Acceso usuarios
Gerard Saucierd, Kathryn Iurino
Contemporary structural models of personality, like the Big Five, are rooted in natural-language lexicons in which socially important individual-differences concepts are sedimented. But key studies of these lexicons have been narrow in scope and mainly sought confirmatory evidence for one model of interest, rather than the maximum number of meaningful dimensions obtainable from the lexicon. Here, building on established methods for determining the appropriate number of factors, and comparing various methods of data-treatment and factor-rotation, analyses allowed higher-dimensionality structures to emerge from the same data. Factor-number-determination methods always recommended well more than 5 or 6 factors. In data using the largest 1,710-term set, we compared 18 candidate-structures derived from different method-combinations, identifying 15-, 21-, and 28-factor structures as most robust and promising. Among these, in a larger sample using a smaller 540-term set, the factors related to the 21-dimensional structure were most advantageously robust. Robustness-comparisons in an even larger sample with a 449-term subset of the original 1,710 converged on a similar number of factors. Though such a high-dimensionality model is slightly less robust than the Big Five across method-variations, we were able to confirm its clearly superior predictive capacity. And as comprehensiveness would imply, one can readily identify a low-dimensionality structure like the Big Five from within this higher-dimensionality structure, but one cannot generate this structure from the Big Five. A high-dimensionality structure can function as an improved scientific framework for cataloging trait variables and dimensions, including those that fall outside popular classifications involving only 5 or 6 factors. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
