By Jason Hreha

Personality

Personality and life outcomes: what a major replication found

What a large personality replication project found, why effects often shrank, how the links hold up in other samples, and what associations can tell you about individuals.

Published Evidence checked through October 2, 2026 Prepared with AI assistance 4 min read

Some of the most useful behavioral research is about how people differ: what they tend to enjoy, how they usually act, and which situations bring out their strengths. Claims about those differences deserve the same scrutiny as claims about a clever intervention. Do the reported links between personality and life outcomes show up again when researchers collect new data?

In a large project that retested selected links between the Big Five personality traits and life outcomes, most did. The results give good reason to take personality seriously. They also give reason to be careful about how strong the relationships are and what they can predict. They do not show that a trait determines anyone's future. Soto, 2019.

What the project tested

Christopher Soto's Life Outcomes Of Personality Replication project, or LOOPR, selected 78 trait–outcome associations from an earlier review that could feasibly be tested again. The project gathered new survey data from four US online samples. It measured personality with the Big Five Inventory–2 and assessed outcomes through self-report, sometimes adapting or shortening the original measures.

Eighty-seven percent of the selected associations were statistically significant in the expected direction. The replication effects were typically 77 percent as large as the original effects. That 77 percent is the median ratio of new to original effect sizes; it does not mean a personality test predicts 77 percent of a person's life. The hypotheses and analysis plan were registered during data collection, before analysis. Project methods and results.

These were selected associations, not a random sample of every claim made about personality. And some original studies used different designs, including longitudinal data that followed people over time. Reproducing an association in a one-time (cross-sectional) questionnaire is useful evidence, but it does not reproduce every feature of those studies.

What a successful replication tells you

Suppose people with higher and lower scores on a trait differ, on average, in some outcome. Finding a similar relationship in a new sample makes the original observation more credible. It reduces the worry that the first result depended entirely on one unusual group or one lucky analysis.

That still leaves several questions. How strong is the relationship? How much overlap is there between people with different scores? Does the measurement capture what matters for the decision you want to make? Would the relationship remain useful in another population or setting?

A statistically detectable association can be too weak to support a confident judgment about one person. Imagine a trait score that helps distinguish average preferences in a large group. It would not follow that everyone with a high score wants the same job, prefers the same exercise, or will respond to the same advice. A group-level tendency is information, not an assignment.

That is also why the shrinking effect sizes matter. A relationship can survive replication and still be less useful than its original estimate suggested. “It replicated” should open the next question (how well, and under what conditions?), not end the discussion.

The question about other populations has started to get answers, and so far they call for caution. The Africa Long Life Study used data on 2,770 young adults in Kenya, Namibia and South Africa, with outcomes measured over two to three years. It used a different trait framework, the “Big Two” of agency and communion, rather than the Big Five. Higher scores on both predicted better self-rated health and less anxiety and depression later, and communion predicted being employed. But the associations were smaller than those previously observed in Western countries (correlations between .02 and .11 in absolute size), and they depended on analytic choices such as which country and which time lag were analyzed. Haehner and colleagues, 2026.

Association is not a recipe for change

An association between a trait and an outcome does not show that deliberately changing the trait will change the outcome. Nor does it show whether an apparent personality difference is partly connected to other traits or circumstances.

A later LOOPR analysis asked whether the associations held across demographic groups, and what happened when other personality traits were taken into account. Many relationships held across groups. But adjusting for overlapping traits weakened many of the associations. This was a further analysis of the same project data, not a second independent replication. Soto, 2021.

A larger analysis that followed people forward in time reached a similar two-part conclusion. Emorie Beck and Joshua Jackson pooled raw data from 171,395 people in 10 longitudinal panel studies and tested whether Big Five traits predicted 14 later life outcomes. Most associations held up, and few depended on who was studied. But 21 of the 70 trait–outcome associations were not robust across most analyses. For 38 of the 70, the result depended on which background variables were included, and in some models, adding or removing one of those variables flipped the direction of the association. Beck and Jackson, 2022.

Here is an everyday example. Imagine that people who describe themselves as organized also report finishing projects on time. That hypothetical pattern would be worth knowing. But it would not, by itself, tell a manager whether to teach organization, reduce an excessive workload, clarify priorities, or change how deadlines are set. Those are intervention questions. They require evidence about interventions.

The same boundary applies to assessment products. Evidence for links between Big Five traits and outcomes does not automatically validate every questionnaire, score interpretation, or recommendation sold under the label “personality.” A product that promises to identify your ideal career or prescribe your routine makes additional claims, and those claims need their own evidence. The guide to evaluating personality tests shows how to check a specific instrument, score and intended use.

How to use this evidence

Personality is one useful way to understand how people differ. Use it alongside what you know about skills, interests, opportunities and actual experience. If an activity keeps clashing with what you enjoy or can sustain, that is worth looking into. It is not proof that you must force yourself harder, or that a test score has settled the matter.

LOOPR is also a good model of what replication can do. It neither wiped the picture away nor left it untouched. Many associations returned, effect sizes often shrank, and measurement limits stayed in view. That tells you more than sorting a whole discipline into “works” or “doesn't work.”

If the trait framework is unfamiliar, start with the Big Five personality traits or the broader personality definition. For a different kind of evidence question, compare retrieval practice, where the central issue is what happens when a learning activity is changed.

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