The Importance of Item Wording
Bibliographic record
Abstract
In the current research, we illustrate the impact that item wording has on the content of personality scales and how differences in item wording influence empirical results. We present evidence indicating that items in certain scales used to measure “adaptive” perfectionism fail to capture the disabling all-or-nothing approach that is synonymous with the individual who is driven to attain perfection. Original and modified versions of two perfectionism measures of high personal standards and modified perfectionistic standards versions of these scales were administered to three samples of participants. A series of analyses established that item wording does indeed matter. In particular, our results differed for a modified version of the Almost Perfect Scale–Revised when the focus was on a conceptualization and assessment of perfectionism that is fundamentally different from conscientious striving. The current findings are discussed in terms of their implications for scale construction and item wording in general and for the measurement of perfectionism in particular. The specific implications of these findings are examined in terms of understanding dysfunctional perfectionism and the current debate about whether certain aspects of perfectionism are adaptive versus maladaptive.
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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 | 0.233 | 0.660 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".