Unpacking the personality–cognitive ability link: A cross-national facet-level analysis of the Big Five

Unpacking the personality–cognitive ability link: A cross-national facet-level analysis of the Big Five | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Unpacking the personality–cognitive ability link: A cross-national facet-level analysis of the Big Five Beatrice Rammstedt, Vera Vogel, Matthias Roth, Clemens Lechner This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7431184/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jan, 2026 Read the published version in Large-scale Assessments in Education → Version 1 posted You are reading this latest preprint version Abstract Background Numerous studies have demonstrated robust—albeit modest—associations between personality and cognitive ability, with a shared variance of 5–10%. Previous research has largely focused on global personality dimensions, such as the Big Five. Recent studies suggest that narrower personality facets may enhance our understanding of the personality–cognitive ability link. However, these studies typically rely on selective samples from single countries, limiting insights into populational and cross-cultural variation. Findings This study is the first to examine the associations between personality facets and cognitive ability using comprehensive, population-representative data from 11 countries. We investigated the relationships between 15 Big Five facets and cognitive ability by analyzing shared variance and bivariate associations. Three main findings emerged: (1) Personality facets account for approximately twice the variance in cognitive ability compared to broad personality domains. (2) Facets provide a more nuanced picture of the personality–cognitive ability link and reveal associations masked at the domain level. (3) Associations at the facet level differ across countries, with the strongest variation observed in facets of Openness. Conclusions Our findings underscore the added value of examining personality at the facet level. This more granular approach offers deeper insights into the interplay between personality and cognitive ability and highlights the importance of considering cultural variability in psychological research. Big Five personality facets cognitive ability cross-cultural PIAAC Figures Figure 1 Figure 2 1. Introduction In recent decades, a substantial body of research has demonstrated that cognitive ability and personality are systematically related (e.g., Anglim et al., 2022 ; Stanek & Ones, 2023 ). Most of this work has focused largely on the Big Five personality traits, the most widely used and validated personality model. Numerous studies, including meta-analyses and mega-analyses, have consistently shown two main findings: First, the proportion of variance shared by personality and cognitive ability is modest at 5–10% (Furnham et al., 2007 ). Second, among the Big Five traits, Openness to Experience—and, to a lesser extent, Emotional Stability—show the strongest positive associations with cognitive ability. By contrast, Conscientiousness sometimes shows negative associations with cognitive ability, whereas the associations shown by Agreeableness and Extraversion are typically negligible (Anglim et al., 2022 ). These findings were further supported by a recent analysis of large-scale, population-representative datasets from 26 countries (Rammstedt et al., 2025 ): Pooled across countries, the share of the variance in cognitive ability explained by the Big Five was around 4%, placing it at the lower bound of previous estimates. Further, and most importantly, as the country values ranged between 1% and 10%, the study showed for the first time that associations between personality and cognitive ability vary markedly across countries. In most of the 26 countries, Openness and Emotional Stability were most predictive of cognitive ability, whereas cross-national variations in the associations between Conscientiousness and cognitive ability were considerable both in size and direction. In addition, recent studies on associations between personality and external criteria such as academic achievement, career success, or life satisfaction (e.g., Danner et al., 2021 ) have demonstrated that the domain level of personality is often too broad to capture nuanced relationships. This may also apply to the personality–cognitive ability link, where finer-grained analyses at the facet level could reveal differential associations that are masked at the domain level. Previous studies have shown that the shared variance between cognitive ability and personality tends to be greater at facet level than at domain level. Further, and more crucially, the associations between cognitive ability and personality facets within the same domain often vary markedly. For example, different facets within the same domain can show opposing relationships with cognitive ability, which can suppress or neutralize associations at the domain level (e.g., Rammstedt et al., 2018 ; see also the meta-analysis by Anglim et al., 2022 ). However, these studies demonstrating differential facet-level associations between cognitive ability and personality suffer from two important limitations. First, to the best of our knowledge, they were all based on self-selected samples and/or restricted subgroups of the adult population, such as students or job applicants. Such samples do not reflect the general adult population, raising concerns about selection bias and limiting the generalizability of findings to the broader adult population (Henrich et al., 2010 ). There is therefore a pressing need for studies utilizing more representative samples that reflect the heterogeneity of the adult population at large. Second, prior investigations into the personality–cognitive ability link have been conducted predominantly within Anglo-American or Western European contexts (DeYoung, 2020 ; Schmitt et al., 2007 ). As both personality and cognitive ability can be influenced by cultural practices, norms, and educational systems (Nisbett et al., 2001 ; McCrae & Terracciano, 2005 ), one might also expect the associations between personality facets and cognitive ability to vary cross-culturally. The cultural homogeneity of existing studies, however, overlooks such potential variations. The present study addresses these two limitations and aims to provide a more nuanced and generalizable understanding of the personality–cognitive ability link at the facet level. Based on comprehensive, population-representative datasets from 11 countries, we sought to (a) determine how much shared overall variance in cognitive ability can be explained by personality facets compared with broader personality domains, (b) analyze the strength and direction of bivariate associations between individual facets and cognitive ability, and (c) explore how these relationships differ across cultural contexts. 2. Method 2.1 Sample and design Data were collected as part of the Programme for the International Assessment of Adult Competencies (PIAAC; OECD, 2024 ), an initiative of the Organisation for Economic Co-operation and Development (OECD). The PIAAC survey consists of a detailed background questionnaire (≈ 45 minutes), followed by an in-depth assessment of cognitive abilities (≈ 60 minutes), focusing on key skills in literacy, numeracy, and problem-solving among the adult population. The PIAAC dataset offers a unique empirical foundation for studying individual and societal differences in adulthood. It includes data from over 20 countries, combining detailed information on sociodemographic background, education, and employment with validated assessments of cognitive skills (e.g., literacy, numeracy, problem-solving) and personality (Big Five). The dataset is particularly valuable due to its large, nationally representative samples, the international comparability of cognitive outcomes, and the broad age range (16–65 years), which goes well beyond typical student or youth samples. To ensure valid cross-national comparisons, PIAAC follows rigorous methodological standards in sampling, instrument translation, and cross-cultural adaptation. These standards include the use of nationally representative probability samples (typically 4,000–5,000 adults aged 16–65 per country), standardized translation procedures (including double translation and back-translation), and extensive quality control measures (e.g., interviewer training, response rate monitoring, and detailed documentation of fieldwork procedures). For further details on the study design and quality assurance processes, see OECD ( 2025 ). For the present study, we used data from the second PIAAC cycle, conducted in 2022–2023. A total of 31 countries participated, of which 28 included Big Five personality traits in the background questionnaire, and 12 employed an extended version allowing facet-level analysis. Data from 11 countries are available as public-use files (PUFs) via the OECD website ( https://www.oecd.org/en/data/datasets/piaac-2nd-cycle-database.html ). Probability samples of adults aged 16–65 years were drawn in each participating nation. The resulting sample sizes ranged between 3,160 respondents in Poland and 11,697 respondents in Canada. Our final sample includes N = 61,729 individuals from 11 countries. Detailed sample characteristics by country and pooled across countries are provided in Table 1 . Table 1 Sociodemographic composition of the samples by country and pooled across countries. Country n Age M Female [in %] Higher education [in %] Employment status [in %] Migration background [in %] Missings 1 [in %] Pooled 61,729 40.7 49.9 30.2 74.8 30.1 3.0 Canada 11,697 40.7 49.9 30.2 74.8 30.1 3.0 Chile 4,726 39.4 50.0 20.1 72.0 8.6 2.9 Croatia 4,316 42.3 50.2 21.7 65.3 8.7 4.7 Czechia 5,057 41.7 49.1 22.6 71.0 3.1 2.9 Estonia 6,665 41.3 50.0 38.5 78.8 10.3 4.3 Germany 4,793 42.2 49.4 32.6 74.3 18.6 6.3 Italy 4,847 42.9 50.1 16.0 60.2 13.3 5.1 New Zealand 5,359 40.0 50.3 33.0 79.1 35.1 2.3 Poland 3,160 42.2 51.5 22.5 69.1 22.0 4.6 Slovak Republic 5,238 41.6 49.3 25.2 74.0 1.6 4.2 Spain 5,871 42.2 50.1 25.4 65.6 18.3 4.0 Note . Sociodemographic values are derived from the following PIAAC 2023 variables: numerical age (midpoint of 10-year interval, AGEG10LFS); higher education (EDCAT6_TC1 > = 5); gender (GENDER_R); employment status (C2_D05, 1 = employed, all other values = unemployed/out of the labor force); nativity/migration (born in country/not born in country; A2_Q03a_T). 