2009
DOI: 10.1093/erae/jbp012
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Modelling attribute non-attendance in choice experiments for rural landscape valuation

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Cited by 310 publications
(303 citation statements)
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“…Class 2, which accounts for 28.97 % of the sample has an insignificant parameter estimate for the money variable indicating that these respondents were ignoring, or non-attending, this Standard errors in parentheses * p < 0.10; ** p < 0.05; *** p < 0.01 attribute or may have negligible utility from this attribute (see for example Scarpa et al 2009;Lagarde 2013). Those respondents' whose households have received a negative arsenic test are more likely to be a member of Class 2 than those with a positive result.…”
Section: Latent Class Analysismentioning
confidence: 99%
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“…Class 2, which accounts for 28.97 % of the sample has an insignificant parameter estimate for the money variable indicating that these respondents were ignoring, or non-attending, this Standard errors in parentheses * p < 0.10; ** p < 0.05; *** p < 0.01 attribute or may have negligible utility from this attribute (see for example Scarpa et al 2009;Lagarde 2013). Those respondents' whose households have received a negative arsenic test are more likely to be a member of Class 2 than those with a positive result.…”
Section: Latent Class Analysismentioning
confidence: 99%
“…This potential confounding may lead to a misinterpretation of the insignificant parameter estimate as non-attendance when it could be related to low or insignificant preferences. A commonly used method in the literature is to impose a parameter restriction to account for non-attendance of an attribute in the latent class model (see for instance Burton and Rigby 2009;Scarpa et al 2009;Campbell et al 2011;Lagarde 2013). To investigate this issue further we re-estimate the latent class models of Table 11 with the parameters for the money and work attributes constrained to be zero for the MAP and WAP models respectively.…”
Section: Latent Class Analysismentioning
confidence: 99%
“…It is hypothesised that respondents who state that they have considered several or all attributes when stating their choices have taken more time for this task. Further, an equality-constrained latent class model is used to infer attribute attendance from choice data and test the influence of response time on class membership (Scarpa et al 2009(Scarpa et al , 2012. These analyses are intended to shed greater light on the question of whether response time is an appropriate proxy for cognitive effort in stated preference surveys.…”
Section: Research Questionsmentioning
confidence: 99%
“…The two econometric models employed in this study are the equality-constrained latent class (ECLC) (Scarpa et al 2009(Scarpa et al , 2012 and the generalised multinomial logit (GMNL) models (Fiebig et al 2010;Gu et al 2013). In all models, utility of respondent n from choosing option i in choice occasion t is assumed to consist of representative utility explained by the vector x nit consisting of alternative-and respondent-specific attributes and its coefficient vector β n and an unobserved error term ε nit according to…”
Section: Equality-constrained Latent Class (Eclc) and Generalised Mulmentioning
confidence: 99%
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