The HEXACO–100 Across 16 Languages: A Large-Scale Test of Measurement Invariance

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.

Acknowledgments

This article has earned the Center for Open science badges for Open Data. The data and materials are openly accessible at https://osf.io/bwtnr.

Notes

1 The distinction between cross-language versus cross-country measurement invariance is commonly confounded in corresponding tests given that different language versions of an inventory are usually compared across native-speaking samples in different countries. Indeed, the same also applies to the test of measurement invariance provided here. Thus, although we primarily refer to cross-language invariance in what follows, one might likewise interpret the results in terms of cross-country invariance.

2 In addition to this sequence of nested models, further restrictions can be imposed and tested (see Marsh et al., 2009, for a taxonomy of invariance models). However, given that our primary focus is on testing whether indicator and factor means are comparable across languages, we confine our analysis to the previously mentioned sequence, with the scalar invariance model being the most restrictive (e.g., Rutkowski & Svetina, 2014; Thompson & Green, 2013).

3 Note that it is also possible to specify cross-loadings in CFA. However, especially when multiple cross-loadings are to be expected or when there is no strong a priori theory about which cross-loadings to expect—as is typically the case with omnibus personality inventories—ESEM provides a useful alternative to CFA models.

4 In addition, the inventory includes four items to measure altruism as an interstitial facet, thus bringing the total number of items to 100. The altruism facet is an interstitial facet because it is expected to divide its loadings across three factors, namely Honesty-Humility, Emotionality, and Agreeableness—which are interpreted as representing different aspects of reciprocal or kin altruistic tendencies, respectively (Ashton & Lee, 2007). Therefore, we refrained from consideration of the altruism facet but focused on the six HEXACO dimensions (and the respective 24 facets) only.

5 The Spanish sample consists of two subsamples in which different translations of the HEXACO–PI–R were used. However, given that group-based ESEM analysis provided support for metric and scalar invariance across the two subsamples (for results, see Table S1 in the supplemental materials on the OSF), we merged them for the following analyses.

6 In addition, we ran the ESEM scalar invariance model with factor means being fixed to zero and to be equal across groups (note that in both these models, the factor means in the reference group were fixed to be zero by default). Corresponding model fit statistics are available in Table S2 in the supplemental materials (https://osf.io/bwtnr).

7 Note that the data set provided on the OSF does not contain the English (Canadian), Hungarian, and Russian data given that the conditions of participant consent in these data sets were not compatible with the posting of the data in an online repository. The data are, however, available on request from the first author.

8 Note that the various samples were, in different respects, not representative of the national populations from which they were drawn. Therefore, differences in mean scores across our various samples do not necessarily imply national-level differences.

9 To estimate the alignment model, we used the “free” optimization option as implemented in Mplus, in line with recommendations (Asparouhov & Muthén, 2014). Using this option, the factor means are freely estimated.

10 Percentages of invariant parameters for HEXACO factors represent the total number of approximate invariant groups across facets per factor divided by the total number of groups across facets (i.e., 4 facets *16 groups = 64). In turn, percentages of invariant parameters for HEXACO facets represent the total number of approximate invariant groups divided by the total number of groups (i.e., 16).