The European Journal of Personality (EJP) invites articles that estimate how strongly individual differences in personality traits and their changes can be predicted from combinations of multiple life experiences.
One of the key questions of current personality trait science is: which life experiences contribute to personality trait change?
Most studies have linked a few life experiences with changes in the Big Five personality domains, looking at one correlation at a time. But so far, this approach has not revealed many experiences that reliably explain even a relatively small fraction (say 1%) of trait variance (Bleidorn et al., 2020). Future studies with larger samples and more accurate measurements of both traits and experiences may identify some “hits”, especially if/when the subjective meaning of events is considered. However, it currently seems unlikely that the effect sizes will be much larger than explaining 1% of trait variance – if such effects existed, we would probably already know of them.
The one-trait-one-experience approach reminds us of the “candidate gene” approach to identifying genetic variants responsible for observable trait variance, which has not revealed many replicable “hits” (Munafò & Flint, 2011). A more data-driven approach that considers many associations at a time, genome-wide associations studies, has been somewhat more successful in linking observable trait variance with genetic variants (Nagel et al., 2018).
However, the effects of individual genetic variants have proven extremely small. Predicting trait scores from genetic variance, therefore, requires building models that include many genetic variants; these prediction models are known as polygenic scores and are now widely used in quantitative genetics for various purposes. Their complexity requires that these models are routinely cross-validated in independent samples or sample partitions to avoid statistical over-fitting (capitalizing on chance findings).
A similar approach has explored used to link personality trait variance with the brain structure, constructing models from sets of brain parameters and validating them in independent sample partitions (e.g., Hyatt et al., 2021).
To our knowledge, this reasoning has not yet been empirically applied to linking personality trait variance with life experiences, although it has been discussed by von Stumm & d’Apice (2021). We recognize that parallels with genetics and brain imaging only very loosely apply to life experiences. For example, geneticists and neuroscientists can objectively measure thousands of more or less clearly distinguishable variables, whereas capturing individual differences in life experiences comprehensively, with high precision and in a standardized, psychologically meaningful way is likely much more complicated. Such high-quality data may not exist yet.
However, the EJP hopes to promote and encourage work in which multiple aspects of the environment are measured and considered in relation to personality traits or trait change, while addressing the increased risk of type 1 errors. We think some potentially relevant data may exist, where multiple life experiences (say at least 10) have been measured alongside multiple measurements of personality traits. For example, studies may predict trait change with models that include several life experiences as predictors, essentially producing “polyexperience scores”.
This call is flexible as to the specifics of the proposed studies, as long as personality traits/their changes are predicted from multiple experiences and over-fitting (type 1 error risk) is accounted for, preferably by cross-validation in independent samples or sample partitions. Almost inevitably, sample sizes in many thousands are required for sufficient statistical power; this may be achieved by combining multiple comparable datasets. It may be particularly useful to include the subjective meaning of events in the models, as the psychological impacts of life events may partly or even entirely depend on how people perceive these events. We encourage pre-registration and also welcome null findings (little or no predictive accuracy).
We think that such an approach may go some way in helping the field to a) estimate the overall predictability of personality traits and their changes from life experiences and b) see if multivariable approaches are likely to yield benefits over linking traits and their changes with individual experiences.
Importantly, this approach would not contradict but complement the approaches linking traits and their changes with individual life experiences – such papers are also welcome. However, they do not fall within the scope of this call.
We would also be happy to discuss proposals for manuscripts describing multivariable data collection efforts, such as, for example, a relevant cohort profile or a technical tutorial of smartphone sensing tools applied to life events. To fit within this call, the manuscript would need to do more than simply point out that measuring multiple life experiences is both important and difficult. Clear, concrete steps should be proposed that would be easy for others to apply to their multivariable data collection efforts.
Interested authors are encouraged to get in touch with René Mõttus (rene.mottus@ed.ac.uk).
René Mõttus, Daniel Briley, Wiebke Bleidorn, and Chris Hopwood
References
Bleidorn, W., Hopwood, C. J., Back, M. D., Denissen, J. J. A., Hennecke, M., Jokela, M., Kandler, C., Lucas, R. E., Luhmann, M., Orth, U., Roberts, B. W., Wagner, J., Wrzus, C., & Zimmermann, J. (2020). Longitudinal Experience-Wide Association Studies—A Framework for Studying Personality Change. European Journal of Personality, 34, 285–300. https://doi.org/10.1002/per.2247
Hyatt, C. S., Sharpe, B. M., Owens, M. M., Listyg, B. S., Carter, N. T., Lynam, D. R., & Miller, J. D. (2021). Searching high and low for meaningful and replicable morphometric correlates of personality. Journal of Personality and Social Psychology.https://doi.org/10.1037/pspp0000402
Munafò, M. R., & Flint, J. (2011). Dissecting the genetic architecture of human personality. Trends in Cognitive Sciences, 15, 395–400. https://doi.org/10.1016/j.tics.2011.07.007
Nagel, M., Watanabe, K., Stringer, S., Posthuma, D., & Sluis, S. (2018). Item-level analyses reveal genetic heterogeneity in neuroticism. Nature Communications, 9, 905. https://doi.org/10.1038/s41467-018-03242-8
von Stumm, S., & d’Apice, K. (2022). From Genome-Wide to Environment-Wide: Capturing the Environome. Perspectives on Psychological Science, 17, 30–40. https://doi.org/10.1177/1745691620979803