Toward an Integrative Psychometric Model of Emotions

Abstract

Emotions are part and parcel of the human condition, but their nature is debated. Three broad classes of theories about the nature of emotions can be distinguished: affect-program theories, constructionist theories, and appraisal theories. Integrating these broad classes of theories into a unifying theory is challenging. An integrative psychometric model of emotions can inform such a theory because psychometric models are intertwined with theoretical perspectives about constructs. To identify an integrative psychometric model, we delineate properties of emotions stated by emotion theories and investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow (a) identifying distinct emotions (central in affect-program theories), (b) between- and within-person variations of emotions (central in constructionist theories), and (c) causal relationships between emotion components (central in appraisal theories). Evidence suggests that the popular reflective and formative latent variable models-in which emotions are conceptualized as unobservable causes or consequences of emotion components-cannot account for all properties. Conversely, a psychometric network model-in which emotions are conceptualized as systems of causally interacting emotion components-accounts for all properties. The psychometric network model thus constitutes an integrative psychometric model of emotions, facilitating progress toward a unifying theory.

Keywords: emotion; formative latent-variable model; psychometric network model; reflective latent-variable model.

Fig. 1.

Illustration of a reflective latent-variable model of anger. Anger is a latent (i.e., unobserved) variable that causes multiple emotion components. λ_i denotes the effects of anger on the emotion components. δ_i denotes variance unexplained by anger in the emotion components (i.e., residual).

Fig. 2.

Illustration of a formative latent-variable model of anger. Anger is a latent (i.e., unobserved) variable that is caused by multiple emotion components. γ_i denotes the effects of the emotion components on anger; λ_i denotes the effects of anger on the external variables necessary to identify the model; and ζ₁ denotes variance unexplained by the emotion components in anger (i.e., residual).

Fig. 3.

Illustration of a network model of anger. Emotion components represent nodes. The connections of emotion components denote edges, representing conditional dependencies ω_i between emotion components controlling for all other components in the network. The entire network structure constitutes anger.