scholarly journals Going “Up” to Move Forward: S-1 Bifactor Models and the Study of Neurocognitive Abilities in Psychopathology

2021 ◽  
Vol 18 (14) ◽  
pp. 7413
Author(s):  
Darren Haywood ◽  
Frank D. Baughman ◽  
Barbara A. Mullan ◽  
Karen R. Heslop
Keyword(s):  
Structural Models ◽  
General Factor ◽  
Neurocognitive Performance ◽  
Diagnostic Stability ◽  
Psychopathological Symptoms ◽  
Simulation Techniques ◽  
Wide Range ◽  
Bifactor Models ◽  
Specific Factors ◽  
P Factor

Recently, structural models of psychopathology, that address the diagnostic stability and comorbidity issues of the traditional nosological approach, have dominated much of the psychopathology literature. Structural approaches have given rise to the p-factor, which is claimed to reflect an individual’s propensity toward all common psychopathological symptoms. Neurocognitive abilities are argued to be important to the development and maintenance of a wide range of disorders, and have been suggested as an important driver of the p-factor. However, recent evidence argues against p being an interpretable substantive construct, limiting conclusions that can be drawn from associations between p, the specific factors of a psychopathology model, and neurocognitive abilities. Here, we argue for the use of the S-1 bifactor approach, where the general factor is defined by neurocognitive abilities, to explore the association between neurocognitive performance and a wide range of psychopathological symptoms. We use simulation techniques to give examples of how S-1 bifactor models can be used to examine this relationship, and how the results can be interpreted.

Going “Up” to Move Forward: S-1 Bifactor Models and the Study of Neurocognitive Abilities in Psychopathology

2021 ◽  
Author(s):  
Darren Haywood ◽  
Frank Baughman ◽  
Barbara Mullan ◽  
Karen R. Heslop
Keyword(s):  
Structural Models ◽  
General Factor ◽  
Diagnostic Stability ◽  
Psychopathological Symptoms ◽  
Simulation Techniques ◽  
Wide Range ◽  
Bifactor Models ◽  
P Factor ◽  
The Relationship

Recently, structural models of psychopathology, that address the diagnostic stability and comorbidity issues of the nosological approach, have dominated much of the literature. Structural approaches have given rise to the p factor, which is claimed to reflect an individual’s propensity toward all common psychopathological symptoms. Neurocognitive abilities are argued to be important to the development and maintenance of a wide range of disorders, and have between suggested as an important driver of the p factor. However, recent evidence argues against p being an interpretable substantive construct, limiting conclusions that can be drawn from associations between p and neurocognitive abilities. Here, we argue for the use of the S-1 bifactor approach, where the general factor is defined by neurocognitive abilities, to explore the association. We use simulation techniques to give examples of how S-1 bifactor models can be used to examine the relationship, and how the results can be interpreted.

scholarly journals Misbegotten Methodologies and Forgotten Lessons from Tom Swift’s Electric Factor Analysis Machine: A Demonstration with Competing Structural Models of Psychopathology

2021 ◽  
Author(s):  
Ashley Lauren Greene ◽  
Ashley L. Watts ◽  
Miriam K. Forbes ◽  
Roman Kotov ◽  
Robert Krueger ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Model Misspecification ◽  
Theory Development ◽  
Structural Models ◽  
General Factor ◽  
Psychological Constructs ◽  
Methodological Choices ◽  
Bifactor Models ◽  
Specific Factors ◽  
P Factor

Confirmatory factor analysis (CFA) and its bifactor models are popular in empirical investigations of the factor structure of psychological constructs. CFA offers straightforward hypothesis testing but has notable pitfalls, such as the imposition of strict assumptions (i.e., simple structure) that obscure unmodeled complexity. Due to the limitations of bifactor CFAs, they have yielded anomalous results across samples and studies that suggest model misspecification (e.g., evaporating specific factors and unexpected loadings). We propose the use of exploratory factor analysis (EFA) to evaluate the structural validity of CFA solutions—either before or after the estimation of more restrictive CFA models—to (1) identify model misspecifications that may drive anomalous estimates and (2) confirm CFA models by examining whether hypothesized structures emerge with limited researcher input. We evaluated the degree to which predominant factor structures were invariant across contexts along the exploratory-confirmatory continuum and demonstrate how poor methodological choices can distort results and impede theory development. All models fit well, but there were numerous differences in replicability and substantive interpretability. Several similarities emerged between bifactor CFA and EFA models, including evidence of overextraction, the collapse of specific factors onto the general factor, and subsequent shifts in how the general factor was defined. We situate these methodological shortcomings within the broader literature on structural models of psychopathology, articulate implications for theories (such as the p-factor) that are borne out of factor analysis, outline several remedies for problems encountered when performing exploratory bifactor analysis, and propose alternative specifications for confirmatory bifactor models.

