Predictors of school absenteeism severity at multiple levels: A classification and regression tree analysis

Skedgell, Kyleigh K.; Kearney, Christopher A.

doi:10.1016/j.childyouth.2018.01.043

Cited by 49 publications

(81 citation statements)

References 48 publications

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“…As the aforementioned studies illustrate, different cut offs are used to define when absenteeism becomes problematic. This is an important issue as Skedgell and Kearney (2018) found that associations between problematic levels of absenteeism and contributing factors differ according to the cut-off applied (1, 10, 15% absence).…”

Section: Introductionmentioning

confidence: 99%

The Kids and Teens at School (KiTeS) Framework: An Inclusive Bioecological Systems Approach to Understanding School Absenteeism and School Attendance Problems

et al. 2019

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Absence from school jeopardizes children's and adolescents' education and their social and emotional development. Proximal and distal individual, parental, familial, and environmental factors have been linked to absenteeism and the development and maintenance of school attendance problems. The complex interaction among these factors necessitates a multifactorial approach to understanding school absenteeism and attendance problems. The current paper builds on recent calls to apply bioecological systems frameworks when studying risk factors for school absenteeism and attendance problems. The Kids and Teens at School (KiTeS) Framework presented here is an application of Bronfenbrenner's bioecological systems model, incorporating candidate factors of particular relevance to school absenteeism and attendance problems. The current paper is also a response to the Warnock report which highlighted the need to individualize educational supports for children and adolescents with disabilities, to foster optimal educational outcomes. The KiTeS Framework is an inclusive framework, inclusive of students with and without disabilities. It is envisaged that the KiTeS Framework will provide guidance to researchers aiming to improve understanding of the factors influencing absenteeism among all school-aged students, including those from minority or vulnerable populations.

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Section: Introductionmentioning

confidence: 99%

The Kids and Teens at School (KiTeS) Framework: An Inclusive Bioecological Systems Approach to Understanding School Absenteeism and School Attendance Problems

et al. 2019

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“…In addition, youth in the present study were examined at different points of the academic year, but anxiety levels may be more pronounced at the beginning of a year (Ingul and Nordahl, 2013). Higher levels of absenteeism severity also mean more time out of school and thus relief from school-based anxiety symptoms (Skedgell and Kearney, 2018). Other variables such as family or school environment may thus be better predictors of absenteeism severity (Fornander and Kearney, 2019).…”

Section: Discussionmentioning

confidence: 87%

“…In addition, for those students with a mental disorder, absences due to a particular disorder accounted for 13.4% of all days absent from school (rising to 16.6% in years 11-12). Skedgell and Kearney (2016) also examined internalizing symptoms among youth with 0-14% and 15-100% absenteeism severity, finding the latter group (and particularly those at 20-39%) to display significantly more general and separation anxiety and depression. Stempel et al (2017) similarly compared youth who had missed less than versus more than 15 days of school, finding that more chronic absenteeism was associated with more adverse childhood experiences such as financial hardship, divorce, parental incarceration, domestic or neighborhood violence, and family mental disorder or substance use.…”

Section: Introductionmentioning

confidence: 99%

Internalizing Symptoms as Predictors of School Absenteeism Severity at Multiple Levels: Ensemble and Classification and Regression Tree Analysis

Fornander

Kearney

2020

Front. Psychol.

