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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17462469 |
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Table of Contents:
- <div>India’s rapid digitization has expanded connectivity, but how stratified media use translates into educational and cognitive outcomes remains largely unexplored. This study implements a cross-sectional, stratified multivariate design across four North Indian regions, with a final analytic sample of N = 1992, retention = 91.4%, to model links between media exposure and three focal outcomes, educational interest, digital literacy, and information evaluation, while testing demographic contrasts. Instruments for cognitive–psychosocial constructs achieved high reliability (α > 0.84). Media-exposure variables were z-standardized prior to analysis. Independent-samples t-tests showed no gender differences in educational interest (Male: M = 34.70, SD = 5.96; Female: M = 34.94, SD = 5.88; t(1990) = −0.87, p = 0.382, d = 0.04), information evaluation (Male: M = 69.23, SD = 18.90; Female: M = 69.75, SD = 18.45; t(1990) = −0.61, p = 0.542, d = 0.03), trust in media (Male: M = 88.45, SD = 13.94; Female: M = 88.11, SD = 13.36; t(1990) = 0.54, p = 0.589, d = 0.02), peer influence (Male: M = 108.72, SD = 13.79; Female: M = 108.11, SD = 14.37; t(1990) = 0.95, p = 0.342, d = 0.04), standardized media-hours (t(1990) = 0.11, p = 0.910, d = 0.01), or platforms used (t(1990) = 0.00, p = 0.998, d = 0.00). One-way ANOVAs likewise indicated no age effects on educational interest (F(2,1989) = 0.29, p = 0.745), digital literacy (F(2,1989) = 1.38, p = 0.253), information evaluation (F(2,1989) = 1.48, p = 0.228), trust in media (F(2,1989) = 1.81, p = 0.164), or peer influence (F(2,1989) = 0.02, p = 0.981). Findings reveal that demographic convergence by gender and age is robust across outcomes, while stratification is better explained outside simple demographic contrasts. Large-scale, stratified, India-specific multivariate design that integrates cognitive–psychosocial constructs with standardized media-exposure metrics, yielding precise nulls that refine digital-divide theory beyond gender/age heuristics.</div>