Background/Context Chronic absenteeism has received increased attention from educational leaders and policy makers, in part because of the association between attendance and important student outcomes. Student attendance is influenced by a range of student-, school-, and community-level characteristics, suggesting that a comprehensive and multilayered approach to addressing chronic absenteeism is warranted, particularly in high-poverty urban districts. Given the complexity of factors associated with chronic absenteeism, we draw from ecological systems theory to study absenteeism in Detroit, which has the highest rate of chronic absence of major cities in the country. Purpose/Research Questions We use administrative and public data to advance the ecological approach to chronic absenteeism. In particular, we ask: (1) How are student, neighborhood, and school characteristics associated with individual absenteeism? (2) How are structural and environmental conditions associated with citywide rates of absenteeism? Our study helps to fill a gap in the research on absenteeism by moving beyond a siloed focus on student, family, or school factors, instead placing them in relationship to one another and in their broader socioeconomic context. It also illustrates how researchers, policy makers, and administrators can take a theoretically informed approach to chronic absenteeism and use administrative data to conceptualize the problem and the potential routes to improving it. Research Design Using student-level administrative data on all students living and going to school in Detroit in the 2015–2016 school year, we estimate a series of multilevel logistic regressions that measure the association between student-, neighborhood-, and school-level factors and the likelihood of a Detroit student being chronically absent. We also use publicly available data to examine how macrosystemic conditions (e.g., health, crime, poverty, racial segregation, weather) are correlated with citywide rates of absenteeism in the 2015–2016 school year, and we compare Detroit with other large cities based on those conditions. Findings/Results Student-, neighborhood-, and school-level factors were significant predictors of chronic absenteeism in Detroit. Students were more likely to be chronically absent if they were economically disadvantaged, received special education services, moved schools or residences during the year, lived in neighborhoods with more crime and residential blight, and went to schools with more economically disadvantaged students and less stable student populations. Macro-level factors were also significantly correlated with citywide rates of absenteeism, highlighting Detroit's uniquely challenging context for attendance. Conclusions/Recommendations Our ecological understanding of absenteeism suggests that school-based efforts are necessary but not sufficient to substantially decrease rates of chronic absenteeism in Detroit and other high-absenteeism contexts. Policies that provide short-term relief from economic hardship and aim to reduce inequalities in the long-run must be understood as part of, rather than separate from, a policy agenda for reducing chronic absenteeism.
A Coterminous Collaborative Learning Model: Interconnectivity of Leadership and Learning
