Award Date


Degree Type


Degree Name

Master of Arts (MA)



First Committee Member

Christopher Kearney

Second Committee Member

Michelle Paul

Third Committee Member

Murray Millar

Fourth Committee Member

Courtney Coughenour

Number of Pages



Attendance is a crucial component of the education system and may be addressed utilizing Multi-Tiered System of Supports (MTSS) models. To effectively utilize such models, clear demarcations between tiers must exist to classify students appropriately. Additionally, relevant risk and protective factors must be identified as targets for intervention and prevention efforts at each tier. Little research exists that identifies school climate factors, and other student-based contextual factors such as academic mindset and social emotional learning, that impact absenteeism. The present study aimed to identify suitable tier demarcations as well as school climate and student-based contextual factors relevant at each tier. Ensemble analysis was utilized and included chi-squared automatic interaction detection (CHAID), support vector machines, and neural network analyses. Confusion matrices identified CHAID as the best classifier. CHAID analyses were then conducted at varying levels of absenteeism severity (3+%, 5+%, 10+%, 15+%, 20+%) for 128,381 students (Mage = 13.98; SD = 2.48). Models favored specificity over sensitivity and may be more useful for interpreting protective factors than risk factors for absenteeism. Homogeneity of predictors and overall model classification accuracy, keeping in consideration the practicality of policy implementation, suggest the 5+% and 15+% severity cutoffs as suitable demarcations for tier cutoffs in MTSS models. Pathways revealed specific school climate and academic mindset items present at all levels of absenteeism severity. Social emotional learning items became relevant at higher levels of absenteeism. Findings reveal the importance of examining and integrating school climate with other student-based contextual factors. Results have implications for intervention and prevention efforts at the school level.


Academic mindset; Chi squared automatic interaction detection; School climate; School refusal; Social emotional learning; Truancy


Clinical Psychology | Education | Education Policy | Student Counseling and Personnel Services

File Format


File Size

7386 KB

Degree Grantor

University of Nevada, Las Vegas




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