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Since their introduction in health economics in the early 1990s, research in the area of health care benefits valuation has seen an increased interest in the use of discrete choice experiments (DCEs). This is shown by the explosion of literature applying this technique to direct evaluation of different policy-relevant attributes of health care interventions as well as to look at other issues such as understanding labour supply characteristics, time preferences or uptake or demand forecasting (see Ryan and Gerard, 2003; Fiebig et al., 2005 for recent reviews).

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References

  • Adamowicz, W., Louviere, J. and Williams, M. 1994. Combining revealed and stated preference methods for valuing environmental amenities. Journal of Environmental Economics and Management, vol 26 (3) 271–292.

    Google Scholar 

  • Adamowicz, W., Louviere, J. and Swait, J. 1998. Introduction to attribute-based stated choice methods. Report to NOAA Resource Valuation Branch, Damage Assessment Centre, Advanis.

    Google Scholar 

  • Alberini, A., Longo, A. and Veronesi, M. 2006. Basic statistical models for conjoint choice experiments. In: Valuing Environmental Amenities using Choice Experiments: A Common Sense Guide to Theory and Practice. Kanninen, B. (ed.). Springer, pp 203–228. Series: The Economics of Non-Market Goods and Resources, vol 8, Series ed.: Bateman, I.

    Google Scholar 

  • Amaya-Amaya, M. and Ryan, M. 2003. Decision strategy switching in choice experiments: an exploratory analysis in health economics. Paper presented at Advancing the Methodology of Discrete Choice Experiments in Health Care Workshop, Oxford, 22–23 September.

    Google Scholar 

  • Amaya-Amaya, M. and Ryan, M. (forthcoming). Between contribution and confusion: an investigation of the impact of complexity in stated preferences choice experiments. Journal of Health Economics.

    Google Scholar 

  • Andrews, R.L. and Currim, I.S. 2003. A Comparison of segment retention criteria for finite mixture logit models. Journal of Marketing Research, vol XL (2).

    Google Scholar 

  • Bateman, I. et al. 2006. http://www.uea.ac.uk/env/cserge/pub/wp/edm/edm_2006_16.pdf.

  • Ben-Akiva, M.E. and Bolduc, D. 1996. “Multinomial probit with a logit kernel an a general parametric specification of the covariance structure”. Working paper, Massachusetts Institute of Technology, Cambridge, MA.

    Google Scholar 

  • Ben-Akiva, M. and Lerman, S. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge, MA: MIT Press. http://elsa.berkeley.edu/reprints/misc/multinomial.pdf. Last accessed November 2006.

  • Ben-Akiva, M., Morikawa, T. and Shiroishi, F. 1992. Analysis of the reliability of preference ranking data. Journal of Business Research, vol 24, 149–164.

    Google Scholar 

  • Ben-Akiva, M.E. 1973. Structure of passenger travel demand models. Ph.D. thesis. Department of Civil Engineering, MIT Press, Cambridge, MA.

    Google Scholar 

  • Bennett, J. and Adamowicz, W. 2001. The Choice modelling approach to environmental valuation. In: Some Fundamentals of Environmental Choice Modelling. Bennett, J. and Blamey, R. (eds). Northampton: Edward Elgar, pp 37–69.

    Google Scholar 

  • Bhat, C.R. 1995. A heteroscedastic extreme value model of intercity travel mode choice. Transportation research, vol 29 (6), 471–486.

    Google Scholar 

  • Blamey, R.J. Bennett, J., Louviere, M., et al. 2000. A test of policy labels in environmental choice modeling studies. Ecological Economics, vol 32, 269–286.

    Google Scholar 

  • Bliemer, M.C.J. and Rose, J.M. 2005. Efficiency and sample size requirements for stated choice studies. Submitted for publication to Transportation Research B. Working paper can be accessed at http://www.its.usyd.edu.au/publications/working_papers/wp2005/itls_wp_05–08.pdf.

  • Bock, R.D. and Jones, L. 1968. The Measurement and Prediction of Judgement and Choice. San Francisco: Holden-Day.

    Google Scholar 

  • Bolduc, D., Fortin, B. and Fournier, M.A. 1996. The impact of incentive policies to influence practice location of general practitioners: a multinomial probit analysis. Journal of Labor Economics, vol 14, 703–732.

