Data SGP is a system that utilizes longitudinal student data to produce statistical growth plots. These plots display a student’s current test score percentile rank relative to others with similar prior achievement levels and are a more accurate measure of achievement than unadjusted test scores. However, the estimates that are used to create these graphs can be noisy due to large estimation errors. SGPs attempt to reduce these errors by leveraging the correlation structure of students’ standardized test score histories. Statistical methods that are commonly used in education, such as statistical models and regression analyses, can be very time consuming. SGPs are a quicker alternative to these methods and provide more insight into students’ academic progress than raw test scores alone.
SGPs are a critical tool in measuring student progress, but they can be inaccurate if the assumptions behind the methodology are not met. To address these issues, researchers have developed a new method to construct SGPs that is less susceptible to estimation error and produces more valid measures of student achievement. The new approach uses a more robust model to calculate the mean and standard deviation of the score distribution, and it incorporates a more complete estimate of the latent achievement trait that underlies the score.
The result is a more accurate estimate of student performance, more powerful analysis tools to investigate trends in individual student’s performance, and a much simpler way to generate SGPs. The new approach is also easier to implement in practice. To facilitate its adoption, we have provided a set of SGP vignettes that provide examples of how to use the new functions and how to apply the new assumptions to existing data sets.
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When preparing SGP data for analysis, it is important to decide whether the data should be formatted in WIDE or LONG data formats. The lower level functions in the SGP package, such as studentGrowthPercentiles and studentGrowthProjections, can process data in either format, but higher level wrapper functions require the LONG data format. For operational analyses, a LONG format is recommended since it has many preparation and storage benefits over WIDE formatted data. See the SGP documentation for more information about formatting and storing SGP data.
