Comparing the State-of-the-Art Efficient Stated Choice Designs Based on Empirical Analysis

Table 5

Comparison of orthogonal, OOC, and 𝐷-efficient design.

Method	Advantage	Disadvantage
Orthogonal	(i) It is the most widely used method and easy to construct or obtain; (ii) There are no correlations between attribute levels; thus it allows for an independent estimation of the influence of each attribute on choice	(i) There are too many choice situations/questions for a single respondent; (ii) Orthogonally it is hard to maintain in actual design: subsets replicated unevenly, introducing sociodemographic variable and allocation bias of the implausible choice situation; (iii) It may contain “useless” choice situations
Optimal orthogonal choice	(i) Attribute level differences are maximized; (ii) Choice situations will be reduced as well as attaining the design’s orthogonality	(i) It can only generate designs for generic attributes; the rules for setting up alternative-specific attributes are not clear right now; (ii) Unreasonable combinations of attribute levels may appear; thus the “real choice” of respondents is hard to capture
𝐷-efficient	(i) The smaller the asymptotic standard errors achieved, the smaller the width of the confidence intervals observed around the parameters estimates will be; (ii) 𝑡-Radios will be maximized thus producing more reliable study results and analyst is able to minimize the sample size	(i) In general not orthogonal (not that important); (ii) Advanced knowledge of the parameter estimates is needed; (iii) It needs more computation power