Abstract
Convergent correlations between traits and state aggregates from experience sampling cannot fully establish trait-state homomorphy (the extent to which the same constructs are measured). With a nomological vector correlation and lens model approach, we test how similar nomological networks of traits and state aggregates are to each other: A trait and state-aggregate capture the same construct when both show highly similar nomological associations to a common set of correlates. In large experience sampling (N = 209) and life-logging studies (N = 298), Extraversion, Conscientiousness, and Agreeableness tended to show more and Openness, Honesty/Humility, and Neuroticism/Emotionality tended to show less trait-state homomorphy. However, these general findings differed somewhat at the aspect level, with Neuroticism and Extraversion aspects tending to show more versus Openness and Honesty/Humility aspects tending to show less homomorphy. The proposed nomological approaches can be flexibly applied to other traits, states, and correlates.
Highlights
Traits and StatesPersonality traits are conceptualized as stable interindividual differences in thoughts, feelings, desires, and behaviors (Funder, 2001)
We proposed a way of estimating the extent to which traits and state aggregates tap the same construct—in addition to looking at the raw convergent correlation
While high trait-state manifestation correlations are desirable, it is more stringent to test to what extent the nomological networks of traits and state aggregates are similar to each other
Summary
Traits and StatesPersonality traits are conceptualized as stable interindividual differences in thoughts, feelings, desires, and behaviors (Funder, 2001). We propose an additional focus on nomological validity—the extent to which two constructs, or their measures, show similar associations with a set of correlates (Cronbach & Meehl, 1955; Hough, Oswald, & Ock, 2015; Rauthmann & Sherman, 2016a, 2016b). ) the interlocking system of laws which constitute a theory [shall be referred to] as a nomological network.”. This means that we can learn something about a construct by examining how it is associated with a set of other variables. It follows that we can define and compare scales in terms of their nomological networks. If two scales share a highly similar nomological network (nomological homomorphy), we may pragmatically conclude that they both tap a highly similar construct—even if they are not highly correlated with each other
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