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Network Analysis of ADHD and ODD Symptoms: Novel Insights or Redundant Findings with the Latent Variable Model?

Jonathan PreszlerG Leonard Burns
Published in: Journal of abnormal child psychology (2020)
A latent variable model (LVM) and network analysis (NA) were applied to mother and father ratings of attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) symptoms to determine if NA offers unique insights relative to the LVM. ADHD-inattention (IN), ADHD-hyperactivity/impulsivity (HI), and ODD symptoms along with academic competence behaviors (reading, arithmetic, and writing skills) were rated by mothers and fathers of Brazilian (n = 894), Thai (n = 2075), and United States (n = 817) children (Mage = 9.04, SD = 2.12, 49.5% females). LVM indicated that (1) the ADHD-IN, ADHD-HI, and ODD three-factor model yielded a close global-fit with no localized ill-fit; (2) nearly all loadings were substantial; (3) like-symptom loadings, like-symptom thresholds, and like-factor means showed invariance across mothers and fathers; (4) the three factors showed convergent and discriminant validity across mothers and fathers; and (5) only the ADHD-IN showed a unique negative relationship with academic competence. NA indicated that (1) a walktrap community analysis resulted in ADHD-IN, ADHD-HI, and ODD symptom communities; (2) the three symptom communities were consistent across mothers and fathers; (3) only three ADHD-IN symptoms showed unique relationships with the three academic competence items. NA has proven useful for numerous mental disorders. In the current study, NA results were mostly congruent with the LVM model, with a few notable exceptions. The results are discussed in the context of model assumptions and application considerations in the context of ADHD/ODD symptoms relative to other symptom dimensions.
Keyphrases
  • attention deficit hyperactivity disorder
  • autism spectrum disorder
  • working memory
  • network analysis
  • healthcare
  • mental health
  • young adults
  • sleep quality
  • medical students
  • data analysis