Data SGP (Student Growth Plots) is a statistical growth plot analysis tool for longitudinal student assessment data that creates statistical growth plots depicting students’ academic growth relative to their peers. This can be used for informing instruction, assessing student/teacher performance evaluation systems and supporting educator evaluation systems. Unlike traditional percentile scores, SGP estimates are calculated based on latent achievement trait models estimated from each student’s test score history using covariate information established as growth standards – thus helping reduce estimation errors while simultaneously increasing validity in their results trajectories/projections.
While many educators utilize SGP data, some errors may diminish its usefulness. This article highlights those mistakes while providing guidance to avoid them.
Educators can access SGP data for their students by selecting one student in the district report card and clicking on the SGP Data tab. The sgpData spreadsheet then presents comprehensive SGP information over five years for that student – starting with their unique identifier (ID), followed by assessment scores from Badger and Forward tests in 2013, 2014, 2015 and 2016 and finally their overall SGP assessment score as measured against state requirements (SS_2013 through SS_2016).
SGP scores for each student are determined by comparing their most recent standardized test score with that of someone from their academic profile who took the same assessment, and deriving an SGP percentage difference as their current SGP score. Furthermore, SGP data are estimated using latent achievement trait models estimated using teacher evaluation criteria and historical test score histories as growth standards; thus providing more effective accounting of student/teacher differences than standard percentiles do.
SGP analyses may seem intimidating to newcomers to SGP analysis, yet they can be carried out on relatively small relational databases compared to global Facebook interactions for instance. Yet this does not make the process any less time consuming or complex and any neglect in preparation steps may lead to inaccurate or misleading analysis results.
To run SGP analyses on a computer, the R software environment must be installed – this free download can be found for Windows, Mac OSX and Linux operating systems. CRAN offers numerous resources to get you up and running in R and learn how to conduct SGP analyses.
At OSPI, we recognize that SGP data can provide invaluable insight into a student’s learning; however, its sheer volume may be daunting to educators and administrators. OSPI staff understand this is a complex topic and are available for training or support as necessary; investing time and energy in training on SGP can ensure educators gain maximum benefit from it.
