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Early social interactions and young school‐aged children's behavioral problems: Converging evidence from theory‐ and data‐driven approaches.

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Title: Early social interactions and young school‐aged children's behavioral problems: Converging evidence from theory‐ and data‐driven approaches.
Authors: Liang, Jiahao, Wang, Yiji
Source: Journal of Child Psychology & Psychiatry. Oct2025, Vol. 66 Issue 10, p1539-1550. 12p.
Subjects: Behavior disorders, Predictive tests, Chaos theory, Maternal age, Income, Research funding, T-test (Statistics), Affinity groups, Spouses, Structural equation modeling, Descriptive statistics, Internalizing behavior, Chi-squared test, Behavior disorders in children, Psychology of preschool children, Longitudinal method, Teachers, Child development, Mathematical models, Social skills, Communication, Acquisition of data, Child Behavior Checklist, Interpersonal relations, Mother-child relationship, Theory, Machine learning, Data analysis software, Externalizing behavior, Comparative studies, Child behavior, Inter-observer reliability, Educational attainment
Abstract: Background: Although prior studies have established the relation between social interactions and behavioral adjustment, it remains unclear whether aspects of early social interactions are uniquely related to behavioral problems and the relative importance of each in predicting internalizing and externalizing problems. Using traditional theory‐driven and novel data‐driven perspectives, this longitudinal study simultaneously evaluated the role of preschool mother–child, teacher –child, and peer interactions in predicting internalizing and externalizing problems in early grade school. Methods: At 36 months, the quality of children's social interactions with mothers, teachers, and peers were observed and coded (N = 1,028). Mothers later reported children's internalizing and externalizing problems in first grade. Theory‐driven structural equation modeling (SEM) and data‐driven machine learning models (i.e., random forests and support vector machines) were performed separately for data analysis. Results: The results showed that machine learning models, particularly support vector machines, outperformed SEM in model performance. Regarding the relative importance of predictors, SEM suggested that indicators of early peer interactions uniquely predicted behavioral problems in early grade school when those of teacher–child and mother–child interactions were considered simultaneously. Machine learning models consistently demonstrated that indicators of early peer interactions had the highest feature importance and were among the highest ranking predictors of children's subsequent behavioral adjustment. Conclusions: The findings contribute converging evidence from theory‐ and data‐driven approaches to better understand the longitudinal associations between preschoolers' social interactions and later behavioral adjustments in early grade school. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Early social interactions and young school‐aged children's behavioral problems: Converging evidence from theory‐ and data‐driven approaches.
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  Data: <searchLink fieldCode="AR" term="%22Liang%2C+Jiahao%22">Liang, Jiahao</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yiji%22">Wang, Yiji</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Child+Psychology+%26+Psychiatry%22">Journal of Child Psychology & Psychiatry</searchLink>. Oct2025, Vol. 66 Issue 10, p1539-1550. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Behavior+disorders%22">Behavior disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+tests%22">Predictive tests</searchLink><br /><searchLink fieldCode="DE" term="%22Chaos+theory%22">Chaos theory</searchLink><br /><searchLink fieldCode="DE" term="%22Maternal+age%22">Maternal age</searchLink><br /><searchLink fieldCode="DE" term="%22Income%22">Income</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22T-test+%28Statistics%29%22">T-test (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Affinity+groups%22">Affinity groups</searchLink><br /><searchLink fieldCode="DE" term="%22Spouses%22">Spouses</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Internalizing+behavior%22">Internalizing behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Chi-squared+test%22">Chi-squared test</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+disorders+in+children%22">Behavior disorders in children</searchLink><br /><searchLink fieldCode="DE" term="%22Psychology+of+preschool+children%22">Psychology of preschool children</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Teachers%22">Teachers</searchLink><br /><searchLink fieldCode="DE" term="%22Child+development%22">Child development</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Social+skills%22">Social skills</searchLink><br /><searchLink fieldCode="DE" term="%22Communication%22">Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Child+Behavior+Checklist%22">Child Behavior Checklist</searchLink><br /><searchLink fieldCode="DE" term="%22Interpersonal+relations%22">Interpersonal relations</searchLink><br /><searchLink fieldCode="DE" term="%22Mother-child+relationship%22">Mother-child relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Theory%22">Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Externalizing+behavior%22">Externalizing behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Child+behavior%22">Child behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Inter-observer+reliability%22">Inter-observer reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+attainment%22">Educational attainment</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Although prior studies have established the relation between social interactions and behavioral adjustment, it remains unclear whether aspects of early social interactions are uniquely related to behavioral problems and the relative importance of each in predicting internalizing and externalizing problems. Using traditional theory‐driven and novel data‐driven perspectives, this longitudinal study simultaneously evaluated the role of preschool mother–child, teacher –child, and peer interactions in predicting internalizing and externalizing problems in early grade school. Methods: At 36 months, the quality of children's social interactions with mothers, teachers, and peers were observed and coded (N = 1,028). Mothers later reported children's internalizing and externalizing problems in first grade. Theory‐driven structural equation modeling (SEM) and data‐driven machine learning models (i.e., random forests and support vector machines) were performed separately for data analysis. Results: The results showed that machine learning models, particularly support vector machines, outperformed SEM in model performance. Regarding the relative importance of predictors, SEM suggested that indicators of early peer interactions uniquely predicted behavioral problems in early grade school when those of teacher–child and mother–child interactions were considered simultaneously. Machine learning models consistently demonstrated that indicators of early peer interactions had the highest feature importance and were among the highest ranking predictors of children's subsequent behavioral adjustment. Conclusions: The findings contribute converging evidence from theory‐ and data‐driven approaches to better understand the longitudinal associations between preschoolers' social interactions and later behavioral adjustments in early grade school. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Child Psychology & Psychiatry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/jcpp.14166
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1539
    Subjects:
      – SubjectFull: Behavior disorders
        Type: general
      – SubjectFull: Predictive tests
        Type: general
      – SubjectFull: Chaos theory
        Type: general
      – SubjectFull: Maternal age
        Type: general
      – SubjectFull: Income
        Type: general
      – SubjectFull: Research funding
        Type: general
      – SubjectFull: T-test (Statistics)
        Type: general
      – SubjectFull: Affinity groups
        Type: general
      – SubjectFull: Spouses
        Type: general
      – SubjectFull: Structural equation modeling
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Internalizing behavior
        Type: general
      – SubjectFull: Chi-squared test
        Type: general
      – SubjectFull: Behavior disorders in children
        Type: general
      – SubjectFull: Psychology of preschool children
        Type: general
      – SubjectFull: Longitudinal method
        Type: general
      – SubjectFull: Teachers
        Type: general
      – SubjectFull: Child development
        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Social skills
        Type: general
      – SubjectFull: Communication
        Type: general
      – SubjectFull: Acquisition of data
        Type: general
      – SubjectFull: Child Behavior Checklist
        Type: general
      – SubjectFull: Interpersonal relations
        Type: general
      – SubjectFull: Mother-child relationship
        Type: general
      – SubjectFull: Theory
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Externalizing behavior
        Type: general
      – SubjectFull: Comparative studies
        Type: general
      – SubjectFull: Child behavior
        Type: general
      – SubjectFull: Inter-observer reliability
        Type: general
      – SubjectFull: Educational attainment
        Type: general
    Titles:
      – TitleFull: Early social interactions and young school‐aged children's behavioral problems: Converging evidence from theory‐ and data‐driven approaches.
        Type: main
  BibRelationships:
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          Name:
            NameFull: Liang, Jiahao
      – PersonEntity:
          Name:
            NameFull: Wang, Yiji
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          Dates:
            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
          Identifiers:
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              Value: 00219630
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              Value: 66
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              Value: 10
          Titles:
            – TitleFull: Journal of Child Psychology & Psychiatry
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