A Contrast of Hunter Characteristics and Attitudes between Random and Convenience Samples in the 2010, 2012, and 2013 Surveys of Louisiana Waterfowl Hunters
Bibliographic record
Abstract
I surveyed random (mail and internet panel surveys) and convenience samples (web surveys) of Louisiana waterfowl hunters following the 2009-2010, 2011-2012, and 2012-2013 seasons. I hypothesized significant differences in effort, success, satisfaction and demographics, but no difference in attitudes, in responses between random and convenience survey methods. I mailed to stratified random samples of 2,500 in 2010, 6,400 in 2012, and 2,500 each for mail and on-line internet panel response in 2013. An identical web survey, hosted each year during the same time period, facilitated open-access response. I received 1,676 usable responses in 2010 (727 mail and 949 web), 2,382 usable responses in 2012 (1,096 mail and 1,286 web), and 2,121 usable responses in 2013 (590 mail, 145 internet panel, and 1,407 web). Cost per usable response averaged $39.10 for mail surveys, $41.70 for the internet panel survey, and $3.20 for web surveys. Compared with the mail surveys and internet panel, respondents to the web surveys hunted more frequently, harvested more waterfowl, and placed higher levels of self-identity or importance on waterfowl hunting. However, I noted similarities in attitudes toward regulatory alternatives across survey methods in all 3 studies. I tested the random and convenience samples using binary logistic regression of variables measuring effort, success, satisfaction, and demographics, exceeding statistical standards for classification accuracy in 2010 and 2012, but not 2013, resulting in a mixed conclusion for these characteristics. Identical tests of attitudinal variables failed to meet statistical standards for classification accuracy in all 3 studies, confirming the inability to distinguish respondents by survey method using attitudinal variables. Polar reclassification of attitudinal responses into bichotomous categories led to identical managerial conclusions in 12 of 13 Likert-scaled questions, irrespective of survey method. Results support my hypothesis of no significant differences in hunter attitudes between random and convenience samples. These findings identify an opportunity to increase stakeholder feedback at reduced costs using web-based surveys. I suggest that survey methodology be carefully linked to survey objectives, and that open web surveys may be used to supplement random surveys in investigations of stakeholder attitudes to inform development of natural resource policy.
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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 | 0.030 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".