Disentangling the Roles of Institutional and Individual Poverty in the Identification of Gifted Students

Abstract

Although the relationships between family income and student identification for gifted programming are well documented, less is known about how school and district wealth are related to student identification. To examine the effects of institutional and individual poverty on student identification, we conducted a series of three-level regression models. Students of poverty are generally less likely to be identified for gifted services, even after controlling for prior math and reading achievement. Furthermore, school poverty predicts the percentage of gifted students identified in a school. Within districts, even after controlling for reading and math scores, the poorer schools in a district have lower identification rates. Whereas students of poverty are generally less likely to be identified for gifted services, poor students in poor schools are even less likely to be identified as gifted.

The Relationship of Gifted Program Enrollment to Race/Ethnicity, Gender, and SES.

“…Enrollment data for these programs reflect differences in proportional distribution among students (Civil Rights Data Collection [CRDC], 2014). Students of color, as well as students living in poverty, have faced limited access to gifted education opportunities (Callahan, 2005;Ford, Grantham, & Whiting, 2008;Hamilton et al, 2018;Wright, Ford, & Young, 2017). Both students of color and those living in poverty have been underrepresented as long as gifted education has existed (Callahan; Ford et al).…”

The Potential Promises and Pitfalls of Using Local Norms for Gifted Identification

“…school, yet in both schools there exists a group of students who will require additional educational opportunities, beyond those provided to the general school population, in order to be educated to their highest potential (Callahan & Miller, 2005;Hamilton et al, 2017;Renzulli, 1984). (Callahan & Miller, 2005;Hamilton et al, 2017;Renzulli, 1984) Upon implementing school-based norming procedures school locale and proportion of FRL students did not predict proportion of identified gifted students. Implementing school norms in this state would help better foster the academic talent of economically disadvantaged students, particularly in high-poverty schools.…”

“…It is well established in the literature that students receiving free or reduced priced lunches are underrepresented in gifted education programs relative to their proportion in the population, further indicating current inequities of educational opportunities (J. Borland, 2004;Hamilton et al, 2017;Olszewski-Kubilius & Thomson, 2010). There is some debate over what methods could be used to increase identification of FRL students because they typically underperform on the standardized achievement tests used to make identification decisions (National Association for Gifted Children, 2015;Michelmore and Dynarski, 2016;Olszewski-Kubilius & Thomson, 2010, Reardon, 2011.…”

“…A few scholars have suggested the use of schoolbased local norms as a possible way to reduce the underrepresentation problem (McBee, 2010;Olszewski-Kubilius & Corwith, 2018;Peters & Engerrand, 2016;Plucker & Peters, 2018). Using district-wide norming procedures among schools with different levels of achievement could lead to very few students being identified in schools with the highest proportion of students receiving FRL within districts (Hamilton et al, 2017). However, even within the poorest schools there are a group of students who are academically more advanced than their peers and thus require additional services outside the general classroom to be challenged by the curriculum (Ford, 2003;Peters, Rambo-Hernandez, Makel, Matthews, & Plucker, 2017;Reis & Renzulli, 2010) Improved methods for identifying students from low-income backgrounds for gifted education services could help provide a more responsive education to economically disadvantaged students (Lohman, 2005;Olszewski-Kubilius & Corwith, 2018;Peters & Engerrand, 2016;Peters & Gentry, 2012;Plucker & Peters, 2018).…”

“…Based on the literature, I expect suburban schools to have the highest proportion of identified students across all norming procedures and thresholds due to the lower concentration of poverty and the higher concentration of highly-educated parents (Duncan & Magnuson, 2005;Leventhal & Brooks-Gunn, 2000;Michelmore & Dynarski, 2016). I also expect the slope of the FRL variable to be less negative when applying district norms when compared to state norms and the FRL slope to be statistically nonsignificant when school norming procedures are in place (Hamilton et al, 2017;Lohman, 2005;Reis & Renzulli, 1982).…”