IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i24p13166-d701927.html
   My bibliography  Save this article

The Role of Strategic Emotional Intelligence in Predicting Adolescents’ Academic Achievement: Possible Interplays with Verbal Intelligence and Personality

Author

Listed:
  • Zorana Jolić Marjanović

    (Department of Psychology, Faculty of Philosophy, University of Belgrade, 11000 Belgrade, Serbia
    These authors have equally contributed to this paper and should be regarded as co-first authors.)

  • Ana Altaras Dimitrijević

    (Department of Psychology, Faculty of Philosophy, University of Belgrade, 11000 Belgrade, Serbia
    Institute for Educational Psychology “Rosa & David Katz”, Faculty of Philosophy, University of Rostock, 18051 Rostock, Germany
    These authors have equally contributed to this paper and should be regarded as co-first authors.)

  • Sonja Protić

    (Institute for Criminological and Sociological Research, 11000 Belgrade, Serbia
    International Psychoanalytic University, 10555 Berlin, Germany)

  • José M. Mestre

    (University Institute of Social and Sustainable Development (INDESS), University of Cádiz, 11405 Jerez de la Frontera, Spain
    Department of Psychology, University of Cádiz, 11519 Puerto Real, Spain)

Abstract

As recent meta-analyses confirmed that emotional intelligence (EI), particularly strategic EI, adjoins intelligence and personality in predicting academic achievement, we explored possible arrangements in which these predictors affect the given outcome in adolescents. Three models, with versions including either overall strategic EI or its branches, were considered: (a) a mediation model, whereby strategic EI partially mediates the effects of verbal intelligence (VI) and personality on achievement; the branch-level version assumed that emotion understanding affects achievement in a cascade via emotion management; (b) a direct effects model, with strategic EI/branches placed alongside VI and personality as another independent predictor of achievement; and (c) a moderation model, whereby personality moderates the effects of VI and strategic EI/branches on achievement. We tested these models in a sample of 227 students ( M = 16.50 years) and found that both the mediation and the direct effects model with overall strategic EI fit the data; there was no support for a cascade within strategic EI, nor for the assumption that personality merely moderates the effects of abilities on achievement. Principally, strategic EI both mediated the effects of VI and openness, and independently predicted academic achievement, and it did so through emotion understanding directly, “skipping” emotion management.

Suggested Citation

  • Zorana Jolić Marjanović & Ana Altaras Dimitrijević & Sonja Protić & José M. Mestre, 2021. "The Role of Strategic Emotional Intelligence in Predicting Adolescents’ Academic Achievement: Possible Interplays with Verbal Intelligence and Personality," IJERPH, MDPI, vol. 18(24), pages 1-20, December.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:24:p:13166-:d:701927
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/24/13166/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/24/13166/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nhu Ngoc Nguyen & Phong Tuan Nham & Yoshi Takahashi, 2019. "Relationship between Ability-Based Emotional Intelligence, Cognitive Intelligence, and Job Performance," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    2. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiwon Lee & Midam An & Yongku Kim & Jung-In Seo, 2021. "Optimal Allocation for Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(18), pages 1-10, September.
    2. Benjamin G Schultz & Catherine J Stevens & Peter E Keller & Barbara Tillmann, 2013. "A Sequence Identification Measurement Model to Investigate the Implicit Learning of Metrical Temporal Patterns," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-1, September.
    3. Daniela Andreini & Diego Rinallo & Giuseppe Pedeliento & Mara Bergamaschi, 2017. "Brands and Religion in the Secularized Marketplace and Workplace: Insights from the Case of an Italian Hospital Renamed After a Roman Catholic Pope," Journal of Business Ethics, Springer, vol. 141(3), pages 529-550, March.
    4. Andreas Wienke & Anne M. Herskind & Kaare Christensen & Axel Skytthe & Anatoli I. Yashin, 2002. "The influence of smoking and BMI on heritability in susceptibility to coronary heart disease," MPIDR Working Papers WP-2002-003, Max Planck Institute for Demographic Research, Rostock, Germany.
    5. Byrd, T. A. & Marshall, T. E., 1997. "Relating information technology investment to organizational performance: a causal model analysis," Omega, Elsevier, vol. 25(1), pages 43-56, February.
    6. Berry, Brian J.L. & Okulicz-Kozaryn, Adam, 2008. "Are there ENSO signals in the macroeconomy," Ecological Economics, Elsevier, vol. 64(3), pages 625-633, January.
    7. Nicos Nicolaou & Scott Shane, 2019. "Common genetic effects on risk-taking preferences and choices," Journal of Risk and Uncertainty, Springer, vol. 59(3), pages 261-279, December.
    8. Stephen Richards, 2010. "Author's response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(4), pages 920-924, October.
    9. Ken B Hanscombe & Maciej Trzaskowski & Claire M A Haworth & Oliver S P Davis & Philip S Dale & Robert Plomin, 2012. "Socioeconomic Status (SES) and Children's Intelligence (IQ): In a UK-Representative Sample SES Moderates the Environmental, Not Genetic, Effect on IQ," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-16, February.
    10. Zhang, Quanzhong & Wei, Haiyan & Liu, Jing & Zhao, Zefang & Ran, Qiao & Gu, Wei, 2021. "A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data," Ecological Modelling, Elsevier, vol. 450(C).
    11. Oh, Man-Suk, 2014. "Bayesian comparison of models with inequality and equality constraints," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 176-182.
    12. Satonori Nasu & Yu Ishibashi & Junichi Ikuta & Shingo Yamane & Ryuji Kobayashi, 2022. "Reliability and Validity of the Japanese Version of the Assessment of Readiness for Mobility Transition (ARMT-J) for Japanese Elderly," IJERPH, MDPI, vol. 19(21), pages 1-14, October.
    13. Bonaiuto, M. & Mosca, O. & Milani, A. & Ariccio, S. & Dessi, F. & Fornara, F., 2024. "Beliefs about technological and contextual features drive biofuels’ social acceptance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    14. 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.
    15. Golob, Thomas F. & Regan, A C, 2002. "Trucking Industry Preferences for Driver Traveler Information Using Wireless Internet-enabled Devices," University of California Transportation Center, Working Papers qt40q8h6sf, University of California Transportation Center.
    16. Schreier, Alayna & Stenersen, Madeline R. & Strambler, Michael J. & Marshall, Tim & Bracey, Jeana & Kaufman, Joy S., 2023. "Needs of caregivers of youth enrolled in a statewide system of care: A latent class analysis," Children and Youth Services Review, Elsevier, vol. 147(C).
    17. Daisuke Matsumoto & Fujio Inui & Chika Honda & Rie Tomizawa & Mikio Watanabe & Karri Silventoinen & Norio Sakai, 2020. "Heritability and Environmental Correlation of Phase Angle with Anthropometric Measurements: A Twin Study," IJERPH, MDPI, vol. 17(21), pages 1-10, October.
    18. Sanjay Gupta & Kushagra Sinha, 2022. "Assessing the Factors Impacting Transport Usage of Mobility App Users in the National Capital Territory of Delhi, India," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    19. Schomaker Michael & Heumann Christian, 2011. "Model Averaging in Factor Analysis: An Analysis of Olympic Decathlon Data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-15, January.
    20. Naiara Escalante Mateos & Eider Goñi Palacios & Arantza Fernández-Zabala & Iratxe Antonio-Agirre, 2020. "Internal Structure, Reliability and Invariance across Gender Using the Multidimensional School Climate Scale PACE-33," IJERPH, MDPI, vol. 17(13), pages 1-24, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:24:p:13166-:d:701927. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.