What is Data SGP?
Data sgp is a collective of aggregated student performance data that teachers and administrators use to make decisions about instruction and assessment. It includes individual-level measures like test scores and growth percentiles as well as aggregated measures at the school and district levels such as class size, attendance rates and graduation rates. This data can be used to identify areas for improvement, inform classroom practices, evaluate teachers/schools/districts and support broader research initiatives.
The SGPdata package installed when one installs the SGP package contains exemplar WIDE and LONG formatted data sets (sgpData and sgpData_LONG, respectively). In WIDE formatted data, each case/row represents a unique student and columns represent variables associated with the student at different times. In contrast, in long formatted data, time dependent variables are spread out across multiple rows. Generally speaking, the higher level functions in the SGP package are designed for use with long formatted data.
A number of districts, such as Macomb and Clare-Gladwin, have made their SGP data readily available in formats compatible with operational SGP analyses. However, many districts are struggling with the complexities involved in implementing SGP analyses in their operational systems. For example, in order to calculate student growth projections, districts need a reliable way of matching students who have multiple instructors for the same content area to their instructors through unique identifiers associated with the assessment records. This is accomplished by using the sgpData_INSTRUCTOR_NUMBER field in sgpData.
Another issue that districts face is the difficulty of using their current year growth data to assess teacher effectiveness. In most states, teachers are evaluated based on their students’ performance on a standardized test and compared to the average score of all students in the grade or school. Hence, it is important to use longitudinal or cohort-referenced SGPs. Unfortunately, these types of SGPs require additional years of stable assessment data to produce and are more difficult to compute than their baseline-referenced counterparts.
SGPs that are baseline-referenced also have the potential to introduce substantial bias into interpretation of results. This is because correlations between baseline SGPs and the scale score of a previous year are unlikely to be exactly zero. Consequently, it is crucial for educators to understand the assumptions and limitations of SGPs when they are designing evaluation systems that include student growth measures. This is especially important since the proliferation of SGP-based evaluation systems has raised expectations about what a “good” educator looks like. To mitigate these concerns, it is recommended that educators consider using a combination of longitudinal and cohort-referenced SGPs. This will help ensure that teachers’ evaluations are based on the most accurate and objective measures possible. This will reduce the likelihood of misguided evaluations based solely on current year growth. In addition, it will improve the reliability and validity of these evaluations by ensuring that they are not biased by prior year growth. This is particularly important when evaluating teachers for professional development purposes.