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      School Absenteeism and Academic Achievement: Does the Reason for Absence Matter?

      , 1 , 2
      AERA Open
      SAGE Publications

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          Abstract

          Studies consistently show associations between school absences and academic achievement. However, questions remain about whether this link depends on the reason for children’s absence. Using a sample of the Scottish Longitudinal Study (n = 4,419), we investigated whether the association between school absenteeism and achievement in high-stakes exams at the end of compulsory and postcompulsory schooling varies with the reason for absence. In line with previous research, our findings show that overall absences are negatively associated with academic achievement at both school stages. Likewise, all forms of absences (truancy, sickness absence, exceptional domestic circumstances, and family holidays) are negatively associated with achievement at the end of compulsory and postcompulsory schooling. First difference regressions confirm these negative associations, except for family holidays. These findings suggest that, in addition to lost instruction, other mechanisms such as behavioral, health-related, and psychosocial pathways may account for the association between absenteeism and achievement. The findings have implications for designing tailored absenteeism interventions to improve pupils’ academic achievement.

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          Most cited references41

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          Fixed Effects Regression Models

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            Withdrawing From School

            J. D. Finn (1989)
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              Estimating causal effects from epidemiological data.

              In ideal randomised experiments, association is causation: association measures can be interpreted as effect measures because randomisation ensures that the exposed and the unexposed are exchangeable. On the other hand, in observational studies, association is not generally causation: association measures cannot be interpreted as effect measures because the exposed and the unexposed are not generally exchangeable. However, observational research is often the only alternative for causal inference. This article reviews a condition that permits the estimation of causal effects from observational data, and two methods -- standardisation and inverse probability weighting -- to estimate population causal effects under that condition. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a generalisation of inverse probability weighting.
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                Author and article information

                Contributors
                Journal
                AERA Open
                AERA Open
                SAGE Publications
                2332-8584
                2332-8584
                January 2022
                February 13 2022
                January 2022
                : 8
                : 233285842110711
                Affiliations
                [1 ]University of Strathclyde
                [2 ]University of Dundee
                Article
                10.1177/23328584211071115
                9b12fba3-0b0f-4af4-9323-dc43a6a36ae3
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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