A Comparison of Criteria to Design Efficient Choice Experiments

By Roselinde Kessels, Peter Goos, Martina Vandebroek. 2006

252 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4f804303f192d8b5f8a2fd99aa7b4396a639253d

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Citations in the corpus

Contemporary Guidance for Stated Preference Studies (2017, methodology): “Statistical design properties (e.g., D-efficiency, D-optimality, C-efficiency, S-efficiency) are derived based on an assumed statistical model and set of intended hypothesis tests (Kessels, Goos, and Vandebroek 2006).”
Bayesian optimal designs for discrete choice experiments with partial profiles (2011, methodology): “Huber and Zwerina (1996), Sándor and Wedel (2001), Kessels et al. (2006, 2009, 2011a), Scarpa and Rose (2008), among others, implemented different design criteria or functions of the information matrix (5) for constructing optimal discrete choice designs.”
Models and Optimal Designs for Conjoint Choice Experiments Including a No-Choice Option (2008, methodology): “For the NLMNL model however, we used 250 draws only because its complicated information matrix slowed down the design construction algorithm, which was a modified Fedorov algorithm like the one in Kessels, Goos and Vandebroek (2006a) .”
Working Paper: The Fragmentation of Cities and its Decline: Global Evidence of Sprawl, 1990-2000 (2009, unknown): “Recent new and innovative methods have been developed to allocate the attribute levels to the design matrix that do not rely on orthogonality as a design principle (see e.g., Bliemer and Rose 2006; Carlsson and Martinsson 2002; Ferrini and Scarpa 2007; Huber and Zwerina 1996; Kanninen 2002; Kessels ”
Nudging Energy Efficiency Behavior: The Role of Information Labels (2014, unknown): “…but also depends on the underlying choice probabilities, the choice sets fielded for each treatment were constructed using Bayesian methods for statistically efficient experimental design (Sándor and Wedel 2002; Kessels, Goos, and Vandebroek 2006; Ferrini and Scarpa 2007; Kessels et al. 2009).”
An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs (2009, methodology): “Table 1 compares the criterion values of these designs with the criterion values obtained by Kessels et al. (2006) using the MCMF algorithm.”
Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information (2008, background): “Each Choice Set Louviere et al. (1999) and Hensher et al. (1999) discuss various ways to collect responses from persons consistent with RUT, showing how various response modes can be used to obtain extra choice information that can be expanded into implied choices (see also, Brazell et al., 2006).”
Estimating the willingness-to-pay and value of risk reduction for car occupants in the road environment (2009, methodology): “Recent new and innovative methods have been developed to allocate the attribute levels to the design matrix that do not rely on orthogonality as a design principle (see e.g., Bliemer and Rose 2006; Carlsson and Martinsson 2002; Ferrini and Scarpa 2007; Huber and Zwerina 1996; Kanninen 2002; Kessels”
Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why* (2008, unknown): “This measure, proposed by Kessels et al. (2006), is not explicitly meant to be used as a measure of design efficiency, however we use it here as a means of attempting to remove alternatives that may be dominated.”
Efficient stated choice experiments for estimating nested logit models (2009, methodology): “…a small but growing number of researchers have called into question this practice (e.g., Bliemer and Rose, 2006; Carlsson and Martinsson, 2003; Ferrini and Scarpa, 2007; Huber and Zwerina, 1996; Kanninen, 2002; Kessels et al., 2006; Sándor and Wedel, 2001, 2002, 2005; Rose and Bliemer, 2006).”
Experimental design influences on stated choice outputs: An empirical study in air travel choice (2009, methodology): “…since including, but not limited to work undertaken by Bliemer et al. (2009), Burgess and Street (2003), Ferrini and Scarpa (2006), Kanninen (2002), Kessels et al. (2006), Sándor and Wedel (2001, 2002, 2005), Rose and Bliemer (2008), Scarpa and Rose (2008), Street and Burgess (2004) and Toner et…”
Approximation of bayesian efficiency in experimental choice designs (2008, unknown): “(2) Other efficiency measures have been proposed in the literature, such as the Gerror and V-error, both focusing on prediction accuracy instead of estimation accuracy (see Kessels et al. 2006).”
Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study (2007, methodology): “However, the concept of D-optimality or, more appropriately, D-efficiency [45,7]—which is based on the minimization of the variance–covariance matrix—has dominated the design literature for CEs because—despite being geared towards parameter estimation— it still performs well in prediction, and it is”
Construction of experimental designs for mixed logit models allowing for correlation across choice observations (2010, methodology): “Several researchers have already addressed the issue of how to construct experimental designs that display favorable statistical properties (e.g., Bliemer and Rose, 2006; Bliemer et al., 2007; Carlsson and Martinsson, 2002; Ferrini and Scarpa, 2007; Huber and Zwerina, 1996; Kanninen, 2002; Kessels e”
Constructing Efficient Stated Choice Experimental Designs (2009, background): “Other researchers however set up the design matrix such that each row of the matrix represents an individual alternative and each column a different attribute (see, e.g. Huber and Zwerina, 1996; Sándor and Wedel, 2001, 2002; Carlsson and Martinsson, 2002; Kanninen, 2002; Kessels et al., 2006).”
The state of the art of environmental valuation with discrete choice experiments (2010, background): “However, theuseof orthogonaldesigns inDCEshas been recently challenged (Huber and Zwerina, 1996; Kessels et al., 2006).”