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

Junghänel, Michaela; Rodenacker, Klaas; Dose, Christina; Döpfner, Manfred

doi:10.1007/s10802-020-00637-4

Cited by 18 publications

(22 citation statements)

References 22 publications

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“…Given these findings, we adopted Model 7 as our preferred model for both parent and teacher ratings. Our conclusion is consistent with existing literature that have also reported the strongest support for the bi-factor S−1 CFA model with verbal HY/IM as the reference factor (see 30).…”

Section: Discussionsupporting

confidence: 93%

“…As our sample was a community sample, the findings may not be applicable to clinic-referred children. As noted by Junghänel et al (30), specific factors could embody higher variance in clinic-referred samples than non-clinical samples, as distinct subtypes may be less observable in the latter. Because teachers are likely to rate more than one child, their ratings may lack independence.…”

Section: Limitationsmentioning

confidence: 95%

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

et al. 2021

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Attention Deficit/hyperactivity disorder (ADHD) is conceptualized differently in the Diagnostic and Statistical Manual (DSM-5), the International Classification of Diseases-10 (ICD-10), and the Hierarchical Taxonomy of Psychopathology (HiTOP) frameworks. This study applied independent cluster confirmatory factor analysis (ICM-CFA), exploratory structure equation model with target rotation (ESEM), and the S-1 bi-factor CFA approaches to evaluate seven ADHD models yielded by different combinations of these taxonomic frameworks. Parents and teachers of a community sample of children (between 6 and 12 years of age) completed the Disruptive Behavior Rating Scale (for ADHD symptoms) and the Strengths and Difficulties Questionnaire (for validation). Our findings for both parent and teacher ratings provided the most support for the S-1 bi-factor CFA model comprised of (i) a g-factor based on ICD-10 impulsivity symptoms as the reference indicators and (ii) inattention and hyperactivity as specific factors. However, the hyperactivity-specific factor lacked clarity and reliability. Thus, our findings indicate that ADHD is best viewed as a disorder primarily reflecting impulsivity, though with a separable inattention (but no hyperactivity) component, i.e., “ADID (attention deficit/impulsivity disorder).” This model aligns with the HiTOP proposals.

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Section: Discussionsupporting

confidence: 93%

Section: Limitationsmentioning

confidence: 95%

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

et al. 2021

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“…We now illustrate the effect of collapsing factors on the meaning of the general factor in the BFSYM model, as well as fundamental properties of BFS-1 models based on an empirical example (for other applied examples see: Burns et al, 2020a;Demkowicz et al, 2019;Gäde et al, 2017;Heinrich et al, 2020;Junghänel et al, 2020). First, we show that the BFSYM model produces anomalous results and that the general factor in the BFSYM model becomes the specific latent variable underlying the indicators of a collapsing factor.…”

Section: Illustrative Examplementioning

confidence: 87%

“…(3) a single-factor model that only comprises the indicators of the vanishing S factor. Thus, regardless of whether a reference domain is defined a priori (e.g., Burns et al, 2020a;Heinrich et al, 2020;Junghänel et al, 2020), S factors are removed based on empirical results (e.g., Caspi et al, 2014;Lahey et al, 2012;Tackett et al, 2013), or non-significant factor loadings lead to reduction of a BFSYM model to an empirical BFS-1 model (e.g., Gluschkoff et al, 2019;Martel et al, 2017), consequences for the change in interpretation of P and S are the same: The general factor is no longer interpretable as an "overarching" factor, but instead carries a meaning defined by a specific set of symptoms/domains, and the S factors are contrasted against that factor. To avoid a sample-specific, data-driven result as to what the general factor depicts, researchers should define the general factor a priori using the BFS-1 approach.…”

Section: Overall Summary Of the Illustrative Examplementioning

confidence: 99%

On the Meaning of the General Factor of Psychopathology (“P-Factor”) in Symmetrical Bifactor Models

Heinrich¹,

Geiser²,

Zagorscak³

et al. 2020

Preprint

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Symmetrical bifactor models are frequently applied to diverse symptoms of psychopathology to identify a general P factor. This factor is assumed to mark shared liability across psychopathology dimensions and mental disorders. Despite their popularity, however, symmetrical bifactor models often yield anomalous results, including but not limited to non-significant or negative specific factor variances and non-significant or negative factor loadings. To date, these anomalies have often been treated as nuisances to be explained away. In this paper, we demonstrate why these anomalies alter the substantive meaning of P such that it (1) does not reflect general liability to psychopathology and (2) differs in meaning across studies. We then describe an alternative modeling framework, the bifactor-(S − 1) approach. This approach avoids anomalous results, provides a framework for explaining unexpected findings in published symmetrical bifactor studies, and yields a general factor with well-defined meaning across studies. We present an empirical example to illustrate these points and provide concrete recommendations to help researchers decide for or against a specific variant of bifactor structures. In summary, bifactor-(S − 1) models provide an approach to answer questions posed in symmetrical bifactor models in a more comparable and replicable manner.

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“…Of note, however, if a priori specified and embedded in a theoretical context, a bifactor model excluding one specific factor might be psychometrically sounder and allow for a clearer interpretation than a bifactor model with weakly defined specific factors (as for example indicated by non-significant or negative item loadings; cf. Eid et al 2017 ; Junghänel et al 2020 ). In such an a priori defined model, the items of the domain which is not modeled as specific factor mainly define the meaning of the general factor (Eid et al 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Dimensions of Autistic Traits Rated by Parents of Children and Adolescents with Suspected Autism Spectrum Disorders

Giudice

Dose

Görtz-Dorten

et al. 2021

J Autism Dev Disord

Self Cite

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To examine the factor structure of autism spectrum disorder (ASD) and the psychometric properties of the German Symptom Checklist for Autism Spectrum Disorders (SCL-ASD). Data were collected from 312 clinical referrals with suspected ASD (2–18 years). Confirmatory factor analyses and analyses of reliability, convergent and divergent validity were performed. A bifactor model with one general ASD factor and two specific factors (interaction-communication; restricted, repetitive behaviors) provided an adequate data fit. Internal consistencies of the SCL-ASD subscales and the total scale were > .70. Correlations with measures of ASD traits were higher than correlations with measures of externalizing and internalizing symptoms. The results support a factor structure consistent with DSM-5/ICD-11 criteria. The SCL-ASD has sound psychometric properties.

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

Cited by 18 publications

References 22 publications

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

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

On the Meaning of the General Factor of Psychopathology (“P-Factor”) in Symmetrical Bifactor Models

Dimensions of Autistic Traits Rated by Parents of Children and Adolescents with Suspected Autism Spectrum Disorders

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