How Should Discrepancy Be Assessed in Perfectionism Research? A Psychometric Analysis and Proposed Refinement of the Almost Perfect Scale–Revised
Why this work is in the frame
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Bibliographic record
Abstract
Research on perfectionism with the Almost Perfect Scale–Revised (APS-R) distinguishes adaptive perfectionists versus maladaptive perfectionists based primarily on their responses to the 12-item unidimensional APS-R Discrepancy subscale, which assesses the sense of falling short of standards. People described as adaptive perfectionists have high standards but low levels of discrepancy (i.e., relatively close to attaining these standards). Maladaptive perfectionists have perfectionistic high standards and high levels of discrepancy. In the current work, we re-examine the psychometric properties of the APS-R Discrepancy subscale and illustrate that this supposedly unidimensional discrepancy measure may actually consists of more than one factor. Psychometric analyses of data from student and community samples distinguished a pure five-item discrepancy factor and a second four-item factor measuring dissatisfaction. The five-item factor is recommended as a brief measure of discrepancy from perfection and the four-item factor is recommended as a measure of dissatisfaction with being imperfect. Overall, our results confirm past suggestions that most people with maladaptive perfectionism are characterized jointly by chronic dissatisfaction as well as a sense of being discrepant due to having fallen short of expectations. These findings are discussed in terms of their implications for the assessment of perfectionism, as well as the implications for research and practice.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it