Abstract
Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes.
Highlights
Latent Variable models are commonly used in psychological research to model the measurements of psychological attributes and causal relations between these measures (Bollen, 1989; Kelava and Brandt, 2014)
Based on a pragmatist-realist epistemology, we argue that Latent Variable models are efficient approaches to infer psychological attributes
We can consider that manifestations have a common cause and that they interact. This new approach is a major opening in response to the issue of classic Structural Equation Modeling (SEM) versus Network Analysis: we retain latent variables and we introduce the possibility of cyclic relations between manifestations or latent variables
Summary
Latent Variable models are commonly used in psychological research to model the measurements of psychological attributes and causal relations between these measures (Bollen, 1989; Kelava and Brandt, 2014). With Network Analysis, a psychological attribute is not considered as an underlying common cause that explains perceptible manifestations. A psychological attribute is a complex system of perceptible components, i.e., a system in which each component interacts with every other without these perceptible components being linked to an underlying common cause (Cramer et al, 2010, 2012a; Borsboom and Cramer, 2013; Bringmann et al, 2013; Schmittmann et al, 2013; De Schryver et al, 2015; Fried, 2015; McNally et al, 2015; Dalege et al, 2016). Abandoning latent variables raises the issue of the ontology of psychological attributes
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