Optimality and efficiency requirements for the design of stated choice experiments

To assess the value of aspects of transport alternatives researchers often apply survey techniques that allow them to explore public preferences for hypothetical scenarios. A widely-used standard survey technique for this purpose has been the contingent valuation method. In the last twenty years, stated preferences (SP) approaches have been used in several contexts of interest, including transport analysis. SP is a technique that can be used to assess values for attributes of market or non-market goods based on survey respondents' willingness to trade-off different bundles of these attributes. Most recently, the Stated Choice approach has been the most widely used of the Stated Preference methods, as it is believed to approximate most closely to actual consumer behaviour. Because of cost considerations, sample sizes are often limited to the smallest that researchers consider necessary. By employing optimal survey design techniques, practitioners can increase the informational content of each observation, producing the equivalent effect of a larger sample size. In this context, the main goal of this paper is to describe a procedure to construct optimal SP designs that, given a fixed number of observations, will provide the most information possible about parameter estimators of interest, such as mean or median willingness to pay. In so doing, the paper aims to extend the existing literature on the optimal design of surveys to apply discrete choice modelling (DCM) in three ways: first, by arguing the limited applicability of the concepts of traditional conjoint analysis to build choice sets to apply DCM, second, by analysing the different and sometimes competing ways to define the optimality required, and third, by discussing the influence on design optimality of the ultimately more important issues of reliability and credibility of the responses. The paper also addresses with the dissimilar criteria that have been used in the literature to measure the covariance matrices of the parameters to be estimated. The different ways to calculate these matrices and the assumptions on which their use is based are assessed, aiming to give practical advice to choice designers. The type of DCM that is going to be applied over the responses collected also affects the process to design such survey. The issue of orthogonality in SP designs is addressed; this is a property often sought by designers, since when it applies the parameter estimates are independent in their explanation of the observed responses, which does not necessarily imply that these estimates will be significant. This issue is particularly relevant for larger and more diverse designs. In conclusion, the paper offers a number of insights and results that will be of assistance to the designers of SP surveys in their attempts to maximise the effectiveness of survey budgets. For the covering abstract see ITRD E137145.

• Authors:
  • IBANEZ, J N
  • TONER, J
  • DALY, A
• Publication Date: 2007

Language

• English

Media Info

• Pagination: 22p
• Serial:
  • PROCEEDINGS OF THE EUROPEAN TRANSPORT CONFERENCE (ETC) 2007 HELD 17-19 OCTOBER 2007, LEIDEN, THE NETHERLANDS

Subject/Index Terms

• TRT Terms: Alternatives analysis; Behavior; Conferences; Design; Interviewing; Mathematical models; Stated preferences; Transportation
• Uncontrolled Terms: Selection
• ITRD Terms: 9000: Alternative; 9001: Behaviour; 8525: Conference; 9011: Design (overall design); 9147: Interview; 6473: Mathematical model; 9072: Selection; 6587: Stated preference; 1155: Transport
• Subject Areas: Design; Planning and Forecasting; Transportation (General); I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01095440
• Record Type: Publication
• Source Agency: Transport Research Laboratory
• Files: ITRD
• Created Date: Apr 25 2008 9:22AM