Evaluation of the use of psychometric scales in human–wildlife interaction research to determine attitudes and tolerance toward wildlife
Bibliographic record
Abstract
Studies evaluating human-wildlife interactions (HWIs) in a conservation context often include psychometric scales to measure attitudes and tolerance toward wildlife. However, data quality is at risk when such scales are used without appropriate validation or reliability testing, potentially leading to erroneous interpretation or application of findings. We used 2 online databases (ProQuest Psych Info and Web of Science) to identify published HWI studies that included attitude and tolerance. We analyzed these studies to determine the methods used to measure attitudes or tolerance toward predators and other wildlife; determine the proportion of these methods applying psychometric scales; and evaluate the rigor with which the scales were used by examining whether the psychometric properties of validity and reliability were reported. From 2007 to 2017, 114 published studies were identified. Ninety-four (82%) used questionnaires and many of these (53 [56%]) utilized a psychometric scale. Most scales (39 [74%]) had at least 1 test of reliability reported, but reliance on a single test was notable, contrary to recommended practice. Fewer studies (35 [66%]) reported a test of validity, but this was primarily restricted to structural validity rather than more comprehensive testing. Encouragingly, HWI investigators increasingly utilized the necessary psychometric tools for designing and analyzing questionnaire data, but failure to assess the validity or reliability of psychometric scales used in over one-third of published HWI attitude research warrants attention. We advocate incorporation of more robust application of psychometric scales to advance understanding of stakeholder attitudes as they relate to HWI.
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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 | 0.556 | 0.684 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.022 | 0.031 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".