Data SGP provides statistical tools to analyze longitudinal student assessment data in order to generate statistical growth plots (SGPs), which show students’ relative progress over time compared with their academic peers. SGPs enable teachers and administrators to quickly identify those students whose development exceeds expectation so that instruction may be adjusted appropriately.
SGPs can be calculated from various sources; OSPI’s analyses typically utilize longitudinal student assessment data as the foundation. SGP data sets — often known by their acronym “SGP data sets” — are then created through multivariate growth models used on an individual student level to create state-level longitudinal performance indicators (LPI).
Student achievement targets (SGPs) are an integral component of any effective learning program, providing a concise outline of what students must accomplish to be successful at any level. SGPs also allow teachers to more clearly express their achievement goals by quantifying how much growth students must demonstrate to reach those targets.
SGP analyses can be complex and time consuming to complete correctly; much of their effort goes into data preparation. When used properly however, SGP analyses can become invaluable tools in meeting educational objectives.
Due to their complexity, many districts find it challenging to allocate enough resources and train staff effectively on how to use SGPs. Furthermore, time spent compiling data often saps energy that should be dedicated to other district or school initiatives.
SGPs have long been recognized as an essential tool in education, yet individual schools and districts must decide how best to prioritize training on them. Spending the time and energy into developing SGPs will prove highly worthwhile over time as educators use these metrics to more accurately evaluate programs and identify areas for enhancement.
This is an excerpt of an article originally published by the Oregon Department of Education; to view the complete piece click here.
Key to the SGP model is using student growth percentiles as a measure of relative gains. These measures are calculated using students’ standardized test scores and previous assessments as measurement standards, which allows for easy comparison among academic peers regardless of testing environment. SGPs can also be generated through various other sources like longitudinal statistical models.
The SGP package provides high-level functions that facilitate easier SGP analyses for operational analysis purposes. These “wrap” around studentGrowthPercentiles and studentGrowthProjections functions and simplify source code associated with each analysis.
The SGP package also contains the sgpdata_long dataset, an anonymized panel data set containing 8 windows (3 windows annually) of assessment data in LONG format for three content areas. This dataset includes several variables necessary for SGP analyses such as VALID_CASE, CONTENT_AREA, YEAR and CLASS variables as well as demographic/student categorization variables required by summariSGP function.