Comparison of questionnaire items for discomfort glare studies in daylit spaces
Bibliographic record
Abstract
When studying discomfort glare, researchers tend to rely on a single questionnaire item to obtain user evaluations. It is unclear whether the choice of questionnaire item affects the distribution of user responses and leads to inconsistencies between studies. This study aims to investigate if different glare questionnaire items yield similar distributions of user discomfort in daylit environments. We conducted a comparative study of selected questionnaire items from previous glare experiments, testing them in three independent user studies with different lighting conditions and glare stimuli. We compared the resulting outputs across questionnaire items with 540 data points from 149 participants. Results indicated that ordinal questionnaire outputs show strong correlations (0.68 < ρ < 0.85), high internal reliability (α = 0.93) and captured the same latent construct. Binary questionnaire items reflected different glare thresholds but still correlated well with ordinal items. The construct validity of tested questionnaire items was confirmed through responses to an open-ended question. These findings suggest that the tested questionnaire items may be used for category rating-type discomfort glare evaluations and consistently capture the same construct.
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".