Personality and Healthy Aging in Adulthood by Patrick L. Hill & Mathias Allemand

Author:Patrick L. Hill & Mathias Allemand
Language: eng
Format: epub
ISBN: 9783030320539
Publisher: Springer International Publishing

Two other models that are mathematically equivalent to the reflective model – the formative model (or common cause model) and an idiographic network model (Fig. 7.1). In a formative model, a latent variable is regressed on its indicators. A classic example of this is socioeconomic status, where indicators like education, household income, and location are indicators of the construct SES. However, there is no assumption that education, income, or location cause SES. In personality, in other words, observable manifestations of Extraversion, like being talkative, are not caused by some Extraversion trait that a person has. Rather, their position on the latent construct Extraversion is a function of their tendency to be talkative, as well as a number of other indicators. However, although the formative model relaxes assumptions of the reflective model that often do not hold (perhaps especially the independence of errors), it fails to provide a satisfying explanation for why latent traits emerge at all.

The idiographic network model, though, not only relaxes reflective model assumptions but also suggests explanations for why latent trait dimensions emerge. Based in dynamic systems theory, an idiographic network model asserts that latent traits emerge as emergent properties of interactions among a set of indicators. These emergent properties may appear to be “trait like” but they are only a property of the complex interrelations among lower order components. As a result, these models make none of the assumptions of the common, reflective model. In doing so, these models offer up new perspectives on how to assess and capture personality processes (Beck & Jackson 2017).

Instead of focusing on the causal properties of broad traits, lower order components are assumed to be causally responsible for personality-health associations. By looking at these lower order components, compared to standard trait models, idiographic models introduce more unique personality → health pathways, allowing for more complex models that capture personality-health associations in the complexity that many theories assert. For example, Idiographic network models allow one to systematically estimate the associations between indicators and target behaviors for each person. One can then ask whether (1) properties of the structure of someone’s personality (not their trait standing) is associated with health? Or (2) whether a specific pathway is found (e.g., feeling stressed results in more or less than desired exercise) – and does that association hold for everyone? Currently questions of how idiographic personality structure and network processes relate to health processes is completely unknown.

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