Data sgp is the name of the data format used by the SGP package to represent time dependent student assessment scores. This format is used by the SGP package when performing student growth projections and student achievement plots. It is also used by the SGP package when interpreting the results of SGP analyses.
The sgpData format supports a variety of student characteristics and can be easily customized to suit your needs. In addition, the SGP package includes exemplar WIDE and LONG data sets to assist in setting up your data.
In the sgpData format, each case/row represents a unique student and columns are used to represent variables associated with the student at different times. For example, in the sgpData below, the first row of data (ID) provides the student’s unique identifier and the next 5 rows provide grade level and scale score information for that student.
This format is ideal for analyzing data from multiple years of standardized tests. For example, a student could have 5 years of test scores and then a single year of SGPs.
SGP models compare students’ true SGPs to those of their peers who have the same combination of prior-year test scores. The results of this comparison may reveal a nontrivial source of variance that is due to the relationships between student characteristics and true SGPs at the individual level rather than to measurement errors at the teacher or school levels.
As such, a student’s SGP score is not always a good predictor of her future academic success. Hence, it is important to account for these relationships and analyze the relationship between student characteristics and actual SGPs at the individual level.
To address these problems, we constructed a model that compared SGPs at the student, school, and teacher levels. The model was designed to overcome the excessive measurement error problem at the student level by comparing true SGPs to those of their peers with similar characteristics.
This model has a number of benefits over the traditional methods for interpreting SGPs at the student level. For instance, the model is less susceptible to changes in student demographics and more robust in predicting student performance. In addition, the model is able to identify differences in student SGPs between groups of students that may not have a significant impact on the overall SGP scores of the group.
The model was tested on a sample of more than 3000 students. The results showed that the model was able to detect student-level differences in true SGPs in math and ELA. In general, the group mean differences in SGPs were larger than those found at the individual level.
In this study, the group mean differences in SGPs for math were notably larger than those for ELA. This is in part because math and ELA differ in the way that SGPs are measured. For example, math scores are weighted more heavily than ELA scores. In addition, students with high math SGPs are more likely to be in the highest quartiles than students with low math SGPs.