School dropout among children aged 5-17 in Türkiye and related factors: a logistic regression analysis.

BACKGROUND: School dropout remains a significant concern in Türkiye, especially for children aged 5–17. This study aims to identify key factors associated with dropout risk using a nationally representative dataset. METHODS: Using microdata from the 2019 Child Labour Survey conducted by TurkStat, binary logistic regression analysis with sampling weights was performed. Logistic regression, a non-parametric statistical method, is used when the dependent variable is categorical with exactly two outcomes. Average marginal effects were calculated to interpret the influence of independent variables, including demographic, parental, and household characteristics. RESULTS: The likelihood of school dropout was higher among girls, older children (ages 15–17), those from large households, working children, and those with caregiving responsibilities. For instance, children working in agriculture were 30.6% more likely to drop out. Conversely, parental employment, maternal education, and participation in household chores reduced dropout probability. CONCLUSION: Effective interventions should promote girls’ education, reduce caregiving responsibilities, and improve economic support for families. Policies must be regionally adapted and culturally sensitive to ensure equal educational opportunities for all children in Türkiye.