How You Identify Determines Who You Identify: The Implications of the Choice of Talent Measures,Norms,Cut-Offs,and Combination Rules on the Academic Profile and Diversity of Students Identified as Gifted

Abstract

This study investigates the impact of variation in gifted student identification systems on the size, equity, and academic profiles of identified students. Utilizing data from 40,834 students across 519 elementary schools in seven districts, we modeled 64 hypothetical systems, focusing on measures (ability test scores, cognitive scores, and teacher ratings), norms (district and building), cut-off levels (5% and 10%), and data point combinations. Findings reveal that OR rules and teacher rating scales significantly increase identification rates among traditionally underrepresented groups. However, these methods lead to a gifted population with more varied abilities and lower average achievement. Also, different rules with similar average characteristics often identify markedly different student groups. No single identification system achieved complete equity in identifying gifted students from underserved racial/ethnic backgrounds or low-income families. This study provides practitioners guidance on the implications of different identification systems and an online tool to examine differences (https://id-app.shinyapps.io/id_shiny_app/).

Plain Language Summary

How you identify determines who you identify: The implications of the choice of talent measures, norms, cut-offs, and combination rules on the academic profile and diversity of students identified as gifted

This study examines how different gifted identification systems affect student numbers, equity, and achievement. Using data from 40,000+ students, we modeled 64 approaches. Teacher ratings and OR rules increase diversity but lower average achievement. No system fully achieves equity. Explore impacts and compare options using our online tool: https://id-app.shinyapps.io/id_shiny_app/.

Keywords

equity gifted identification multiple criteria observational research secondary data analysis simulation study traditionally underrepresented populations

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