The Effect of Digital Literacy and Reading Literacy on the Statistical Literacy of Higher Education Students - Edi Susanto; Pratiwi Disha Stanggo; Ringki Agustinsa; Syafdi Mayzora (Journal of Research in Science and Mathematics Education, 2026)
Purpose: Statistical literacy has emerged as a fundamental competency in higher education, enabling students to critically interpret, evaluate, and communicate data in an increasingly data-driven society. This study investigated the contributions of digital literacy and reading literacy to university students' statistical literacy. Methodology: A quantitative correlational design was employed with 147 students from three academic departments within the Faculty of Teacher Training and Education, University of Bengkulu. Data were collected using validated instruments measuring digital literacy, reading literacy, and statistical literacy and analyzed through descriptive statistics and multiple linear regression. Findings: Both digital literacy (β = 0.344, p < .001) and reading literacy (β = 0.431, p < .001) exerted significant positive effects on statistical literacy, with the regression model explaining 43.5% of the variance. Reading literacy demonstrated a stronger predictive influence, underscoring the importance of comprehending, evaluating, and synthesizing written information in developing statistical reasoning. Although participants reported relatively high levels of digital and reading literacy, their statistical literacy remained comparatively lower across all departments, suggesting that technological proficiency alone is insufficient to cultivate robust statistical competence. Significance: The findings conceptualize statistical literacy as a multidimensional competency requiring the integration of digital, cognitive, and interpretive skills. Strengthening digital literacy alongside reading literacy can substantially enhance students' capacity to interpret and apply statistical information. Accordingly, higher education institutions should embed authentic data-analysis tasks, digital learning resources, and critical reading practices within statistics instruction to better prepare graduates for evidence-based decision-making in increasingly data-intensive contexts.