Data sgp is a tool that allows users to compare student assessment data with a similar group of students. This can be done for a single school or district, an entire state, or a national sample. The results can be used to identify patterns of achievement that may be difficult to see when examining individual student performance. The information can also help educators and administrators focus on areas for improvement.
An SGP describes a student’s growth compared to the performance of other students with similar prior test scores (their academic peers). The calculation for each SGP is complex, but the results are communicated in percentile terms that are familiar to teachers and parents. This makes SGPs an ideal means of sharing student growth with the public and identifying gaps in instruction that need to be addressed.
SGPs are calculated for each student who has a valid score on a recent MCAS test in ELA and mathematics. When available, growth percentiles are also provided for grade 10 students in each content area. SGPs are not generated for grade 3 (the first grade of MCAS testing) or science because these subjects are only tested in grades 5 and 8.
In the sgpData set above, the first column, ID, provides the unique student identifier. The next five columns provide the corresponding values of the assessment variables. The final column, GRADE, specifies the assessment being evaluated. The summarySGP function is a convenient way to create SGPs at the student level for multiple schools or districts, allowing educators and administrators to quickly identify trends in student learning and identify potential gaps in instruction.
The SGPs can also be displayed at the school/district and subgroup levels to inform decision-making about student needs and opportunities for improvement. To do this, the average SGP for each student in the selected group is computed by pooling all of the individual student growth percentiles and identifying the mean (average) for that group. The average SGP is the best representation of typical student growth within that group.
As with any statistical analysis, the bulk of the time spent using this tool is in preparation and analysis of the data. Once the data is prepared properly, the analyses are straightforward and should be conducted in a matter of minutes. We have worked to make this process as simple as possible, and most of the analyses that we assist with follow these steps.
In addition to sgpData, sgptData_LONG is a complete anonymized data set with 7 additional variables for SGP analyses. These variables include VALID_CASE, CONTENT_AREA, YEAR, ID, SCALE_SCORE, GRADE and ACHIEVEMENT_LEVEL. The additional variables are demographic/student categorization fields that are necessary if running student growth projections and plots. The final two variables, LAST_NAME and FIRST_NAME, are required if creating individual-level student growth and achievement plots. To learn more, please see the sgptData_LONG overview page.