Open AccessPromises and Pitfalls of Latent Variable Approaches to Understanding Psychopathology: Reply to Burke and Johnston, Eid, Junghänel and Colleagues, and Willoughby
Author(s) -
G. Leonard Burns,
Christian Geiser,
Mateu Servera,
Stephen P. Becker,
Theodore P. Beauchaine
Publication year - 2020
Publication title -
journal of abnormal child psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 142
eISSN - 1573-2835
pISSN - 0091-0627
DOI - 10.1007/s10802-020-00656-1
Subject(s) - psychopathology , psychology , latent variable , latent variable model , context (archaeology) , leverage (statistics) , cognitive psychology , developmental psychology , clinical psychology , artificial intelligence , computer science , paleontology , biology
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.