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School attendance problems are highly prevalent worldwide, leading researchers to investigate many different risk factors for this population. Of considerable controversy is how internalizing behavior problems might help to distinguish different types of youth with school attendance problems. In addition, efforts are ongoing to identify the point at which children and adolescents move from appropriate school attendance to problematic school absenteeism. The present study utilized ensemble and classification and regression tree analysis to identify potential internalizing behavior risk factors among youth at different levels of school absenteeism severity (i.e., 1+%, 3+%, 5+%, 10+%). Higher levels of absenteeism were also examined on an exploratory basis. Participants included 160 youth aged 6-19 years (M = 13.7; SD = 2.9) and their families from an outpatient therapy clinic (39.4%) and community (60.6%) setting, the latter from a family court and truancy diversion program cohort. One particular item relating to lack of enjoyment was most predictive of absenteeism severity at different levels, though not among the highest levels. Other internalizing items were also predictive of various levels of absenteeism severity, but only in a negatively endorsed fashion. Internalizing symptoms of worry and fatigue tended to be endorsed higher across less severe and more severe absenteeism severity levels. A general expectation that predictors would tend to be more homogeneous at higher than lower levels of absenteeism severity was not generally supported. The results help confirm the difficulty of conceptualizing this population based on forms of behavior but may support the need for early warning sign screening for youth at risk for school attendance problems.

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“…A regression tree is an effective method for predictive modeling and can be useful for exploring data characteristics. It is widely used in many fields of application [30][31][32]. Unlike the classical linear regression, this method does not require an assumption of linearity in the data, such that potential nonlinear relationships among configuration parameters do not affect the performance of the regression tree.…”

Section: Regression Treementioning

confidence: 99%

“…In Reference [30], a logistic regression tree-based method was applied to American older adults to identify fall risk factors and interaction effects of those risk factors. In Reference [31], the regression tree method was used to predict the school absenteeism severity from various demographic and academic variables such as youth age and grade level, and to find the main factors that lead to the absence. In Reference [32], a coastal ecosystem was simulated to study the growth behavior of bivalve species, and the regression tree analysis was conducted to investigate the leading factors associated with the water conditions that promote the quality and growth rate of species the most.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway

2019

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For a better understanding of the nature of complex systems modeling, computer simulations and the analysis of the resulting data are major tools which can be applied. In this paper, we study a statistical modeling problem of data coming from a simulation model that investigates the correctness of autonomous agents’ decisions in learning to cross a cellular automaton-based highway. The goal is a better understanding of cognitive agents’ performance in learning to cross a cellular automaton-based highway with different traffic density. We investigate the effects of parameters’ values of the simulation model (e.g., knowledge base transfer, car creation probability, agents’ fear and desire to cross the highway) and their interactions on cognitive agents’ decisions (i.e., correct crossing decisions, incorrect crossing decisions, correct waiting decisions, and incorrect waiting decisions). We firstly utilize canonical correlation analysis (CCA) to see if all the considered parameters’ values and decision types are significantly statistically correlated, so that no considered dependent variables or independent variables (i.e., decision types and configuration parameters, respectively) can be omitted from the simulation model in potential future studies. After CCA, we then use the regression tree method to explore the effects of model configuration parameters’ values on the agents’ decisions. In particular, we focus on the discussion of the effects of the knowledge base transfer, which is a key factor in the investigation on how accumulated knowledge/information about the agents’ performance in one traffic environment affects the agents’ learning outcomes in another traffic environment. This factor affects the cognitive agents’ decision-making abilities in a major way in a new traffic environment where the cognitive agents start learning from existing accumulated knowledge/information about their performance in an environment with different traffic density. The obtained results provide us with a better understanding of how cognitive agents learn to cross the highway, i.e., how the knowledge base transfer as a factor affects the experimental outcomes. Furthermore, the proposed methodology can become useful in modeling and analyzing data coming from other computer simulation models and can provide an approach for better understanding a factor or treatment effect.

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Predictors of school absenteeism severity at multiple levels: A classification and regression tree analysis

Cited by 49 publications

References 48 publications

The Kids and Teens at School (KiTeS) Framework: An Inclusive Bioecological Systems Approach to Understanding School Absenteeism and School Attendance Problems

The Kids and Teens at School (KiTeS) Framework: An Inclusive Bioecological Systems Approach to Understanding School Absenteeism and School Attendance Problems

Internalizing Symptoms as Predictors of School Absenteeism Severity at Multiple Levels: Ensemble and Classification and Regression Tree Analysis

Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway

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