Ehh the study is a bit more complicated then that. They had to control for many factors including teacher, classroom, and school characteristics. They also controlled for test scores , socioeconomic background , sex ,age , kindergarten attendance, previous scores in math and reading using various linear regression and statistical models.
They also noted many limitations in their methods
< Finally, several school characteristics are associated with assignment probability, including school racial/ethnic composition; students in schools with larger fractions of Black and Hispanic students have higher assignment probabilities, while those in schools with larger Asian populations have lower assignment probabilities.
<A limitation of this approach is that it does not take into account unobserved school characteristics, such as the school’s gifted referral and evaluation process, that may affect a student’s probability of being assigned to gifted services. A typical strategy for accounting for such factors is to include a school fixed effect, which would estimate the impact of teacher race congruence or other factors on gifted assignment by comparing assignment patterns across classrooms within the same school. Unfortunately, the infrequency of gifted assignment in the data—and, for the race congruence analysis, the lack of variation in teacher race across classrooms—makes a school fixed-effect approach infeasible <irst, we lack data on teachers’ actual identification, referral, and diagnosis behaviors and instead approximate this complex process with information on whether the student moves from not receiving gifted services to receiving them in consecutive survey periods. This approximation is more problematic in some years because the ECLS-K did not collect data in second and fourth grades. Second, data limitations prevent us from more fully accounting for unobserved characteristics that might bias our results. For example, it could be that motivated parents both push for their children to be assigned to same-race teachers and advocate for them to be tested for gifted classification. If so, we risk attributing the impact of parental involvement to the presence of a race-congruent teacher. We attempt to account for a number of student, teacher, and school characteristics to avoid these sources of bias, but larger data sets of the type maintained for state administrative purposes could permit other modeling strategies, such as the inclusion of student or teacher fixed effects, that would provide more convincing estimates. An additional data limitation is that the ECLS-K data contain no measures of student aptitude. We include math and reading achievement scores in our models, but if schools aim to target gifted services at high-aptitude students, regardless of past achievement, these scores may not be sufficient. Rather than interpreting our results as definitive evidence that teacher discretion contributes to disproportionalities in student gifted assignment, we interpret them as suggesting new avenues for further research to better understand a complex set of social and educational processes.
There main conclusion is with all these statistical limitations, control settings and sample size differences. <White students have a predicted probability of assignment of 6.2%, whereas Black students have only a 2.8% probability, a statistically and substantively important difference.