Data SGP is a software package that can calculate student growth percentiles and projections/trajectories from large scale, longitudinal education assessment data. This data can include scores from standardized tests, portfolios or grading scales used to measure students’ progress over time. This data can be useful for identifying students who may require additional support, evaluating current educational systems and finding ways to improve them.
A program fits into this category if a significant percentage of the students in the program fall behind the class median. This can be caused by accelerated programs that push students beyond what their knowledge, skills and ability can handle or by programs with very rapid curriculum progressions that are not well-matched to the skill level of the students. In addition, if the class median is defined by a student’s average score, the growth of students who do not keep up with the rest of the class can be artificially inflated because they are dragging down the average score for the entire class.
To address these issues, it is essential that the school and district use data to identify programs that need additional support for their students. This can be accomplished by using data to identify which students are below the national average for their grade and by analyzing student growth over time by comparing individual student’s scores to those of their classmates and the state average. Data can also be analyzed by various factors including gender, race and socioeconomic status to identify patterns of student success.
Getting the most out of data sgp requires careful planning and preparation before starting to analyze it. However, the benefits of doing so are enormous. Any errors that arise in the analysis process typically revert back to problems in data preparation, making it necessary to spend some time upfront on data preparation before running analyses.
The sgptData_LONG data set contains an anonymized panel of 8 windows (3 windows annually) of student assessment data in LONG format for 3 content areas. Each window contains a complete assessment record for each of the students in the sample. There are 7 required variables in this data set: VALID_CASE, CONTENT_AREA, YEAR, ID, SCALE_SCORE, GRADE and ACHIEVEMENT_LEVEL (required if running student growth projections). The other variables are demographic/student categorization variables used for creating student aggregates by the SGP function.
The SGP software is designed to work with this type of data and allows users to create a variety of student groupings. The output from the SGP analyses is easy to read and provides important information about student achievement that could otherwise be hidden in a sea of numbers. This makes the SGP software a powerful tool for analyzing student data, and for identifying effective programs that can help students achieve their potential. In addition to identifying programs that need improvement, this data can also be used to compare the performance of schools and districts across the state. It can also help identify patterns in student achievement by gender, race and socioeconomic status that can be used to inform future decisions about school improvement efforts.