Data sgp is an informational database that can be used for a number of different purposes, including tracking student growth and development over time. It can also be used to evaluate teachers and help them improve their teaching methods. It is also useful for evaluating the effectiveness of education policies.
Student Growth Percentiles are a popular measure of student progress, especially for students who enter schools at lower levels than their peers. However, there are several issues with using this measure for evaluation purposes. For one, it can lead to biased results if the teacher is not taking into account certain factors such as student background characteristics. Moreover, it can be difficult to interpret aggregated SGPs because they may contain a large amount of variance that is not related to the teacher’s effects.
To address these issues, we have developed a new analysis model that removes variance due to unobserved student covariates and allows us to estimate teacher-level effects. This model is based on the notion that true SGPs are latent achievement attributes (i.e., unobserved variables that predict student performance) and that these attributes have their own distributional properties. It has been applied to a dataset containing standardized test scores and demographic data for each student in the state of California. We have found that the new model yields more valid and interpretable SGP estimates than previous approaches that ignore variation due to unobserved student covariates.
SGPs are a convenient and widely used measure of student learning, but they have limitations. Two features make them appealing: the percentile rank scale is familiar and easily interpreted, even when test scores are not vertically or intervally scaled; and they are sensitive to student prior achievement. Therefore, SGPs are often used as an alternative to standard measures of achievement such as mean and median scores, which are sensitive to student backgrounds and can obscure the impact of instruction on learning.
sgptData_LONG is an anonymized panel data set with 8 windows (3 windows annually) of assessment data in LONG format for 3 content areas. There are 7 required variables for SGP analyses: VALID_CASE, CONTENT_AREA, YEAR, ID, SCALE_SCORE, GRADE and ACHIEVEMENT_LEVEL. sgptData_INSTRUCTOR_NUMBER contains a similar set of variables but without the demographic data and an additional variable DATE, which indicates the date associated with each student assessment record. The sgptData_INSTRUCTOR_NUMBER dataset can be used to create student growth and achievement plots, and it can also be used to generate teacher-level value added models. These models can be analyzed to determine which teachers have the greatest influence on student performance, and they can also be used to evaluate the effects of specific policies such as supplemental instruction, interventions, and classroom resources. They can also be used to examine the extent to which teachers’ practices are consistent with recommended best practices. They can be visualized in a variety of formats, and the results can be exported to spreadsheets for further analysis. To learn more, please see the documentation.