Generalizability of Results From Dyadic Data: Participation of One Versus Two Members of a Romantic Couple Is Associated With Breakup Likelihood
Bibliographic record
Abstract
With a growing body of relationship research relying on dyadic data (i.e., in which both members of a couple are participants), researchers have raised questions about whether such samples are representative of the population or unique in important ways. In this research, we used two large data sets (Study 1: n = 5,118; Study 2: n = 5,194) that included participants with and without a romantic partner participating to examine if co-participation status has substantive relationship implications. Results showed that co-participation status predicted breakup even after controlling for other known predictors such as satisfaction, although the effect weakened over time (Study 2). There was also tentative evidence that factors such as conflict may be differentially related to breakup among couples in which one versus both partners participated. These findings raise caution in interpreting effects found in dyadic studies and highlight the need to be mindful of potential bias in recruitment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".