    Google Scholar 

  • Borsch-Supan, A. 1987. Econometric analysis of discrete choice: with applications on the demand for housing in the US and West Germany. Lectures notes in Economics and Mathematical Systems, Heidelberg: Springer-Verlag.

    Google Scholar 

  • Boxall, P.C. and Adamowicz, V.L. 2002. Understanding heterogeneous preferences in random utility models: the use of latent class analysis. Environmental and Resource Economics, vol 23 (4), 421–446.

    Google Scholar 

  • Boyle, K.J., Holmes, T.P., Teisl, M.F. and Roe, B. 2001. A Comparison of conjoint analysis response formats. American Journal of Agricultural Economics, vol 83 (2), 441–454.

    Google Scholar 

  • Bozdogan, H. 1993. Choosing the number of component clusters in the mixture model using a new informational complexity criterion of the inverse Fisher information matrix. In: Information and Classification. Concepts, Methods and Applications, Proceedings of the 16th annual conference of the “Gesellschaft für Klassifikation e.V”.. Opitz, O., Lausen, B. and Klar, R. (eds). Universität Dortmund, 1–3 April 1992, Berlin: Springer-Verlag, pp 40–54.

    Google Scholar 

  • Bozdogan, H. 1994. Mixture model cluster analysis using model selection criteria and a new informational measure of complexity. In: Multivariate Statistical Modeling. Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling. An Informational Approach. Bozdogan, H. (ed.). Dordrecht: Kluwer Academic, pp 69–113.

    Google Scholar 

  • Brownstone, D. and Train, K. 1999. Forecasting new product penetration with flexible substitution patterns. Journal of Econometrics, vol 89, 109–129.

    Google Scholar 

  • Bryan, S., Buxton, M., Sheldon, R. and Grant, A. 1998. Magnetic resonance imaging for the investigation of knee injuries: an investigation of preferences. Health Economics, vol 7, 595–603.

    Google Scholar 

  • Burgess, L. and Street, D.J. 2003. Optimal designs for 2k choice experiments. Communications in Statistics–Theory and Methods, vol 32, 2185–2206.

    Google Scholar 

  • Burgess, L. and Street, D. 2005. Optimal designs for choice experiments with asymmetric attributes. Journal of Statistical Planning and Inference, vol 134, 288–301.

    Google Scholar 

  • Butler, J. and Moffit, R. 1982. A computationally efficient quadrature procedure for the one-factor multinomial probit model. Econometrica, vol 50, 761–764.

    Google Scholar 

  • Caussade, S., Ortúzar, J., de D Rizzi, L.I. and Hensher, D.A. 2005. Assessing the influence of design dimensions on stated choice experiment estimates. Transportation Research Part B, vol 39 (7), 621–640.

    Google Scholar 

  • Champ, P.A. 2003. Collecting survey data collection for nonmarket valuation. In: A Primer on Nonmarket Valuation. Champ, P.A., Boyle, K. and Brown, T.C. (eds). Boston: Kluwer Academic. Series: The Economics of Non-Market Goods and Resources. vol 3, Series ed.: Bateman, I., Chapter 3.

    Google Scholar 

  • Champ, P.A. and Welsh, M.P. 2006. Survey Methodologies for stated choice studies. In: Valuing Environmental Amenities using Choice Experiments: A Common Sense Guide to Theory and Practice. Kanninen, B. (ed.). Boston: Springer. Series: The Economics of Non-Market Goods and Resources, vol 8, Series ed.: Bateman, I., pp 21–42.

    Google Scholar 

  • Chipman, J. 1960. The Foundations of utility. Econometrica, vol 28, 193–224.

    Google Scholar 

  • Chipman, J. and Moore, J. 1990. Acceptable indicators of welfare change. In: Preferences, Uncertainty, and Optimality. Chipman, J., McFadden, D. and Moore, J. (eds). Boulder: Westview Press.

    Google Scholar 

  • Cochran, W.G. 1977. Sampling Techniques. New York: Wiley.

    Google Scholar 

  • Daganzo, C. 1979. Multinomial Probit: The Theory and its Applications to Demand Forecasting. New York: Academic Press.