1 Percentage of respondents with missing values on at least one Big Five facet. 2.2 Instruments 2.2.1 Personality traits The Big Five were assessed using the 30-item short form of the Big Five Inventory–2 (BFI-2S; Soto & John, 2017 ), which allows the investigation of the three most prototypical facets per Big Five domain, each measured with two items. Items were answered on a 5-point scale from 1 ( strongly disagree ) to 5 ( strongly agree ). To improve readability, all items were adapted slightly (e.g., “I tend to be quiet” instead of “[I am someone who…] …tends to be quiet”). All PIAAC instruments, including the BFI-2-XS, underwent a rigorous, multi-step translation process (see Rammstedt et al., 2015). Measurement invariance analyses across all 28 countries, conducted by the international consortium mandated by the OECD, provided evidence that the Big Five domains achieved at least partial metric invariance using traditional methods (OECD, in press). In addition, the alignment method further showed that most items had highly similar factor loadings across countries (GESIS, 2024), supporting the cross-national comparability of associations between the Big Five and other constructs. In the OECD public-use files, item-level data for the personality variables are not available. Instead, only country z -standardized Big Five mean-scores for the domain- and facet-level are provided that were calculated by the internation consortium. For deriving these scores, they averaged the corresponding domain or facet items per respondent and then z -standardized the resulting mean-scores using the mean and standard deviation of the respective scores in each country. Since item-level information is not available in the public-use files, we were not able to conduct independent tests of measurement invariance for the 11 countries in the present research. As a result, mean-level comparisons across countries should be interpreted with caution. Nevertheless, the data remain well-suited for analyzing personality and its correlates both within and across national contexts, given the measurement invariance tests in the full PIAAC datasets by the OECD. 2.2.2 Cognitive ability Following prior research (Rammstedt et al., 2025 ), we used the competencies assessed in PIAAC 2023—literacy, numeracy, and adaptive problem-solving—as indicators for cognitive ability, as they have strong empirical associations with intelligence (Engelhardt et al., 2021 ). Each competency was assessed using a multistage adaptive design comprising 80 items each for literacy and numeracy and 65 items for adaptive problem-solving (for further details, see OECD, 2025 ; for sample items, see OECD, 2024 , pp. 38–40). 2.3 Data analysis To examine the relationship between personality and cognitive ability, we conducted regression analyses with cognitive ability as the dependent variable and personality traits as predictors. In PIAAC Cycle 2, three different cognitive domains were assessed (see above). Cognitive skill scores were z-standardized within each country. This ensures that the scale of cognitive skills matches the scale of the Big Five variables, which are also z-standardized within each country (see above). All reported regression coefficients are standardized. We conducted two sets of regression analyses: In the first analyses, we predicted cognitive skills simultaneously by all Big Five domains (domain-level analyses: 5 domains in total): In the second analyses, instead of the domains, we predicted cognitive skills simultaneously by the three facets underlying each Big Five domain (facet-level analyses: 15 facets in total): For both domain-level and facet-level analyses, we conducted the regression analyses separately for each of the 11 countries. To account for the PIAAC-specific data structure, we followed established procedures used plausible values (PVs) and replicate weights provided in the Cycle 2 public-use files (OECD, 2025 ). The basic idea of PVs and replicate weights is that any analysis needs to be repeated multiple times to calculate the unbiased variance of the model estimates. For each cognitive ability, there are 10 PVs to take measurement uncertainty into account. Additionally, 80 replicate weights (SPFWT1-80) are provided to account for uncertainty due to the complex sampling design. As a result, both analyses – the domain-level and the facet-level – were conducted 10 × 80 = 800 per country. The distribution of these 800 model estimates was then used to calculate standard errors and other measures of uncertainty. Final point estimates were obtained by applying the final weight (SPFWT0) to each plausible value, resulting in 10 models per country. These point estimates and their standard errors form the basis for the country results of each model. To obtained pooled effect sizes across countries, we averaged within country results giving each country unit weight. Analyses were conducted in R. The survey R package (Lumley, 2021) was used to account for plausible values and replicate weights. Reproducible R code and packages used are available at https://osf.io/mwkb4/?view_only=b297b74723284bd4a524390e19e070c3 . 3. Results To investigate whether analyzing facets gives a clearer picture of the personality–cognitive ability link across and within the eleven countries, we tested whether (a) facets explained more variance in cognitive ability than domains, and (b) facet-level results helped clarify the domain-level patterns typically found in prior research. These questions were investigated both with an aggregated view and with a focus on cross-national variations. We calculated the results for each cognitive skill (literacy, numeracy, adaptive problem solving) separately. As, however, results are highly similar for the three domains (not surprisingly as the three domains are correlated on average to .88), we focus in the main text on the findings for literacy. The results for numeracy and problem-solving are reported in the Online Supplement. 3.1 Variance explained by personality facets vs. domains Results pooled across the 11 countries showed that personality facets explained 10% of the variance in cognitive ability (for both domains literacy and numeracy; 8% for adaptive problem solving), compared with 4 to 6% explained by the personality domains (Table 2 and S1.1 and S1.2). Thus, on a general level, the explained variance approximately doubled when moving from domains to facets. Table 2 Regression results of Big Five Domains and Facets on the Cognitive Skill Domain Literacy Domains only Facets only PE SD 95% CI PE SD 95% CI Extraversion -0.02 0.06 [-0.03, 0.00] Assertiveness 0.08 0.03 [0.06, 0.09] Energy Level -0.03 0.08 [-0.05, -0.02] Sociability -0.05 0.05 [-0.06, -0.04] Agreeableness 0.01 0.04 [-0.01, 0.02] Compassion 0.02 0.06 [0.00, 0.03] Respectfulness 0.02 0.05 [0.00, 0.03] Trust -0.03 0.04 [-0.05, -0.02] Conscientiousness -0.04 0.09 [-0.06, -0.02] Organization -0.07 0.05 [-0.09, -0.05] Productiveness -0.04 0.08 [-0.06, -0.03] Responsibility 0.07 0.07 [0.06, 0.09] Emotional stability 0.09 0.04 [0.07, 0.10] Anxiety 0.01 0.06 [0.00, 0.03] Depression 0.03 0.04 [0.01, 0.05] Emotional Volatility 0.08 0.06 [0.06, 0.09] Open-Mindedness 0.15 0.07 [0.14, 0.17] Aesthetic Sensitivity 0.12 0.08 [0.10, 0.13] Intellectual Curiosity 0.07 0.08 [0.05, 0.08] Creative Imagination -0.01 0.06 [-0.03, 0.00] Adjusted R² 0.05 0.02 [0.05, 0.06] 0.10 0.03 [0.09, 0.10] Note. Domain-level scores and facet-level scores were analyzed separately. PE = point estimate from regression analyses; SD = standard deviation of the point estimate; 95% CI = 95% confidence interval. Across most countries, the variance in cognitive ability (literacy) explained by the facets ranged from 8–12%, indicating relatively consistent explanatory power (see Supplement S3). Only in the Slovak Republic and Spain were explained variances comparatively lower, but this trend was also present for the Big Five domains (5% for facets vs. 2% for domains). [1] Importantly, however, in all countries, facet-level models explained a greater proportion of variance than did domain-level models, with improvements for literacy ranging from 1.1 times more explained variance in Croatia (9% vs. 8%) to 3.7 times more in Chile (11% vs. 3%). For numeracy and adaptive problem solving, results were largely comparable. 