scholarly journals Riskier Tests of the Validity of Bifactor Models of Psychopathology

2019 ◽  
Author(s):  
Ashley L. Watts ◽  
Holly Poore ◽  
Irwin Waldman
Keyword(s):  
Community Sample ◽  
General Factor ◽  
Symptom Dimensions ◽  
Bifactor Models ◽  
Specific Factors ◽  
Psychopathology Symptom ◽  
Using Data ◽  
P Factor ◽  
Correlated Factors ◽  
Additional Variance

We advanced several “riskier tests” of the validity of bifactor models of psychopathology, which included that the general and specific factors should be reliable and well-represented by their indicators, and that including a general factor should improve the correlated factor model’s external validity. We compared bifactor and correlated factors models using data from a community sample of youth (N=2498) whose parents provided ratings on psychopathology and external criteria (i.e., temperament, aggression, antisociality). Bifactor models tended to yield either general or specific factors that were unstable and difficult to interpret. The general factor appeared to reflect a differentially-weighted amalgam of psychopathology rather than a liability for psychopathology broadly construed. With rare exceptions, bifactor models did not explain additional variance in psychopathology symptom dimensions or external criteria compared with correlated factors models. Together, our findings call into question the validity of bifactor models of psychopathology, and the p-factor more broadly.

scholarly journals The p Factor and the n Factor: Associations Between the General Factors of Psychopathology and Neuroticism in Children

2019 ◽  
Author(s):  
Cassandra M Brandes ◽  
Kathrin Herzhoff ◽  
Avante J Smack ◽  
Jennifer L Tackett
Keyword(s):  
Mental Disorders ◽  
Age Groups ◽  
Negative Emotionality ◽  
General Factor ◽  
General Psychopathology ◽  
Bifactor Models ◽  
Specific Factors ◽  
Latent Dimension ◽  
P Factor ◽  
Internalizing And Externalizing

Research across age groups has consistently indicated that psychopathology has a general factor structure, such that there is a broad latent dimension (or p factor) capturing variance common to all mental disorders, as well as specific internalizing and externalizing factors. This research has found that the p factor overlaps substantially with trait negative emotionality (or neuroticism). However, less is known about the psychological substance of the specific factors of the general psychopathology model, or how lower-order facets of neuroticism may relate to each psychopathology factor. We investigated the structure of neuroticism and psychopathology, as well as associations between these domains in a sample of 695 pre-adolescent children using multi-method assessments. We found that both psychopathology and neuroticism may be well-characterized by bifactor models, and that there was substantial overlap between psychopathology (p) and neuroticism (n) general factors, as well as between specific factors (Internalizing with Fear, Externalizing with Irritability).

scholarly journals The p Factor and the n Factor: Associations Between the General Factors of Psychopathology and Neuroticism in Children

2019 ◽  
Vol 7 (6) ◽  
pp. 1266-1284 ◽  
Author(s):  
Cassandra M. Brandes ◽  
Kathrin Herzhoff ◽  
Avanté J. Smack ◽  
Jennifer L. Tackett
Keyword(s):  
Mental Disorders ◽  
Age Groups ◽  
Negative Emotionality ◽  
General Factor ◽  
General Psychopathology ◽  
Bifactor Models ◽  
Specific Factors ◽  
Latent Dimension ◽  
P Factor ◽  
Internalizing And Externalizing

Research across age groups has consistently indicated that psychopathology has a general factor structure such that a broad latent dimension (or p factor) captures variance common to all mental disorders as well as specific internalizing and externalizing factors. This research has found that the p factor overlaps substantially with trait negative emotionality (or neuroticism). However, less is known about the psychological substance of the specific factors of the general psychopathology model or how lower-order facets of neuroticism may relate to each psychopathology factor. We investigated the structure of neuroticism and psychopathology as well as associations between these domains using multimethod assessments in a sample of 695 preadolescent children. We found that both psychopathology and neuroticism may be well characterized by bifactor models and that there was substantial overlap between psychopathology (p) and neuroticism (n) general factors as well as between specific factors (Internalizing with Fear, Externalizing with Irritability).

