| Literature DB >> 31184949 |
Isabel Thielmann1, Nazar Akrami2, Toni Babarović3, Amparo Belloch4, Robin Bergh5, Antonio Chirumbolo6, Petar Čolović7, Reinout E de Vries8, Daniel Dostál9, Marina Egorova10, Augusto Gnisci11, Timo Heydasch12, Benjamin E Hilbig1, Kung-Yu Hsu13, Paweł Izdebski14, Luigi Leone15, Bernd Marcus16, Janko Međedović17, János Nagy18, Oksana Parshikova10, Marco Perugini19, Boban Petrović17, Estrella Romero20, Ida Sergi11, Kang-Hyun Shin21, Snežana Smederevac7, Iva Šverko3, Piotr Szarota22, Zsofia Szirmák23, Arkun Tatar24, Akio Wakabayashi25, S Arzu Wasti26, Tereza Záškodná9, Ingo Zettler27, Michael C Ashton28, Kibeom Lee29.
Abstract
The HEXACO Personality Inventory-Revised (HEXACO-PI-R) has become one of the most heavily applied measurement tools for the assessment of basic personality traits. Correspondingly, the inventory has been translated to many languages for use in cross-cultural research. However, formal tests examining whether the different language versions of the HEXACO-PI-R provide equivalent measures of the 6 personality dimensions are missing. We provide a large-scale test of measurement invariance of the 100-item version of the HEXACO-PI-R across 16 languages spoken in European and Asian countries (N = 30,484). Multigroup exploratory structural equation modeling and confirmatory factor analyses revealed consistent support for configural and metric invariance, thus implying that the factor structure of the HEXACO dimensions as well as the meaning of the latent HEXACO factors is comparable across languages. However, analyses did not show overall support for scalar invariance; that is, equivalence of facet intercepts. A complementary alignment analysis supported this pattern, but also revealed substantial heterogeneity in the level of (non)invariance across facets and factors. Overall, results imply that the HEXACO-PI-R provides largely comparable measurement of the HEXACO dimensions, although the lack of scalar invariance highlights the necessity for future research clarifying the interpretation of mean-level trait differences across countries.Year: 2019 PMID: 31184949 DOI: 10.1080/00223891.2019.1614011
Source DB: PubMed Journal: J Pers Assess ISSN: 0022-3891