Analysing decision behavior styles in contingent valuation: The latent class and the factor analysis
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".