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Dimensional Latent Structure of Early Disruptive Behavior Disorders: A Taxometric Analysis in Preschoolers.

Soeren KliemNina HeinrichsAnna LohmannRegina BussingGudrun SchwarzerWolfgang Briegel
Published in: Journal of abnormal child psychology (2019)
Although disruptive behavior disorders (DBDs) are used as a distinct categorical diagnosis in clinical practice, they have repeatedly been described as having a dimensional structure in taxometric analyses. In the current study the authors analyzed the latent status of disruptive behaviors (DB) in a large sample (N = 2,808) of German preschool children (2-6 years old, mean age 53.7 months, SD = 13.5, 48.4% girls). The Eyberg Child Behavior Inventory (ECBI) as well as the Strengths and Difficulties Questionnaire (SDQ) were used to compile indicators of the DB core dimensions (Temper Loss, Aggression, Noncompliance, and Low Concern for others). Three widely used taxometric methods (a) MAXEIG, (b) MAMBAC, and (c) L-Mode were applied. Simulation data were created to evaluate the Comparison Curve Fit Index values (CCFIs), which were below 0.45, supporting a dimensional solution. Hence, in the current study the latent structure of DB in preschoolers encompassed differences in degree rather than kind. Researchers and practitioners should be mindful of the dimensional latent status of DB in theory building, assessment, classification, and labeling.
Keyphrases
  • clinical practice
  • primary care
  • machine learning
  • deep learning
  • electronic health record
  • psychometric properties