DayMay 14, 2024

How to Interpret Data SGP

Data SGP refers to aggregated student performance data accumulated over time which teachers and administrators use to understand student progress and inform instruction, evaluate schools/districts, and support larger research initiatives. It includes individual measures like student test scores and growth percentiles as well as aggregate measures at both school and district levels like class sizes, attendance rates and graduation rates.

Teachers and school administrators need to be familiar with how SGP metrics are generated for optimal interpretation. Furthermore, it’s crucial that teachers and administrators comprehend any assumptions made when using these metrics in educator evaluation systems that might introduce bias.

SGPs are calculated by comparing latent achievement trait models to the performance of a reference group, typically via least squares regression or Bayesian inference estimation methods. Estimations generated from these models are then compared with growth standards established via teacher evaluation criteria and student covariates; this comparison attempts to eliminate uncertainties associated with tracking one student over time while simultaneously decreasing errors when making inferences about individuals or groups of students.

Unfortunately, error-free measurement is unrealistic in education as students’ raw scores often vary considerably and correlations between prior year scale scores and current year ones are unlikely to be zero. This introduces bias into SGP interpretation and makes objective evaluation of educators difficult due to students’ growth.

To overcome these limitations, many SGP analyses rely on “baseline-referenced” data sets which assume that an individual student’s current year growth rate resembles the average growth rate experienced by their cohort in a prior assessment window. While this method helps minimize estimation errors, it requires at least three years of stable student data as well as sufficient assessments occurrences per student for comparison purposes.

Alternately, some educators may opt to assess students’ current year growth relative to a predetermined growth standard’s median median. While this approach reduces some of the error in interpreting SGPs, it does not eliminate the need to set growth standards beforehand and may lead to teachers being evaluated solely on whether their students met or exceeded a defined pass/fail rate.

To address these limitations, the SGP package offers several functions to ease the complex process of operational SGP analyses: prepareSGP, analyzeSGP and combineSGP. prepareSGP takes an exemplary LONG format data set (sgpData_LONG) and an INSTRUCTOR-STUDENT lookup file (sgpData_INSTRUCTOR_NUMBER), then uses these elements to produce the master longitudinal record Demonstration_SGP@Data. analyzeSGP is designed to perform student growth analysis on this record and produce outputs such as student growth percentiles, projections and lagged baseline projections. combineSGP merges these results back into the master longitudinal record while also creating student growth percentiles for additional content areas and windows.