Marital Satisfaction: Toward an Integrated Understanding. Structural Equation Modelling Helps Unravel the Complexity of Factors that Impact Marital Success
Bibliographic record
Abstract
A data set of 1030 individuals (including 392 married couples) was employed to create a comprehensive picture of the interactive impact of many variables on marital satisfaction. Predictor variables were eventually combined into 20 composite variables and structural equation modeling resulted in 78.6% of the variance in marital satisfaction being explained for men; 79.8% for women. The primary dependent variable was Relational Satisfaction. Primary predictors (all composite variables) included emotional engagement, emotional-regulation skills, destructive interactions, shared activities, family and friend support, compatibility, strength of personal identity, accuracy of perception (of their partner), personality traits, temperaments (from the DISC measure), improvement over time, and positive illusions. To measure change over time, participants answered questions for both “now” and in the “first year of marriage”. Further, a criss-cross technique (rate self and partner across all variables) facilitated many comparative predictors. The structural models found the primary predictors of relational satisfaction (with only minor differences between mens’ and womens’ models) to be: emotional engagement (with β values of .56 for both), family and friend support, improvement over time, accuracy of perception, (absence of) destructive interactions, compatibility and positive traits. Equally important were predictors of emotional engagement—the greatest predictor of relational satisfaction: emotional-regulation skills (men), emotional-regulation skills (women), shared activities, accuracy of perception, family and friend support, and looking for the good explained 75% of the variance in the emotional engagement.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".