    Google Scholar 

  • Debreu, G. 1960. Review of R. D. Luce individual choice behavior. American Economic Review, vol 50, 186–188.

    Google Scholar 

  • Dellaert, B., Brazell, J. and Louviere, J. 1999. The effect of attribute variation on consumer choice consistency. Marketing Letters, vol 10 (2), 139–147.

    Google Scholar 

  • Deshazo, J.R. and Fermo, G. 2002. Designing choice sets for stated preference methods: the effects of complexity on choice consistency. Journal of Environmental Economics and Management, vol 44 (1), 123–143.

    Google Scholar 

  • Diamond, P. and McFadden, D. 1974. Some uses of the expenditure function in public finance. Journal of Public Economics, vol 3, 3–21.

    Google Scholar 

  • Domencich, T. and McFadden, D. 1975. Urban Travel Demand: A Behavioral Analysis. Amsterdam: North-Holland.

    Google Scholar 

  • Fiebig, D., Louviere, J. and Waldman, D. 2005. Contemporary issues in modelling discrete choice experimental data in health economics. Working paper, University of New South Wales. http://wwwdocs.fce.unsw.edu.au/economics/staff/DFIEBIG/ContemporaryissuesHEv120Apr05.pdf. Last accessed 13 July 2006.

  • Greene, W. 2001. Fixed and random effects in nonlinear models. Working Paper EC-01–01, Stern School of Business, Department of Economics. http://www.stern.nyu.edu/eco/wkpapers/workingpapers01/EC-01–01.pdf. Last accessed November 2006.

  • Greene, W.H. 2000. Econometric Analysis. New Jersey: Prentice Hall.

    Google Scholar 

  • Gyrd-Hansen, D. 2000. Cost benefit analysis of mammography screening in Denmark based on discrete ranking data. International Journal of Health Technology Assessment in Health Care, vol 16 (3), 811–821.

    Google Scholar 

  • Haaijer, R., Wagner Kamakura, A. and Wedel, M. 2001. The “no choice” alternative in conjoint choice experiments. International Journal of Market Research, vol 43, 93–106.

    Google Scholar 

  • Hajivassiliou, V., McFadden, D. and Ruud, P. 1996. Simulation of multivariate normal rectangle probabilities and their derivatives: theoretical and computational results. Journal of Econometrics, vol 72, 85–134.

    Google Scholar 

  • Hall, J., Kenny, P., King, M., Louviere, J.J., Viney, R. and Yeoh, A. 2002. Using stated preference discrete choice modelling to evaluate the introduction of varicella vaccination. Health Economics, vol 11, 457–465.

    Google Scholar 

  • Hall, J., Fiebig, D., King, M., Hossain, I. and Louviere, J.J. 2006. What influences participation in genetic carrier testing? Results from a discrete choice experiment. Journal of Health Economics, vol 25, 520–537.

    Google Scholar 

  • Hanemann, W.M. 1982. Applied welfare analysis with qualitative response models. Working paper No. 241. Department of Agricultural and Resource Economics, University of California, Berkeley.

    Google Scholar 

  • Hanemann, W.M. 1984. Discrete/continuous models of consumer demand. Econometrica, vol 52 (3), 541–561.

    Google Scholar 

  • Hanley, N., Mourato, S. and Wright, R. 2001. Choice modelling approaches: a superior alternative for environmental evaluation? Journal of Economic Surveys, vol 15 (3), 453–557.

    Google Scholar 

  • Hausman, J. and McFadden, D. 1984. Specification tests for the multinomial logit model. Econometrica, vol 52, 1219–1240.

    Google Scholar 

  • Hausman, J.A. and Wise, D.A. 1978. A conditional probit model for qualitative choice: discrete decisions recognising interdependence and heterogeneous preferences. Econometrica, vol 46, 403–426.

    Google Scholar 

  • Hensher, D. 2006. Revealing differences in willingness to pay due to the dimensionality of stated choice designs: an initial assessment. Environmental and Resource Economics, vol 34 (1), 7–44.

    Google Scholar 

  • Hensher, D., Rose, J. and Greene, W. 2005. Applied Choice Analysis: A Primer. Cambridge: Cambridge University Press.

