A Meta–Analytic and Conceptual Update on the Associations between Procrastination and Multidimensional Perfectionism
Bibliographic record
Abstract
The equivocal and debated findings from a 2007 meta–analysis, which viewed perfectionism as a unidimensional construct, suggested that perfectionism was unrelated to procrastination. The present meta–analysis aimed to provide a conceptual update and reanalysis of the procrastination–perfectionism association guided by both a multidimensional view of perfectionism and self–regulation theory. The random–effects meta–analyses revealed a small to medium positive average effect size ( r = .23; k = 43, N = 10 000; 95% confidence interval (95% CI) [0.19, 0.27]) for trait procrastination and perfectionistic concerns and a small to medium negative average effect size ( r = −.22; k = 38, N = 9544; 95% CI [−0.26, −0.18]) for procrastination and perfectionistic strivings. The average correlations remained significant after statistically accounting for the joint variance between the two perfectionism dimensions via semi–partial correlations. For perfectionistic concerns, but not perfectionistic strivings, the effects depended on the perfectionism measure used. All effects did not vary by the trait procrastination measure used or the respondent's sex. Our findings confirm that from a multidimensional perspective, trait procrastination is both positively and negatively associated with higher–order perfectionism dimensions and further highlights the value of a self–regulation perspective for understanding the cognitive, affective and behavioural dynamics that characterise these traits. Copyright © 2017 European Association of Personality Psychology
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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 | 0.086 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.018 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".