title: CAPTION-ing the situation: A lexically-derived taxonomy of psychological situation characteristics.
abstract: In comparison with personality taxonomic research, there has been much less advancement toward establishing an integrative taxonomy of psychological situation characteristics (similar to personality characteristics for persons). One of the main concerns has been the limited content coverage of the characteristics being used. To address this issue, we present a collection of 4 lexically based studies using the largest-to-date number of situation characteristics to identify the major dimensions of the psychological situation. These studies each implemented a unique sampling and analytic methodology-namely, a qualitative dimensional exploration; the factor analyses of 2, independent samples of large-scale in situ ratings of situations; and the use of lexical-vector representations from neural-network-based models derived from millions of sources of natural-language usage with a total of 146.7 billion words. Across these studies, a clear 7-dimensional structure emerged: Complexity, Adversity, Positive Valence, Typicality, Importance, Humor, and Negative Valence-collectively referred to as the "CAPTION" model, which parsimoniously integrates the diversity of dimensions found in the extant literature. We then introduce both full- and short-form measures of these CAPTION. Data from 2 additional diverse samples of native English speakers suggest that the measures have good psychometric properties, and are able to predict a broad range of important psychological outcomes (e.g., behaviors, affect, motivation, and need satisfaction), even when pitted against extant situation taxonomic frameworks. We conclude by discussing how the CAPTION framework may serve as a useful tool for conceptualizing and measuring a broad range of psychological situations across all areas of psychology. (PsycINFO Database Record
authors.affiliation: Department of Psychological Sciences, Purdue University.
authors.forename: Scott
authors.initials: S
authors.lastname: Parrigon
authors.affiliation: Department of Psychological Sciences, Purdue University.
authors.forename: Sang Eun
authors.initials: SE
authors.lastname: Woo
authors.affiliation: Department of Psychological Sciences, Purdue University.
authors.forename: Louis
authors.initials: L
authors.lastname: Tay
authors.affiliation: Department of Computer Science, University of Toronto.
authors.forename: Tong
authors.initials: T
authors.lastname: Wang
mesh_terms: D000328:Adult; D000339:Affect; D002965:Classification / Q000379:methods*; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D010551:Personality; D011594:Psychometrics / Q000379:methods*