Data sgp is an educational assessment database used for various purposes including student progress analysis. This type of information can help teachers and administrators determine how well their students are performing academically as well as identify areas that require improvement. This is important in order to ensure students are receiving the proper education they need and deserve.
Despite the benefits of aggregated SGPs, the relationships between true SGPs and student characteristics create significant issues in interpretation. A nontrivial part of this variance may be due to unobserved student-level factors correlated with both the prior achievement attribute and the estimated teacher effects. While this source of variance is easy to address in a value-added model that regresses student test scores on teacher fixed effects, prior achievement attributes, and student background variables, the aggregated SGPs still present a potential source of bias.
In addition, the estimated SGPs are not necessarily comparable across time periods because they are based on a single measurement of a student’s performance. This is a major problem when comparing the results of different teachers who teach students with similar backgrounds and characteristics. To solve this problem, the SGP analyses rely on a correlation matrix that can be used to adjust for differences in the distribution of prior achievement. The correlation matrix is calculated using a statistical method called “smoothing”. This process takes a standard distribution of multiple years of compiled student test data and applies it to each individual year in order to reduce the impact of any random variation that might occur in any one particular year’s test scores.
SGP analyses also take advantage of the fact that students’ historical growth trajectories are available from previous Star exams. These growth trajectories are used to estimate what future performance (i.e., what future growth percentiles) a student will be required to reach in order to meet or maintain proficiency. This information is then projected forward to future time periods when a new Star exam is administered.
A student’s current performance can be compared to these projections in order to determine how close they are to meeting or exceeding the proficiency goal. This data can then be used to help make instructional decisions for each student, such as determining what courses or programs they should enroll in to maximize their chances of success.
To facilitate these types of analyses, the SGPD provides a standard data set called sgpData that contains five years of annual, vertically scaled student assessment results in WIDE format. This exemplar data set models the format required for the lower level functions studentGrowthPercentiles and studentGrowthProjections as well as higher level wrapper functions that simplify operational SGP analyses when applied to a large set of data. For operational analyses conducted on a regular basis, we recommend formatting data in the LONG format instead of WIDE in order to take advantage of the many preparation and storage benefits associated with LONG. This is especially true for analyses that will be repeated annually.
