Abstract
Background/Context
Parents, policymakers, and researchers uphold that missing school has negative implications on schooling success, particularly for students in urban schools. However, it has thus far been an empirical challenge within educational research to estimate the true effect that absences have on achievement outcomes. This study addresses this issue by applying multiple quasi-experimental methods and, subsequently, contributes a more accurate understanding of the pervasive, negative effects of missing school.
Purpose/Objective/Research Question/Focus of the Study
The purpose of this study is to determine the effects of individual-level absences on individual-level standardized testing achievement (reading and math) in an urban school district.
Population/Participants/Subjects
The dataset compiled for this study is multilevel and longitudinal and is comprised of five elementary school cohorts within the School District of Philadelphia, for a total of N=20,932 student observations over three academic years. Individual student records were linked to teacher, classroom, and school administrative data as well as to census residential-block neighborhood information.
Research Design
This study combines secondary data analyses and quasi-experimental methods. This study begins with a baseline, linear model of achievement, where the dependent variables are Stanford Achievement Test Ninth Edition (SAT9) reading and math scores. To address issues pertaining to omitted variable bias, this study employs three methods: fixed effects, value-added, and instrumental variable models.
Findings
Consistently across all methods employed in this study, the results indicate a potentially causal, detrimental negative effect of absences on both reading and math standardized achievement. The effects remain significant even after accounting for additional student, neighborhood, teacher, classroom, and school factors.
Conclusions/Recommendations
This study demonstrates that after accounting for omitted variable bias in multiple capacities, the negative relationship between absences and achievement is even more detrimental than reported in previous research. With a more detailed and realistic prediction of the negative ramifications of missing school, it now becomes possible to develop data-driven educational policy.
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