Construct-irrelevant item attributes: a framework to classifying items based on context and referent
Bibliographic record
Abstract
Construct-irrelevant items attributes (CIIAs) are characteristics of psychometric scale items that relate to how item stems are worded, rather than the construct they measure. For instance, an item can be framed from a first-hand (e.g., "How would you describe yourself?") or second-hand (e.g., "How would others describe you?") perspective. These attributes meaningfully change the way respondents interpret and answer scale items, so knowing what they are and how they impact data is essential to the construction of valid scales. The present paper serves as a taxonomy of known CIIAs and offers general suggestions on their use. Through this review, we hope to both introduce scale users to the intricacies of item design and offer experienced scale developers a much-needed resource on the types and uses of CIIAs. In doing so, we aim to contribute to the development of more effective, valid scales. We also aim to unify the research literature on item attributes under one taxonomy, to the benefit of scale developers and researchers alike.
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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 | 0.105 | 0.133 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.030 | 0.032 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".