A Closer Look at Decision and Analyst Error by Including Nonlinearities in Discrete Choice Models: Implications on Willingness-to-Pay Estimates Derived from Discrete Choice Data in Healthcare

Complex indirect utility functions reduce error arisen from researchers, which can have important implications for measures in healthcare such as the WTP, whereas EMNL provides insights into the behaviour of respondents when answering DCEs. Understanding how respondents answer DCE questions may allow researchers to construct DCEs that minimise scale differences, so that the decision error made across respondents is more homogeneous and therefore taken out as additional noise in the data. Hence, better estimates of preferences in healthcare can be provided. </AbstractSection> Copyright Springer International Publishing Switzerland 2013