Data sgp is a database that contains a variety of information about different cities and regions in Singapore. It is updated regularly and it is based on multiple sources of information. This makes it a valuable resource for businesses looking to make informed decisions. It also provides a platform for discussion of economic issues.
The vignettes in this section provide background on the SGP analysis process and the technical aspects of its implementation. This background is important for understanding the results of SGP analyses and how to interpret them. It also helps to understand the limitations of SGP analyses, which should be kept in mind when interpreting results.
The SGP analyses are intended to be simple and straightforward, and the vast majority of errors that occur during analyses can be traced back to data preparation issues. This is why the SGP vignettes are designed to be a two step process: data preparation and analysis.
SGPs are a measure of student academic growth relative to students with similar prior achievement history. They are calculated by regressing current test scores on teacher fixed effects, student background variables, and prior achievement. SGPs can be interpreted in the same way as percentile ranks on standardized tests, with higher percentages of student growth indicating better performance.
However, a number of studies have shown that SGPs estimated from standardized tests are noisy measures of their corresponding latent achievement attributes (Akram, Erickson, & Meyer, 2013; McCaffrey, Castellano, & Lockwood, 2015). These noises come from both the limited number of items on the test and from measurement error in the models used to estimate the latent achievement traits.
In addition, SGPs have a non-Gaussian distribution. Specifically, their distribution has a peak at the 50th percentile and then tapers off to near zero at the 95th percentile. This distribution is not optimal for the identification of teachers who have high impact on student outcomes, a key purpose of SGP analyses.
These limitations mean that SGPs are not suitable for use in current accountability systems that rely on test score-based measures, but they could be useful in future accountability systems that focus more on student learning. SGPs can also be a useful tool for school leaders and administrators as they evaluate the effectiveness of their teachers.
The sgpData_INSTRUCTOR_NUMBER data set contains an anonymized, student-instructor lookup table that provides instructor information associated with each students test record. This allows us to identify the teacher that a particular student was taught by during a given year. Each student may have more than one teacher associated with their test records, as each teacher might be assigned to teach a single student in a content area for multiple years. The data set also includes a set of teacher-student association tables, which are useful for creating individual student growth and achievement plots.