A Clarified Examination of the Item Wording Effect: Item Valence (Good vs. Bad) Versus Semantic Framing (I Am vs. I Am Not)
Bibliographic record
Abstract
The Item Wording Effect (IWE) in psychological testing describes how individuals respond differently to positively and negatively worded items. Previous IWE research faced challenges due to measures varying beyond item valence. This study aimed to address this problem by developing an inventory, the Positive and Negative Descriptor Inventory (PANDI), with items varying solely on valence. Semantic framing was manipulated to examine which factor (valence vs. framing) was more causal of the IWE. Using an online survey on Mechanical Turk, 336 Canadian participants responded to PANDI items in different experimental conditions. Results indicated that item valence had a bigger impact on IWE than semantic framing. PANDI-Good items in the Affirming Condition exhibited lower reliability but higher means and response variance than other groups, emphasizing the significant difference in how individuals interpret positive and negating inventory items. This study recommends using negatively worded items sparingly, and not using negating items at all.
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How this classification was reachedexpand
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".