Data sgp is an important tool for education leaders to identify students who are most likely to succeed in school and improve educational outcomes. It provides valuable information about how much growth a student needs to meet or exceed their achievement target, and it can be used to identify and inform targeted interventions that can increase student performance. SGP analyses can also help determine trends over time and across various subgroups such as gender, race, or socioeconomic status.

Unlike standardized test scores, which are influenced by many factors other than the underlying learning traits measured by those tests, SGPs are unbiased measures of students’ latent achievement traits. They are a measure of the percentile rank of current achievement relative to other students who have similar prior achievement, defined for each student as the estimated mean difference (EMD) between his or her current test score and the mean test score of students matched to him or her on the basis of prior test scores.

Unfortunately, estimating these EMDs from standardized test scores is not without error. In addition to the within-student correlations, there are a number of other sources of error that make estimated SGPs noisy and unreliable. The most obvious of these is the fact that both prior and current test scores are error-prone measures of their corresponding latent achievement traits, as well as the fact that those trait estimates are subject to finite measurement accuracy.

These errors are not trivial, and they cause the estimated SGPs to be highly variable, even at the individual student level. In turn, this produces a large amount of variance in the aggregated SGPs reported at the teacher and school levels. The relationships between true SGPs and student characteristics created by these error-prone measurements further exacerbate the problems with interpreting such aggregated SGPs.

While a variety of formats can be used for SGP calculations, it is generally better to use the LONG format for all but the simplest, one-off, analyses. This is because the lower level functions that do the SGP calculations, studentGrowthPercentiles and studentGrowthProjections, require WIDE formatted data while the higher level wrapper functions rely on LONG data.

Once the data is prepared correctly, SGP analyses are relatively simple and straightforward. In our experience, the majority of any problems that arise with SGP analyses revert back to issues related to data preparation and analysis, so it is crucial to be clear on how to prepare and analyze the data correctly. Almost all of the SGP analyses that we assist with follow this two step process.