A Renewed Call for Disaggregation of Racial and Ethnic Data: Advancing Scientific Rigor and Equity in Gifted and Talented Education Research

Abstract

Researchers in gifted and talented education (GATE) have increasingly taken on the role of advocating equity and access for minoritized populations. However, subgroups of racially and ethnically diverse students are rarely disaggregated from monolithic racial and ethnic categories. Studies on academic achievement of Asian American and White students, based on aggregated data, risk straying from scientific rigor and may lead to conclusions that further contribute to the masking of inequities and disparities of nested subgroups. The roots of this phenomenon can be traced to the practice of racial/ethnic data aggregation from the national level on down. We contend that fair and equitable access should be afforded to all students and call for the normalization of racial/ethnic data disaggregation in GATE research to increase scientific rigor in our scholarship and unmask intra-ethnic inequities.

What Happens After Nomination? Evaluating the Probability of Gifted Identification With the Torrance Test of Creative Thinking

“…Any other intentional modifications to the identification selection process discussed within individual campus or district committees were not able to be modeled quantitatively. Future research should also disaggregate the students categorized as Asian to better understand the differences within this specific racial/ethnic category and better express the wide heterogeneity that exists within the Asian racial category (Yeung & Mun, 2022). For example, additional points were provided to ELs that encompass students from multiple racial groups.…”

“…The comparison within each generalized linear regression to White students would change with a different reference group and should be further explored. More specifically, there should be additional analyses that are inclusive of disaggregation by race/ethnicity, socioeconomic status, and geographic region (Hodges et al, 2022; Yeung & Mun, 2022).…”

Promoting Equity in Methodological Considerations for Disaggregated Data

“…Data disaggregation can be defined as breaking down summarized data findings into smaller components based on some characteristic of a sample population instead of the aggregate. It is also a process by which researchers can examine findings in data with more nuance and better understand heterogeneity within a sample population (EdSource, 2022; Yeung & Mun, 2022). There are many uses for quantitative data in social science research (e.g., casual studies, descriptive studies, predictive analytics), particularly for large datasets that examine disaggregated findings by populations of interest (e.g., race, gender, class).…”

“…Aggregated data can lead to incorrect conclusions about the behavior and outcomes of individuals. Aggregated data can mask the outcomes we seek to observe and can fail to adequately represent the diversity of experiences within a particular area (Price, 2019; Roegman et al., 2018; Yeung & Mun, 2022). Nevertheless, reliable and valid data are necessary for developing, administering, and promoting social policy interventions.…”

“…Considering the widespread use of disaggregated data to make decisions at the local, state, and federal levels, researchers can significantly influence and shape public policy (Roegman et al., 2018; Yeung & Mun, 2022). Therefore, researchers should employ more inclusive, equitable, and critical methodological practices, such as critical quantitative inquiry, to effectively analyze and address social problems.…”