    Google Scholar 

  • Hensher, D.A. and Greene, W. 2003a. The mixed logit model: the state of practice. Transportation, vol 30 (2), 133–176.

    Google Scholar 

  • Hensher, D.A. and Greene, W. 2003b. A latent class model for discrete choice analysis: contrasts with mixed logit. Transportation Research Part B, vol 37, 681–698.

    Google Scholar 

  • Hensher, D.A. and Johnson, L.W. 1981. Behavioural response and form of the representative component of the indirect utility function in travel mode choice. Regional Science and Urban Economics, vol 11, 559–572.

    Google Scholar 

  • Herriges, J. and Kling, C. 1999. Nonlinear income effects in random utility models. Review of Economics and Statistics, vol 81, 62–72.

    Google Scholar 

  • Hicks, J. 1939. Value and Capital. Oxford: Oxford University Press.

    Google Scholar 

  • Huber, J. and Zwerina, K. 1996. The importance of utility balance in efficient choice designs. Journal of Marketing Research, vol 33, 307–317.

    Google Scholar 

  • Johnson, F.R., Desvousges, W.H., Ruby, M.C, Stieb, D. and De Civita, P. 1998. Eliciting stated preferences: an application to willingness to pay for longevity. Medical Decision Making, vol 18 (Suppl), S57–S67.

    Google Scholar 

  • Johnson, F.R., Banzhaf, M.R. and Desvousges, W.H. 2000. Willingness to pay for improved respiratory and cardiovascular health: a multiple-format stated-preference approach. Health Economics, vol 9, 295–317.

    Google Scholar 

  • Kanninen, B. 2007. Valuing environmental amenities using stated choice studies: A common sense approach to theory and practice. Holland: Springer.

    Google Scholar 

  • Kanninen, B.J. 2002. Optimal design for multinomial choice experiments. Journal of Marketing Research, vol 39, 214–217.

    Google Scholar 

  • Kanninen, B.J. 2005. Optimal design for binary choice experiments with quadratic or interactive terms. Paper presented at the 2005 International Health Economics Association conference, Barcelona, July.

    Google Scholar 

  • Lancaster, K. 1966. A new approach to consumer theory. Journal of Political Economy, vol 74, 132–157.

    Google Scholar 

  • Lancsar, E. and Louviere, J.J. 2006, Deleting “irrational” responses from discrete choice experiments: a case of investigating or imposing preferences? Health Economics, vol 15 (8), 797–811.

    Google Scholar 

  • Lancsar, E. and Savage, E. 2004. Deriving welfare measures from discrete choice experiments: inconsistency between current methods and random utility and welfare theory. Health Economics, vol 13 (9), 901–907.

    Google Scholar 

  • Leigh, T., MacKay, D. and Summers, J. 1984. Reliability and validity of conjoint analysis and self explicated weights: a comparison. Journal of Marketing Research, vol 21, 456–462.

    Google Scholar 

  • Louviere, J.J. 2000. “Why Stated Preference Discrete Choice Modelling is NOT Conjoint Analysis (and what SPDCM IS)”, Memetrics white paper.

    Google Scholar 

  • Louviere, J.J. 2001. Choice experiments: an overview of concepts and issues. In: The Choice Modelling Approach to Environmental Valuation. Bennett, J. and Blamey, R. (eds). Northhampton: Edward Elgar, pp 13–36.

    Google Scholar 

  • Louviere, J.J. 2006. What you don’t know might hurt you: some unresolved issues in the design and analysis of discrete choice experiments. In: Special Issue: Frontiers on Stated Choice Methods. Adamowicz, W. and Deshazo, J.R. (eds). Environmental and Resource Economics, vol 34, 173–188.Louviere, J.J., Hensher, D. and Swait, J. 2000. Stated Choice Methods: Analysis and Applications in Marketing, Transportation and Environmental Valuation. Cambridge/England: Cambridge University Press.

    Google Scholar 

  • Louviere, J.J., Street, D., Carson, R., et al. 2002. Dissecting the random component of utility. Marketing Letters, vol 13, 177–193.

    Google Scholar 

  • Manski, C. 1977. The Structure of random utility models. Theory and Decision, vol 8, 29–254.

