Background: Traditionally, SREE has traditionally focused on examining the effects of educational interventions with rigorous causal analysis. However, studies of racial/ethnic inequities in education do not always lend themselves to experimental or even quasi-experimental designs. Further, well formulated descriptive studies can provide crucial information about the current state of education and can lead to the generation of causal questions and designs. This study documents racial/ethnic, class, and English learner (EL) disparities in gifted identification through the lens of intersectionality using administrative data from seven school districts. Race, ethnicity, SES, and EL are not independent and unitary concepts. Instead, they intersect and interact in ways that must be considered in educational research (Collins, 2015). Substantial work has documented disproportional representation in school discipline, special education placement, and gifted education placement (Hamilton et al., 2018; Hamilton et al., 2020; Long et al., in press; Shores et al., 2020). A subset of studies examined the same educational outcomes after controlling for student poverty, prior achievement, or special education eligibility (e.g., Backes et al., 2021; Grissom & Redding, 2019; Morgan et al., 2015; Long et al., in press). The results of studies comparing students of different racial/ethnic groups are very different, depending on whether or not studies control for academic achievement or socio-demographic factors such as poverty. For example, although Latinx students are less likely than their White peers to be identified as gifted, after controlling for background factors, Grissom and Redding found the identification gap between White and Latinx students disappeared. As more educational interventions, particularly post-pandemic, focus on improving equity, it is essential to understand the various factors that relate to disproportional outcomes. Otherwise, efforts to mitigate disproportional outcomes may emphasize or implement ineffective solutions. Intersectionality, as an analytic strategy, may provide an important perspective the complex interplay between various sociodemographic factors (Collins, 2015). Purpose: The purpose of this study is to understand how race/ethnicity and free- or reduced-price lunch (FRL) eligibility and EL intersect and interact to produce disparities in gifted identification rates across intersectional groups. Setting: Our sample includes student level data on academic achievement, ability, and student demographics across 6 districts. The combined dataset includes 19463 students nested within 1119 classrooms in 285 schools (see table 1). These districts have an average of 19% black, 46% Latino, 26% White, and 66% FRL. They also have notable variation in racial composition and FRL composition. For example, among the largest four districts, one is predominantly Latino, two are predominantly White, and one has nearly equal proportions of Black, Latino, and White students. We also complement this analysis with a replication in a 7th district that provided two years of 2nd grade data collected under a separate data agreement. Across the two cohorts in district 7, there are a total of 25,114 students in 937 classrooms. Research Design: We conducted a series of mixed-effect logistic regression analyses to determine the intersectional effects of race/ethnicity, FRL and EL status on gifted identification. We tested three models: Model 1 included race/ethnicity, FRL and the race/ethnicity by FRL interaction; Model 2 added EL to Model 1 plus the race/ethnic by EL interaction, and the FRL by EL interactions; and Model 3 added achievement and ability to the Model 2 variables. (see table 2). Our analysis allowed us to directly evaluate the relative contribution of each variable in explaining the odds of a student being identified as gifted. We first estimated these models with a students within classrooms within schools within district model that included the pooled data from districts 1-6. Next we estimated a subset of multilevel models of students within classrooms within schools for the largest districts (districts 1, 2, 3 plus the supplemental district 7). Findings Models 1 and 2 show a Black, Latino, FRL, and EL disadvantage in gifted identification (see Table 3 and Figure 1). When ability, and achievement are added to the models the lower levels of identification for Black students, Latino Students, low-income students, and EL students all diminish and are no longer statistically significant (see Tables 3-6 and Figures 1-4). To better understand the differences in rates of gifted identification in models with and without ability and achievement we examined the differences in ability, achievement, and teacher rating scales by subgroup (see Table 6). There is about a 0.5 standard deviation difference in achievement, ability, and TRS between Non-FRL and FRL students. And there is a 0.6, 0.5, and 0.3 difference in achievement, ability, and TRS, respectively, for Black versus White students. In the joint models for districts 1-6 there are few statistically significant interaction effects, however there is a positive interaction effect by FRL and race/ethnicity in district 7's unconditional model. Conclusion: First, this study shows that Black and Latinx students in poverty are at a double disadvantage in gifted identification. There is a persistent effect of both race and poverty when both are included in the model and these disadvantages have a similar magnitude. However, in most districts (District 7 is the exception) there are no statistically significant interaction. Second, this study shows that identification gaps largely disappear after controlling for ability and achievement. The different results in the conditional and unconditional model appears to be driven by very large sub-group differences in ability and achievement. These findings suggest that the most effective policy to address under-representation would be to address early achievement and ability gaps instead of addressing potential "bias" in the gifted identification process. Third, our analysis revealed some notable between district differences. For example, there is a positive interaction effect for Black and FRL and Latinx and FRL in district 7 only. Also, in district 7, holding ability and achievement constant, Black students are actually more likely to be identified as gifted. Although non-causal, we believe that well-designed descriptive analysis of unconditional and conditional disparities can help identify the relative influence of race versus class on educational inequalities and can help to inform education policy.
