Data sgp is a database that provides a variety of different types of information. It can be used for many purposes including analyzing educational assessment data. It can also provide a variety of other useful information for teachers and administrators. For example, it can provide information on student growth percentiles. These percentiles are a measure of how much a student has grown relative to their academic peers. Students who have higher growth percentages are considered to be growing at a faster rate than their peers. This information can help teachers and administrators determine whether a student is making progress or not.

The sgpdata_LONG data set is an anonymized panel data set that contains 8 windows (3 windows annually) of assessment data in LONG format for 3 content areas (Early Literacy, Mathematics and Reading). This data set can be used to run SGP analyses and to create individual level student growth and achievement plots. There are 7 required variables when using LONG data with SGP analyses: VALID_CASE, CONTENT_AREA, YEAR, ID, SCALE_SCORE, GRADE and ACHIEVEMENT_LEVEL. The last two are required if running student growth projections and the other 5 variables are demographic/student categorization variables that are used for creating student aggregates by the summarizeSGP function.

A Student Growth Percentile (SGP) is the percentile rank of a student’s current achievement on MCAS compared to the performance of students matched to them by their prior achievement history. The advantage of the percentile rank scale is that it is familiar and easily interpretable even if test scores are not vertically or intervally scaled (Betebenner, 2009).

While estimated SGPs may provide valuable information for many uses, they should be interpreted with caution because of the relationships between true SGPs and student characteristics. This problem is particularly severe when aggregating estimated SGPs to the teacher or school levels because it suggests that a nontrivial portion of the variability in aggregated estimates is due to student characteristics.

This vignette describes an approach for estimating student growth standards based on official state achievement targets/goals. Specifically, it illustrates how the percentile ranking of a student’s MCAS score can be translated into a multi-year growth standard and what this growth standard reflects about a student’s likely progress toward achieving their achievement target. It also demonstrates how to construct student growth curves from the SGP data. These growth curves are designed to show how a student’s current level of achievement is expected to change over time in response to their effort and teacher support.