    Google Scholar 

  • March, J.G. 1978. Bounded rationality, ambiguity and the engineering of choice. Bell Journal of Economics, vol 9, 587–608.

    Google Scholar 

  • Marschak, J. 1960. Binary choice constraints on random utility indicators. In: Stanford Symposium on Mathematical Methods in the Social Sciences. Arrow, K. (ed.). Stanford: Stanford University Press.

    Google Scholar 

  • Mas-Collel, A., Whinston, M.D. and Green, J.R. 1995. Microeconomic Theory. Oxford: Oxford University Press.

    Google Scholar 

  • McFadden, D. 1974. Conditional logit analysis of qualitative choice behavior. In: Frontiers in Econometrics. Zarembka, P. (ed.). New York: Academic Press, pp 105–142.

    Google Scholar 

  • McFadden, D. 1978. Modeling the choice of residential location. In: Spatial Interaction Theory and Planning Models. Karlqvist, A., Lundqvist, L., Snickars, F. and Weibull, J. (eds). Amsterdam: North-Holland, pp 75–96.

    Google Scholar 

  • McFadden, D. 1979. Quantitative methods for analyzing travel behavior of individuals: some recent developments. In: Behavioural Travel Modelling. Hensher, D. and Stopher, P. (eds). London: Croom Heml, pp 279–318.

    Google Scholar 

  • McFadden, D. 1981. Econometric models of probabilistic choice. In: Structural Analysis of Discrete Data with Econometric Applications. Manski, C. and McFadden, D. (eds). Cambridge: MIT Press, pp 198–272.

    Google Scholar 

  • McFadden, D. 1986. The choice theory approach to market research. Marketing Science, vol 5 (4), 275–297.

    Google Scholar 

  • McFadden, D. 1997. Measuring willingness-to-pay in discrete choice models. In: Essays in Honor of John Chipman. Moore, J. and Hartman, R. (eds). London/New York: Routledge.

    Google Scholar 

  • McFadden, D. 1999. Computing willingness-to-pay in random utility models. In: Trade Theory and Econometrics. Moore, J., Reizman, R. and Melvin, J. (eds). London: Routledge, pp 253–274.

    Google Scholar 

  • McFadden, D. and Train, K. 2000. Mixed MNL models for discrete response. Journal of Applied Econometrics, vol 15, 447–470.

    Google Scholar 

  • Morey, E.R. and Rossman, K.R. 2004. Calculating with varying types of income effects, closed-form solutions for the compensating variation associated with a change in the state of the world. July 2004. http://www.colorado.edu/Economics/morey/papers/MoreyStatetoStateCV06282006.pdf.

  • Payne, J.W., Bettman, J.R. and Johnson, E.J. 1993. The Adaptive Decision Maker. Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Puig-Junoy, J., Saez, M., Martinez-Garcia, E. (1998). “Why do patients prefer hospital emergency visits? A nested multinomial logit analysis for patient-initiated contacts”. Health Care Management Science, vol 1 (1), 39–52.

    Google Scholar 

  • Revelt, D. and Train, K. 2000. Specific taste parameters and mixed logit. Working paper No. E00–274, Department of Economics, University of California, Berkeley.

    Google Scholar 

  • Revelt, D. and Train, T. 1998. Mixed logit with repeated choices: households’ choice of appliance efficiency level. Review of Economics and Statistics, vol LXXX (4), 647–657.

    Google Scholar 

  • Rose, J. and Bliemer, M. 2004. The design of stated choice experiments: the state of the practice and future challenges Institute Transport and Logistics Studies. Working paper 04–09 http://www.its.usyd.edu.au/publications/working_papers/wp2004/its_wp_04–09.pdf.

  • Rose, J. and Bliemer, M. 2005. Constructing efficient choice experiments. ITLS Working Paper ITLS-WP-05–07. Download/http://www.its.usyd.edu.au/publications/working-papers/wp2005/itls-wp-05–7.pdf.

  • Rose, J. and Bliemer, M. (forthcoming). Stated preference experimental design strategies. In: Handbook in Transport Modelling. Hensher, D.A. and Button, K. (Series and volume eds). Oxford: Pergamon Press.