3.2 Facet-level personality–cognitive ability associations At domain level, results largely replicated previous findings (see Fig. 1 for literacy and Figures S1 .1 for numeracy and adaptive problem solving; Figure S1 .2 shows the results for all three cognitive domains in parallel): Openness (β = .15, .14, and 13 for literacy, numeracy, and adaptive problem solving) and Emotional Stability (β = .09, .14. and .09, respectively) showed the strongest positive associations with cognitive ability, Conscientiousness showed a negative association (β = –.08, − .04, and − .05, respectively), and the associations for Extraversion and Agreeableness were negligible. Next, we examined whether facet-level associations allow for a more nuanced understanding of personality–ability relationship than do domain-level associations. The differential associations [1] that emerged at facet level clearly support this notion: The three facets within each domain varied notably in their associations with cognitive ability—both in terms of magnitude and direction—reaching absolute differences in beta values of up to .17. This pattern of differential effects across facets was most pronounced for Conscientiousness, where—for all three cognitive domains—Organization (β = –.07, − .07, and − .08 for literacy, numeracy, and adaptive problem solving) and Productiveness (β = –.04 for all three domains) showed negative associations, whereas Responsibility (β = .07, .05, and .06, respectively) showed a positive one. Similarly, for Extraversion, where a negligible association emerged only at domain level (β = –.02, .0, − .01, respectively), the three facets diverged markedly in their associations with cognitive ability, with two showing substantial but inverse correlations. Whereas Sociability (β = –.05, − .07, and − .06, respectively)—and to a lesser extent Energy Level (β = –.03, − .02, and − .02, respectively)—were negatively associated with cognitive ability, Assertiveness showed one of the strongest positive associations with cognitive ability overall (β = .08, .1, and .08, respectively). For Openness, the strong domain-level effect was for all three cognitive domains driven primarily by the facets Aesthetic Sensitivity (β = .12, .09, .09, respectively) and Intellectual Curiosity (β = .07 for all three cognitive domains), whereas Creative Imagination showed no significant association with cognitive ability (β = –.01, .0, and − .01, respectively). Looking at cross-national variations, the pattern of associations between personality facets and cognitive ability varied substantially across the 11 countries (see Fig. 2 and bars in Fig. 1). Overall, the highest variations across countries were found for the Extraversion facet Energy Level, the Conscientiousness facets Productiveness and Responsibility, and the Openness facets Aesthetic Sensitivity and Intellectual Curiosity ( SD = .07–.08) [3] . For the Openness facets, the comparatively large variations stem primarily from deviating effects in Sweden, where the relationships differed markedly from those of other countries. By contrast, cross-national consistency was highest for the Extraversion facet Assertiveness, which showed a stable positive association with cognitive ability across all countries ( SD = .03). 4. Discussion This study is the first to use large-scale, cross-national data representative of the adult population to examine the associations between personality facets and cognitive ability. In doing so, it extends prior research (e.g., Anglim et al., 2022 ; Rammstedt et al., 2018 ) and provides novel insights into the personality–cognitive ability link from both a facet-level and a cross-cultural perspective. Consistent with existing research (Danner et al., 2021 ; Rammstedt et al., 2018 ), our results highlight the added value of the more fine-grained, facet-level approach over the domain-level approach in two respects. First, facet-level models explained approximately twice as much variance in cognitive ability as did domain-level models, confirming that the finer-grained approach improves predictive power. Second, and more importantly, the facet-level perspective revealed differential associations within all but one of the Big Five domains, offering new insights into the personality–cognitive ability link: Except for Emotional Stability, associations between cognitive ability and personality facets from the same domain varied considerably in magnitude and direction. For Openness, the trait most strongly linked to cognitive ability, it became obvious that the facets Curiosity and Aesthetic Sensitivity are particularly relevant, whereas the facet Creative Imagination is not. For Conscientiousness, positive associations were driven by its facets Organization and Productiveness, but not Responsibility. Thus, the facet-level approach helps explain why negative domain-level associations with Conscientiousness are only inconsistently observed: when Organization and Productiveness are underrepresented in a scale, the domain-level effect may be underestimated or missed entirely. For Extraversion, we found a suppression effect, with opposing facet-level associations cancelling each other out and leading to an underestimation of the domain's overall effect. In addition, our study is the first to uncover systematic cross-cultural differences in the strength and direction of personality–cognitive ability associations. While some facets (e.g., the Extraversion facet Assertiveness) showed relatively consistent effects across countries, others (particularly within Openness) varied considerably. These findings underscore the importance to investigate these cultural differences more deeply and across a wider and even more heterogeneous set of countries. Overall, our findings highlight the need to move beyond broad personality domains when studying the personality–cognitive ability link. Facet-level analysis not only improves predictive accuracy but also provides a more nuanced understanding of the underlying associations—both within and across cultural contexts. Future studies should build on these insights to refine personality theory and its applications in cognitive and cross-cultural research. Declarations Author Contribution •BR developed the research questions and wrote the initial version of the paper.•VV and MR performed the statistical analyses.•VV and CL were major contributors in writing the manuscript.•CL also reviewed the analyses.•All authors reviewed and revised the manuscript critically for important intellectual content.•All authors read and approved the final manuscript. Data Availability The datasets analyzed during the current study are available as public-use files (PUFs) via the OECD website (https://www.oecd.org/en/data/datasets/piaac-2nd-cycle-database.html). Reproducible R code and packages used for the analysis of the data are available at https://osf.io/mwkb4/?view_only=b297b74723284bd4a524390e19e070c3. References Anglim, J., Dunlop, P. D., Wee, S., Horwood, S., Wood, J. K., & Marty, A. (2022). Personality and intelligence: A meta-analysis. Psychological Bulletin , 148 (5–6), 301–336. https://doi.org/10.1037/bul0000373 Danner, D., Lechner, C. M., Soto, C. J., & John, O. P. (2021). Modelling the incremental value of personality facets: The Domains-Facets-Acquiescence-Bifactor (DFAB) model. European Journal of Personality, 35 (1), 67–87. https://doi/10.1002/per.2268 DeYoung, C. G. (2020). Intelligence and Personality. In I. J. Deary & S. M. McGue (Eds.), The Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 507–529). Cambridge University Press. https://doi.org/10.1017/9781108647687.025 Engelhardt, L., Goldhammer, F., Lüdtke, O., Köller, O., Baumert, J., & Carstensen, C. H. (2021). Separating PIAAC competencies from general cognitive skills: A dimensionality and explanatory analysis. Studies in Educational Evaluation , 71 , Article 101069. https://doi.org/10.1016/j.stueduc.2021.101069 Furnham, A., Dissou, G., Sloan, P., & Chamorro-Premuzic, T. (2007). Personality and intelligence in business people: A study of two personality and two intelligence measures. Journal of Business and Psychology, 22 (1), 99–109. https://doi.org/10.1007/s10869-007-9051-z GESIS – Leibniz Institute for the Social Sciences. (2024). PIAAC Cycle 2 MS international report social and emotional skills (Section K ) [Unpublished report]. Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2-3), 61–83. https://doi.org/10.1017/S0140525X0999152X McCrae, R. R., & Terracciano, A. (2005). Personality profiles of cultures: Aggregate personality traits. Journal of Personality and Social Psychology, 89, 407–425. Nisbett, R. E., Peng, K., Choi, I., & Norenzayan, A. (2001). Culture and systems of thought: Holistic versus analytic cognition. Psychological Review, 108, 291–310. https://doi.org/10.1037//0033-295X.108.2.291 OECD. (2024). Do adults have the skills they need to thrive in a changing world? Survey of Adult Skills 2023 . OECD Publishing. https://www.oecd.org/en/publications/do-adults-have-the-skills-they-need-to-thrive-in-a-changing-world_b263dc5d-en.html OECD. (2025). Survey of Adult Skills 2023 Technical Report, OECD Skills Studies. OECD Publishing. https://doi.org/10.1787/80d9f692-en. Rammstedt, B., Lechner, C., & Danner, D. (2018). Relationships between personality and cognitive ability: A facet-level analysis. Journal of Intelligence, 6 (2), Article 28. http://www.mdpi.com/2079-3200/6/2/28 Rammstedt, B., Roemer, L., Behr, D., Bluemke, M., Lechner, C. M., Dept, S., Wäyrynen, L., Soto, C. J., & John, O. P. (2025). Going global: 39 language versions of the BFI-2-XS. Measurement Instruments for the Social Sciences, 7, Article e14067. https://doi.org/10.5964/miss.14067 Rammstedt, B., Roth, M., Roemer, L., & Lechner. C. M. (2025). Revisiting the links between personality and cognitive ability: A generalization study in 26 countries. SSRN. http://dx.doi.org/10.2139/ssrn.5239330 Schmitt, D. P., Allik, J., McCrae, R. R., & Benet-Martínez, V. (2007). The geographic distribution of Big Five personality traits: Patterns and profiles of human self-description across 56 nations. Journal of Cross-Cultural Psychology, 38(2), 173–212. https://doi.org/10.1177/0022022106297299 Soto, C. J., & John, O. P. (2017). Short and extra-short forms of the Big Five Inventory–2: The BFI-2-S and BFI-2-XS. Journal of Research in Personality , 68 , 69–81. https://doi.org/10.1016/j.jrp.2017.02.004 Stanek, K. C., & Ones, D. S. (2023). Meta-analytic relations between personality and cognitive ability. Proceedings of the National Academy of Sciences , 120 (23), Article e2212794120. https://doi.org/10.1073/pnas.2212794120 Footnotes For numeracy and adaptive problem solving explained variances were more heterogenous among countries with proportions for the different countries ranging between 1% and 12%. The associations reported here refer to standardized regression coefficients. Highly similar results emerged for the corresponding bivariate correlations. For the cognitive skills numeracy and adaptive problem solving similar patterns were found. Again, the highest cross-country variations were observed for the Extraversion facet Energy Level, the Conscientiousness facet Productiveness, and all three Openness facets (Aesthetic Sensitivity, Intellectual Curiosity, and Creative Imagination; SD = .07–.08). Additional Declarations No competing interests reported. Supplementary Files MsfacetsOnlineSupplementLSAErevisedfinal.docx Cite Share Download PDF Status: Published Journal Publication published 19 Jan, 2026 Read the published version in Large-scale Assessments in Education → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7431184","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":524883573,"identity":"5a8a7ebf-b6de-46a1-adbc-2e9c5aaa6abf","order_by":0,"name":"Beatrice 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Science","correspondingAuthor":false,"prefix":"","firstName":"Vera","middleName":"","lastName":"Vogel","suffix":""},{"id":524883575,"identity":"3055af29-5b9b-46c6-a770-207c91fc2b7f","order_by":2,"name":"Matthias Roth","email":"","orcid":"","institution":"GESIS - Leibniz Institute for the Social Science","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Roth","suffix":""},{"id":524883576,"identity":"35bd682c-ef4b-498c-84a7-753a2a207145","order_by":3,"name":"Clemens Lechner","email":"","orcid":"","institution":"GESIS - Leibniz Institute for the Social Science","correspondingAuthor":false,"prefix":"","firstName":"Clemens","middleName":"","lastName":"Lechner","suffix":""}],"badges":[],"createdAt":"2025-08-22 06:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7431184/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7431184/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40536-026-00281-2","type":"published","date":"2026-01-19T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92974956,"identity":"c5db0b70-d1a5-40b7-8861-96e74abc92e0","added_by":"auto","created_at":"2025-10-07 17:54:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":409160,"visible":true,"origin":"","legend":"","description":"","filename":"MsCognskillsB5facetsLSAErev2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7431184/v1/d2ee498dd360d73b6f7964dd.docx"},{"id":92974954,"identity":"beec9578-67bf-4ac3-872b-bc0be6e154f8","added_by":"auto","created_at":"2025-10-07 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17:54:54","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":38769,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7431184/v1/ab8f3ac900ab6bd63702e9be.png"},{"id":92974965,"identity":"6019693a-265e-4e20-8702-ab11e4368a47","added_by":"auto","created_at":"2025-10-07 17:54:54","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77854,"visible":true,"origin":"","legend":"","description":"","filename":"191f0f8219fc4f7b8c1b1cba07e28fbb1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7431184/v1/bfbb1f37b50c41d807b9a81a.xml"},{"id":92974966,"identity":"8874ecb3-1c31-4e0d-9f49-b2d4e6c9a593","added_by":"auto","created_at":"2025-10-07 17:54:54","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86410,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7431184/v1/c7c6b52fc83581ad36d54911.html"},{"id":92974953,"identity":"2681ced6-dda7-4387-9ca4-5128458b1294","added_by":"auto","created_at":"2025-10-07 17:54:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138920,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAssociations between the Big Five and the Cognitive Skill Domain Literacy Pooled Across Countries\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e Standardized regression coefficients of the Big Five domains (dark colors) and facets (corresponding light colors) on cognitive ability pooled across 11 PIAAC 2023 countries. Bars represent standard deviations across the 11 countries.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7431184/v1/0d662ce9922509e0b6cc3afe.png"},{"id":92974957,"identity":"bd70fa64-084d-4a1e-89ef-2b7a12e7ef52","added_by":"auto","created_at":"2025-10-07 17:54:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":185958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAssociations between the Big Five and the Cognitive Skill Domain Literacy per Country\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote. \u003c/strong\u003e\u003c/em\u003eStandardized regression coefficients for the Big Five domains and facets for 11 PIAAC 2023 countries separately. Bars represent standard errors. CAN = Canada, CHL = Chile, CZE = Chechia, DEU = Germany, ESP = Spain, EST = Estonia, HRV = Croatia, ITA = Italy, NZL = New Zealand, PRT = Portugal, SVK = Slovak Republic.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7431184/v1/1aa170dd96192f98a614fcca.png"},{"id":101151831,"identity":"2135d212-b559-416e-a558-af90e2a1da84","added_by":"auto","created_at":"2026-01-26 16:06:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1064704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7431184/v1/26eed9c4-ce1e-4e40-86b4-6e782ed0cd62.pdf"},{"id":92975424,"identity":"a25cf568-ae75-4ea9-8be6-97e66143065e","added_by":"auto","created_at":"2025-10-07 18:02:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":667186,"visible":true,"origin":"","legend":"","description":"","filename":"MsfacetsOnlineSupplementLSAErevisedfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7431184/v1/c5e6ae7f7983da83199c4a48.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unpacking the personality–cognitive ability link: A cross-national facet-level analysis of the Big Five","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent decades, a substantial body of research has demonstrated that cognitive ability and personality are systematically related (e.g., Anglim et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stanek \u0026amp; Ones, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Most of this work has focused largely on the Big Five personality traits, the most widely used and validated personality model. Numerous studies, including meta-analyses and mega-analyses, have consistently shown two main findings: First, the proportion of variance shared by personality and cognitive ability is modest at 5\u0026ndash;10% (Furnham et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Second, among the Big Five traits, Openness to Experience\u0026mdash;and, to a lesser extent, Emotional Stability\u0026mdash;show the strongest positive associations with cognitive ability. By contrast, Conscientiousness sometimes shows negative associations with cognitive ability, whereas the associations shown by Agreeableness and Extraversion are typically negligible (Anglim et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings were further supported by a recent analysis of large-scale, population-representative datasets from 26 countries (Rammstedt et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e): Pooled across countries, the share of the variance in cognitive ability explained by the Big Five was around 4%, placing it at the lower bound of previous estimates. Further, and most importantly, as the country values ranged between 1% and 10%, the study showed for the first time that associations between personality and cognitive ability vary markedly across countries. In most of the 26 countries, Openness and Emotional Stability were most predictive of cognitive ability, whereas cross-national variations in the associations between Conscientiousness and cognitive ability were considerable both in size and direction.