The sgp data set contains a variety of different types of information that can be used for many purposes. It is an important resource for analyzing educational assessment data. It contains data that can be used to calculate student growth percentiles, projections and trajectories. It is also useful for evaluating the impact of teacher training and school policies. The sgp data set includes a number of different indicators, including the teacher effectiveness index and the percentage of students who pass their content area tests. This information can help administrators make decisions about hiring, firing and reshaping the classroom.
The gps data set provides the means for calculating student growth percentiles (SGPs) from large scale, longitudinal education assessment datasets. It also enables the calculation of projected and trajectory SGPs using existing longitudinal data. The sgp data set also contains a student-instructor lookup database that allows teachers to be associated with each test record. This data can be used to determine which teachers are most effective at achieving student growth.
SGPs are an estimate of the current percentile rank of a student’s latent achievement attribute, as compared to all students who took the same prior test. They are error-prone measures of the underlying achievement trait due to the finite number of items in the test, and they suffer from correlations between a student’s current and prior tests. These errors make aggregated SGPs susceptible to teacher-level bias. This bias can be avoided by regressing the current test score on teacher fixed effects and prior test scores in a value-added model.
One of the most important uses for sgp data is to assess the quality of a student’s education. Several studies have shown that SGPs are a strong indicator of the achievement gap between students from high-income and low-income families, as well as between ethnic groups. Moreover, SGPs can be used to identify underperforming schools and districts.
It is recommended that the sgp data set be formatted in WIDE or LONG format depending on the specific needs of your analysis. In general, the lower level functions studentGrowthPercentiles and studentGrowthProjections use the WIDE format whereas the higher level wrapper functions rely on the LONG data format. In addition, LONG formatting is typically more efficient for running analyses operationally year after year than WIDE formatting. Consequently, it is more practical to use LONG format for all SGP analyses.