Serial Choice Conjoint Analysis for Estimating Discrete Choice Models

Abstract

Stated choice experiments can be used to estimate the parameters in discrete choice models by showing hypothetical choice situations to respondents. These attribute levels in each choice situation are determined by an underlying experimental design. Often, an orthogonal design is used, although recent studies have shown that better experimental designs exist, such as efficient designs. These designs provide more reliable parameter estimates. However, they require prior information about the parameter values, which is often not readily available. Serial efficient designs are proposed in this paper in which the design is updated during the survey. In contrast to adaptive conjoint, serial conjoint only changes the design across respondents, not within-respondent thereby avoiding endogeneity bias as much as possible. After each respondent, new parameters are estimated and used as priors for generating a new efficient design. Results using the multinomial logit model show that using such a serial design, using zero initial prior values, provides the same reliability of the parameter estimates as the best efficient design (based on the true parameters). Any possible bias can be avoided by using an orthogonal design for the first few respondents. Serial designs do not suffer from misspecification of the priors as they are continuously updated. The disadvantage is the extra implementation cost of an automated parameter estimation and design generation procedure in the survey. Also, the respondents have to be surveyed in mostly serial fashion instead of all parallel.

Citation

Bliemer, M.C.J. and Rose, J.M. (2010), "Serial Choice Conjoint Analysis for Estimating Discrete Choice Models", Hess, S. and Daly, A. (Ed.) Choice Modelling: The State-of-the-art and the State-of-practice, Emerald Group Publishing Limited, Leeds, pp. 137-161. https://doi.org/10.1108/9781849507738-006