In data sgp, latent achievement trait models are estimated to provide an objective measure of student performance that can be compared across time and between teachers. This allows educators to identify areas of concern and focus on instructional strategies that will most likely lead to improved performance. The goal is to minimize estimation error by comparing student growth against a baseline cohort that has been matched for covariates and standardized test scores. SGP analyses are conducted in a variety of ways, with many different steps involved. This article describes an approach to conducting operational SGP analyses using the Python SGP Package.
sgpData_LONG
The sgpData_LONG data set contains anonymized LONG assessment records for 8 windows (3 windows annually) in 3 content areas (Early Literacy, Mathematics, and Reading). It includes 7 required variables: VALID_CASE, CONTENT_AREA, YEAR, ID, SCALE_SCORE, GRADE and ACHIEVEMENT_LEVEL. The rest of the variables are demographic/student categorization variables that are used for creating student aggregates by the summarizeSGP function.
The SGP Package provides higher level functions, abcSGP and updateSGP that “wrap” the above 6 steps into a single function call, simplifying the source code associated with an analysis. The lower level functions studentGrowthPercentiles and studentGrowthProjections are also available in the Package.
SGP analyses are often conducted using a model-based method that uses least squares regression and Bayesian inference to estimate latent achievement trait models and compare them against growth standards established via teacher evaluation criteria and student covariates. The results of these comparisons can then be used to identify areas for improvement and determine the likelihood that a student will perform at a certain level in the future. The resulting prediction probabilities (probability of a student scoring above or below a specified target score) are known as Student Growth Percentiles, or SGPs.
It is important to be aware of the limitations of SGP calculations and interpretations. There are several factors that can lead to inaccurate estimates, including measurement error, student covariates, and model assumptions. As a result, the use of SGPs in education should always be considered within the context of the particular study and its goals. The most robust estimates are derived from multiple trials of the same model and ideally, with similar student population characteristics. SGPs are most reliable when they are based on a large number of samples over a long period of time and with high levels of replication. This is particularly important when examining the accuracy of predictions in a longitudinal study. The sgpData_LONG and sgpData_INSTRUCTOR_STUDENT lookup files, which contain student-instructor data, can help researchers validate their SGP methods and reduce model errors. This is an essential step in ensuring that SGPs are accurate and meaningful. It is also important to understand the potential for bias in SGPs when making inferences about students and schools. A careful review of the data, a thorough understanding of the SGP process, and appropriate statistical checks can help ensure that the results are valid. This will in turn provide confidence that conclusions based on the SGPs are valid and can be trusted.
