Understanding the Multifaceted Nature of Educational Advantage Through an Expanded Composite Measure of Socioeconomic Status

• In Event: Measuring Socioeconomic Status to Contextualize Educational Achievement: A Discussion on Methods, Applications, and Challenges

Abstract

Previous studies showed that students from high socio-economic status (SES) backgrounds perform better on standardized assessments than those from low SES backgrounds (Borman & Hewes, 2002; Harwell & LeBeau, 2010; Lubienski & Lubienski, 2006). In particular, van Ewijk and Sleegers (2010) found that an increase of one standard deviation in school SES was associated with an increase of .32 standard deviations in student achievement. The eligibility for free or reduced price lunch as an SES measure is becoming increasingly limited and subject to change due to policy uncertainty. The recent proposal by the Office of Management and Budget to change the federal poverty line would result in over 100,000 children losing their eligibility for NSLP (Karni, 2019). Moreover, NSLP eligibility fails to take into account the multifaceted nature of the SES construct that represents the educational advantage of students with privileged backgrounds. Not all financial, social, and human capital resources available to the student come from the family (NCES, 2012). Students with high SES backgrounds also have access to more educational resources within their neighborhoods that are predictive of academic success, such as afterschool programs and extra-curricular opportunities. Additionally, due to school choice initiatives and other factors related to current educational policies, more students are able to attend schools outside of their neighborhood. Therefore, the characteristics of the school community may have a unique impact on the academic success of students who live in poor neighborhoods.

Drawing on SES-related proxy items in the NAEP student background questionnaire and Census Bureau data, this study proposes to create an expanded measure of SES that incorporates accessibility to resources at family, neighborhood and school levels. For school-level SES variables, the study will use the percentage of students who are eligible for NSLP and the School Neighborhood Poverty Estimates developed by the Education Demographic and Geographic Estimates (EDGE) Program at NCES (Geverdt & Nixon, 2018). For neighborhood factors, the 5-year estimates of median household income from the American Community Survey (ACS) are extracted and merged with the NAEP data by the zip code of the student’s home location. The study intends to apply multilevel models to examine relationship between family, neighborhood, and school-level SES factors and the academic performance of students, as measured by the 2015 NAEP math and science composite scores at elementary and secondary grade levels. Variation in educational performance of children from financially disadvantaged backgrounds will be explored by various family, school and neighborhood contextual factors. Random-coefficients model is applied with student-level characteristics as predictors and student-level intercept having random element, which will estimate the proportion of within-school variance that can be explained by a set of student and family characteristics while considering the variability across schools. The significance of the study lies in advancing the understanding of multi-faceted nature of SES and its impact on educational outcomes as well as addressing the need for new ways to look at educational advantages and more effective measures of achieving educational equity between the rich and the poor with the same potentials.

Authors

• Ning Rui, Amazon

• Judy H. Tang, Westat