Machine‑Learning Identification of Subtypes of Adolescent Perfectionism Based on Concern Over Mistakes, Parental Expectations, Cognitive Rigidity, and Negative Affect
Keywords:
Adolescent perfectionism, Machine learning, Gaussian Mixture Modeling, Cognitive rigidity, Negative affect, Parental expectationsAbstract
Objective: The objective of this study was to identify distinct latent subtypes of adolescent perfectionism by applying unsupervised machine-learning algorithms to a comprehensive set of variables, specifically concern over mistakes, parental expectations, cognitive rigidity, and negative affect.
Methods and Materials: The research utilized a cross-sectional, school-based design, sampling adolescents from Germany. Data were gathered using standardized self-report instruments to measure the four target variables, with all raw scores converted to standardized -scores. Following data preprocessing, unsupervised machine learning via Gaussian Mixture Modeling (GMM) was employed to uncover latent subpopulations, evaluating models ranging from two to six classes using fit indices such as the Bayesian Information Criterion and the Bootstrapped Likelihood Ratio Test.
Findings: The GMM algorithm identified an optimal three-class structural model (Entropy = ). The sample was categorized into Class : Low Perfectionism ( , ); Class : Externally Pressured Perfectionism ( , ), defined predominantly by elevated perceived parental expectations ( ); and Class : Maladaptive Perfectionism ( , ), characterized by pervasive elevations across all domains, notably severe negative affect ( ) and extreme cognitive rigidity ( ). Demographic analyses revealed significant differences, indicating that older adolescents ( , ) and females ( , ), who comprised of Class , were significantly overrepresented in the Maladaptive Perfectionism profile.
Conclusion: Adolescent perfectionism manifests in distinct multivariate phenotypes, highlighting the critical necessity of identifying high-risk, maladaptive subtypes to deliver highly targeted clinical interventions focusing on cognitive flexibility and emotion regulation.
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References
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