Measuring the Inequality of Opportunity Using Individual-Level Advantage.

다국어 초록 (Multilingual Abstract)

Educational inequality has primarily been defined and measured as group-centered differences in achievement. Often described using the achievement-gap framework, a substantial portion of the literature has highlighted the pervasive inequalities in educational outcomes among ethnic-racial groups or by socioeconomic status. The focus on group-level disparities in achievement has yielded important insights into systematic inequalities and informed policies at both the state and national level, including the No Child Left Behind Act, Every Student Succeeds Act, and California’s Local Control Funding Formula. Despite this, the achievement-gap framework has been criticized for (a) reinforcing stereotypes, (b) essentializing students based on group membership and ignoring their multiple identities, (c) disregarding within-group heterogeneity, (d) frequently omitting groups with fewer members, and (e) positioning White or middle class as the norm. Alternatively, some scholars have suggested that research on educational inequality should focus instead on gaps in opportunity as the precursors to group-based differences in achievement.Although reframing achievement gaps as opportunity gaps provides a different perspective, by focusing at the group level, this approach remains vulnerable to many of the same concerns as the achievement-gap framework, both of which lack student-level specificity. Importantly, although there is general agreement regarding the measurement of achievement, the measurement of opportunity is beset by a number of challenges, most prominently by the lack of a commonly shared definition, an essential factor for its application in policy and practice. In this dissertation, I proposed and tested the Advantage framework as a potential solution to the weaknesses and limitations of the gap frameworks for measuring inequality of opportunity and evaluating achievement.I had three requirements when developing the Advantage framework. These included that the model should (a) include all students, (b) embrace the complexity of students’ multiple overlapping group memberships, and (c) yield individual-level measures. I built upon the method demonstrated by Ferreira and Gignoux (2013) in which they measured the inequality of opportunity using the variance in achievement explained by students’ background characteristics. Whereas their method yielded overall sample-level measures of inequality, I provided each student with an individual-level measure of advantage, defined by the Cambridge dictionary as the conditions giving greater chance of success. As an individual-level continuous measure, Advantage explained the inequality of opportunity at multiple levels including at the population level, among and within groups, and even between two individuals.In the first study, I proposed and evaluated the framework for defining and measuring opportunity based on individual-level Advantage, estimated using six educationally relevant demographic variables and aggregated using students’ demographic profiles. I evaluated the Advantage framework using data from two school districts, multiple academic years, and over a range of outcomes. Advantage was found reliable and valid across both districts. Compared to the ethnic-racial and socioeconomic achievement-gap frameworks, Advantage explained up to 4.5 times more of the variance in achievement, suggesting that group-centered methods may significantly understate the actual magnitude of educational inequality. Advantage explained up to 44% of the heterogeneity of achievement seen within ethnic-racial groups and up to 38% within socioeconomic groups. Advantage correctly classified proficiency status on a standardized assessment for over 80% of the students and came with significantly lower false positive and false negative rates, an important characteristic for practitioners working to address inequality. Controlling for Advantage eliminated gaps in proficiency between ethnic-racial and socioeconomic groups on standardized assessments in one district, and largely so in the other. This study suggested that the Advantage framework is a valid and reliable method for measuring the inequality of opportunity.In the second study, I used the Advantage framework to examine longitudinal data. Students exposed to greater educational opportunities prior to starting school typically demonstrate higher initial achievement and grow at faster rates compared to their less advantaged peers. The inequalities in opportunity prior to starting school frequently translate into inequalities in achievement in the later grades. In this study, I compared two methods for translating student demographics into indicators of inequality: California’s Local Control Funding Formula (LCFF) and the Advantage framework. Whereas the LCFF dichotomizes inequality into two categories, Advantage generates individual-level measures along a continuum. I applied the two methods to longitudinal achievement data from two school districts and compared the extent to which the models explained students’ initial and long-term achievement. Compared to the LCFF, the Advantage framework explained up to 3.3 times more of the variance in Grade 3 achievement, up to 3.4 times more of the variance in growth, and was between 7.5% and 9% more accurate in predicting proficiency on standardized assessments. Although both models demonstrated evidence for the Matthew effect, the Advantage framework better portrayed it as a heterogenous phenomenon. Based on the findings of this study, the Advantage framework could improve policies designed to mitigate educational inequality.For the third study, I applied the Advantage framework at the school-level. In 2013, California implemented the Local Control Funding Formula to reduce inequalities in both educational achievement and opportunity. In this study, I used hierarchical linear modeling to examine the inequality of opportunity in California’s over 9,000 public schools from 2015–2024 with a specific emphasis on the time periods pre- and post-COVID. I used the Advantage framework to calculate school-specific measures of Advantage using a combination of demographic, enrollment, and test data. School-level measures of Advantage were then used to measure the inequality of opportunity in both cross-sectional and longitudinal analyses. Results showed that as achievement was increasing during the years pre-COVID, the inequality of opportunity was decreasing, suggesting that raising test scores and reducing inequality can work in tandem. Due to the COVID interruption in learning, inequality increased and achievement declined substantially from 2019–2022, but the positive trends resumed between 2022–2024. Compared to pre-COVID, school-level Advantage explained 95% more of the variance in test scores for math post-COVID and 32% more for English language arts, indicating school-level characteristics played a more prominent role in the post-pandemic recovery.