Reciprocal Personality Assessment of Both Partners in a Romantic Relationship and Its Correlates to Dyadic Adjustment
Bibliographic record
Abstract
This project examines the effects of self- and partner-rated personality and their reciprocal interaction between two partners. Personality in 113 young dating couples was measured with the Five-Factor Model and maladaptive personality trait model of the DSM-5. Partners completed self- and partner-reports of the NEO-FFI-3 and the Personality Inventory for DSM-5 (PID-5) as well as the self-report Dyadic Adjustment Scale (DAS). Three sets of Actor-Partner Interdependence Models (APIMs) were run to estimate actor and partner effects of self-rated personality, partner-rated personality, and of both sets of effects simultaneously in an integrated model. When self- and partner-rating models were examined separately, several significant actor and partner effects were observed. However, the strongest effects were observed in the partner-rating models. When self- and partner-rated personality were examined at the same time, most effects from the self-rating models disappeared. Furthermore, most of the effects as well as the strongest one observed were associated with an individual’s perception of their partner’s personality, particularly men’s perception of women’s personality. This study demonstrates the incremental predictive utility of individuals’ perception of their partner’s personality for explaining their own dyadic adjustment.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".