How to Interpret and Understand Student Growth Percentile (SGP) Data
A student’s progress is a key factor in the success of their education. A popular measure of a student’s academic performance is their student growth percentile (SGP). The SGP is based on the student’s current test score compared to the scores of students with similar prior achievement. The higher the SGP, the more a student has improved their academic performance.
SGPs are an attractive measure for evaluating student achievement because they provide a direct measure of how well a student has performed in a particular subject and can be used to identify underperforming teachers. However, it is important to keep in mind that SGPs are only one component of a much broader assessment system that should include several measures.
In addition to SGPs, there are other ways to evaluate student progress such as growth models and social-emotional learning (SEL). These methods can help educators track students’ academic and developmental progress over time. However, they can also lead to inaccurate interpretations of student data and misleading conclusions about a teacher’s effectiveness.
This article examines how to better interpret and understand student SGPs, and how these results can be influenced by underlying assumptions about the nature of teacher effects. It argues that the benefits of SGPs as an aggregated measure of teacher effectiveness must be weighed against the potential for bias in their estimation from individual-level correlations among students’ prior and current test scores. This bias is easy to avoid by using a value-added model that regresses student test scores on teacher fixed effects and student background variables.
The SGP is an estimate of a student’s current achievement relative to the achievement of peers with comparable prior test scores (Betebenner, 2009). It is defined for each student by calculating their percentile rank on the current test, which is then compared to the percentile ranks of students who scored at least as high as the student in the previous year. Separate SGPs are calculated for each grade level and testing subject.
Recent research has shown that SGPs estimated from standardized test scores are noisy measures of their corresponding latent achievement traits. This is because of the limited number of items on each test and the fact that both current and prior test scores are error-prone measures of the underlying trait. These errors can make it difficult to distinguish between the true SGP of a student and the noise generated by the statistical process. This article presents a simple model for this problem and provides conditional mean estimators that can be used to assess the distributional properties of true SGPs. The results indicate that the true SGPs of students are correlated with their prior test scores and related to their student background characteristics. This information can help to inform the interpretation and transparency of SGPs, while avoiding bias that might otherwise be introduced by other modeling approaches. It is important to note, however, that the true SGP of a student can only be interpreted in the context of a value-added model that explains the variance in both current and prior test scores.