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Alleviating the Constant Stochastic Variance Assumption in Decision Research: Theory, Measurement, and Experimental Test

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  • Linda Court Salisbury

    () (Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467)

  • Fred M. Feinberg

    () (Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

Analysts often rely on methods that presume constant stochastic variance, even though its degree can differ markedly across experimental and field settings. This reliance can lead to misestimation of effect sizes or unjustified theoretical or behavioral inferences. Classic utility-based discrete-choice theory makes sharp, testable predictions about how observed choice patterns should change when stochastic variance differs across items, brands, or conditions. We derive and examine the implications of assuming constant stochastic variance for choices made under different conditions or at different times, in particular, whether substantive effects can arise purely as artifacts. These implications are tested via an experiment designed to isolate the effects of stochastic variation in choice behavior. Results strongly suggest that the stochastic component should be carefully modeled to differ across both available brands and temporal conditions, and that its variance may be relatively greater for choices made for the future. The experimental design controls for several alternative mechanisms (e.g., flexibility seeking), and a series of related models suggest that several econometrically detectable explanations like correlated error, state dependence, and variety seeking add no explanatory power. A series of simulations argues for appropriate flexibility in discrete-choice specification when attempting to detect temporal stochastic inflation effects.

Suggested Citation

  • Linda Court Salisbury & Fred M. Feinberg, 2010. "Alleviating the Constant Stochastic Variance Assumption in Decision Research: Theory, Measurement, and Experimental Test," Marketing Science, INFORMS, vol. 29(1), pages 1-17, 01-02.
  • Handle: RePEc:inm:ormksc:v:29:y:2010:i:1:p:1-17
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    File URL: http://dx.doi.org/10.1287/mksc.1080.0464
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    References listed on IDEAS

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    Cited by:

    1. Mikołaj Czajkowski & Nick Hanley & Jacob LaRiviere, 2016. "Controlling for the Effects of Information in a Public Goods Discrete Choice Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(3), pages 523-544, March.
    2. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    3. Cameron, Trudy Ann & DeShazo, J.R., 2013. "Demand for health risk reductions," Journal of Environmental Economics and Management, Elsevier, vol. 65(1), pages 87-109.
    4. Harmsen-van Hout, Marjolein J.W. & Dellaert, Benedict G.C. & Herings, P. Jean-Jacques, 2008. "Behavorial Effects in Individual Decisions of Network Formation," Research Memorandum 019, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
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    7. N. Flynn, Terry & J. Peters, Tim & Coast, Joanna, 2013. "Quantifying response shift or adaptation effects in quality of life by synthesising best-worst scaling and discrete choice data," Journal of choice modelling, Elsevier, vol. 6(C), pages 34-43.
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    9. Jordan Louviere & Joffre Swait, 2010. "—Discussion of “Alleviating the Constant Stochastic Variance Assumption in Decision Research: Theory, Measurement, and Experimental Test”," Marketing Science, INFORMS, vol. 29(1), pages 18-22, 01-02.
    10. Demi Chung & David A. Hensher, 2015. "Modelling Risk Perceptions of Stakeholders in Public–Private Partnership Toll Road Contracts," Abacus, Accounting Foundation, University of Sydney, vol. 51(3), pages 437-483, September.
    11. Linda Court Salisbury & Fred M. Feinberg, 2010. "—Temporal Stochastic Inflation in Choice-Based Research," Marketing Science, INFORMS, vol. 29(1), pages 32-39, 01-02.
    12. Harmsen - van Hout, Marjolein J.W. & Dellaert, Benedict G.C. & Herings, P. Jean-Jacques, 2016. "Heuristic decision making in network linking," European Journal of Operational Research, Elsevier, vol. 251(1), pages 158-170.
    13. Hoyos, David, 2010. "The state of the art of environmental valuation with discrete choice experiments," Ecological Economics, Elsevier, vol. 69(8), pages 1595-1603, June.
    14. J. Wesley Hutchinson & Gal Zauberman & Robert Meyer, 2010. "—On the Interpretation of Temporal Inflation Parameters in Stochastic Models of Judgment and Choice," Marketing Science, INFORMS, vol. 29(1), pages 23-31, 01-02.
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