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Estimating the Associations between Big Five Personality Traits, Testosterone, and Cortisol

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Abstract

Objective

Hormones are often conceptualized as biological markers of individual differences and have been associated with a variety of behavioral indicators and characteristics, such as mating behavior or acquiring and maintaining dominance. However, before researchers create strong theoretical models for how hormones modulate individual and social behavior, information on how hormones are associated with dominant models of personality is needed. Although there have been some studies attempting to quantify the associations between personality traits, testosterone, and cortisol, there are many inconsistencies across these studies.

Methods

In this registered report, we examined associations between testosterone, cortisol, and Big Five personality traits. We aggregated 25 separate samples to yield a single sample of 3964 (50.3% women; 27.7% of women were on hormonal contraceptives). Participants completed measures of personality and provided saliva samples for testosterone and cortisol assays.

Results

The results from multi-level models and meta-analyses revealed mostly weak, non-significant associations between testosterone or cortisol and personality traits. The few significant effects were still very small in magnitude (e.g., testosterone and conscientiousness: r = −0.05). A series of moderation tests revealed that hormone-personality associations were mostly similar in men and women, those using hormonal contraceptives or not, and regardless of the interaction between testosterone and cortisol (i.e., a variant of the dual-hormone hypothesis).

Conclusions

Altogether, we did not detect many robust associations between Big Five personality traits and testosterone or cortisol. The findings are discussed in the context of biological models of personality and the utility of examining heterogeneity in hormone-personality associations.

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Notes

  1. We used the secondary data pre-registration template. A blinded copy of this document can be found on the OSF page for this project (https://osf.io/z8xfu/). We faithfully executed all of the specified analyses outlined in the Stage 1 manuscript with one exception—due to some missing data on study variables, the sample size fell just below 4000 participants. The pre-registration and Stage 1 manuscript predicted “at least 4000 participants” and our actual sample size was 3964 for testosterone (and 3518 for cortisol); we note the deviation here but view this as a minor problem given that we had approximately the same statistical power to estimate small effects.

  2. There is some controversy over the correct effect size to quantify the magnitude of fixed effects in the content of multi-level modeling (Lorah 2018). We chose partial correlation coefficients derived from the estimates in the multi-level model. We did so because of their intuitive nature and the ease with which they could be subjected to a meta-analysis. However, there is also the perspective that computing correlation coefficients in this manner does not adequately account for the Level-2 unit measurement (in this case, study membership). Partial standardization also relies on sample characteristics (e.g., the standard deviation of each variable), which might also vary across samples. Rather, it is recommended to use an index like f2 because it is measure of variance explained that has clearer benchmarks of interpretation (Aiken et al. 1991; Cohen 1992). By using an effect size conversion based on the critical estimate from the models, the pseudo-partial-r’s presented in the tables account for the Level-2 membership. However, upon estimating the f2 measure for the substantive effects of interest, we also found that the effect sizes present were very small (f2 ≤ .003), consistent with the inferences made using correlation coefficients as effect sizes. For a full report of the magnitude of the effects across different effect size indices, please contact the second author for more details.

  3. The use of alpha reliabilities is far from the ideal solution to correct for measurement unreliability in personality-hormone associations. Internal consistencies are not a good reflection of the reliability of short-form measures of personality. Correcting for unreliability using these indices likely leads to an overestimation of the association between personality traits and hormones. As a result, under this likely possibility, the corrected associations should be viewed with skepticism, particularly when they depart from the results found in other analyses. The results did not dramatically differ across analyses in the current project, but future research can more closely examine this question using different criteria than those that we pre-registered. For example, a better index for reliability might be test-retest correlations, which have been shown to be adequate when using these short-form scales (Gosling et al. 2003).

  4. In our experience and a review of the literature, there is some ambiguity with respect to how we should have standardized the hormone values. Some studies did little or no standardizing beyond log-transformations, depending on the distribution of the hormone values. Other studies have standardized the hormone values within gender, given the different distributions between men and women (Zilioli et al. 2015). Yet others have standardized within a sample (across gender) to test questions related to mediation of gender differences (Schultheiss et al. 2020). One immediate implication is that the different standardization approaches affect the rank-ordering of men (who have higher and more variable testosterone values) and women, which has implications for the estimation of a gender difference and possibly estimations of personality-hormone associations. In a series of exploratory supplementary analyses, we examined how robust our findings were to different standardization procedures (e.g., standardizing hormones within a sample, standardizing hormones within men and women, within a sample). The findings were robust across all of these methods—the association between testosterone and cortisol was reproduced and all other effects were non-significant or did not surpass our pre-registered effect size cut-off.

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Authors and Affiliations

  1. Oakland University, Rochester, MI, USA

    Zachary W. Sundin

  2. Michigan State University, East Lansing, MI, USA

    William J. Chopik

  3. University of Massachusetts, Boston, Boston, MA, USA

    Keith M. Welker

  4. University of Michigan, Ann Arbor, MI, USA

    Esra Ascigil, Kristi Chin, Zachary A. Reese, Lester Sim & Robin S. Edelstein

  5. Northwestern University, Evanston, IL, USA

    Cassandra M. Brandes & Jennifer L. Tackett

  6. University of Hartford, West Hartford, CT, USA

    Sarah Ketay

  7. University of Colorado, Boulder, Boulder, CO, USA

    Erik L. Knight

  8. University of Goettingen, Göttingen, Germany

    Tobias L. Kordsmeyer, Julia Stern & Lars Penke

  9. Alpena Community College, Alpena, MI, USA

    Amber R. McLarney-Vesotski

  10. Marshall School of Business, University of Southern California, Los Angeles, CA, USA

    Smrithi Prasad

  11. University of California, San Francisco, San Francisco, CA, USA

    Ashlin R. K. Roy

  12. Nipissing University, North Bay, Canada

    Justin M. Carré

  13. University College London, London, England

    Pranjal H. Mehta

  14. University of Georgia, Athens, GA, USA

    Richard B. Slatcher

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Sundin, Z.W., Chopik, W.J., Welker, K.M. et al. Estimating the Associations between Big Five Personality Traits, Testosterone, and Cortisol. Adaptive Human Behavior and Physiology 7, 307–340 (2021). https://doi.org/10.1007/s40750-020-00159-9

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