“Will you complete this survey too?” Differences between individual versus dyadic samples in relationship research.
Bibliographic record
Abstract
This study examines the ways in which collecting data from individuals versus couples affects the characteristics of the resulting sample in basic research studies of romantic relationships. From a nationally representative sample of 1,294 individuals in a serious romantic relationship, approximately half of whom were randomly selected to invite their partner to participate in the study, we compare relationship, individual, and demographic characteristics among 3 groups: individuals randomized to invite their partner and whose partner participated in the study, individuals randomized to invite their partner but whose partner did not participate, and individuals who were not randomized to invite their partner. Results indicated that individuals whose partner participated reported the highest levels of relationship and individual well-being relative to comparison groups, as well as individuals who participated alone despite being asked to invite their partner, reported the lowest levels of relationship and individual well-being relative to comparison groups. Effect size magnitudes indicated the strongest group differences with respect to relationship variables, particularly cognitive appraisals of overall relationship stability and satisfaction. Implications for romantic relationship research and study design are discussed. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".