Biased estimates in discrete choice models: the appropriate inclusion of psychometric data into the valuation of recycled wastewater
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The introduction of measurement bias in parameter estimates into non-linear discrete choice models,
as a result of using factor analysis, was identified by Train et al. (1987). They found that the inclusion
of factor scores, used to represent relationships amongst like variables, into a subsequent discrete
choice models introduced measurement bias as the measurement error associated with each factor
score is excluded. This is an issue for non-market valuation given the increase in popularity of
including psychometric data, such as primitive beliefs, attitudes and motivations, in willingness to pay
estimates. This study explores the relationship between willingness to pay and primitive beliefs
through a case study eliciting Perth community values for drinking recycled wastewater. The standard
discrete decision model, with sequential inclusion of factor scores, is compared to an equivalent
discrete decision model, which corrects for the measurement bias by simultaneously estimating the
underlying latent variables using a measurement model. Previous research has focused on the issue of
biased parameters. Here we also consider the implications for willingness to pay estimates.