Sample size requirements for stated choice experiments

Abstract

Stated choice (SC) experiments represent the dominant data paradigm in the study of behavioral responses of individuals, households as well as other organizations, yet in the past little has been known about the sample size requirements for models estimated from such data. Traditional orthogonal designs and existing sampling theories does not adequately address the issue and hence researchers have had to resort to simple rules of thumb or ignore the issue and collect samples of arbitrary size, hoping that the sample is sufficiently large enough to produce reliable parameter estimates, or are forced to make assumptions about the data that are unlikely to hold in practice. In this paper, we demonstrate how a recently proposed sample size computation can be used to generate so-called S-efficient designs using prior parameter values to estimate panel mixed multinomial logit models. Sample size requirements for such designs in SC studies are investigated. In a numerical case study is shown that a D-efficient and even more an S-efficient design require a (much) smaller sample size than a random orthogonal design in order to estimate all parameters at the level of statistical significance. Furthermore, it is shown that wide level range has a significant positive influence on the efficiency of the design and therefore on the reliability of the parameter estimates.