Landmark study on 11,196 couples pinpoints what dating apps get so wrong

If you have ever labored over how to convey your personality through a dating app bio — or judged someone else's through theirs — research on romance suggests you place your efforts elsewhere.

It's taken 20 years of relationship science to get here, but scientists now argue that there's something far more important than your personality or even your partner's when it comes to cultivating happy relationships.

The most powerful predictors of relationship quality are the characteristics of the relationship itself — the life dynamic you build with your person. This is according to an analysis of 11,196 couples gleaned from 43 studies.

At the outset of relationships, relationship-related characteristics are likely to account for about 45 percent of the differences in relationship satisfaction. Actor reported traits (or your own personality) can account for 19 percent of differences.

By contrast, a partner's personality may only account for about 5 percent of that relationship satisfaction. Over time, the estimates become smaller, but the hierarchy remains the same: relationship characteristics trumping individual ones.

Samantha Joel, the study's first author and the director of the Relationships Decision Lab at Western University, says that her study crystallizes one thing:

"Really, it suggests that the person we choose is not nearly as important as the relationship we build," she tells Inverse.

The study was published Monday in Proceedings of the National Academy of Sciences.

What makes relationships successful – This study breaks down all the individual ingredients that go into romantic relationships (or as many that can be captured through asking people questions about their dating lives). They fell into two categories: individual characteristics of each partner and relationship characteristics.

Individual characteristics included attributes like income, satisfaction with life, age, or empathy, amongst many others. Relationship characteristics included things like perceived partner satisfaction, affection, power dynamics, or sexual satisfaction.

In every relationship, both of these categories will intermix, but not all traits will have equal sway.

The study pooled data from 43 separate studies and 11,196 couples who were interviewed at least twice (the interval between interviews ranged from two months to four years, depending on the study). Those interviews showed which attributes within each category were most tightly tied to relationship quality.

"The shared norms, the in-jokes, the shared experiences – is so much more than the separate individuals who make up that relationship."

The top five individual variables that explained differences in relationship satisfaction were:

1. Life satisfaction
2. Negative affect (feeling distressed or irritable)
3. Depression or feelings of hopelessness
4. Attachment anxiety (in a phrase: "I worry a lot about my relationships")
5. Attachment avoidance (preferring to not become too attached)

The five most powerful relationship-based variables that explained differences in satisfaction were:

1. Perceived partner commitment (in a phrase: "my partner wants this relationship to last forever")
2. Appreciation (feeling lucky to have your partner)
3. Sexual satisfaction
4. Perceived partner satisfaction (how happy you think the relationship makes your partner)
5. Conflict

Those individual characteristics are important as they impact how you approach the relationship in the first place, Joel explains. But they still paled in comparison to the nature of the relationship itself.

"The dynamic that you build with someone — the shared norms, the in-jokes, the shared experiences — is so much more than the separate individuals who make up that relationship," Joel says.

Using science for relationship advice – This study comes about 20 years after relationship science became a "mature discipline," the study team writes. Joel adds that the field has seen a surge in both popularity and scientific efforts.

"Our conferences have record numbers of attendees, and our journals have record numbers of submissions," she says.

This study exists to pool all that information into one place and see what conclusions might be drawn. It's not all about gleaning science-backed dating advice — but when asked to provide it, Joel is game.

"It really seems that having a great relationship is less about finding the perfect partner or changing your current partner, and more about building that relationship itself – setting up the conditions that will allow the relationship to flourish," she says.

Abstract: Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explain- ing why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual- difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship- specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.