\u003c/p\u003e\u003cp\u003eIn addition, recent studies on associations between personality and external criteria such as academic achievement, career success, or life satisfaction (e.g., Danner et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have demonstrated that the domain level of personality is often too broad to capture nuanced relationships. This may also apply to the personality\u0026ndash;cognitive ability link, where finer-grained analyses at the facet level could reveal differential associations that are masked at the domain level. Previous studies have shown that the shared variance between cognitive ability and personality tends to be greater at facet level than at domain level. Further, and more crucially, the associations between cognitive ability and personality facets within the same domain often vary markedly. For example, different facets within the same domain can show opposing relationships with cognitive ability, which can suppress or neutralize associations at the domain level (e.g., Rammstedt et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; see also the meta-analysis by Anglim et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, these studies demonstrating differential facet-level associations between cognitive ability and personality suffer from two important limitations. First, to the best of our knowledge, they were all based on self-selected samples and/or restricted subgroups of the adult population, such as students or job applicants. Such samples do not reflect the general adult population, raising concerns about selection bias and limiting the generalizability of findings to the broader adult population (Henrich et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). There is therefore a pressing need for studies utilizing more representative samples that reflect the heterogeneity of the adult population at large. Second, prior investigations into the personality\u0026ndash;cognitive ability link have been conducted predominantly within Anglo-American or Western European contexts (DeYoung, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schmitt et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). As both personality and cognitive ability can be influenced by cultural practices, norms, and educational systems (Nisbett et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; McCrae \u0026amp; Terracciano, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), one might also expect the associations between personality facets and cognitive ability to vary cross-culturally. The cultural homogeneity of existing studies, however, overlooks such potential variations.\u003c/p\u003e\u003cp\u003eThe present study addresses these two limitations and aims to provide a more nuanced and generalizable understanding of the personality\u0026ndash;cognitive ability link at the facet level. Based on comprehensive, population-representative datasets from 11 countries, we sought to (a) determine how much shared overall variance in cognitive ability can be explained by personality facets compared with broader personality domains, (b) analyze the strength and direction of bivariate associations between individual facets and cognitive ability, and (c) explore how these relationships differ across cultural contexts.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cem\u003e2.1 Sample and design\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eData were collected as part of the Programme for the International Assessment of Adult Competencies (PIAAC; OECD, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), an initiative of the Organisation for Economic Co-operation and Development (OECD). The PIAAC survey consists of a detailed background questionnaire (\u0026asymp;\u0026thinsp;45 minutes), followed by an in-depth assessment of cognitive abilities (\u0026asymp;\u0026thinsp;60 minutes), focusing on key skills in literacy, numeracy, and problem-solving among the adult population.\u003c/p\u003e\n \u003cp\u003eThe PIAAC dataset offers a unique empirical foundation for studying individual and societal differences in adulthood. It includes data from over 20 countries, combining detailed information on sociodemographic background, education, and employment with validated assessments of cognitive skills (e.g., literacy, numeracy, problem-solving) and personality (Big Five). The dataset is particularly valuable due to its large, nationally representative samples, the international comparability of cognitive outcomes, and the broad age range (16\u0026ndash;65 years), which goes well beyond typical student or youth samples.\u003c/p\u003e\n \u003cp\u003eTo ensure valid cross-national comparisons, PIAAC follows rigorous methodological standards in sampling, instrument translation, and cross-cultural adaptation. These standards include the use of nationally representative probability samples (typically 4,000\u0026ndash;5,000 adults aged 16\u0026ndash;65 per country), standardized translation procedures (including double translation and back-translation), and extensive quality control measures (e.g., interviewer training, response rate monitoring, and detailed documentation of fieldwork procedures). For further details on the study design and quality assurance processes, see OECD (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor the present study, we used data from the second PIAAC cycle, conducted in 2022\u0026ndash;2023. A total of 31 countries participated, of which 28 included Big Five personality traits in the background questionnaire, and 12 employed an extended version allowing facet-level analysis. Data from 11 countries are available as public-use files (PUFs) via the OECD website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.oecd.org/en/data/datasets/piaac-2nd-cycle-database.html\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eProbability samples of adults aged 16\u0026ndash;65 years were drawn in each participating nation. The resulting sample sizes ranged between 3,160 respondents in Poland and 11,697 respondents in Canada. Our final sample includes \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61,729 individuals from 11 countries. Detailed sample characteristics by country and pooled across countries are provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic composition of the samples by country and pooled across countries.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e[in %]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigher education\u003c/p\u003e\n \u003cp\u003e[in %]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmployment status\u003c/p\u003e\n \u003cp\u003e[in %]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMigration background\u003c/p\u003e\n \u003cp\u003e[in %]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMissings\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e[in %]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePooled\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e61,729\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e49.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e74.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e30.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCroatia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCzechia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Zealand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlovak Republic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. Sociodemographic values are derived from the following PIAAC 2023 variables: numerical age (midpoint of 10-year interval, AGEG10LFS); higher education (EDCAT6_TC1\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;5); gender (GENDER_R); employment status (C2_D05, 1\u0026thinsp;=\u0026thinsp;employed, all other values\u0026thinsp;=\u0026thinsp;unemployed/out of the labor force); nativity/migration (born in country/not born in country; A2_Q03a_T).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Percentage of respondents with missing values on at least one Big Five facet.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Instruments\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Personality traits\u003c/h2\u003e\n \u003cp\u003eThe Big Five were assessed using the 30-item short form of the Big Five Inventory\u0026ndash;2 (BFI-2S; Soto \u0026amp; John, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), which allows the investigation of the three most prototypical facets per Big Five domain, each measured with two items. Items were answered on a 5-point scale from 1 (\u003cem\u003estrongly disagree\u003c/em\u003e) to 5 (\u003cem\u003estrongly agree\u003c/em\u003e). To improve readability, all items were adapted slightly (e.g., \u0026ldquo;I tend to be quiet\u0026rdquo; instead of \u0026ldquo;[I am someone who\u0026hellip;] \u0026hellip;tends to be quiet\u0026rdquo;). All PIAAC instruments, including the BFI-2-XS, underwent a rigorous, multi-step translation process (see Rammstedt et al., 2015).\u003c/p\u003e\n \u003cp\u003eMeasurement invariance analyses across all 28 countries, conducted by the international consortium mandated by the OECD, provided evidence that the Big Five domains achieved at least partial metric invariance using traditional methods (OECD, in press). In addition, the alignment method further showed that most items had highly similar factor loadings across countries (GESIS, 2024), supporting the cross-national comparability of associations between the Big Five and other constructs.