    Google Scholar 

  • Ryan, M. 1999. Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilization. Social Science and Medicine, vol 48 (4), 535–546.

    Google Scholar 

  • Ryan, M. and Farrar, S. 1994. A pilot study using conjoint analysis to establish the views of users in the provision of orthodontic services in Grampian. Health Economics Research Unit Discussion Paper No 07/94, University of Aberdeen, Aberdeen.

    Google Scholar 

  • Ryan, M. and Gerard, K. 2003. Using discrete choice experiments to value health care programmes: current practice and future research reflections. Applied Health Economics and Health Policy, vol 2 (1), 55–64.

    Google Scholar 

  • Ryan, M. and Hughes, J. 1997. Using conjoint analysis to assess women’s preferences for miscarriage management. Health Economics, vol 6, 261–273.

    Google Scholar 

  • Ryan, M. and Skåtun, D. 2004. Modelling non-demanders in discrete choice experiments. Health Economic Letters, vol 13, 397–402.

    Google Scholar 

  • Ryan, M. and Wordsworth, S. 2000. Sensitivity of willingness to pay estimates to the level of attributes in discrete choice experiments. Scottish Journal of Political Economy, vol 47, 504–524.

    Google Scholar 

  • Ryan, M., McIntosh, E., Dean, T. and Old, P. 2000. Trade-offs between location and waiting times in the provision of health care: the case of elective surgery on the Isle of Wight. Journal of Public Health Medicine, vol 22, 202–210.

    Google Scholar 

  • Ryan, M., Major, K. and Skåtun, D. 2005. Using discrete patient choices to go beyond clinical outcomes when evaluating clinical practice. Journal of Evaluation in Clinical Practice, vol 11, 328–339.

    Google Scholar 

  • San Miguel, F., Ryan, M. and Amaya-Amaya, M. 2004. Irrational stated preferences: a quantitative and qualitative investigation. Health Economics, vol 14 (13), 307–322.

    Google Scholar 

  • Sándor, Z. and Wedel, M. 2001. Designing conjoint choice experiments using managers’ prior beliefs. Journal of Marketing Research, vol 38, 430–444.

    Google Scholar 

  • Sándor, Z. and Wedel, M. 2002. Profile construction in experimental choice designs for mixed logit models. Marketing Science, vol 21 (4), 455–475.

    Google Scholar 

  • Sándor, Z. and Wedel, M. 2005. Heterogeneous conjoint choice designs. Journal of Marketing Research, vol 42, 210–218.

    Google Scholar 

  • Santos Silva, J.M.C. 2004. Deriving welfare measures in discrete choice experiments: a comment to Lancsar and Savage. Health Economics, vol 13 (9), 913–918.

    Google Scholar 

  • Scarpa, R., Willins, K.G. and Acutt, M. 2004. Comparing individual-specific benefit estimates for public goods: finite versus continuous mixing in logit models. Foundation Eni Enrico Mattei Nota Di Lavoro 132.2004.

    Google Scholar 

  • Scott, A., Watson, M.S. and Ross, S. 2003. Eliciting preferences of the community for out of hours care provided by general practitioners: a stated preference discrete choice experiment. Social Science and Medicine, vol 56, 803–814.

    Google Scholar 

  • Severin, V. 2001. Comparing statistical and respondent efficiency in choice experiments. Unpublished Ph.D. dissertation. Department of Marketing, University of Sydney, Sydney, Australia.

    Google Scholar 

  • Severin, V.C., Burgess, L., Louviere, J. and Street, D.J. 2004. Comparing statistical efficiency and respondent efficiency in choice experiments. Research report to the Department of Mathematical Sciences, University of Technology, Sydney, Australia.

    Google Scholar 

  • Simon, H.A. 1955. A behavioural model of rational choice. Quarterly Journal of Economics, vol 69, 99–118.

    Google Scholar 

  • Small, K.A. and Rosen, H.S. 1981. Applied welfare economics with discrete choice models. Econometrica, vol 49 (3), 105–130.

    Google Scholar 

  • Stern, S. 2000. Simulation based inference in econometrics: motivation and methods. In: Simulations-based inference in econometrics: methods and applications. Mariano, R., Schuermann, T. and Weeks, M.J. (eds). Cambridge: Cambridge University Press.

