Session Information
05 SES 01 A, Addressing School Absence and Drop-out
Paper Session
Contribution
This study examines the effect of absence patterns over the course of a pupil’s entire academic career on achievement in the final year of compulsory schooling in England. There is abundant evidence of the negative consequences of school absenteeism on children’s achievement (Aucejo & Romano, 2016), which subsequently translates into lower educational attainment and poorer labour market outcomes (Cattan et al., 2022). However, most existing research consider the effects of absences over a single year and the limited studies exploring absences across time only examine whether yearly changes in absences result in varying achievement progress. Additionally, existing studies disregard the cumulative measurement of dynamic absence trajectories over time, which may conceal meaningful differences between pupils and likely results in an underestimation of the degree to which absences influence achievement. A holistic measurement of pupils’ absences across their academic careers is crucial to assess whether the timing of absence severity matters for achievement.
Theoretically, there are arguments for early absences being more important for achievement as well as arguments for late absences being more important. On the one hand, we could assume that early absences are more harmful to children’s achievement because skill formation is path dependent: children who lack basic skills will have difficulty acquiring more advanced skills. On the other hand, we could assume that late absences have a greater impact on children’s achievement, given that the content being evaluated on tests is the content taught in the last few years before the test. Existing research overwhelmingly concludes that later absences are more important for academic success (Ansari & Pianta, 2019).
The extent to which absence trajectories influence achievement may not only be influenced by the frequency of absences but also by the type of absence. For instance, unauthorised absences become much more prevalent during later school stages (Department for Education, 2011) and seem to be more detrimental to school performance than authorised absences (Gottfried, 2009). This could be due to teachers being less motivated to support pupils with numerous unauthorised absences (Wilson et al., 2008). Nonetheless, the larger effect of unauthorised absences may also reflect differences in pupils’ school-related attitudes (Hancock et al., 2013), which are rarely measured by surveys. This claim is supported by Klein et al. (2022), who found that sickness absences and absences due to exceptional domestic circumstances are just as damaging to achievement as truancy.
Some studies have examined trajectories of overall absences (Benner & Wang, 2014; Simon et al., 2020) or truancy (Schoeneberger, 2012), but none have jointly investigated type and temporal dynamics across children’s schooling. Moreover, the research on absence trajectories is largely limited to the United States, only considers absences over a few years of schooling, and does not account for all pertinent school absenteeism risk factors which likely also affect academic achievement.
Our study draws on linked school administrative and survey data from England to examine the association between absence trajectories and achievement. Specifically, we address two research questions that enable us to overcome the limitations of previous studies:
- Which absence trajectories emerge across entire school careers?
- How do these absence trajectories affect achievement?
Method
We use linked administrative data on absences and standardised achievement tests from the National Pupil Database (NPD) with survey data from the Millennium Cohort Study (MCS), which enables us to identify pupils’ joint trajectories of authorised and unauthorised absences throughout the entire mandatory school career in England (Years 1 to 11) while simultaneously controlling for a comprehensive set of confounders of the association between absence trajectories and achievement. Linked NPD-MCS data is available for 8,438 pupils. We use the percentage of authorised and unauthorised absences out of all possible sessions in each year for our analysis. Authorised absences are absences with permission from a teacher or other authorised representatives of the school, which is only given if a satisfactory explanation for the absence has been provided, e.g., illness. Unauthorised absences are absences without the permission of the school. As outcomes, we evaluate differences in performance measures on standardised tests at the end of year 11 (key stage 4): 1) Whether pupils passed at least five exams with grades A*-C, including English and Math, 2) The average performance on the eight best exams, 3) Grade in English, 4) Grade in Math. We use multiple imputation for missing values in absenteeism risk factors and weight pupils by the inverse of the probability that they gave consent to data linkage and have complete absence and achievement data to account for selection effects. We use k-medians clustering for longitudinal data to identify clusters with similar joint trajectories on authorised and unauthorised absences from years 1 to 11 (Genolini et al., 2013). To estimate the effect of absence trajectories on achievement, we exploit the fact that the MCS contains all identified risk factors of school absenteeism (Gubbels et al., 2019). Since we evaluate the effect of entire absence trajectories as opposed to absences in a single year, we must appropriately control for time-varying confounders, which have been frequently overlooked in the existing literature. We accomplish this by employing a regression-with-residuals technique, which enables us to condition on time-varying confounders that may be affected by earlier absences without introducing overcontrol bias (Wodtke & Almirall, 2017).
