Comparison of Interconnections between Full-Day and Partial-Day Absences in School Children: A Causal Discovery Analysis

Introduction: Chronic absenteeism (CA) is an administrative term defining extreme failure for students to be present in school. In Minnesota, CA is defined as missing more than 10% of school days in a year. Absenteeism is a national problem that has devastating long-term impacts for students, such as adult substance abuse and poor physical and mental health after graduation. The impact of partial-day absences (PDAs) on student outcomes is less studied, due to diverse policies at the local school district level. However, PDA is much more prevalent than full-day absence (FDA). Leveraging the growing availability of big data, this study applies data-driven research methods to the examination of factors associated with both partial and full-day absences.

Objective: Applying causal discovery analysis techniques to student-level data, this study analyzes the interconnectivity of PDA and FDA by comparing their related factors. Specific student-reported factors were included in the analysis based on components of Bronfenbrenner’s bioecological model of development, which explains children’s development by the environments they are embedded in multiple levels..

Methods: Using TETRAD, a causal discovery analysis (machine-learning method) was conducted on de-identified student-level data (N= 93,329) from the 2016 Minnesota Student Survey. Causal discovery analysis is a data-driven research method that analyzes associations between variables, including the direction of connectivity or pathways between variables when possible.

Results: The analysis produced a model in which both PDA and FDA were linked to student substance use. The study also found disparities between FDA and PDA in their effects and linked factors including the pathways that lead to substance use, parent and adult support and physical condition. For example, PDA was associated with students’ school engagement and relationships with teachers, which wasn’t for FDA. The incomplete list of factors included in the analysis due to restricted computational capacity caused the model to show only the association but not the causal relations which limits the usage of study’s results.

Conclusions: Results suggest that both types of absence ultimately lead to student’s substance use, which highlights PDA’s similarity with FDA. However, PDA was associated with student’s engagement with school or teacher, whereas FDA was related with in-school suspension or students being sent to the office. The similarity of outcomes to students with differences of what they’re associated with calls a need for further study in PDA especially when it’s being inconsistently used in the field of CA. For the important next step, a validation is needed from an expert who is playing an important role in the health of students – which is school nurse. Therefore, next study aims to implement a focus interview to school nurses and acquire better perspective from such experts. The results of both studies will help children who suffer from CA by having a better understanding of the importance of PDA from the perspective of nursing and data science.

Learning Outcome: After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and evaluate the potential of implementing the improvements into practice.