Doctor of Philosophy (PhD)
First Committee Member
Christopher A. Kearney
Second Committee Member
Michelle G. Paul
Third Committee Member
Andrew J. Freeman
Fourth Committee Member
Number of Pages
Contemporary classification models of school absenteeism often employ a multitier approach for organizing assessment and treatment strategies. Researchers have yet to agree, however, on how to objectively define problematic school absenteeism and identify demarcation points for each tier. The present study aimed to inform a multitier approach by determining the most relevant risk factors for problematic school absenteeism. The most useful targets of assessment for problematic school absenteeism are also addressed. The present study examined problematic school absenteeism defined at three distinct cutoffs: 1%, 10%, and 15% of full school days missed. The present study evaluated interactions among several youth- and academic-related variables at each cutoff. Participants included 316,004 elementary, middle, and high school youth from the Clark County School District of Nevada. The present study examined all youth regardless of their school absenteeism. The present study employed Binary Recursive Partitioning (BRP) techniques to identify the most relevant risk factors and highlight profiles of youth exhibiting school absenteeism at each cutoff by constructing classification trees. BRP, a nonparametric statistical approach, is most appropriate for generating, not testing, hypotheses. Anticipated findings were thus offered cautiously. The first hypothesis was that participation in school sports would produce the greatest impurity reduction in the classification tree-model for problematic school absenteeism, defined as equal to or greater than 1% of full school days missed. The second hypothesis was that grade level, letter grades for specific high school core academic courses (i.e., Algebra I, Algebra II, Biology, Chemistry, English 9, English 10, English 11, English 12, and Geometry), and GPA would produce the greatest impurity reductions in the classification tree-model for problematic school absenteeism, defined as equal to or greater than 10% of full school days missed. The third hypothesis was that age, gender, and ethnicity would produce the greatest impurity reductions in the classification tree-model for problematic school absenteeism, defined as equal to or greater than 15% of full school days missed. Models were constructed via Classification and Regression Tree (CART) analysis utilizing SPSS decision tree software. The first hypothesis was not supported but the second and third hypotheses received partial support. Results revealed age, ethnicity, gender, GPA, grade level, and IEP eligibility as relevant risk factors for problematic school absenteeism among the three cutoffs. Implications for clinicians and educators are discussed.
assessment; school absenteeism; truancy
Education | Psychology
University of Nevada, Las Vegas
Skedgell, Kyleigh Kay, "Defining Problematic School Absenteeism Using Nonparametric Modeling" (2017). UNLV Theses, Dissertations, Professional Papers, and Capstones. 3749.
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