Riskier Tests of the Validity of the Bifactor Model of Psychopathology

2019 ◽  
Vol 7 (6) ◽  
pp. 1285-1303 ◽  
Author(s):  
Ashley L. Watts ◽  
Holly E. Poore ◽  
Irwin D. Waldman
Keyword(s):  
Community Sample ◽  
General Factor ◽  
Symptom Dimensions ◽  
Bifactor Models ◽  
Specific Factors ◽  
Psychopathology Symptom ◽  
Using Data ◽  
P Factor ◽  
Correlated Factors ◽  
Additional Variance

We advanced several “riskier tests” of the validity of bifactor models of psychopathology, which included that the general and specific psychopathology factors should be reliable and well represented by their respective indicators and that including a general factor should improve on the correlated factor model’s external validity. We compared bifactor and correlated factors models of psychopathology using data from a community sample of youth ( N = 2,498) whose parents provided ratings on psychopathology and theoretically relevant external criteria (i.e., personality, aggression, antisociality). Bifactor models tended to yield either general or specific factors that were unstable and difficult to interpret. The general factor appeared to reflect a differentially weighted amalgam of psychopathology rather than a liability for psychopathology broadly construed. With rare exceptions, bifactor models did not explain additional variance in first-order psychopathology symptom dimensions or external criteria compared with correlated factors models. Together, our findings call into question the validity of bifactor models of psychopathology and the p factor more broadly.

Issues in Estimating Interpretable Lower Order Factors in Second-Order Hierarchical Models: Commentary on Clark et al. (2021)

2021 ◽  
pp. 216770262110351
Author(s):  
Tyler M. Moore ◽  
Benjamin B. Lahey
Keyword(s):  
Lower Order ◽  
Hierarchical Modeling ◽  
Simulated Data ◽  
Second Order ◽  
General Factor ◽  
Bifactor Models ◽  
Specific Factors ◽  
Factor Modeling ◽  
Highly Correlated ◽  
Criterion Variables

In a previous issue of Clinical Psychological Science, Clark and colleagues asserted that lower order factors in second-order models are comparable with specific factors in bifactor models when residualized on the general factor. Modeling simulated data demonstrated that residualized lower order factors are correlated with bifactor-specific factors only to the extent that factor loadings are proportional. Modeling actual data with violations of proportionality showed that specific and residualized lower order factors are not always highly correlated and have differential correlations with criterion variables even when both models fit acceptably. Because proportionality constraints limit only second-order models, bifactor models should be the first option for hierarchical modeling.

Bifactor Models of Attention-Deficit/Hyperactivity Disorder (ADHD): An Evaluation of Three Necessary but Underused Psychometric Indexes

Assessment ◽  
2016 ◽  
Vol 25 (7) ◽  
pp. 885-897 ◽  
Author(s):  
Víctor B. Arias ◽  
Fernando P. Ponce ◽  
Daniel. E. Núñez
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Measurement Model ◽  
Clinical Samples ◽  
General Factor ◽  
Common Variance ◽  
Hyperactivity Disorder ◽  
Bifactor Models ◽  
Specific Factors ◽  
Inattention And Hyperactivity

Background: In the past decade, the bifactor model of attention-deficit/hyperactivity disorder (ADHD) has been extensively researched. This model consists of an ADHD general dimension and two specific factors: inattention and hyperactivity/impulsivity. All studies conclude that the bifactor is superior to the traditional two-correlated factors model, according to the fit obtained by factor analysis. However, the proper interpretation of a bifactor not only depends on the fit but also on the quality of the measurement model. Objective: To evaluate the model-based reliability, distribution of common variance and construct replicability of general and specific ADHD factors. Method: We estimated expected common variance, omega hierarchical/subscale and H-index from standardized factor loadings of 31 ADHD bifactor models previously published. Results and Conclusion: The ADHD general factor explained most of the common variance. Given the low reliable variance ratios, the specific factors were difficult to interpret. However, in clinical samples, inattention acquired sufficient specificity and stability for interpretation beyond the general factor. Implications for research and clinical practice are discussed.

