Data SGP – Interpreting Student Growth Percentiles

While lotteries may appear to be purely games of chance, statistical tools and probability models can provide helpful insights into improving betting strategies. Data SGP is an open-source application that provides historical results from Singapore Pools lottery games like 4D and Toto; by analyzing trends, patterns, and number frequencies, players can refine their strategies and increase the odds of winning.

A student growth percentile (SGP) describes a student’s growth compared to other students with similar prior test scores, which are known as their academic peers. SGPs are calculated using up to two years of MCAS data. Students’ academic peers are identified from all students statewide in their grade level and include demographic groups such as gender, income, and educational programs such as sheltered English immersion or special education.

SGPs help educators identify and focus on student needs by allowing them to see how much improvement is necessary for each student to reach or maintain proficiency. However, interpreting SGPs can be complicated, especially since a student’s score is reported as a percentile rank that must be converted to a percentage. This vignette will introduce the basics of SGP calculations and how to interpret an individual student’s growth.

The first step in SGP analysis is to prepare the data for analysis. The Data SGP vignette uses the SGP datasets sgpData_LONG and sgpData_INSTRUCTOR_NUMBER to demonstrate this process. The SGP dataset sgpData_INSTRUCTOR_NUMBER is an anonymized lookup table that contains the teacher name and number for each student in a content area.

While the datasets are in the correct format, it is important to note that the sgpData_LONG dataset does not contain data for the 2014-15 school year. This is because the Badger Exam was administered during this school year and statewide performance on this assessment was substantially different from either the WKCE or Forward Exam.

When using the data sgp function, it is important to use the correct input arguments. The studentGrowthPercentiles function requires a SGP data set, while the studentGrowthProjections function can be used to simulate student growth trajectories based on the test-specific Conditional Standard Errors of Measurement (CSEM). The input argument bool(‘AllData’) specifies whether only the sample subset of the data should be returned for inspection or all of the original data should be returned.

In addition, the lower level functions studentGrowthPercentiles and studentGrowthProjections require that data be in WIDE format while higher level functions (wrappers for the lower level functions) expect the data to be in LONG format. As a result, for operational analyses, we recommend the use of the higher level functions. This way, all of the required steps are wrapped into a single function call and there is no need to manage both the WIDE and LONG formats independently. This also simplifies the source code for the analyses. This vignette introduces the lower level functions for SGP analyses but provides examples of how to use the higher level wrapper functions in an operational context.