Applying the Bifactor S-1 Model to Ratings of ADHD/ODD Symptoms: A Commentary on Burns et al. (2019) and a Re-Analysis.

To examine the construct validity of attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD), the bifactor S-1 approach has been applied as an alternative to the fully symmetrical bifactor models in order to eliminate anomalous results and to allow for an unambiguous interpretation of g- and s-factors. We compared and contrasted our results with those of Burns et al. (2019) and extended their analyses by taking into account a two- vs.

Flow Chamber System for the Statistical Evaluation of Bacterial Colonization on Materials.

Biofilm formation on materials leads to high costs in industrial processes, as well as in medical applications. This fact has stimulated interest in the development of new materials with improved surfaces to reduce bacterial colonization. Standardized tests relying on statistical evidence are indispensable to evaluate the quality and safety of these new materials.

Evidence for the Trait-Impulsivity Etiological Model in a Clinical Sample: Bifactor Structure and Its Relation to Impairment and Environmental Risk.

The trait-impulsivity etiological model assumes that a general factor (trait-impulsivity) underlies attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and other externalizing disorders. We investigated the plausibility of this assumption by testing the factor structure of ADHD and ODD in a bifactor framework for a clinical sample of 1420 children between 6 and 18 years of age (M = 9.99, SD = 3.

Bifactor Models Show a Superior Model Fit: Examination of the Factorial Validity of Parent-Reported and Self-Reported Symptoms of Attention-Deficit/Hyperactivity Disorders in Children and Adolescents.

Background: Various studies have demonstrated that bifactor models yield better solutions than models with correlated factors. However, the kind of bifactor model that is most appropriate is yet to be examined. The current study is the first to test bifactor models across the full age range (11-18 years) of adolescents using self-reports, and the first to test bifactor models with German subjects and German questionnaires.

Selectively and progressively disrupted structural connectivity of functional brain networks in Alzheimer's disease - revealed by a novel framework to analyze edge distributions of networks detecting disruptions with strong statistical evidence.

Alzheimer's disease (AD) disrupts selectively and progressively (increasing with severity) functional connectivity of intrinsic brain networks (IBNs), most prominent in the default mode network. Given that IBNs' functional connectivity depends on structural connectivity, we hypothesize for our study selective and progressive changes of IBN based structural connectivity in AD. To achieve strong statistical evidence, we introduce a novel statistical method based on the edge frequency distributions of structural connectivity networks.