Data sgp is an important tool for assessing student achievement and growth. It provides a way to identify trends in student performance over time, evaluate teachers, and compare students across groups. The sgp website provides access to a wealth of information, but it is also important to understand how to interpret and utilize the data.
SGP measures student growth relative to peers with similar academic histories. It is reported on a 1-99 scale, with lower numbers indicating less relative growth and higher numbers indicating greater relative growth. For example, a student with a SGP score of 75 would have demonstrated growth that is about average for academically-similar students.
Students are compared with their academically-similar peers using a series of standardized tests over time. The test scores from each assessment are transformed to a common scale and then combined to form a single index of student performance. These indexes are then ranked and reported to the student, teacher, school, district, state, and nation. In the SGP model, a student’s performance is compared with other students who have a history of similar performance on standardized tests. This information is used to predict the probability that the student will make it to proficient by a specified date.
The sgpData data set is an anonymized, panel data set comprising 5 years of annual, vertically scaled assessment data. It models the format of data used with the lower level studentGrowthPercentiles and studentGrowthProjections functions. The first column of the data set, ID, provides the unique student identifier. The following five columns, SS_2013, SS_2014, SS_2015, SS_2016, and SS_2017 provide the grade level for the student’s assessment occurrences over the past 5 years.
sgpData_INSTRUCTOR_NUMBER is an anonymized, student-instructor lookup table that provides the insturctor number associated with each students test record. Since a students teachers vary over time and content, variation across teachers in expected aggregated SGP is primarily due to contextual effects or teacher sorting.
The true distributions of SGPs are related to the covariates of interest and have implications for whether improvements in tests or SGP estimation methods can effectively level the playing field and ensure that students are not unfairly assessed. This article describes a model for latent achievement attributes, defines true SGPs under this model, and illustrates their distributional properties from real data.