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Subjective Well-Being and Psychopathology Symptoms: Mental Health Profiles and their Relations with Academic Achievement in Brazilian Children

Author

Listed:
  • Aline Riboli Marasca

    (Universidade Federal do Rio Grande do Sul)

  • Maurício Scopel Hoffmann

    (Universidade Federal de Santa Maria
    London School of Economics and Political Science
    Universidade Federal do Rio Grande do Sul)

  • Anelise Reis Gaya

    (Universidade Federal do Rio Grande do Sul)

  • Denise Ruschel Bandeira

    (Universidade Federal do Rio Grande do Sul)

Abstract

The aim of this study is to examine the differences in children’s academic achievement considering their mental health profiles. Previous studies have started to seek those differences. However, it is not clear what are the academic achievement differences considering distinct children’s mental health profile. We used a cross-sectional study sample of 273 students from an elementary school (6–11 years of age) in Porto Alegre, Brazil. Mental health profiles were empirically investigated using latent class analysis by combining a subjective well-being measure and a psychopathology symptom screening. Standardized tests and school grades were considered to assess academic achievement. Findings reveal an empirical division of the sample into four mental health groups. The adjusted analysis revealed that the group with a high level of symptoms, despite having high subjective well-being, had lower levels of academic achievement when compared with the other groups, which have low to moderate levels of psychopathology. Present findings support the idea that psychopathology is a detrimental factor for educational achievement regardless of the levels of wellbeing.

Suggested Citation

  • Aline Riboli Marasca & Maurício Scopel Hoffmann & Anelise Reis Gaya & Denise Ruschel Bandeira, 2021. "Subjective Well-Being and Psychopathology Symptoms: Mental Health Profiles and their Relations with Academic Achievement in Brazilian Children," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 14(3), pages 1121-1137, June.
  • Handle: RePEc:spr:chinre:v:14:y:2021:i:3:d:10.1007_s12187-020-09792-y
    DOI: 10.1007/s12187-020-09792-y
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    References listed on IDEAS

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    1. Unni Moksnes & Audhild Løhre & Monica Lillefjell & Don Byrne & Gørill Haugan, 2016. "The Association Between School Stress, Life Satisfaction and Depressive Symptoms in Adolescents: Life Satisfaction as a Potential Mediator," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 125(1), pages 339-357, January.
    2. Susana Marques & J. Pais-Ribeiro & Shane Lopez, 2011. "The Role of Positive Psychology Constructs in Predicting Mental Health and Academic Achievement in Children and Adolescents: A Two-Year Longitudinal Study," Journal of Happiness Studies, Springer, vol. 12(6), pages 1049-1062, December.
    3. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    4. Ferran Casas & Sergiu Bălţătescu & Irma Bertran & Mònica González & Adrian Hatos, 2013. "School Satisfaction Among Adolescents: Testing Different Indicators for its Measurement and its Relationship with Overall Life Satisfaction and Subjective Well-Being in Romania and Spain," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 111(3), pages 665-681, May.
    5. Rachel Sun & Daniel Shek, 2013. "Longitudinal Influences of Positive Youth Development and Life Satisfaction on Problem Behaviour among Adolescents in Hong Kong," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(3), pages 1171-1197, December.
    6. Magalhães, Eunice & Calheiros, Maria Manuela, 2017. "A dual-factor model of mental health and social support: Evidence with adolescents in residential care," Children and Youth Services Review, Elsevier, vol. 79(C), pages 442-449.
    7. Peter Greenspoon & Donald Saklofske, 2001. "Toward an Integration of Subjective Well-Being and Psychopathology," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 54(1), pages 81-108, April.
    8. Ed Diener & Shigehiro Oishi & Louis Tay, 2018. "Advances in subjective well-being research," Nature Human Behaviour, Nature, vol. 2(4), pages 253-260, April.
    9. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    10. Shannon Suldo & Michael Frank & Ashley Chappel & Melanie Albers & Lisa Bateman, 2014. "American High School Students’ Perceptions of Determinants of Life Satisfaction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 485-514, September.
    11. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
    12. Michael Lyons & E. Huebner & Kimberly Hills, 2013. "The Dual-Factor Model of Mental Health: A Short-Term Longitudinal Study of School-Related Outcomes," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(2), pages 549-565, November.
    Full references (including those not matched with items on IDEAS)

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