Women and Men are the Barometers of Relationships: Testing the Predictive Power of Women’s and Men’s Relationship Satisfaction
Bibliographic record
Abstract
There is a longstanding belief in relationship science and popular opinion that women are the barometers in mixed-gender relationships such that their perceptions about the partnership carry more weight than men's in predicting future relationship satisfaction, but this idea has yet to be rigorously tested. We analyze data from two studies to test within-person links between men's and women's relationship satisfaction on their own and their partner's next-day and next-year satisfaction. Study 1 combined nine daily diary datasets from Canada and the United States with 901 mixed-gender couples who provided 29,541 daily reports of relationship satisfaction. Study 2 analyzed five annual waves of data from the German Family Panel (pairfam) that surveyed 3,405 mixed-gender couples who provided 21,115 relationship satisfaction reports. Latent curve models with structured residuals (LCM-SR) revealed that in both studies, men's and women's relationship satisfaction significantly predicted their own and their partner's relationship satisfaction, with no gender differences in the magnitude of these effects. Results underscore the interdependence of romantic partners' satisfaction and indicate that both men and women jointly shape romantic relationship satisfaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".