Effects of Response Option Order on Likert-Type Psychometric Properties and Reactions
Bibliographic record
Abstract
The effects of different response option orders on survey responses have been studied extensively. The typical research design involves examining the differences in response characteristics between conditions with the same item stems and response option orders that differ in valence-either incrementally arranged (e.g., strongly disagree to strongly agree) or decrementally arranged (e.g., strongly agree to strongly disagree). The present study added two additional experimental conditions-randomly incremental or decremental and completely randomized. All items were presented in an item-by-item format. We also extended previous studies by including an examination of response option order effects on: careless responding, correlations between focal predictors and criteria, and participant reactions, all the while controlling for false discovery rate and focusing on the size of effects. In a sample of 1,198 university students, we found little to no response option order effects on a recognized personality assessment vis-à-vis measurement equivalence, scale mean differences, item-level distributions, or participant reactions. However, the completely randomized response option order condition differed on several careless responding indices suggesting avenues for future research.
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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 | 0.123 | 0.320 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".