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The elementary and middle grades are a time of great developmental changes with the potential to impact children's longer-term growth. As students progress through their formal schooling during these time periods, the potential exists for children either to follow a course of healthy development associated with positive outcomes or to experience frustration and incompetence associated with disengagement and disinterest in school. The ability of district personnel to detect divergent paths manifest in student data patterns is paramount to the early identification of students at risk of disengagement and subsequent academic failure. The current study explores the use of group-based trajectory modeling to categorize students into longitudinal groups based on attendance patterns. Distinct patterns of attendance are displayed and risk factors associated with group membership are explored. Finally, the relation to attendance patterns and dropout rates is revealed.
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