A Latent Mediated Moderation of Perfectionism, Motivation, and Academic Satisfaction
Bibliographic record
Abstract
The 2 × 2 model of perfectionism conceptualizes perfectionism as the within-person combinations of self-oriented and socially prescribed perfectionism to define four subtypes of perfectionism. This model posits that each subtype is distinctively associated with self-determined motivation and psychological adjustment. Results of latent moderated structural equation model with data from a sample of 559 university students with our newly developed MPLUS syntax codes to estimate simple slopes and their statistical significance supported this hypothesis. As expected, pure self-oriented perfectionism was associated with higher academic self-determination and academic satisfaction relative to mixed perfectionism. Mixed perfectionism was also associated with higher academic self-determination and satisfaction than was pure socially prescribed perfectionism. Results of a latent mediated moderation structural equation model also showed that academic self-determined motivation significantly mediated the relationships between perfectionism subtypes and academic satisfaction. The indirect effects of the four simple slopes, tested with our newly developed MPLUS syntax codes, all reached statistical significance. On substantive grounds, the different amounts of autonomy or self-determination associated with each of the four subtypes of perfectionism of the 2 × 2 model explicate why they are distinctively associated with academic satisfaction. On methodological grounds, this study offered a roadmap to examine the hypotheses of the 2 × 2 model of perfectionism with latent moderated structural equation modeling.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".