Who Cheats?

Data from the Aella Relationship Survey · n=64,273 · April 2026

Cheating by Gender

Men report cheating at roughly double the rate of women (12.1% vs 5.8% “I cheated” alone; 15.5% vs 8.2% including “both cheated”). Women report slightly similar rates of partner cheating (6.5% vs 6.7% for men).

Cheating Over Relationship Length

Cheating climbs steadily with relationship duration — from roughly 8–10% in the first six months to 30–35% at 20+ years. The male-female gap is consistent throughout, with men running about 5 percentage points higher.

Note: this is cross-sectional, not longitudinal — we cannot distinguish “longer relationships accumulate more cheating” from “people who stay together despite cheating have longer relationships.” Also note the x-axis is relationship length, not age — a 25-year-old in a 10-year relationship and a 40-year-old in a 10-year relationship are in the same bin.

Who Cheated: Self vs Partner, Over Time

Breaking it down by who cheated: men’s self-reported cheating (solid blue) climbs steeply with relationship length, reaching 37% at 20+ years. Women’s “partner cheated” rate (dashed red) tracks close to their own “I cheated” rate — while for men, self-reported cheating far outpaces reports of partner cheating.

Cheating by Age

Older respondents report much higher cheating rates — rising from ~5–8% at 18–21 to ~30–40% at 60+. This partly reflects having had more time and more relationships, not just that older people are more prone to cheating.

After controlling for relationship length (faded lines = raw, solid = adjusted), the age gradient flattens substantially — much of the age effect was driven by older people having longer relationships with more opportunity for cheating. But a real residual age effect remains.

Cheating by Lifetime Sexual Partners

The strongest gradient in this report: cheating rates rise from ~5% among those with 0 prior partners to ~45% (male) and ~25% (female) at 100+. The male-female gap also widens substantially at higher partner counts. Causality unclear — people who cheat accumulate more partners mechanically.

After adjusting for relationship length, the gradient persists strongly — this is not just a “more time = more cheating” artifact. Higher lifetime partner count independently predicts cheating even within the same relationship-length strata.

Cheating by Income

Higher income correlates with higher cheating for both sexes. Men go from ~8% at $0 to ~25% at $350k+. Likely confounded by age (older people earn more and have had more time to cheat).

After adjusting for relationship length, the income gradient mostly flattens — suggesting the raw income-cheating link was largely driven by higher earners being in longer relationships. The residual association is weak.

Cheating by Education

Education shows a modest effect. The pattern is noisier than income or age, suggesting education itself is not a strong independent predictor once you control for other demographics.

After adjusting for relationship length, education differences shrink further and largely disappear. Education does not independently predict cheating.

Cheating by Religiosity

Devoutly religious respondents cheat slightly less, while “loosely/culturally” religious cheat slightly more. The differences are relatively modest. Note that religious people are heavily underrepresented in this sample.

Cheating by Mono/Poly Orientation

Interesting inverted-U: cheating peaks among people who are “slightly poly” (~35% male, ~20% female) then drops for “very poly.” This makes sense — very poly people are more likely in open/poly relationships where outside partners are not cheating. The “slightly poly” group may want openness but be in monogamous relationships.

Cheating by Political Orientation

Conservatives cheat more. The gradient is clear and monotonic: from ~24% (significantly conservative men) to ~15% (significantly liberal men), and ~18% to ~10% for women.

Social vs. Economic Politics

Breaking politics into social and economic dimensions: both show the same conservative-cheats-more pattern, but social conservatism has a somewhat steeper gradient than economic conservatism.

Cheating by Sexual Satisfaction

Strong negative gradient: sexually unsatisfied people cheat far more (~30% for the lowest-satisfaction men vs ~10% for the highest). Direction of causality is ambiguous — cheating may cause dissatisfaction, dissatisfaction may cause cheating, or a third variable (e.g., relationship quality) drives both.

Cheating by Jealousy

More jealous people report more cheating in their relationships. The gradient is steeper for men. This likely captures both directions: jealousy may partly be a response to actual or suspected infidelity, and jealous/insecure people may also be more prone to cheating themselves.

Cheating by Relationship Health

The second-strongest gradient: cheating drops from ~30% in the unhealthiest relationships to ~5–8% in the healthiest. The male-female gap nearly closes at the lowest health levels.

Cheating by Perceived Mismatch

People who think they “could do better” cheat more (~25% for the highest-mismatch men vs ~10% for well-matched). Clean dose-response. If you feel like you are settling, you are more likely to look elsewhere.

Cheating by Gender Identity

Cis men report the highest cheating rate (19.3%), followed by cis women (13.2%). Trans and nonbinary respondents report lower rates across the board, though this may partly reflect younger average age in those groups.