Expected Outcomes
Preliminary results indicate that most pupils fall into a cluster with both low authorised absences and very low unauthorised absences throughout their entire school careers. Other clusters are defined by either higher authorised absences, higher unauthorised absences, or high authorised and high unauthorised absences or differ in terms of the persistence and the timing of absences. There are substantial differences in achievement between absence trajectory clusters, even when accounting comprehensively for risk factors. The cluster of pupils with low authorised and very low unauthorised absences throughout their entire school career perform best, but there are also marked differences between different high absences trajectories. (Exact results are subject to the statistical disclosure review of the UK Data Service and will be presented at the conference).
References
Ansari, A., & Pianta, R. C. (2019). School absenteeism in the first decade of education and outcomes in adolescence. Journal of School Psychology, 76, 48–61. https://doi.org/10.1016/j.jsp.2019.07.010 Aucejo, E. M., & Romano, T. F. (2016). Assessing the effect of school days and absences on test score performance. Economics of Education Review, 55, 70–87. https://doi.org/10.1016/j.econedurev.2016.08.007 Benner, A. D., & Wang, Y. (2014). Shifting attendance trajectories from middle to high school: Influences of school transitions and changing school contexts. Developmental Psychology, 50(4), 1288–1301. https://doi.org/10.1037/a0035366 Cattan, S., Kamhöfer, D., Karlsson, M., & Nilsson, T. (2022). The Long-term Effects of Student Absence: Evidence from Sweden. The Economic Journal. https://doi.org/10.1093/ej/ueac078 Department for Education. (2011). A profile of pupil absence in England (DFE-RR171; Research Report). Genolini, C., Pingault, J. B., Driss, T., Côté, S., Tremblay, R. E., Vitaro, F., Arnaud, C., & Falissard, B. (2013). KmL3D: A non-parametric algorithm for clustering joint trajectories. Computer Methods and Programs in Biomedicine, 109(1), 104–111. https://doi.org/10.1016/j.cmpb.2012.08.016 Gottfried, M. A. (2009). Excused Versus Unexcused: How Student Absences in Elementary School Affect Academic Achievement. Educational Evaluation and Policy Analysis, 31(4), 392–415. https://doi.org/10.3102/0162373709342467 Gubbels, J., van der Put, C. E., & Assink, M. (2019). Risk Factors for School Absenteeism and Dropout: A Meta-Analytic Review. Journal of Youth and Adolescence, 48(9), 1637–1667. https://doi.org/10.1007/s10964-019-01072-5 Hancock, K. J., Shepherd, C. C. J., Lawrence, D., & Zubrick, S. R. (2013). Student attendance and educational outcomes: Every day counts (Report for the Department of Education, Employment and Workplace Relations). Klein, M., Sosu, E. M., & Dare, S. (2022). School Absenteeism and Academic Achievement: Does the Reason for Absence Matter? AERA Open, 8, 233285842110711. https://doi.org/10.1177/23328584211071115 Schoeneberger, J. A. (2012). Longitudinal Attendance Patterns: Developing High School Dropouts. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 85(1), 7–14. https://doi.org/10.1080/00098655.2011.603766 Simon, O., Nylund-Gibson, K., Gottfried, M., & Mireles-Rios, R. (2020). Elementary absenteeism over time: A latent class growth analysis predicting fifth and eighth grade outcomes. Learning and Individual Differences, 78, 101822. https://doi.org/10.1016/j.lindif.2020.101822 Wilson, V., Malcolm, H., Edward, S., & Davidson, J. (2008). ‘Bunking off’: The impact of truancy on pupils and teachers. British Educational Research Journal, 34(1), 1–17. https://doi.org/10.1080/01411920701492191 Wodtke, G. T., & Almirall, D. (2017). Estimating Moderated Causal Effects with Time-varying Treatments and Time-varying Moderators: Structural Nested Mean Models and Regression with Residuals. Sociological Methodology, 47(1), 212–245. https://doi.org/10.1177/0081175017701180
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