTRUCTORNUMBER), to generate one master longitudinal record: Demonstration_SGP@Data. The analyzeSGP and combineSGP functions then conduct SGP analyses on this data set, producing various results for each student before merging back into the master longitudinal record using combineSGP. Thus, all six steps involved in an operational SGP analysis are combined into one function call to reduce errors caused by mishandled or incomplete preparation of student assessment data. Once finished, SGPs can then be utilized for instructional purposes.