The Iterative Process of Data SGP Analysis

Data sgp is a tool used by researchers and educators to make inferences about student learning. A student’s performance on an assessment is compared to that of other students who have similar prior test scores (academic peers). The resulting comparison, the student growth percentile or SGP, indicates a relative measure of the students’ growth over time. This enables educators and researchers to assess the effectiveness of teaching methods, educational programs, and school policies.

In most cases, SGP analyses are straightforward if the proper steps are taken in data preparation. Any errors that appear in the analysis often revert back to problems with data preparation. This is why it’s important to have an iterative process between data preparation and data analysis.

The sgp package provides both lower level functions that do the actual calculations, such as studentGrowthPercentiles and studentGrowthProjections, as well as higher level wrapper functions that simplify operational SGP analyses by handling many of the details for you. Generally, the lower level functions require WIDE formatted data whereas the higher level wrappers can take LONG formatted data. If you are planning to run SGP analyses operationally year after year, we recommend setting up your data in the LONG format as it offers numerous preparation and storage advantages over WIDE formatted data.

A key component of SGP is adjusting for the uncertainty associated with estimated prior test scores. This is done by assuming a normal distribution for the student’s score on the previous assessment. By doing this, the SGP will not be influenced by any changes in the student’s performance that are due to variations in their prior test score.

The iterative process of preparing and running SGP analyses is described below. The initial iteration is called “preparation” and consists of cleaning and standardizing the data set and removing outliers. After this iteration, the data is ready for analysis. In the next iteration, the SGP function is called to perform the actual analysis and calculate the results. Finally, the iterative process is repeated until the desired results are obtained.

As the iterations continue, the confidence intervals for each SGP statistic can be calculated. This is useful for assessing the precision of the results, and can help identify potential sources of error in the estimates. The results of the iterations are displayed on a table or graph and can be printed for review.

SGP results are often reported in terms of percentiles, which are familiar to most teachers and parents. For example, a student with an SGP of 85 indicates that she has shown more growth than 85 percent of her academic peers. While the calculation of an SGP is complex, it provides a useful tool for communicating student growth to parents and teachers. By using a percentile scale, SGP can be compared across schools, districts, states, and countries. In addition, it is an effective way to communicate the impact of changes in instructional practices or policy on student outcomes. SGP is a powerful and valuable educational tool that can be easily used by teachers, administrators, and researchers.