Data SGP – Using Longitudinal SGP Data to Assess Educator Effectiveness
Data sgp is a widely used measure that evaluates growth in a student’s subject-matter achievement level relative to students of similar background. SGPs are perceived as more fair and relevant than traditional percentile scores, and they are more commonly used to assess both individual student progress and educator effectiveness.
The most recent SGP data from a large, multi-state longitudinal study was released in 2010 (Betebenner, 2009). This study found that students who grew their score in a subject-matter area by more than 50 percent on average had higher test performance compared to students who did not grow at all. This is in contrast to traditional percentile scores, which tend to fall within the range 0 to 100 for many students.
SGPs are also correlated with covariates that are observed in the data, such as the number of classrooms a teacher teaches. This relationship between student covariates and true SGPs explains a significant portion of the achievement variance seen in our data.
Using LONG data for SGP analyses is relatively straightforward and can be a great option for school districts with a small number of teachers and/or students. However, there are a few additional variables that must be provided when using sgpData with the SGP package.
First, sgpData_INSTRUCTOR_NUMBER is an anonymized, student-instructor lookup table that provides insturctor information associated with each students test record. Each student’s insturctor can be one of two types: the school, or the individual teacher a student had for that year.
Second, sgpData_INSTRUCTOR_NUMBER allows for insturctor-level analysis of student progress by allowing teachers to rank their students based on their growth on their assessment score relative to students of similar background. This provides educators with an opportunity to identify students who may be struggling in a particular content area, and whose teachers might be able to improve their teaching.
Third, sgpData_INSTRUCTOR_NUMBER can be used to examine the impact of covariates on students’ true SGPs. This includes both the observed covariates (such as the amount of time a student spends in the FRL program or the number of classes he or she attends) and latent traits (such as the level of skill, motivation, or family circumstances) that are correlated with students’ test scores.
This vignette provides an example of using sgpData and the SGP package to examine the impact of covariates on student growth in math. The SGP package is available on GitHub, and we hope to add more examples of how to use it in the future.
SGPs are a popular method for assessing individual student progress and educator effectiveness in the United States. They are also widely used in other countries, and have been shown to be a useful way to measure growth in a student’s subject-matter performance relative to students of similar background.
Using data sgp is a convenient and effective method to examine student progress in a wide range of subject-matter areas. It can be a valuable tool in improving teaching and learning for students across the country. It can be used in conjunction with other forms of data and assessment to provide a more comprehensive picture of student progress in the classroom.