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Research on Child and Adolescent Psychopathology

Gepubliceerd in:

16-05-2020

Promises and Pitfalls of Latent Variable Approaches to Understanding Psychopathology: Reply to Burke and Johnston, Eid, Junghänel and Colleagues, and Willoughby

Auteurs: G. Leonard Burns, Christian Geiser, Mateu Servera, Stephen P. Becker, Theodore P. Beauchaine

Gepubliceerd in: Research on Child and Adolescent Psychopathology | Uitgave 7/2020

Abstract

The commentaries by Burke and Johnston (this issue), Eid (this issue), Junghänel et al. (this issue), and Willoughby (this issue) on Burns et al. (this issue) provide useful context for comparing three latent variable modeling approaches to understanding psychopathology—the correlated first-order syndrome-specific factors model, the bifactor S – 1 model, and the symmetrical bifactor model. The correlated first-order syndrome-specific factors model has proven useful in constructing explanatory models of psychopathology. The bifactor S – 1 model is also useful for examining the latent structure of psychopathology, especially in contexts with clear theoretical predictions. Joint use of correlated first-order syndrome-specific model and bifactor S – 1 model provides leverage for explaining psychopathology, and both models can also guide individual clinical assessment. In this reply, we further clarify reasons why the symmetrical bifactor model should not be used to study the latent structure of psychopathology and also discuss a restricted bifactor S – 1 model that is equivalent to the first-order syndrome-specific factors model.

vorige artikel The Bifactor S-1 Model: a Psychometrically Sounder Alternative to Test the Structure of ADHD and ODD?

volgende artikel Physiological Correlates of Sluggish Cognitive Tempo in Children: Examining Autonomic Nervous System Reactivity during Social and Cognitive Stressor Tasks

This can be seen by writing out the measurement and structural equations in the model shown in Figure 1C. The measurement equation for mean-centered variables is Y_if = λ_ifF_f + ε_if (i = indicator, f = facet). The structural (latent regression) equation for the non-reference facets is F_f = β_fF₁ + S_f, where f = 1 indicates the reference facet. Inserting the structural equation for F_f into the measurement equation for the non-reference facets yields Y_if = λ_if (β_fF₁ + S_f) + ε_if = λ_ifβ_fF₁ + λ_ifS_f + ε_if. This is the measurement equation for the restricted bifactor S – 1 model shown in Figure 1D.

Note that the fact that factors have the same loadings does not always mean that the factors have the same meaning. This can be seen by comparing the factors F_f and S_f in Figure 1A and D. Notice that F₂ and F₃ in Figure 1A have the same loadings as do S₂ and S₃ in Figure 1D. Nonetheless, the factors F_f and S_f have different meanings. The F_f factors are true score variables, whereas the S_f factors are true score regression residual variables. This is because the indicators measuring the S_f factors also load on the reference factor F₁ in Figure 1D.

The application of the symmetrical bifactor model to structurally different symptom facets can yield an admissible solution if the correlations among the symptom indicators are homogenous, a pattern that is common with randomly selected and thereby inter-changeable raters. Such an admissible solution, however, still does not yield a psychometrically sound definition of the general factor (see Eid et al. 2017; Heinrich et al. 2018).

Beauchaine, T. P., & Hinshaw, S. P. (in press). The research domain criteria and psychopathology among youth: Misplaced assumptions and an agenda for future research. Journal of Clinical Child and Adolescent Psychology. https://doi.org/10.1080/15374416.2020.1750022.

Beauchaine, T. P., Zisner, A., & Sauder, C. L. (2017). Trait impulsivity and the externalizing spectrum. Annual Review of Clinical Psychology, 13, 343–368. https://doi.org/10.1146/annurev-clinpsy-021815-093253.CrossRefPubMed

Burke, J. D., & Johnston, O. G. (this issue). The bifactor S - 1 model: A psychometrically sounder alternative to test the structure of ADHD and ODD? Journal of Abnormal Child Psychology. doi:https://doi.org/10.1007/s10802-020-00645-4

Burns, G. L., Geiser, C., Servera, M., Becker, S. P., & Beauchaine, T. P. (this issue). Application of the bifactor S - 1 model to multisource ratings of ADHD/ODD symptoms: An appropriate bifactor model for symptom ratings. Journal of Abnormal Child Psychology. https://doi.org/10.1007/s10802-019-00608-4.

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Eid, M. (this issue). Multi-faceted constructs in abnormal psychology: Implications of the bifactor S – 1 model for individual clinical assessment. Journal of Abnormal Child Psychology. https://doi.org/10.1007/s10802-020-00624-9.

Eid, M., Geiser, C., Koch, T., & Heene, M. (2017). Anomalous results in G-factor models: Explanations and alternatives. Psychological Methods, 22, 541–562. https://doi.org/10.1037/met0000083.CrossRefPubMed

Eid, M., Krumm, S., Koch, T., & Schulze, J. (2018). Bifactor models for predicting criteria by general and specific factors: Problems of nonidentifiability and alternative solutions. Journal of Intelligence, 6(3), 42. https://doi.org/10.3390/jintelligence6030042.CrossRefPubMedCentral

Eid, M., Nussbeck, F. W., Geiser, C., Cole, D. A., Gollwitzer, M., & Lischetzke, T. (2008). Structural equation modeling of multitrait-multimethod data: Different models for different types of methods. Psychological Methods, 13, 230–253. https://doi.org/10.1037/a0013219.CrossRefPubMed

Geiser, C., Eid, M., & Nussbeck, F. W. (2008). On the meaning of the latent variables in the CT-C(M–1) model: A comment on Maydeu-Olivares and Coffman (2006). Psychological Methods, 13, 49–57. https://doi.org/10.1037/1082-989X.13.1.49.CrossRefPubMed

Heinrich, M., Zagorscak, P., Eid, M., & Knaevelsrud, C. (2018). Giving G a meaning: An application of the bifactor-(S-1) approach to realize a more symptom-oriented modeling of the Beck depression inventory–II Assessment. Advance online publication. https://doi.org/10.1177/1073191118803738.2016.1144192.

Junghänel, M., Rodenacker, K., Dose, C., & Döpfner, M. (this issue). Applying the bifactor S – 1 model to ratings of ADHD/ODD Symptoms: A commentary on Burns et al. (2019) And a re-analysis. Journal of Abnormal Child Psychology. doi: https://doi.org/10.1007/s10802-020-00637-4.

Preszler, J., & Burns, G. L. (2019). Network analysis of ADHD and ODD symptoms: Novel insights or redundant findings with the latent variable model? Journal of Abnormal Child Psychology, 47, 1599–1610. https://doi.org/10.1007/s10802-019-00549-y.CrossRefPubMed

Willoughby, M. T. (this issue). Commentary on the application of the bifactor S – 1 model to multisource ratings of ADHD/ODD symptoms: An appropriate bifactor model for symptom ratings. Journal of Abnormal Child Psychology. https://doi.org/10.1007/s10802-020-00629-4.

Titel: Promises and Pitfalls of Latent Variable Approaches to Understanding Psychopathology: Reply to Burke and Johnston, Eid, Junghänel and Colleagues, and Willoughby
Auteurs: G. Leonard Burns
Christian Geiser
Mateu Servera
Stephen P. Becker
Theodore P. Beauchaine
Publicatiedatum: 16-05-2020
Uitgeverij: Springer US
Gepubliceerd in: Research on Child and Adolescent Psychopathology / Uitgave 7/2020
Print ISSN: 2730-7166
Elektronisch ISSN: 2730-7174
DOI: https://doi.org/10.1007/s10802-020-00656-1

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