Self-Esteem and Satisfaction with Social Relationships across Time
Bibliographic record
Abstract
Research on the longitudinal association between self-esteem and satisfaction with social relationships led to ambiguous conclusions regarding the temporal order and strength of this relation. Existing studies have examined this association across intervals ranging from days to years, leaving it unclear as to what extent differences in timing may explain differences across studies. In the present study, we used continuous time structural equation models to examine cross-lagged relations between the constructs (i.e., CT-SEM), and also distinguished between-person differences from within-person processes (i.e., RI-CT-SEM). We analyzed 10 years of annual data from the Longitudinal Internet Studies of the Social Sciences (LISS; N = 14,741). When using CT-SEM, we found a bidirectional positive relation between self-esteem and satisfaction with social relationships, with larger effects over longer intervals. When using RI-CT-SEM, we found the largest effects of self-esteem and satisfaction with social relationships across intervals of one year, with smaller effect sizes at both shorter and longer intervals. Additionally, the effect of fluctuations in people’s satisfaction with social relationships on fluctuations in their self-esteem was greater than the reverse effect. Our results highlight the importance of considering time when examining the relation between self-esteem and interpersonal outcomes, and likely psychological constructs in general.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".