Relationship quality is best assessed through your experience of it, not your partner's credentials
How committed and appreciative your partner seems, and how often you fight, say more about relationship quality than their qualifications. Your own reports predict quality two to four times better than your partner's reports. However, questionnaires can barely predict whether the relationship will improve or deteriorate.
What it takes
Free and quick. Put away the partner checklist and look at your actual interactions.
What you may gain
A machine-learning analysis combined 43 longitudinal couples datasets from 29 laboratories. Longitudinal means following the same couples over time. The strongest relationship-specific predictors were perceived partner commitment, appreciation, sexual satisfaction, perceived partner satisfaction and conflict. The strongest individual predictors were life satisfaction, negative affect, depression, attachment avoidance and attachment anxiety. Relationship-specific measures explained up to 45% of variance in relationship quality at baseline and up to 18% at the end. Variance here means differences in relationship quality between couples. A person's own reports had two to four times the predictive power of their partner's reports. Adding individual differences and partner reports to one's own relationship-specific reports provided no additional predictive power.
Context & considerations
The same study found that no combination of self-report measures reliably predicted changes in relationship quality during follow-up. Your own experience is more useful than a checklist of partner attributes for judging the present, but cannot tell you the future. The entry on emotional support and relationship quality addresses the same issue; this entry identifies the specific measures.
Research & references
Joel S, Eastwick PW, Allison CJ, et al. (2020). Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies. Proceedings of the National Academy of Sciences, 117(32), 19061-19071. https://doi.org/10.1073/pnas.1917036117