On the Meaning of the “P Factor” in Symmetrical Bifactor Models of Psychopathology: Recommendations for Future Research From the Bifactor-(S−1) Perspective

Assessment ◽  
2021 ◽  
pp. 107319112110602
Author(s):  
Manuel Heinrich ◽  
Christian Geiser ◽  
Pavle Zagorscak ◽  
G. Leonard Burns ◽  
Johannes Bohn ◽  
...  
Keyword(s):  
Mental Disorders ◽  
Future Research ◽  
Specific Factor ◽  
General Factor ◽  
Modeling Framework ◽  
Factor Loadings ◽  
Negative Factor ◽  
Bifactor Models ◽  
Unexpected Findings ◽  
P Factor

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 all psychopathology dimensions and mental disorders. Despite their popularity, however, symmetrical bifactor models of P often yield anomalous results, including but not limited to nonsignificant or negative specific factor variances and nonsignificant or negative factor loadings. To date, these anomalies have often been treated as nuisances to be explained away. In this article, we demonstrate why these anomalies alter the substantive meaning of P such that it (a) does not reflect general liability to psychopathology and (b) differs in meaning across studies. We then describe an alternative modeling framework, the bifactor-( S−1) approach. This method avoids anomalous results, provides a framework for explaining unexpected findings in published symmetrical bifactor studies, and yields a well-defined general factor that can be compared across studies when researchers hypothesize what construct they consider “transdiagnostically meaningful” and measure it directly. We present an empirical example to illustrate these points and provide concrete recommendations to help researchers decide for or against specific variants of bifactor structure.

Evaluating Bifactor Models of Psychopathology Using Model-Based Reliability Indices

2019 ◽  
Author(s):  
Matthew Constantinou ◽  
Peter Fonagy
Keyword(s):  
Bifactor Model ◽  
Common Variance ◽  
Reliability Indices ◽  
Bifactor Models ◽  
Study Characteristics ◽  
Specific Factors ◽  
P Factor ◽  
Explained Common Variance ◽  
Internalizing And Externalizing ◽  
Variance Explained

There is has been a rapid increase in quantitative researchers applying the bifactor model to psychopathology data. The bifactor model, which typically includes a general p factor and internalizing and externalizing residual factors, consistently demonstrates superior model fit to competing models, including the correlated factors model, which typically includes internalizing and externalizing factors. However, the bifactor model’s superior fit might stem from its tendency to overfit noise and flexibly fit most datasets. An alternative approach to evaluating bifactor models that does not rely on fit statistics is model-based reliability assessment. Reliability indices, including omega/omega hierarchical, explained common variance, and percent uncontaminated correlations can be used to determine the viability of the general and specific psychopathology factors and the extent that the underlying data structure and its measurement is multidimensional. In this methodological review, we identified 49 studies published between 2009 and 2019 that applied the bifactor model to at least two separate symptom domains and calculated reliability indices from the standardized factor loading matrices. We also predicted variation in the p factor’s strength, indexed by the explained common variance, from study characteristics. We found that psychopathology measures tend to be multidimensional, with 57% of the variance explained by the p factor and the remaining variance explained by specific factors. By contrast, most of the variance in observed total scores (74%) was explained by the p factor, while relatively little of the variance in in observed subscale scores (37%) was explained by specific factors beyond the p factor. Finally, 62% of the variability in the p factor’s strength could be predicted by study characteristics, most notably the informant (in a simultaneous regression model), but also age, percent uncontaminated correlations, and the number of items (in separate regression models). We conclude that the latent structure of psychopathology is multidimensional, but its measurement is governed by a single dimension, the strength of which is predicted by study characteristics, particularly the informant.

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