Data Sgp (Student Growth Percentiles) are collected over time by teachers and administrators to better understand students’ progress, from individual-level measures like growth percentiles all the way through school- and district-level measures like class size, attendance rates and graduation rates. Utilizing Data Sgp helps teachers and administrators identify areas for improvement while informing classroom practices, evaluating schools/districts as well as supporting broader research initiatives.

The sgpData spreadsheet offers educators an easy and straightforward way to easily view their students’ SGP results and make comparisons across multiple students, or identify trends within one class, school or district. It has been designed with user friendliness in mind so it can be utilized by educators, administrators, parents and researchers.

SGP measures student growth relative to academic peers who have taken similar tests in the past. SGP scores range from 1-99; higher numbers indicate more relative progress – for instance, 75 SGP indicates greater growth than approximately 75% of peers.

To avoid the limitations associated with using fixed test dates for SGP calculations, the Star Growth Report offers estimates and projections based on historical SGPs of all Star examinees. This allows more accurate forecasting of students’ potential future performances as well as helping reduce estimation errors by decreasing predictions that come due to differences between baseline cohorts or student covariates (e.g. teacher evaluation criteria and growth standards).

As opposed to test scores which only provide one snapshot in time, SGPs provide an indicator of student progress over their educational career. This is crucial because a student’s learning capacity and achievement cannot be assessed with just a single assessment.

While SGP information in sgpData tables may be beneficial to some users, it may not contain all the data necessary for effectively informing instructional decisions and evaluating school/district performance. Therefore, we advise you format your data using LONG format with higher level functions studentGrowthPercentiles and studentGrowthProjections used annually if planning operational analyses over a number of years – this has many preparation and storage advantages compared with WIDE formatted data.

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