A two-study validation of a single-item measure of relationship satisfaction: RAS-1
Bibliographic record
Abstract
Abstract Research addressing relationship satisfaction is a constantly growing area in the social sciences. The aim of the present investigation was to examine the similarities and differences between the seven-item Relationship Assessment Scale (RAS) and the single-item measure of relationship satisfaction (RAS-1), using proximal and distal constructs as correlates. Two studies using two independent samples were conducted, assessing more proximal constructs, such as love and sex mindset in Study 1 ( N = 380; female = 195) and more distant ones, such as loneliness and problematic pornography use in Study 2 ( N = 703; female = 360). Structural equation modeling revealed that love ( β RAS-1 = .55; p < .01; β RAS = .71; p < .01), sex mindset beliefs ( β RAS-1 = .18; p < .01; β RAS = .13; p < .01) and loneliness ( β RAS-1 = −.35; p < .01; β RAS = −.37; p < .01) had significant positive and negative associations with RAS and RAS-1, respectively; while problematic pornography use did not. These results suggest that RAS-1 may be an equally adequate instrument for measuring relationship satisfaction as the RAS with respect to proximal and distal correlates. Thus, RAS-1 is recommended to be used in large-scale studies when the number of items is limited.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".