Stability and change of personality traits, self-esteem, and well-being: Introducing the meta-analytic stability and change model of retest correlations.
Bibliographic record
Abstract
The stability of individual differences is a fundamental issue in personality psychology. Although accumulating evidence suggests that many psychological attributes are both stable and change over time, existing research rarely takes advantage of theoretical models that capture both stability and change. In this article, we present the Meta-Analytic Stability and Change model (MASC), a novel meta-analytic model for synthesizing data from longitudinal studies. MASC is based on trait-state models that can separate influences of stable and changing factors from unreliable variance (Kenny & Zautra, 1995). We used MASC to evaluate the extent to which personality traits, life satisfaction, affect, and self-esteem are influenced by these different factors. The results showed that the majority of reliable variance in personality traits is attributable to stable influences (83%). Changing factors had a greater influence on reliable variance in life satisfaction, self-esteem, and affect than in personality (42%-56% vs. 17%). In addition, changing influences on well-being were more stable than changing influences on personality traits, suggesting that different changing factors contribute to personality and well-being. Measures of affect were less reliable than measures of the other 3 constructs, reflecting influences of transient factors, such as mood on affective judgments. After accounting for differences in reliability, stability of affect did not differ from other well-being variables. Consistent with previous research, we found that stability of individual differences increases with age. (PsycINFO Database Record
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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 | 0.114 | 0.224 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.019 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".