Data Sgp
SGPs display a student’s percentile rank in the current year on standardized test scores, which compare the score to those of students with similar prior achievement levels. Unlike unadjusted test scores, SGPs are a more holistic measure of performance that incorporates multiple stages of learning and growth. They are also perceived as fairer and more relevant to evaluating student progress and educator effectiveness than simply examining unadjusted test scores.
While SGPs are a promising alternative to traditional assessment systems, they can have substantial measurement error and be noisy measures of the latent achievement traits underlying them. As a result, it is important to understand the factors that influence their reliability and validity, especially when using them at the classroom or teacher level. The relationships shown in Figure 2 suggest that a significant proportion of the variance in estimated SGPs is due to covariates that are not associated with the latent trait, rather than from random variation. This raises concerns about the reliability and validity of SGPs aggregated to the teacher or school level, which could mask true student differences in achievement.
In addition to the aggregation issue, there are other concerns with SGPs that should be taken into account when considering their use for classroom or teacher evaluation. One such concern is that SGPs are influenced by student characteristics, which can be difficult to control for when comparing teachers or schools. Another is that student characteristics may have different impacts on SGPs in different grades, and that the results of a single test taken at one point in time do not necessarily indicate how a student will perform in future tests.
A more serious concern is that SGPs may be prone to overstatement, which can occur if they are used as an indicator of a program’s success. When SGPs are compared to the median, they may appear to indicate that the program is accelerating rapidly, even if half of the students cannot keep up. This can mislead educators and policymakers into assuming that the program is successful, when it is actually failing to provide the necessary educational opportunities for all its students.
These concerns can be addressed by using statistical methods to improve the accuracy of SGPs. These include controlling for covariates, adjusting for sample size and sampling variances, and estimating the reliability of SGPs based on the likelihood that they are biased. Ultimately, the best way to improve SGPs is to make them more accurate by collecting higher quality longitudinal data and conducting more rigorous analysis. SGPs can then be used to evaluate the effectiveness of education programs and to inform decision making. The bulk of the effort in SGP analyses is spent on data preparation, and it is recommended that programs follow a two step process for carrying out these calculations. This includes preparing and cleaning up the data, then performing the actual analysis. This should be conducted by a team of individuals who have a deep understanding of the mathematics and statistics behind SGPs.
