Perfectionism and the Five-Factor Model of Personality: A Meta-Analytic Review
Bibliographic record
Abstract
Over 25 years of research suggests an important link between perfectionism and personality traits included in the five-factor model (FFM). However, inconsistent findings, underpowered studies, and a plethora of perfectionism scales have obscured understanding of how perfectionism fits within the FFM. We addressed these limitations by conducting the first meta-analytic review of the relationships between perfectionism dimensions and FFM traits ( k = 77, N = 24,789). Meta-analysis with random effects revealed perfectionistic concerns (socially prescribed perfectionism, concern over mistakes, doubts about actions, and discrepancy) were characterized by neuroticism ([Formula: see text] = .50), low agreeableness ([Formula: see text] = −.26), and low extraversion ([Formula: see text] = −.24); perfectionistic strivings (self-oriented perfectionism, personal standards, and high standards) were characterized by conscientiousness ([Formula: see text] = .44). Additionally, several perfectionism–FFM relationships were moderated by gender, age, and the perfectionism subscale used. Findings complement theory suggesting that perfectionism has neurotic and non-neurotic dimensions. Results also underscore that the (mal)adaptiveness of perfectionistic strivings hinges on instrumentation.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".