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Data SGP, or student growth percentiles, are measures of how much students are learning on subject-matter tests. These measures are often used to evaluate teacher effectiveness, but they also have other applications such as predicting a student’s achievement trajectories over time or for comparing the academic progress of students from different schools. They are calculated by ranking students against peers with similar prior test scores, rather than comparing the student’s own score to the mean. The value of SGPs is a key factor in their increasing use and popularity in education.

In order to calculate a SGP, students must have two or more assessments with different testing windows (the dates for the tests do not need to match the school year). Students are then ranked based on their performance on these assessments. The current SGP is a measure of the student’s growth relative to the students with similar score histories on the same subject-matter test, while the future SGP is a prediction of the student’s expected achievement level on that same test in the future.

The SGPs reported by AIR are based on the average of these calculations, and therefore are not directly comparable to those reported by other organizations. This is important to keep in mind when interpreting the results of other studies or in making decisions about school policies.

SGPs are also influenced by the covariates we include in our model. This can happen in two ways: 1) SGPs may be correlated with student background characteristics, which are related to the students’ ability and motivation to learn. For example, if a student has an absentee parent, the student’s true SGP is likely to be lower than that of other students. This would also be the case if the students were from a family with low socioeconomic status, which is correlated with low SGPs.

2) SGPs can be influenced by the sorting of teachers to classrooms and schools that vary systematically with respect to student covariates. For example, if more effective teachers are more likely to teach students with particular background characteristics, these students’ true SGPs will tend to be higher than those of other students.

This can affect the sensitivity of SGPs to changes in student and teacher covariates, making it more difficult to interpret them as indicators of educator effectiveness. For this reason, it is important to understand how these factors influence SGPs before using them in decision-making. This can be done by looking at the SGP differences by cohort and subject, which are shown in Table 3. Note that the group mean differences for each row of the table contrast the students who belong to the given group over both years, while the SGP differences in the last column compare the same students across two consecutive years.