Confirmatory Factor Analysis and Exploratory Structural Equation Modeling of the Structure of Attention-Deficit/Hyperactivity Disorder Symptoms in Adults

Abstract

This study examined the structure of attention-deficit/hyperactivity disorder (ADHD) symptoms in an adult community sample using first-order confirmatory factor analysis, exploratory structural equation modeling (ESEM), and bifactor confirmatory factor analysis and ESEM models, with two group factors (inattention [IA] and hyperactivity/impulsivity [HY/IM]) and two different three group factors (IA, hyperactivity [HY], and impulsivity [IM]; and IA, motoric HY/IM, and verbal HY/IM). A total of 738 adults (males = 374 and females = 364) between 17 and 72 years of age completed the Adult ADHD Self-Report Scale. The results provided most support for the ESEM model with group factors for IA, motoric HY/IM, and verbal HY/IM. The factors in this model were reasonably well defined, had good internal consistency omega reliabilities, and had support for their external validities, thereby making it a suitable model for ratings of the ADHD symptoms presented in the Adult ADHD Self-Report Scale. The theoretical and clinical implications of the findings are discussed.

Unraveling the Optimum Latent Structure of Attention-Deficit/Hyperactivity Disorder: Evidence Supporting ICD and HiTOP Frameworks

“…Sixth, although there is an emerging trend to examine the structure of the ADHD symptoms using ESEM with target rotation approach (17,27,28), our results provide stronger support for using bi-factor S−1 models, over ESEM models, in research aimed at examining the factor structure of the ADHD symptoms. In our findings, the model with verbal HY/IM symptoms as the reference indicators provided better fit than those of other reference indicators (i.e., with HY/IM, HY, IM, or motoric HY/IM); this means that verbal HY/IM symptoms are likely a more preferable reference factor and should be included in future studies for replication and exploration of bi-factor S-1 ADHD models.…”

“…In general, the bi-factor CFA model has demonstrated better fit for the ADHD symptoms than first-order CFA models (see 16). Additionally, studies involving adults have shown better fit for models with three specific factors (IA, HY, IM; or IA, motoric-HY, verbal HY/IM) than with two specific factors (IA and HY/IM; 5,17,18). Also for adults, the three-factor model corresponding to ICD-10 configuration has shown better fit than DSM-5 configuration (5,16,17).…”

ADHD symptoms, the current symptom scale, and exploratory structural equation modeling: A psychometric study

“…Although many studies have examined the factor structure of the ADHD symptoms, there is still no complete agreement in this area. A recent study showed that an exploratory structural equation model (with three group factors) aligned to ICD-10 symptom configuration (IA, verbal HY/IM, and motoric HY/IM) represented the most appropriate model for ADHD symptom ratings (Gomez & Stavropoulos, 2020). The present study examined the replicability of this factor structure, and the clarity, reliabilities, and convergent and divergent validities of the factors in this model.…”

“…Generally, no support has been found for the one-factor model. At least adequate fit has been found for the two-and three-factor models (e.g., Davis et al, 2011;Gibbins et al, 2012;Gomez, 2016;Gomez, 2014;Gomez et al, 2018;Park et al, 2018;Martel et al, 2012;Morin et al, 2016;Proctor & Prevatt, 2009;Span et al, 2002;Stanton et al, 2018), with the three-factor models showing better fit than the two-factor model (Gomez, 2014(Gomez, , 2016 ICD-10 symptom configuration has shown better fit than the model based on DSM-5 configuration (Gomez & Stavropoulos, 2020;Gomez, 2016;Stanton et al, 2018).…”

“…In addition to these first-order factor models, bi-factor models consisting of one overarching dimension and two (IA and HY/IM) or three specific dimension models (specific dimensions for IA, HY and IM; and specific factors for IA, motoric HY/IM and verbal HY/IM) have also been examined using bi-factor CFA models (Gibbins et al, 2012;Martel et al, 2010;Toplak et al, 2009;Gomez & Stavropoulos, 2020;Stanton et al, 2018). In general, a bi-factor model encompasses orthogonal factor correlations where all items load on one general dimension, and also on their own specific or distinct factors.…”