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.

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).

Core Symptom Index (CSI): testing for bifactor model and differential item functioning

2019 ◽  
Vol 31 (12) ◽  
pp. 1769-1779
Author(s):  
Nahathai Wongpakaran ◽  
Tinakon Wongpakaran ◽  
Surang Lertkachatarn ◽  
Thanitha Sirirak ◽  
Pimolpun Kuntawong
Keyword(s):  
Differential Item Functioning ◽  
Depressive Disorders ◽  
Bifactor Model ◽  
Ordinal Scale ◽  
Common Variance ◽  
Symptom Index ◽  
Core Symptom ◽  
Item Functioning ◽  
Specific Factors ◽  
Explained Common Variance

ABSTRACTObjectives:The Core Symptom Index (CSI) is designed to measure anxiety, depression and somatization symptoms. This study examined the construct validity of CSI using confirmatory factor analysis (CFA) including a bifactor model and explored differential item functioning (DIF) of the CSI. The criterion and concurrent validity were evaluated.Methods:In all, 803 elderly patients, average age 69.24 years, 70% female, were assessed for depressive disorders and completed the CSI and the geriatric depression scale (GDS). A series involving CFA for ordinal scale was applied. Factor loadings and explained common variance were analyzed for general and specific factors; and Omega was calculated for model-based reliability. DIF was analyzed using the Multiple-Indicator Multiple-Cause model. Pearson’s correlation, ANOVA, and ROC analysis were used for associations and to compare CSI and GDS in predicting major depressive disorders (MDD).Results:The bifactor model provided the best fit to the data. Most items loaded on general rather than specific factors. The explained common variance was acceptable, while Omega hierarchical for the subscale and explained common variance for the subscales were low. Two DIF items were identified; ‘crying’ for sex items and ‘self-blaming’ for education items. Correlation among CSI and clinical disorders and the GDS were found. AUC for the GDS was 0.83, and for the CSI was 0.81.Conclusion:CSI appears sufficiently unidimensional. Its total score reflected a single general factor, permitting users to interpret the total score as a sufficient reliable measure of the general factors. CSI could serve as a screening tool for MDD.

scholarly journals Strong Homotypic Continuity in Common Psychopathology-, Internalizing-, and Externalizing-Specific Factors Over Time in Adolescents

2016 ◽  
Vol 5 (1) ◽  
pp. 98-110 ◽  
Author(s):  
Hannah R. Snyder ◽  
Jami F. Young ◽  
Benjamin L. Hankin
Keyword(s):  
Community Sample ◽  
Strong Stability ◽  
General Psychopathology ◽  
Bifactor Models ◽  
Specific Factors ◽  
The Stability ◽  
P Factor ◽  
Heterotypic Continuity ◽  
Internalizing And Externalizing ◽  
Over Time

Dimensional models of psychopathology that posit a general psychopathology factor (i.e., p factor), in addition to specific internalizing and externalizing factors, have recently gained prominence. However, the stability of these factors and the specificity with which they are related to one another over time (e.g., homotypic or heterotypic continuity) have not been investigated. The current study addressed these questions. We estimated bifactor models, with p, internalizing-specific, and externalizing-specific factors, with youth and caretaker reports of symptoms at two time points (18 months apart), in a large community sample of adolescents. Results showed strong stability over time with highly specific links (i.e., p factor at Time 1 to Time 2; internalizing-specific at Time 1 to Time 2 and externalizing-specific at Time 1 to Time 2), suggesting strong homotypic continuity between higher order latent psychopathology factors.

scholarly journals Reliability and Validity of Bifactor Models of Dimensional Psychopathology in Youth from three Continents

2021 ◽  
Author(s):  
Mauricio Scopel Hoffmann ◽  
Tyler Maxwell Moore ◽  
Luiza Kvitko Axelrud ◽  
Nim Tottenham ◽  
Xi-Nian Zuo ◽  
...  
Keyword(s):  
Measurement Invariance ◽  
Child Behavior Checklist ◽  
Reliability And Validity ◽  
Global Model ◽  
Model Fit ◽  
Reliability Indices ◽  
Bifactor Models ◽  
Study Characteristics ◽  
Specific Factors ◽  
Highly Correlated

