Data SGP refers to aggregated student growth percentiles calculated from individual test results. It’s an invaluable statistic that helps educators assess how students are progressing within their classroom and school overall, which in turn allows for improved classroom practices, student learning outcomes and evaluation of school and district accountability measures. Sometimes this data can even be aggregated at group levels for larger studies or research efforts.
Calculating percentages follows a similar process to other percentiles, and it’s essential that users understand how calculations are made before employing them. Percentiles are determined by taking an aggregate look across multiple years of assessment data collection; this allows any irregularities within any single year to be evened out and transformed into percentiles with knots/boundaries being identified as 20th, 40th and 60th quantiles respectively.
As an illustration of their application, consider when a sixth grade student earns a scale score of 300 on this year’s English Language Arts (ELA) state assessment test. Their scaled grade point (SGP) reveals their place within a distribution of students with scores similar to theirs, and how many peers they’ve outshone.
Teachers, administrators and parents can utilize this data when making decisions about student learning and improvement. It should identify students that need extra attention or resources in order to improve their performance; additionally it can inform teacher evaluations and decisions regarding compensation packages.
Although SGPs provide numerous benefits, they also have some drawbacks. One major shortcoming of using SGPs is that they cannot accurately capture variations in student performance across assessment items – this is due to different assessments having differing degrees of difficulty that must be considered when considering an individual student’s overall SGP score. Furthermore, using medians instead of mean SGPs as summaries may distort information presented due to being less centered than mean scores which makes overstating or understating school performances easier due to over or understating due to either extreme when comparing schools using different aggregation methods of aggregation.
