Observed efficiency of a d-optimal design in an interactive agency choice experiment
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Open Access
Type
Working PaperAbstract
There have been a number of recent calls within the choice literature to examine the role of social interactions upon preference formation. McFadden (2001a,b) recently stated that this area should be a high priority research agenda for choice modellers. Manski (2000) has also came ...
See moreThere have been a number of recent calls within the choice literature to examine the role of social interactions upon preference formation. McFadden (2001a,b) recently stated that this area should be a high priority research agenda for choice modellers. Manski (2000) has also came to a similar conclusion and offered a plea for better data to assist in understanding the role of interactions between social agents. The interactive agency choice experiment (IACE) methodology represents a recent development in the area of discrete choice directed towards these pleas (see e.g., Brewer and Hensher 2000). The study of the influences that group interactions have upon choice bring with them not only issues that need to be overcome in terms of modelling, but also in terms of setting up the stated choice experiment itself. Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher and Puckett 2007, Puckett et al. 2007, Puckett and Hensher 2008), one significant empirical constraint was difficulty in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Rose and Bliemer, 2006). The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.
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See moreThere have been a number of recent calls within the choice literature to examine the role of social interactions upon preference formation. McFadden (2001a,b) recently stated that this area should be a high priority research agenda for choice modellers. Manski (2000) has also came to a similar conclusion and offered a plea for better data to assist in understanding the role of interactions between social agents. The interactive agency choice experiment (IACE) methodology represents a recent development in the area of discrete choice directed towards these pleas (see e.g., Brewer and Hensher 2000). The study of the influences that group interactions have upon choice bring with them not only issues that need to be overcome in terms of modelling, but also in terms of setting up the stated choice experiment itself. Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher and Puckett 2007, Puckett et al. 2007, Puckett and Hensher 2008), one significant empirical constraint was difficulty in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Rose and Bliemer, 2006). The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.
See less
Date
2009-04-01Department, Discipline or Centre
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