Analysing decision behavior styles in contingent valuation: The latent class and the factor analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A better understanding of respondents' decision behaviors in contingent valuation (CV) is essential to reveal the true preferences of the public for environmental goods or services. Although the theoretical foundation of CV is based on the assumption of the full rationality of respondents, the literature provides various evidence of limited or partial rationality. In a CV survey of air quality improvement in China, we identified non-rational decision behavior style by adopting the latent class analysis and factor analysis methods, both of which are based on a series of questions related to decision behaviors initially proposed by Frör (2008). The application of the latent class model proposes the identification of two or three classes, with at least one more analytical reasoning group significantly differing from the other group(s). The factor analysis approach allowed us to identify two decision behavior factors, i.e., the analytical reasoning factor and the non-analytical reasoning factor. Our estimation results show that the analytical reasoning style is positively correlated with willingness-to-pay (WTP). Furthermore, the mediation tests conducted in the WTP determination models reveal that simply including respondents' socioeconomic, knowledge and perception characteristic questions in the survey to collect the information does not ensure that all the information conveyed by people's decision behavior style is captured. • Understanding people's decision behaviors in environmental valuation is essential. • Latent class analysis and factor analysis are used to identify decision behaviors. • Both analytical reasoning and non-analytical reasoning styles are identified. • The higher the analytical reasoning, the higher the mean WTP. • Decision behaviors have mutual and partial mediation effects with other variables.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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 it