Testing construct validity of verbal versus numerical measures of preference uncertainty in contingent valuation
The numerical certainty scale (NCS) and polychotomous choice (PC) methods are two widely used techniques for measuring preference uncertainty in contingent valuation (CV) studies. The NCS follows a numerical scale and the PC is based on a verbal scale. This paper presents results of two experiments that use these preference uncertainty measurement techniques. The first experiment was designed to compare and contrast the uncertainty scores obtained from the NCS and the PC method. The second experiment was conducted to test a preference uncertainty measurement scale which combines verbal expressions with numerical and graphical interpretations: a composite certainty scale (CCS). The construct validity of the certainty scores obtained from these three techniques was tested by estimating three separate ordered probit regression models. The results of the study can be summarized in three key findings. First, the PC method generates a higher proportion of ‘Yes’ responses than the conventional dichotomous choice elicitation format. Second, the CCS method generates a significantly higher proportion of certain responses than the NCS and the PC methods. Finally, the NCS method performs poorly in terms of construct validity. We conclude that, overall, the verbal measures perform better than the numerical measure. Furthermore, the CCS method is promising in measuring preference uncertainty in CV studies. However, further empirical applications are required to develop a better understanding of its strengths and the weaknesses.
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- Akter, Sonia & Brouwer, Roy & Brander, Luke & van Beukering, Pieter, 2009. "Respondent uncertainty in a contingent market for carbon offsets," Ecological Economics, Elsevier, vol. 68(6), pages 1858-1863, April.
- Alberini, Anna & Boyle, Kevin & Welsh, Michael, 2003. "Analysis of contingent valuation data with multiple bids and response options allowing respondents to express uncertainty," Journal of Environmental Economics and Management, Elsevier, vol. 45(1), pages 40-62, January.
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