\u003c/p\u003e\n \u003cp\u003eIn the OECD public-use files, item-level data for the personality variables are not available. Instead, only country \u003cem\u003ez\u003c/em\u003e-standardized Big Five mean-scores for the domain- and facet-level are provided that were calculated by the internation consortium. For deriving these scores, they averaged the corresponding domain or facet items per respondent and then \u003cem\u003ez\u003c/em\u003e-standardized the resulting mean-scores using the mean and standard deviation of the respective scores in each country. Since item-level information is not available in the public-use files, we were not able to conduct independent tests of measurement invariance for the 11 countries in the present research. As a result, mean-level comparisons across countries should be interpreted with caution. Nevertheless, the data remain well-suited for analyzing personality and its correlates both within and across national contexts, given the measurement invariance tests in the full PIAAC datasets by the OECD.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Cognitive ability\u003c/h2\u003e\n \u003cp\u003eFollowing prior research (Rammstedt et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), we used the competencies assessed in PIAAC 2023\u0026mdash;literacy, numeracy, and adaptive problem-solving\u0026mdash;as indicators for cognitive ability, as they have strong empirical associations with intelligence (Engelhardt et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Each competency was assessed using a multistage adaptive design comprising 80 items each for literacy and numeracy and 65 items for adaptive problem-solving (for further details, see OECD, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; for sample items, see OECD, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e, pp. 38\u0026ndash;40).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Data analysis\u003c/h2\u003e\n \u003cp\u003eTo examine the relationship between personality and cognitive ability, we conducted regression analyses with cognitive ability as the dependent variable and personality traits as predictors. In PIAAC Cycle 2, three different cognitive domains were assessed (see above). Cognitive skill scores were z-standardized within each country. This ensures that the scale of cognitive skills matches the scale of the Big Five variables, which are also z-standardized within each country (see above). All reported regression coefficients are standardized. We conducted two sets of regression analyses: In the first analyses, we predicted cognitive skills simultaneously by all Big Five domains (domain-level analyses: 5 domains in total):\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg 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\" width=\"746\" height=\"93\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the second analyses, instead of the domains, we predicted cognitive skills simultaneously by the three facets underlying each Big Five domain (facet-level analyses: 15 facets in total):\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\u003cimg 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\" width=\"746\" height=\"222\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eFor both domain-level and facet-level analyses, we conducted the regression analyses separately for each of the 11 countries. To account for the PIAAC-specific data structure, we followed established procedures used plausible values (PVs) and replicate weights provided in the Cycle 2 public-use files (OECD, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The basic idea of PVs and replicate weights is that any analysis needs to be repeated multiple times to calculate the unbiased variance of the model estimates. For each cognitive ability, there are 10 PVs to take measurement uncertainty into account. Additionally, 80 replicate weights (SPFWT1-80) are provided to account for uncertainty due to the complex sampling design. As a result, both analyses \u0026ndash; the domain-level and the facet-level \u0026ndash; were conducted 10 \u0026times; 80\u0026thinsp;=\u0026thinsp;800 per country. The distribution of these 800 model estimates was then used to calculate standard errors and other measures of uncertainty. Final point estimates were obtained by applying the final weight (SPFWT0) to each plausible value, resulting in 10 models per country. These point estimates and their standard errors form the basis for the country results of each model.\u003c/p\u003e\n \u003cp\u003eTo obtained pooled effect sizes across countries, we averaged within country results giving each country unit weight. Analyses were conducted in R. The \u003cem\u003esurvey\u003c/em\u003e R package (Lumley, 2021) was used to account for plausible values and replicate weights. Reproducible R code and packages used are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/mwkb4/?view_only=b297b74723284bd4a524390e19e070c3\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eTo investigate whether analyzing facets gives a clearer picture of the personality\u0026ndash;cognitive ability link across and within the eleven countries, we tested whether (a) facets explained more variance in cognitive ability than domains, and (b) facet-level results helped clarify the domain-level patterns typically found in prior research. These questions were investigated both with an aggregated view and with a focus on cross-national variations.\u003c/p\u003e\n\u003cp\u003eWe calculated the results for each cognitive skill (literacy, numeracy, adaptive problem solving) separately. As, however, results are highly similar for the three domains (not surprisingly as the three domains are correlated on average to .88), we focus in the main text on the findings for literacy. The results for numeracy and problem-solving are reported in the Online Supplement.\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Variance explained by personality facets vs. domains\u003c/h2\u003e\n \u003cp\u003eResults pooled across the 11 countries showed that personality facets explained 10% of the variance in cognitive ability (for both domains literacy and numeracy; 8% for adaptive problem solving), compared with 4 to 6% explained by the personality domains (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and S1.1 and S1.2). Thus, on a general level, the explained variance approximately doubled when moving from domains to facets.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eRegression results of Big Five Domains and Facets on the Cognitive Skill Domain Literacy\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDomains only\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFacets only\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtraversion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.03, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssertiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.06, 0.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[-0.05, -0.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSociability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[-0.06, -0.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgreeableness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.01, 0.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompassion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.00, 0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespectfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.00, 0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[-0.05, -0.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eConscientiousness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.06, -0.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrganization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[-0.09, -0.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProductiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[-0.06, -0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResponsibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.06, 0.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmotional stability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.07, 0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.00, 0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.01, 0.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional Volatility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.06, 0.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOpen-Mindedness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.14, 0.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAesthetic Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.10, 0.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntellectual Curiosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.05, 0.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreative Imagination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[-0.03, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.05, 0.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e[0.09, 0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cstrong\u003eNote.