Modeling Psychological Attributes in Psychology - An Epistemological Discussion: Network Analysis vs. Latent Variables.

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.

An introduction to the partial least squares approach to structural equation modelling: a method for exploratory psychiatric research.

In psychiatry and psychology, relationship patterns connecting disorders and risk factors are always complex and intricate. Advanced statistical methods have been developed to overcome this issue, the most common being structural equation modelling (SEM). The main approach to SEM (CB-SEM for covariance-based SEM) has been widely used by psychiatry and psychology researchers to test whether a comprehensive theoretical model is compatible with observed data. While the validity of this approach method has been demonstrated, its application is limited in some situations, such as early-stage exploratory studies using small sample sizes. The partial least squares approach to SEM (PLS-SEM) has risen in many scientific fields as an alternative method that is especially useful when sample size restricts the use of CB-SEM. In this article, we aim to provide a comprehensive introduction to PLS-SEM intended to CB-SEM users in psychiatric and psychological fields, with an illustration using data on suicidality among prisoners. Researchers in these fields could benefit from PLS-SEM, a promising exploratory technique well adapted to studies on infrequent diseases or specific population subsets. Copyright © 2015 John Wiley & Sons, Ltd.