    Google Scholar 

  • Street, D.J., Bunch, D.S. and Moore, B.J. 2001. Optimal designs for 2k paired comparison experiments. Communications in Statistics, Theory, and Methods, vol 30 (10), 2149–2171.

    Google Scholar 

  • Street, D.J., Burgess, L. and Louviere, J.J. 2005. Quick and easy choice sets: Contructing optimal and nearly optimal stated choice experiments. International Journal of Research in Marketing, vol 22, 459–470.

    Google Scholar 

  • Swait, J. 1994. A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data. Journal of Retail and Consumer Services, vol 1 (2), 77–89.

    Google Scholar 

  • Swait, J. 2006. Advanced choice models. In: Valuing Environmental Amenities Using Stated Choice Studies: A Common Sense Approach to Theory and Practice. Kanninen, B. (ed.). Dordrecht: Springer. Series: The Economics of Non-Market Goods and Resources, vol 8, Series ed.: Bateman, I., Chapter 9.

    Google Scholar 

  • Swait, J. and Adamowicz, W. 2001a. Choice environment, market complexity, and consumer behaviour: a theoretical and empirical approach for incorporating decision complexity into models of consumer choice. Organizational Behaviour and Human Decision Processes, vol 86 (2), 141–167.

    Google Scholar 

  • Swait, J. and Adamowicz, W. 2001b. The influence of task complexity on consumer choice: a latent class model of decision strategy switching. Journal of Consumer Research, vol 28, 135–148.

    Google Scholar 

  • Swait, J. and Louviere, J.J. 1993. The role of the scale parameter in the estimation and comparison of multinational logit models. Journal of Marketing Research, vol 30, 305–314.

    Google Scholar 

  • Swait, J. and Sweeney, J. 2000. Perceived value and its impact on choice behaviour in a retail setting. Journal of Retailing and Consumer Services, vol 7 (2), 77–88.

    Google Scholar 

  • Thurstone, L.L. 1927. A law of comparative judgment. Psychological Review, vol 34, 273–286.

    Google Scholar 

  • Train, K. 2003. Discrete Choice Methods with Simulation. Cambridge: Cambridge University Press.

    Google Scholar 

  • Varian, H. 1984. Microeconomic Analysis, 2nd edn. New York: Norton.

    Google Scholar 

  • Viney, R., Lanscar, E. and Louviere, J. 2002. Discrete choice experiments to measure consumer preferences for health and healthcare. Expert Review of Pharmacoeconomics Outcomes Research, vol 2 (4), 319–326.

    Google Scholar 

  • von Haefen, R.H., Massey, D.M. and Adamowicz, W. 2005. Serial non-participation in repeated discrete choice models. American Journal of Agricultural Economics, vol 87 (4), 1061–1076.

    Google Scholar 

  • Walker, J. 2002. The mixed logit (or logit kernel) model: dispelling misconceptions of identification. Transportation Research Record, vol 1805, 86–98.

    Google Scholar 

  • Watson, V., Ryan, M., Barnett, G., Ellis, B., Emberton, M. and Brown, C. 2004. Eliciting preferences for drug treatment of lower urinary tract symptoms associated with benign prostatic hyperplasia. Journal of Urology, vol 172, 2321–2325.

    Google Scholar 

  • Williams, H.W.C.L. 1977. On the formation of travel demand models and economic evaluation measures of user benefit. Environment and Planning, vol A9, 285–344.

    Google Scholar 

  • Wordsworth, S., Ryan, M., Skåtun, D. and Waugh, N. 2006. Women’s preferences for cervical cancer screening: a study using a discrete choice experiment. International Journal of Technology Assessment in Health Care, vol 22 (3), 344–350.

    Google Scholar 

  • Yatchew, A. and Griliches, Z. 1984. Specification error in probit models. Review of Economics and Statistics, vol 66, 134–139.

    Google Scholar 

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Ryan, M., Gerard, K., Amaya-Amaya, M. (2008). Discrete Choice Experiments in a Nutshell. In: Ryan, M., Gerard, K., Amaya-Amaya, M. (eds) Using Discrete Choice Experiments to Value Health and Health Care. The Economics of Non-Market Goods and Resources, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5753-3_1

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