Abstract
AbstractA child’s socio-economic environment can profoundly affect their development. While existing literature focusses on simplified metrics and pair-wise relations between few variables, we aimed to capture complex interrelationships between several relevant domains using a broad assessment of 519 children aged 7–9 years. Our analyses comprised three multivariate techniques that complimented each other, and worked at different levels of granularity. First, an exploratory factor analysis (principal component analysis followed by varimax rotation) revealed that our sample varied along continuous dimensions of cognition, attitude and mental health (from parallel analysis); with potentially emerging dimensions speed and socio-economic status (passed Kaiser’s criterion). Second, k-means cluster analysis showed that children did not group into discrete phenotypes. Third, a network analysis on the basis of bootstrapped partial correlations (confirmed by both cross-validated LASSO and multiple comparisons correction of binarised connection probabilities) uncovered how our developmental measures interconnected: educational outcomes (reading and maths fluency) were directly related to cognition (short-term memory, number sense, processing speed, inhibition). By contrast, mental health (anxiety and depression symptoms) and attitudes (conscientiousness, grit, growth mindset) showed indirect relationships with educational outcomes via cognition. Finally, socio-economic factors (neighbourhood deprivation, family affluence) related directly to educational outcomes, cognition, mental health, and even grit. In sum, cognition is a central cog through which mental health and attitude relate to educational outcomes. However, through direct relations with all components of developmental outcomes, socio-economic status acts as a great ‘unequaliser’.
Funder
Templeton World Charity Foundation
Medical Research Council
Gates Cambridge Trust
Publisher
Springer Science and Business Media LLC
Reference96 articles.
1. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Association.
2. Andrews, J., Robinson, D., & Hutchinson, J. (2017). Closing the gap? Trends in educational attainment and Disadvantage. Education Policy Institute https://epi.org.uk/wp-content/uploads/2017/08/Closing-the-Gap_EPI-.pdf
3. Barbaranelli, C., Caprara, G. V., Rabasca, A., & Pastorelli, C. (2003). A questionnaire for measuring the big five in late childhood. Personality and Individual Differences, 34(4), 645–664. https://doi.org/10.1016/S0191-8869(02)00051-X
4. Bateman, L. B. (2014). Socioeconomic status, measurement. In W. C. Cockerham, R. Dingwall, & S. Quah (Eds.), The Wiley Blackwell encyclopedia of health, illness, behavior, and society (pp. 2227–2232). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118410868.wbehibs302
5. Bellman, R. (1957). Dynamic programming. Princeton University Press.
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献