Boele De Raad

While trait adjectives may be regarded as convenient summary labels for varieties of behavioral expressions with common themes and outcomes, in turn, behaviors typical of a person may be considered as expressions of personality dispositions. In fact, in daily life, behavioral descriptions are way more frequent than the more abstract adjectival descriptors. For the present study, behavioral, verb-based data sets collected in four languages were used for comparison. The data sets each consisted of ratings from 400 participants on 543 Dutch personality verbs, 1,247 participants on 414 Polish personality verbs, 951 participants on 289 Czech personality verbs, and 512 participants on 319 Serbian personality verbs. Principal component analysis-based structures (of different dimensionalities) were compared pairwise across the languages, making use of congruence coefficients. Moreover, using simultaneous component analysis, the verb-based structure common to the four languages, based on a shared data set with ratings from 3,110 participants on 324 personality verbs, was analyzed. Three- and six-component structures based on the common set are discussed in some detail, both showing portions of the Big Five but with no reference to the Intellect-Imagination factor. It is concluded that verb-based behavioral items, in comparison to adjective- and noun-based items, give rise to a diluted and reduced trait-semantic space. To arrive at a full trait-semantic representation, all linguistic categories should be exploited in a joint operation. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Human social behavior is a function of peoples’ personalities and individual differences. As humans engage in social behaviors, peoples’ traits and other unique attributes come out and get noticed, and community members may feel tempted to talk about these attributes and either encourage them or discourage them: words are created and they become part of the community’s lexicon. In this study we employed the psycho-lexical approach to hunt for those words that describe people’s traits and unique attributes and that have thus become encoded in the lexicon, in particular of the Swahili language, an eastern Africa Bantu language. We used two resources for this purpose, (1) a Swahili dictionary, as the tangible repository of the lexicon, and (2) free descriptions. As a final result we concluded that the trait vocabulary of the Swahili language contains a useful set of 1,338 trait terms, of which 4% are adjectives, 55% are nouns, and 41% are verbs. Thus, nouns form the largest number of trait terms, followed by verbs, while adjectives occupy an insignificant third position in the Swahili trait vocabulary.

This study was an endeavor to map out a personality trait structure of the Swahili language that may be used to develop indigenous eastern African personality assessment instruments. We followed the psycho-lexical approach where we not only identified trait terms from the Swahili dictionary but also from free descriptions collected from indigenous Swahili speakers. In combination, these two routines led to a pool of 3,732 personality-relevant terms, which was reduced in several steps to a set of 948 terms, identified as the most relevant trait-descriptive terms, including a small set of 26 adjectives, a large set of 531 nouns, and a substantial set of 391 verbs. This working set of 948 terms was lastly reduced to a final set of 661 most useful terms, converted into brief communicable sentences based on 439 nouns (comprising 250 type nouns and 189 attribute nouns), 199 verbs, and 23 adjectives. The list of 661 items was used to collect self and peer ratings from 480 university and high school students. An analysis of ratings on the 661 items revealed a six-factor personality trait structure that included Virtue, Imprudence, Negative Valence, Self-importance, Deceptiveness, and Attentive Conversation. Separate analyses were done using the type nouns, the attribute nouns, and the verbs, to assess the differential contribution of these word classes to the makeup of these Swahili Six.

In individual applications, in which the client is the primary audience for the outcome of a personality assessment, integrated reporting is needed in communicating the result. With multidimensional questionnaires, the usual reporting in terms of a profile of separate scale scores is insufficient from that communicative point of view. We present a procedure for finding integral characterizations, consisting of a set of markers for the rotated factor defined by the individual’s score vector. For illustrations, we refer to the Five-Factor Personality Inventory (FFPI-III; see Hendriks et al., 2019).

We investigated whether NEO-PI-R Openness to Experience (Costa & McCrae, 1992) and its six facets could be identified in the natural trait lexicon. To represent the NEO-PI-R Openness, a list of 113 items was selected from a lexically derived trait list developed for the eight-factor trait model of De Raad and Barelds (2008). We used ratings from two samples. The first (N=271) filled out the lexical Openness scales, the NEO-PI-R Openness scales, and scales measuring the eight-factor model. From the second sample (N=1,466), ratings were used to analyze the lexical Openness scales. Correlations between the eight-factor scales and the two sets of Openness scales indicated that Openness scales are fairly covered by the eight factors, except for the Ideas and Values facets of the NEO-PI-R. The lexical Openness scales correlated well with the NEO-PI-R Openness scales. Openness to Experience and its six facets were identified in the natural trait lexicon, but exploratory factor analyses did not support the six-facet structure of the NEO-PI-R Openness, neither did they lead to a similar six-facet structure across samples. Moreover, it did not consistently support a proposed two-facet structure emphasizing internal openness (fantasy, aesthetics) and external openness (ideas, change).

