%{search_type} search results

Multiple price lists are a convenient tool to elicit willingness to pay in surveys and experiments, but choice patterns such as "multiple switching" and "never switching" indicate high error rates. Existing measurement approaches often do not provide accurate standard errors and cannot correct for bias due to framing and order effects. This paper proposes to combine a randomization approach with a random-effects latent utility model to detect bias and account for error. Data from a choice experiment in South Africa shows that significant order effects exist which, if uncorrected, would lead to distorted conclusions about subjects' preferences. Templates are provided to create a multiple price list survey instrument in SurveyCTO and analyze the resulting data using the proposed methods