Data sgp leverages longitudinal student assessment data to produce statistical growth plots (SGP) that quantify students’ relative progress compared to their academic peers. SGPs use a prior achievement standard established using the prior year’s standardized test scores and a variety of covariates to gauge how much a student must grow to meet their expected growth target.
In contrast to the more complicated value-added models used in teacher evaluation systems, SGPs provide a clear and interpretable way for educators to understand student growth in percentile terms. SGP models also provide teachers with an estimate of their students’ progress over time without having to wait for the results of a yearly testing cycle. This makes SGPs an appealing tool for policymakers, educators, and the public.
SGP analyses are straightforward assuming that the data has been properly prepared. Almost any error that might arise in the course of an SGP analysis reverts back to data preparation issues rather than to the analysis itself. This is why it is important to spend time preparing your data before attempting to run SGP analyses.
The sgpData table contains a list of students sorted by the unique ID assigned to them by the district as well as the scores from their previous years’ Star exams. The first column, ID, provides the student’s unique identifier and the next five columns, GRADE_2013, GRADE_2014, GRADE_2015, GRADE_2016, and GRADE_2017 provide the scale scores associated with each student for those five years.
Each year a new SGP model is created for each student in the district using the student’s current and prior year’s standardized test scores and several different sets of covariates. The model generates a growth percentile score for each student, an estimate of how much a student must grow in order to reach their target (e.g. 75% of their academic peers). The model then assigns a teacher’s score by comparing the student’s growth percentile score to the teacher’s mean or median SGP.
Unlike VAMs, SGPs present the results of their calculations in percentile terms, which are familiar to most teachers and parents. This is especially useful for highlighting students’ individual progress and needs in comparison to their peers. In addition, SGPs provide a more meaningful indicator of teacher effectiveness than VAMs because they are designed to be transparent and easy for teachers and parents to understand.
For districts that want to take advantage of SGP’s ability to measure teacher effectiveness, the sgpData table contains an sgpData_INSTRUCTOR_NUMBER field that allows for instructors to be associated with each student. This lookup function, which has been implemented by Macomb and Clare-Gladwin, is essential for operationalizing SGP analyses because it allows districts to associate each instructor with one student. This will ensure that when SGP models are run, students are matched to the correct instructor for their content area. This is particularly crucial if instructors teach multiple sections of a subject. Having this capability will allow districts to identify when students are being improperly assigned to instructors and address these concerns immediately.