IDEAS home Printed from https://ideas.repec.org/a/spr/chinre/v18y2025i1d10.1007_s12187-024-10183-w.html
   My bibliography  Save this article

Identifying the Types of Trajectories for Each of the Five Child Behavior Checklist Sub-Concepts

Author

Listed:
  • Changmin Yoo

    (Inha University)

Abstract

This study aimed to investigate whether the trajectories of change for the sub-concepts of the Child Behavior Checklist — specifically depression and anxiety, attention, withdrawal, delinquency, and aggression — manifest as a single type or as various latent types. The necessity of this study stems from the limitations of current Child Behavior Checklist categorizations, which often fail to capture the nuanced trajectories of child behavior and emotional problems. Also, identifying diverse latent trajectories is crucial for understanding the complex patterns of behavioral and emotional development in children. By providing a more detailed categorization, this research can contribute to a better understanding of child and adolescent development, ultimately informing more targeted interventions and support strategies. Utilizing latent class growth analysis, we analyzed a sample of 747 participants from the Korea Welfare Panel Study’s supplementary survey of children. The major findings are as follows: depression and anxiety trajectories were distinguished into four categories; attention’s trajectories were distinguished into two categories; withdrawal’s trajectories were classified into two categories; delinquency’s trajectories were differentiated into two categories; and aggression’s trajectories were divided into three categories. These findings highlight the diverse patterns of behavioral and emotional development in children and underscore the importance of tailored approaches in supporting their psychosocial, emotional, and behavioral well-being.

Suggested Citation

  • Changmin Yoo, 2025. "Identifying the Types of Trajectories for Each of the Five Child Behavior Checklist Sub-Concepts," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 18(1), pages 137-162, February.
  • Handle: RePEc:spr:chinre:v:18:y:2025:i:1:d:10.1007_s12187-024-10183-w
    DOI: 10.1007/s12187-024-10183-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12187-024-10183-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12187-024-10183-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Bengt Muthén & Kerby Shedden, 1999. "Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm," Biometrics, The International Biometric Society, vol. 55(2), pages 463-469, June.
    2. Song, Juyoung, 2020. "Patterns and explanations of delinquency among Korean youth using general strain theory," Children and Youth Services Review, Elsevier, vol. 114(C).
    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. Marco Guerra & Francesca Bassi & José G. Dias, 2020. "A Multiple-Indicator Latent Growth Mixture Model to Track Courses with Low-Quality Teaching," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 361-381, January.
    2. Michael Prendergast & David Huang & Yih-Ing Hser, 2008. "Patterns of Crime and Drug Use Trajectories in Relation to Treatment Initiation and 5-Year Outcomes," Evaluation Review, , vol. 32(1), pages 59-82, February.
    3. Silvia Bacci & Francesco Bartolucci & Giulia Bettin & Claudia Pigini, 2017. "A mixture growth model for migrants' remittances: An application to the German Socio-Economic Panel," Mo.Fi.R. Working Papers 145, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    4. Patrick Sturgis & Louise Sullivan, 2008. "Exploring social mobility with latent trajectory groups," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 65-88, January.
    5. Getachew A. Dagne, 2016. "A growth mixture Tobit model: application to AIDS studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1174-1185, July.
    6. Bacci, Silvia & Bartolucci, Francesco & Pigini, Claudia & Signorelli, Marcello, 2014. "A finite mixture latent trajectory model for hirings and separations in the labor market," MPRA Paper 59730, University Library of Munich, Germany.
    7. Proust-Lima, Cécile & Joly, Pierre & Dartigues, Jean-François & Jacqmin-Gadda, Hélène, 2009. "Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1142-1154, February.
    8. Francesco Bartolucci & Ivonne Solis-Trapala, 2010. "Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 725-743, December.
    9. Yuan Liu & Hongyun Liu, 2019. "Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 659-677, October.
    10. Benjamin Agbo & Hussain Al-Aqrabi & Richard Hill & Tariq Alsboui, 2022. "Missing Data Imputation in the Internet of Things Sensor Networks," Future Internet, MDPI, vol. 14(5), pages 1-16, May.
    11. Isabelle Archambault & Véronique Dupéré, 2017. "Joint trajectories of behavioral, affective, and cognitive engagement in elementary school," The Journal of Educational Research, Taylor & Francis Journals, vol. 110(2), pages 188-198, March.
    12. Pietro Lovaglio & Mario Mezzanzanica, 2013. "Classification of longitudinal career paths," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 989-1008, February.
    13. Zhou, Xingcai & Liu, Xinsheng, 2008. "The EM algorithm for the extended finite mixture of the factor analyzers model," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3939-3953, April.
    14. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2018. "A mixture latent variable model for modeling mixed data in heterogeneous populations and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(1), pages 95-115, January.
    15. Joanna F. Dipnall & Belinda J. Gabbe & Warwick J. Teague & Ben Beck, 2020. "Identifying Homogeneous Patterns of Injury in Paediatric Trauma Patients to Improve Risk-Adjusted Models of Mortality and Functional Outcomes," IJERPH, MDPI, vol. 17(3), pages 1-20, January.
    16. Lu, Xiaosun & Huang, Yangxin & Zhu, Yiliang, 2016. "Finite mixture of nonlinear mixed-effects joint models in the presence of missing and mismeasured covariate, with application to AIDS studies," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 119-130.
    17. Jost Reinecke & Daniel Seddig, 2011. "Growth mixture models in longitudinal research," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 415-434, December.
    18. Yangxin Huang & Xiaosun Lu & Jiaqing Chen & Juan Liang & Miriam Zangmeister, 2018. "Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 699-718, October.
    19. David Aristei & Silvia Bacci & Francesco Bartolucci & Silvia Pandolfi, 2021. "A bivariate finite mixture growth model with selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 759-793, September.
    20. Maura Mezzetti & Daniele Borzelli & Andrea d’Avella, 2022. "A Bayesian approach to model individual differences and to partition individuals: case studies in growth and learning curves," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1245-1271, December.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:chinre:v:18:y:2025:i:1:d:10.1007_s12187-024-10183-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.