Bifactor models are a promising strategy to parse general from specific aspects of psychopathology in youth. Currently, there are multiple configurations of bifactor models originating from different theoretical and empirical perspectives. Our aim is to identify and test the reliability, validity, measurement invariance, and the correlation of different bifactor models of psychopathology using the Child Behavior Checklist (CBCL). We used data from the Reproducible Brain Charts (RBC) initiative (N=7,011, ages 5 to 22 years, 40.2% females). Factor models were tested using the baseline data. To address our aim, we a) mapped the published bifactor models using the CBCL; b) tested their global model fit; c) calculated model-based reliability indices. d) tested associations with symptoms' impact in everyday life; e) tested measurement invariance across many characteristics and f) analyzed the observed factor correlation across the models. We found 11 bifactor models ranging from 39 to 116 items. Their global model fit was broadly similar. Factor determinacy and H index were acceptable for the p-factors, internalizing, externalizing and somatic specific factors in most models. However, only p- and attention factors were predictors of symptoms' impact in all models. Models were broadly invariant across different characteristics. P-factors were highly correlated across models (r = 0.88 to 0.99). Homotypic specific factors were also highly correlated. Regardless of item selection and strategy to compose CBCL bifactor models, results suggest that they all assess very similar constructs. Our results provide support for the robustness of the bifactor of psychopathology and distinct study characteristics.

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 Pubertal Timing as a Transdiagnostic Risk for Psychopathology in Youth

2018 ◽  
Vol 7 (3) ◽  
pp. 411-429 ◽  
Author(s):  
Elissa J. Hamlat ◽  
Hannah R. Snyder ◽  
Jami F. Young ◽  
Benjamin L. Hankin
Keyword(s):  
Risk Factor ◽  
Factor Model ◽  
Pubertal Timing ◽  
Organizational Models ◽  
General Psychopathology ◽  
Early Pubertal Timing ◽  
Specific Factors ◽  
P Factor ◽  
Internalizing And Externalizing ◽  
Different Levels

Evidence suggests that early pubertal timing may operate as a transdiagnostic risk factor (i.e., shared across syndromes of psychopathology) for both genders. The current study examined associations between pubertal timing and dimensional psychopathology, structured across different levels of three organizational models: (a) DSM-based syndrome model, (b) traditional model of internalizing and externalizing factors, and (c) bifactor (p factor) model, which includes a general psychopathology factor as well as internalizing- and externalizing-specific factors. For study analyses, 567 youth-parent pairs completed psychopathology measures when youths (55.5% female) were 13.58 years old ( SD = 2.37, range = 9–17 years). Findings across all models revealed that early pubertal timing served as a transdiagnostic risk factor and also displayed some syndrome-specific associations. Gender did not moderate any relationships between pubertal timing and psychopathology. Study findings reinforce the importance of examining risk across different levels of psychopathology conceptualization and analysis.

scholarly journals Bifactor Models for Predicting Criteria by General and Specific Factors: Problems of Nonidentifiability and Alternative Solutions

2018 ◽  
Vol 6 (3) ◽  
pp. 42 ◽  
Author(s):  
Michael Eid ◽  
Stefan Krumm ◽  
Tobias Koch ◽  
Julian Schulze
Keyword(s):  
Factor Models ◽  
Formal Proof ◽  
Bifactor Model ◽  
First Order ◽  
Applied Model ◽  
Measurement Models ◽  
Bifactor Models ◽  
Specific Factors ◽  
Order Factor ◽  
Criterion Variables

The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general and specific factors on criterion variables can be scrutinized in latent multiple regression models that are built on bifactor measurement models. This study employs an extended bifactor model to predict mathematics and English grades by three facets of intelligence (number series, verbal analogies, and unfolding). We show that, if the observed variables do not differ in their loadings, extended bifactor models are not identified and not applicable. Moreover, we reveal that standard errors of regression weights in extended bifactor models can be very large and, thus, lead to invalid conclusions. A formal proof of the nonidentification is presented. Subsequently, we suggest alternative approaches for predicting criterion variables by general and specific factors. In particular, we illustrate how (1) composite ability factors can be defined in extended first-order factor models and (2) how bifactor(S-1) models can be applied. The differences between first-order factor models and bifactor(S-1) models for predicting criterion variables are discussed in detail and illustrated with the empirical example.

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.

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.

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