Data sgp is an analysis tool for longitudinal student assessment data that creates statistical growth plots (SGP) which provide visual evidence of student progress relative to academic peers. Using SGP data can help educators pinpoint issues of student achievement and make effective instructional decisions to meet students’ needs. While many people may perceive data sgp as an expensive and time consuming way to analyze student growth, it’s important to remember that this is not necessarily true. SGP analyses are incredibly powerful and efficient when they’re performed correctly.
When performing SGP analyses, it’s essential to properly prepare the data for use. This involves clearing away unnecessary information from databases and making them more readable – often a lengthy process that requires the assistance of trained data analysts. Once the data is prepared, the actual SGP calculations are relatively quick and straightforward. In fact, most errors that occur during the SGP analyses revert back to problems with data preparation.
sgpData_LONG is an anonymized, long format data set with 8 windows (3 windows annually) of assessment data for 3 content areas. This data set contains a number of variables which can be used in SGP analyses such as VALID_CASE, CONTENT_AREA, YEAR, ID, SCALE_SCORE, GRADE and ACHIEVEMENT_LEVEL. All of these variables are required if running SGP analyses, however, a student’s first name and last name are only required if the sgpTData_LONG function is used to create individual student level growth/achievement plots.
It is also important to note that the SGP distribution across the state is not a bell-shape curve. Instead, there is a relatively large number of students in each percentile, with the bottom half of the distribution typically consisting of students who have not made adequate gains. This is expected given the fact that SGPs are based on a combination of prior year test scores, including the Badger exam, and a student’s current grade level test score.
In addition to this, there is a clear relationship between achievement and growth, with lower achievement students tending to have higher growth, while higher achievement students tend to have lower growth. This is a key finding in the SGP research literature and is an important point to keep in mind when interpreting growth data.
Lastly, it’s important to recognize that SGPs are not meant as a substitute for teacher evaluation. They are a powerful tool that provides educators with a much more complete picture of student learning, but should only be used as one component of an evaluation system. This is especially true if a district decides to apply the SGP framework to educator evaluators, as it will require substantial training and support for teachers in order to be meaningful.