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How to Use Data SGP to Evaluate Student Growth and Achievement

data sgp

Data sgp is an analysis package that can be used to evaluate student growth and achievement. It can be applied to longitudinal (time dependent) student assessment data. This package includes functions for analyzing student growth and achievement, creating individual level student growth and achievement plots, and calculating student growth and achievement indices.

To run an SGP analysis, the user must have access to a copy of the student assessment data. This can be a WIDE file, which is typically the format used for most student assessment data.

The format can be customized to fit the needs of an organization. The following variables are required when using a WIDE file with SGP: VALID_CASE, CONTENT_AREA, YEAR, ID, SCALE_SCORE, GRADE and ACHIEVEMENT_LEVEL.

If running student growth and achievement projections, additional variables are required: LAST_NAME and FIRST_NAME. These are demographic/student categorization variables that are used to create student aggregates by the summarizeSGP function.

Once the students have been categorized, the next step is to create the SGP objects and the corresponding plots of student growth and achievement. These include student growth indices, which are used to identify students who have above average or below average growth rates.

This is done by examining the student’s growth rate for each grading period and identifying students with above average or below average growth. The student growth indices can then be used to predict future growth for individual students and groups of students.

In general, these indices are derived from the relationship between the student’s growth rates and his or her prior performance on standardized assessments. They are a useful tool for monitoring a student’s progress over time and evaluating teachers and schools based on their effectiveness.

When interpreting SGPs as indicators of educator effectiveness, they should be used with caution. These indices may be influenced by non-academic factors, such as the amount of time a teacher spends teaching the content of a subject or the number of teachers in a classroom. These indices also can be affected by the degree to which an individual instructor is proficient at teaching a particular subject.

For example, if a student has had a particularly strong year, his or her true SGP for math will be above average. This may be because the student is likely to be highly proficient at a particular subject, or because the instructor is a very effective teacher at that subject.

However, if a student has had an otherwise poor year, his or her true SGP for mathematics will be below average. This is due in part to the smaller within-subject, cross-year correlations of the latent achievement traits in Grades 7 and 8 compared to earlier grades.

These smaller correlations can be problematic for interpreting SGPs aggregated to teacher or school levels as indicators of educator effectiveness. They may lead to a false sense of confidence in the reliability of student growth and achievement scores. The underlying problem is that the estimated SGPs can be influenced by non-academic sources, such as the amount of time a student spends teaching a particular subject or the number of teachers in a room.