Age-Related Nuances in Knowledge Assessment

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Description: Although crystallized intelligence (gc) is a prominent factor in contemporary theories of individual differences in intelligence, its structure and optimal measurement are elusive. Analogously to the personality trait hierarchy, we propose the following hierarchy of declarative fact knowledge as a key component of gc: a general fact knowledge factor at the apex, followed by broad knowledge areas (e.g., natural sciences, social sciences, humanities), knowledge domains (e.g., chemistry, law, art), and nuances. In most scientific contexts we are predominantly concerned with aggregate levels, but we argue that the sampling of knowledge items strongly affects distinctions at higher levels of the hierarchy. We illustrate the magnitude of item-level heterogeneity by predicting chronological age differences through knowledge differences at different levels of the hierarchy. Analyses were based on an online sample of 1,629 participants between 18 and 70 years old who completed 120 broadly sampled declarative knowledge items across twelve domains. The results of linear and elastic net regressions, respectively, demonstrated that the majority of the age variance was located at the item level, and the strength of the prediction decreased with increasing aggregation. Knowledge nuances seem to tap important variance that is not covered with aggregate scores (e.g., sum or factor scores) and that is useful in the prediction of age. In turn, these effects extend our understanding how knowledge is acquired and imparted. On a more general stance, to gain new insights into the nature of knowledge, its optimal measurement and psychometric representation, item and person sampling issues should be considered.

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