\u003c/strong\u003e Domain-level scores and facet-level scores were analyzed separately. \u003cem\u003ePE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;point estimate from regression analyses; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standard deviation of the point estimate; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;95% confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAcross most countries, the variance in cognitive ability (literacy) explained by the facets ranged from 8\u0026ndash;12%, indicating relatively consistent explanatory power (see Supplement S3). Only in the Slovak Republic and Spain were explained variances comparatively lower, but this trend was also present for the Big Five domains (5% for facets vs. 2% for domains).\u003csup\u003e[1]\u003c/sup\u003e Importantly, however, in all countries, facet-level models explained a greater proportion of variance than did domain-level models, with improvements for literacy ranging from 1.1 times more explained variance in Croatia (9% vs. 8%) to 3.7 times more in Chile (11% vs. 3%). For numeracy and adaptive problem solving, results were largely comparable.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Facet-level personality\u0026ndash;cognitive ability associations\u003c/h2\u003e\n \u003cp\u003eAt domain level, results largely replicated previous findings (see Fig. 1 for literacy and Figures \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.1 for numeracy and adaptive problem solving; Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.2 shows the results for all three cognitive domains in parallel): Openness (\u0026beta;\u0026thinsp;=\u0026thinsp;.15, .14, and 13 for literacy, numeracy, and adaptive problem solving) and Emotional Stability (\u0026beta;\u0026thinsp;=\u0026thinsp;.09, .14. and .09, respectively) showed the strongest positive associations with cognitive ability, Conscientiousness showed a negative association (\u0026beta; = \u0026ndash;.08, \u0026minus;\u0026thinsp;.04, and \u0026minus;\u0026thinsp;.05, respectively), and the associations for Extraversion and Agreeableness were negligible.\u003c/p\u003e\n \u003cp\u003eNext, we examined whether facet-level associations allow for a more nuanced understanding of personality\u0026ndash;ability relationship than do domain-level associations. The differential associations\u003csup\u003e[1]\u003c/sup\u003e that emerged at facet level clearly support this notion: The three facets within each domain varied notably in their associations with cognitive ability\u0026mdash;both in terms of magnitude and direction\u0026mdash;reaching absolute differences in beta values of up to .17. This pattern of differential effects across facets was most pronounced for Conscientiousness, where\u0026mdash;for all three cognitive domains\u0026mdash;Organization (\u0026beta; = \u0026ndash;.07, \u0026minus;\u0026thinsp;.07, and \u0026minus;\u0026thinsp;.08 for literacy, numeracy, and adaptive problem solving) and Productiveness (\u0026beta; = \u0026ndash;.04 for all three domains) showed negative associations, whereas Responsibility (\u0026beta;\u0026thinsp;=\u0026thinsp;.07, .05, and .06, respectively) showed a positive one.\u003c/p\u003e\n \u003cp\u003eSimilarly, for Extraversion, where a negligible association emerged only at domain level (\u0026beta; = \u0026ndash;.02, .0, \u0026minus;\u0026thinsp;.01, respectively), the three facets diverged markedly in their associations with cognitive ability, with two showing substantial but inverse correlations. Whereas Sociability (\u0026beta; = \u0026ndash;.05, \u0026minus;\u0026thinsp;.07, and \u0026minus;\u0026thinsp;.06, respectively)\u0026mdash;and to a lesser extent Energy Level (\u0026beta; = \u0026ndash;.03, \u0026minus;\u0026thinsp;.02, and \u0026minus;\u0026thinsp;.02, respectively)\u0026mdash;were negatively associated with cognitive ability, Assertiveness showed one of the strongest positive associations with cognitive ability overall (\u0026beta;\u0026thinsp;=\u0026thinsp;.08, .1, and .08, respectively).\u003c/p\u003e\n \u003cp\u003eFor Openness, the strong domain-level effect was for all three cognitive domains driven primarily by the facets Aesthetic Sensitivity (\u0026beta;\u0026thinsp;=\u0026thinsp;.12, .09, .09, respectively) and Intellectual Curiosity (\u0026beta;\u0026thinsp;=\u0026thinsp;.07 for all three cognitive domains), whereas Creative Imagination showed no significant association with cognitive ability (\u0026beta; = \u0026ndash;.01, .0, and \u0026minus;\u0026thinsp;.01, respectively).\u003c/p\u003e\n \u003cp\u003eLooking at cross-national variations, the pattern of associations between personality facets and cognitive ability varied substantially across the 11 countries (see Fig.\u0026nbsp;2 and bars in Fig.\u0026nbsp;1). Overall, the highest variations across countries were found for the Extraversion facet Energy Level, the Conscientiousness facets Productiveness and Responsibility, and the Openness facets Aesthetic Sensitivity and Intellectual Curiosity (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.07\u0026ndash;.08)\u003csup\u003e[3]\u003c/sup\u003e. For the Openness facets, the comparatively large variations stem primarily from deviating effects in Sweden, where the relationships differed markedly from those of other countries. By contrast, cross-national consistency was highest for the Extraversion facet Assertiveness, which showed a stable positive association with cognitive ability across all countries (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.03).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first to use large-scale, cross-national data representative of the adult population to examine the associations between personality facets and cognitive ability. In doing so, it extends prior research (e.g., Anglim et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rammstedt et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and provides novel insights into the personality\u0026ndash;cognitive ability link from both a facet-level and a cross-cultural perspective.\u003c/p\u003e\u003cp\u003eConsistent with existing research (Danner et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rammstedt et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), our results highlight the added value of the more fine-grained, facet-level approach over the domain-level approach in two respects. First, facet-level models explained approximately twice as much variance in cognitive ability as did domain-level models, confirming that the finer-grained approach improves predictive power. Second, and more importantly, the facet-level perspective revealed differential associations within all but one of the Big Five domains, offering new insights into the personality\u0026ndash;cognitive ability link: Except for Emotional Stability, associations between cognitive ability and personality facets from the same domain varied considerably in magnitude and direction.\u003c/p\u003e\u003cp\u003eFor Openness, the trait most strongly linked to cognitive ability, it became obvious that the facets Curiosity and Aesthetic Sensitivity are particularly relevant, whereas the facet Creative Imagination is not. For Conscientiousness, positive associations were driven by its facets Organization and Productiveness, but not Responsibility. Thus, the facet-level approach helps explain why negative domain-level associations with Conscientiousness are only inconsistently observed: when Organization and Productiveness are underrepresented in a scale, the domain-level effect may be underestimated or missed entirely. For Extraversion, we found a suppression effect, with opposing facet-level associations cancelling each other out and leading to an underestimation of the domain's overall effect.\u003c/p\u003e\u003cp\u003eIn addition, our study is the first to uncover systematic cross-cultural differences in the strength and direction of personality\u0026ndash;cognitive ability associations. While some facets (e.g., the Extraversion facet Assertiveness) showed relatively consistent effects across countries, others (particularly within Openness) varied considerably. These findings underscore the importance to investigate these cultural differences more deeply and across a wider and even more heterogeneous set of countries.\u003c/p\u003e\u003cp\u003eOverall, our findings highlight the need to move beyond broad personality domains when studying the personality\u0026ndash;cognitive ability link. Facet-level analysis not only improves predictive accuracy but also provides a more nuanced understanding of the underlying associations\u0026mdash;both within and across cultural contexts. Future studies should build on these insights to refine personality theory and its applications in cognitive and cross-cultural research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026bull;BR developed the research questions and wrote the initial version of the paper.\u0026bull;VV and MR performed the statistical analyses.\u0026bull;VV and CL were major contributors in writing the manuscript.\u0026bull;CL also reviewed the analyses.\u0026bull;All authors reviewed and revised the manuscript critically for important intellectual content.