Validation of Two Short Personality Inventories Using Self-Descriptions in Natural Language and Quantitative Semantics Test Theory

Frontiers in Psychology

Kevin M. Cloninger¹  Patricia Rosenberg²  Danilo Garcia³  Sverker Sikström⁴  Alexandre Granjard⁵  Ali Al Nima⁵ 

[1] Anthropedia Foundation, St. Louis, MO, United States;Blekinge Center of Competence, Karlskrona, Sweden;Department of Behavioral Science and Learning, Linköping University, Linköping, Sweden;Department of Psychology, Lund University, Lund, Sweden;Department of Psychology, University of Gothenburg, Gothenburg, Sweden;

关键词: character;    identity;    quantitative semantic test theory;    narrative self;    personality;   

DOI  :  10.3389/fpsyg.2020.00016

来源: DOAJ

【 摘 要 】

BackgroundIf individual differences are relevant and prominent features of personality, then they are expected to be encoded in natural language, thus manifesting themselves in single words. Recently, the quantification of text data using advanced natural language processing techniques offers innovative opportunities to map people’s own words and narratives to their responses to self-reports. Here, we demonstrate the usefulness of self-descriptions in natural language and what we tentatively call Quantitative Semantic Test Theory (QuSTT) to validate two short inventories that measure character traits.MethodIn Study 1, participants (N1 = 997) responded to the Short Character Inventory, which measures self-directedness, cooperativeness, and self-transcendence. In Study 2, participants (N2 = 2373) responded to Short Dark Triad, which measures Machiavellianism, narcissism, and psychopathy. In both studies, respondents were asked to generate 10 self-descriptive words. We used the Latent Semantic Algorithm to quantify the meaning of each trait using the participants’ self-descriptive words. We then used these semantic representations to predict the self-reported scores. In a second set of analyses, we used word-frequency analyses to map the self-descriptive words to each of the participants’ trait scores (i.e., one-dimensional analysis) and character profiles (i.e., three-dimensional analysis).ResultsThe semantic representation of each character trait was related to each corresponding self-reported score. However, participants’ self-transcendence and Machiavellianism scores demonstrated similar relationships to all three semantic representations of the character traits in their respective personality model. The one-dimensional analyses showed that, for example, “loving” was indicative of both high cooperativeness and self-transcendence, while “compassionate,” “kind,” and “caring” was unique for individuals high in cooperativeness. The words “kind” and “caring” indicated low levels of Machiavellianism and psychopathy, whereas “shy” or “introvert” indicated low narcissism. We also found specific keywords that unify or that make the individuals in some profiles unique.ConclusionDespite being short, both inventories capture individuals’ identity as expected. Nevertheless, our method also points out some shortcomings and overlaps between traits measured with these inventories. We suggest that self-descriptive words can be quantified to validate measures of psychological constructs (e.g., prevalence in self-descriptions or QuSTT) and that this method may complement traditional methods for testing the validity of psychological measures.

【 授权许可】

Unknown