In this study, we describe the taxonomy of personality descriptive trait terms in the Albanian language according to the psycho-lexical procedure, in two parts. In the first part the selection of trait terms from a standard Albanian dictionary took place, largely according to standard procedures. This resulted in a useful set of 607 personality relevant terms. In the second part this list of trait terms was administered to 497 participants to obtain self-ratings. During the rating process, participants could indicate their familiarity with the terms, leading to another reduction to a final set of 434 trait terms with self-ratings. Principal Components Analysis followed by Varimax rotation was applied both using raw data and using ipsatized data. Structures with one up to seven factors were discussed and represented in a hierarchy of factor solutions. As an aid to the interpretation of the factors, use was made of markers of the Big Five, selected from the full list of 434 terms. The most comprehensive and clearest structure was found with seven factors, which included the Big Five and both Negative Valence and Positive Valence.

We reply to each of the issues raised by Schwartz in a commentary on our article on a comparison of value taxonomies. We discuss two approaches, mentioned in that commentary, the lexical approach and the theory-driven approach, especially with respect to their capacities in covering the domain of values and with respect to the representation of important values in a useful structure. We refute the critique by Schwartz that the lexical approach is superfluous, because his theory “toward universals in values” would already cover all values, and that their mutual relationships are relevant to individuals around the globe. We explain the necessity and strength of the lexical approach in taxonomizing the value domain, both within and across languages. Furthermore, we argue that principal components analysis (PCA) and simultaneous component analysis (SCA) are most adequate in arriving at a satisfactory structuring of the great many values in terms of both underlying constructs and their facets. We point to a misrepresentation in Schwartz’s circular model, and we review some misunderstandings on the side of Schwartz with respect to our results in comparison with those proceeding from his circular model.

This study aimed at finding the kernel structure common to three different value taxonomies in European languages (Dutch with 641 values and 634 participants, Austrian with 496 values and 456 participants, and Spanish with 566 values and 532 participants). Values from those three taxonomies were translated into English, thus forming the basis for the connections between the taxonomies. Using the common values between the three pairs of languages, factor structures resulting from a principal components analysis (PCA) were pairwise compared based on congruence coefficients after Procrustes rotation. Moreover, PCA was applied to a set of 139 values that was common to the three taxonomies. Furthermore, a joint matrix of values was formed with 1,703 values and 1,622 participants. Using only the common values after translation into English, this matrix was compressed to a set with 413 values and 1,622 participants, which was subjected to simultaneous components analysis (SCA). The different procedures ultimately led to a common structure with five value components, each specified in four facets. Those value components are Interpersonal Relatedness, Status and Respect, Commitment and Tradition, Competence, and Autonomy.

We developed the Lithuanian taxonomy of personality traits according to the psycho‐lexical approach. This was done in two studies. First, trait descriptive terms were selected from a Lithuanian dictionary. This selection led to a list of 435 personality‐relevant adjectives was thus collected. This list was reduced to the more useful terms and ultimately led to the 194 most frequently used trait adjectives. Second, self‐ratings from 212 participants were collected on both those 194 terms and on the 44‐item BFI. Principal Components Analysis followed by Varimax rotation was applied on the collected ratings, and also on the ratings after ipsatization. For both these types of analysis structures with two up to seven factors were discussed. Each of the structures was also related to the five BFI‐scales. The slightly clearer structure was found in the ipsatized ratings, in which clear support was found for the two‐factor model (with Dynamism and Social Propriety), for the three‐factor model (Dynamism, Affiliation and Order). A five‐factor solution was fully presented with the Big Five factors Extraversion, Agreeableness and Conscientiousness and with a factor that had Intellect traits on the one pole and Neurotic traits on the other, and finally a factor called Toughness.

We described the development of a taxonomy of Persian personality-descriptive terms in two studies. In Study 1, judges scanned Persian dictionaries and several Persian novels for person-descriptive terms. The resulting set of person-descriptive terms was classified into different categories of description, including the category of dispositional trait-descriptive adjectives. Of the 544 most familiar traits, 126 traits were selected to collect self-ratings. In Study 2, self-ratings were provided by 2400 students. Ratings were factor analysed (Principal Components Analysis) followed by Varimax rotation. Factor structures with two to six factors were discussed. The two-factor structure and the three-factor structure confirm the Big Two and the Big Three, respectively. Of the structures with five and six factors, the five-factor structure appears to be the more appropriate choice, with the factors labelled Morality, Positive versus Negative Emotionality, Achievement, Thoughtfulness and Affection. Gender differences were measured on both the five-factor structure and the six-factor structure. The discussion section emphasised both cross-cultural commonalities and cultural deviations in reference to the Big Five.