\u0026bull;All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are available as public-use files (PUFs) via the OECD website (https://www.oecd.org/en/data/datasets/piaac-2nd-cycle-database.html). Reproducible R code and packages used for the analysis of the data are available at https://osf.io/mwkb4/?view_only=b297b74723284bd4a524390e19e070c3.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnglim, J., Dunlop, P. D., Wee, S., Horwood, S., Wood, J. K., \u0026amp; Marty, A. (2022). Personality and intelligence: A meta-analysis. \u003cem\u003ePsychological Bulletin\u003c/em\u003e, \u003cem\u003e148\u003c/em\u003e(5\u0026ndash;6), 301\u0026ndash;336. https://doi.org/10.1037/bul0000373\u003c/li\u003e\n\u003cli\u003eDanner, D., Lechner, C. M., Soto, C. J., \u0026amp; John, O. P. (2021). Modelling the incremental value of personality facets: The Domains-Facets-Acquiescence-Bifactor (DFAB) model. \u003cem\u003eEuropean Journal of Personality, 35\u003c/em\u003e(1), 67\u0026ndash;87. https://doi/10.1002/per.2268\u003c/li\u003e\n\u003cli\u003eDeYoung, C. G. (2020). Intelligence and Personality. In I. J. Deary \u0026amp; S. M. McGue (Eds.), \u003cem\u003eThe Cambridge Handbook of Intelligence and Cognitive Neuroscience (pp. 507\u0026ndash;529). \u003c/em\u003eCambridge University Press. https://doi.org/10.1017/9781108647687.025\u003c/li\u003e\n\u003cli\u003eEngelhardt, L., Goldhammer, F., L\u0026uuml;dtke, O., K\u0026ouml;ller, O., Baumert, J., \u0026amp; Carstensen, C. H. (2021). Separating PIAAC competencies from general cognitive skills: A dimensionality and explanatory analysis. \u003cem\u003eStudies in Educational Evaluation\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e, Article 101069. https://doi.org/10.1016/j.stueduc.2021.101069\u003c/li\u003e\n\u003cli\u003eFurnham, A., Dissou, G., Sloan, P., \u0026amp; Chamorro-Premuzic, T. (2007). Personality and intelligence in business people: A study of two personality and two intelligence measures. \u003cem\u003eJournal of Business and Psychology, 22\u003c/em\u003e(1), 99\u0026ndash;109. https://doi.org/10.1007/s10869-007-9051-z\u003c/li\u003e\n\u003cli\u003eGESIS \u0026ndash; Leibniz Institute for the Social Sciences. (2024). \u003cem\u003ePIAAC Cycle 2 MS international report social and emotional skills (Section K\u003c/em\u003e) [Unpublished report].\u003c/li\u003e\n\u003cli\u003eHenrich, J., Heine, S. J., \u0026amp; Norenzayan, A. (2010). The weirdest people in the world? \u003cem\u003eBehavioral and Brain Sciences, 33(2-3), \u003c/em\u003e61\u0026ndash;83. https://doi.org/10.1017/S0140525X0999152X\u003c/li\u003e\n\u003cli\u003eMcCrae, R. R., \u0026amp; Terracciano, A. (2005). Personality profiles of cultures: Aggregate personality traits. \u003cem\u003eJournal of Personality and Social Psychology, 89,\u003c/em\u003e 407\u0026ndash;425.\u003c/li\u003e\n\u003cli\u003eNisbett, R. E., Peng, K., Choi, I., \u0026amp; Norenzayan, A. (2001). Culture and systems of thought: Holistic versus analytic cognition. \u003cem\u003ePsychological Review, 108,\u003c/em\u003e 291\u0026ndash;310. https://doi.org/10.1037//0033-295X.108.2.291\u003c/li\u003e\n\u003cli\u003eOECD. (2024). \u003cem\u003eDo adults have the skills they need to thrive in a changing world? Survey of Adult Skills 2023\u003c/em\u003e. OECD Publishing. https://www.oecd.org/en/publications/do-adults-have-the-skills-they-need-to-thrive-in-a-changing-world_b263dc5d-en.html\u003c/li\u003e\n\u003cli\u003eOECD. (2025). \u003cem\u003eSurvey of Adult Skills 2023 Technical Report, OECD Skills Studies.\u003c/em\u003e OECD Publishing. https://doi.org/10.1787/80d9f692-en.\u003c/li\u003e\n\u003cli\u003eRammstedt, B., Lechner, C., \u0026amp; Danner, D. (2018). Relationships between personality and cognitive ability: A facet-level analysis. \u003cem\u003eJournal of Intelligence, 6\u003c/em\u003e(2), Article 28. http://www.mdpi.com/2079-3200/6/2/28\u003c/li\u003e\n\u003cli\u003eRammstedt, B., Roemer, L., Behr, D., Bluemke, M., Lechner, C. M., Dept, S., W\u0026auml;yrynen, L., Soto, C. J., \u0026amp; John, O. P. (2025). Going global: 39 language versions of the BFI-2-XS. \u003cem\u003eMeasurement Instruments for the Social Sciences, 7,\u003c/em\u003e Article e14067. https://doi.org/10.5964/miss.14067\u003c/li\u003e\n\u003cli\u003eRammstedt, B., Roth, M., Roemer, L., \u0026amp; Lechner. C. M. (2025). \u003cem\u003eRevisiting the links between personality and cognitive ability: A generalization study in 26 countries.\u003c/em\u003e SSRN. http://dx.doi.org/10.2139/ssrn.5239330\u003c/li\u003e\n\u003cli\u003eSchmitt, D. P., Allik, J., McCrae, R. R., \u0026amp; Benet-Mart\u0026iacute;nez, V. (2007). The geographic distribution of Big Five personality traits: Patterns and profiles of human self-description across 56 nations. \u003cem\u003eJournal of Cross-Cultural Psychology, 38(2),\u003c/em\u003e 173\u0026ndash;212. https://doi.org/10.1177/0022022106297299\u003c/li\u003e\n\u003cli\u003eSoto, C. J., \u0026amp; John, O. P. (2017). Short and extra-short forms of the Big Five Inventory\u0026ndash;2: The BFI-2-S and BFI-2-XS. \u003cem\u003eJournal of Research in Personality\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e, 69\u0026ndash;81. https://doi.org/10.1016/j.jrp.2017.02.004\u003c/li\u003e\n\u003cli\u003eStanek, K. C., \u0026amp; Ones, D. S. (2023). Meta-analytic relations between personality and cognitive ability. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e120\u003c/em\u003e(23), Article e2212794120. https://doi.org/10.1073/pnas.2212794120\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e For numeracy and adaptive problem solving explained variances were more heterogenous among countries with proportions for the different countries ranging between 1% and 12%.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The associations reported here refer to standardized regression coefficients. Highly similar results emerged for the corresponding bivariate correlations.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e For the cognitive skills numeracy and adaptive problem solving similar patterns were found. Again, the highest cross-country variations were observed for the Extraversion facet Energy Level, the Conscientiousness facet Productiveness, and all three Openness facets (Aesthetic Sensitivity, Intellectual Curiosity, and Creative Imagination; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.07\u0026ndash;.08).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Big Five, personality facets, cognitive ability, cross-cultural, PIAAC","lastPublishedDoi":"10.21203/rs.3.rs-7431184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7431184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eNumerous studies have demonstrated robust\u0026mdash;albeit modest\u0026mdash;associations between personality and cognitive ability, with a shared variance of 5\u0026ndash;10%. Previous research has largely focused on global personality dimensions, such as the Big Five. Recent studies suggest that narrower personality facets may enhance our understanding of the personality\u0026ndash;cognitive ability link. However, these studies typically rely on selective samples from single countries, limiting insights into populational and cross-cultural variation.\u003c/p\u003e\u003ch2\u003eFindings\u003c/h2\u003e\u003cp\u003eThis study is the first to examine the associations between personality facets and cognitive ability using comprehensive, population-representative data from 11 countries. We investigated the relationships between 15 Big Five facets and cognitive ability by analyzing shared variance and bivariate associations. Three main findings emerged: (1) Personality facets account for approximately twice the variance in cognitive ability compared to broad personality domains. (2) Facets provide a more nuanced picture of the personality\u0026ndash;cognitive ability link and reveal associations masked at the domain level. (3) Associations at the facet level differ across countries, with the strongest variation observed in facets of Openness.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings underscore the added value of examining personality at the facet level. This more granular approach offers deeper insights into the interplay between personality and cognitive ability and highlights the importance of considering cultural variability in psychological research.\u003c/p\u003e","manuscriptTitle":"Unpacking the personality–cognitive ability link: A cross-national facet-level analysis of the Big Five","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 17:54:49","doi":"10.21203/rs.3.rs-7431184/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"578d2d8b-6a01-4fb1-ac48-d03de59d39bb","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:02:49+00:00","versionOfRecord":{"articleIdentity":"rs-7431184","link":"https://doi.org/10.1186/s40536-026-00281-2","journal":{"identity":"large-scale-assessments-in-education","isVorOnly":false,"title":"Large-scale Assessments in Education"},"publishedOn":"2026-01-19 15:58:07","publishedOnDateReadable":"January 19th, 2026"},"versionCreatedAt":"2025-10-07 17:54:49","video":"","vorDoi":"10.1186/s40536-026-00281-2","vorDoiUrl":"https://doi.org/10.1186/s40536-026-00281-2","workflowStages":[]},"version":"v1","identity":"rs-7431184","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7431184","identity":"rs-7431184","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}