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A Comparison of Conjoint Methods When There Are Many Attributes

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Abstract

This paper compares several methods of performing conjoint analysis when there is a large number of attributes. National parks were described in terms of 17 attributes and 56 levels. Subjects were randomly assigned to one of four groups and each person responded to a calibration questionnaire that allowed the estimation of one of the following conjoint analysis models: full profile, ACA, individual-level hybrid, or full profile on the person's eight stated most important attributes. Validations were performed in terms of individual choices and aggregate choice shares. Reliabilities were assessed on both ratings and choices.

Surprisingly even with a large number of attributes, the full profile method consistently validated best. Second was a full profile model estimated on the respondent's stated eight most important attributes. ACA and individual hybrid conjoint analysis performed similarly, but worse than these two methods on most measures. Validation differences were more strongly related to differences in attribute importances than desirabilities for levels within an attribute. It appears that these respondents were not able to accurately report self-explicated importances with a large number of attributes.

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References

  • Brazell, Jeff, Jordan Louviere. (1998). “Length Effects in Conjoint Choice Experiments and Surveys: An Explanation Based on Cumulative Cognitive Burden,” Working Paper, Marketing Department, The University of Sydney.

  • Green, Paul E., Stephen M. Goldberg, and James B. Wiley. (1982). “A Cross Validation Test of Hybrid Conjoint Models,” Proceedings of the 1982 annual meeting of the Association for Consumer Research, San Francisco, October.

  • -and Abba M. Krieger. (1996). “Individualized Hybrid Models for Conjoint Analysis,” Management Science 42 (June), 850–67.

    Google Scholar 

  • -,-, and M. K. Agarwal. (1991). “Adaptive Conjoint Analysis: Some Caveats and Suggestions,” Journal of Marketing Research 28 (May), 215–22.

  • -,-, and-. (1993). “A Cross Validation Test of Four Models for Quantifying Multiattribute Preferences,” Marketing Letters 4 (October), 369–80.

    Google Scholar 

  • -and V. Srinivasan. (1990). “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice,” Journal of Marketing 54 (October), 3–19.

    Google Scholar 

  • Greenberg, Marshall G. (1986). “Commentary,” Marketing Science 5 (Fall), 320–1.

    Google Scholar 

  • Huber, Joel C., Dick R. Wittink, John A. Fiedler, and Richard L. Miller. (1991). “The Effectiveness of Alternative Preference Elicitation Procedures in Predicting Choice,” Journal of Marketing Research 30 (February), 105–14.

  • Johnson, Richard M. (1986). “Commentary,” Marketing Science 5 (Fall), 322.

    Google Scholar 

  • -. (1987). “Adaptive Conjoint Analysis,” in Proceedings of the Sawtooth Software Conference on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing (March), Sawtooth Software: Sun Valley, ID, 253–66.

    Google Scholar 

  • -. (1996). Personal Communication.

  • Leigh, Thomas W., David B. McKay, and John O. Summers. (1984). “Reliability and Validity of Conjoint Analysis and Self-Explicated Weights: A Comparison,” Journal of Marketing Research 21 (November), 456–62.

  • Louviere, Jordan J., Harmen Oppewal, Harry Timmermans, and T. Thomas. (1993). “Handling Large Numbers of Attributes in Conjoint Applications: Who Says Existing Techniques Can't Be Applied? But If You Want an Alternative, How About Hierarchical Choice Experiments,” paper presented at the ARTS forum.

  • Malhotra, Naresh K., Arun K. Jain, and Stephen W. Lagakos. (1982). “The Information Overload Controversy: An Alternative Viewpoint,” Journal of Marketing Spring, 27–37.

  • Mehta, Raj B., William L. Moore and Teresa M. Pavia. (1992). “A Note on the Use of Unacceptable Levels in Conjoint Analysis,” Journal of Consumer Research December, 470–476.

  • Moore, illiam L., Raj B. Mehta, and Teresa M. Pavia. (1994). “A Simplified Method of Constrained Parameter Estimation in Conjoint Analysis,” Marketing Letters (April), 173–81.

  • -and Richard J. Semenik. (1988). “Measuring preferences with Hybrid Conjoint Analysis: The Impact of a Different Number of Attributes in the Master Design,” Journal of Business Research 16, 261–74.

    Google Scholar 

  • Moorthy, Sridhar, Brian T. Ratchford, and Debabrata Talukdar. (1997). “Consumer Information Search Revisited: Theory and Empirical Analysis,” Journal of Consumer Research March, 263–77.

  • Pavia, Teresa M., William L. Moore, Raj B. Mehta and Brian D. Ottum. (1993). “How Many Attributes are Needed for Prediction in Conjoint Analysis: An Empirical Investigation,” paper presented at the Marketing Science Conference, March.

  • Phelps, Ruth H. and James Shanteau. (1978). “Livestock Judges: How Much Information Can an Expert Use?” Organizational Behavior and Human Performance (21), 209–19.

    Google Scholar 

  • Rossi, Peter H. and S. L. Nock. (1982). Measuring Social Judgments: The Factorial Survey Approach, BeverlyHills, CA: Sage Publications, Inc.

    Google Scholar 

  • Sawtooth Software Inc. (1994). ACA System: Version 4.0, Margo Metegrano (ed.), Evanston IL.

  • -. (1997). “Using Utility Constraints to Improve the Predictability of Conjoint Analysis,” Sawtooth Solutions Summer, 3, 4, 6.

  • Shepard, Roger N. (1964). “On subjectively Optimum Selections Among Multiattribute Alternatives,” in Human Judgements and Optimality, (M. W. Shelley, II and G. L. Bryan (eds.), New York: John Wiley, 257–81.

    Google Scholar 

  • Srinivasan, V. (1988). “A Conjunctive-Compensatory Approach to the Self-Explication of Multiattributed Preferences,” Decision Sciences 19 (Spring), 295–305.

    Google Scholar 

  • Wilkie, William L. and Edgar A. Pessemier. (1973). “Issues in Marketing's Use of Multi-Attribute Attitude Models,” Journal of Marketing Research 10 (November), 428–41.

  • Wright, Peter and Mary Ann Kriewall. (1980). “State of Mind Effects on the Accuracy With Which Utility Functions Predict Marketplace Choice,” Journal of Marketing Research 19 (August), 277–93.

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Authors and Affiliations

  1. Edwin L. Cox School of Business, Southern Methodist University, Dallas, Texas

    Madeleine E. Pullman

  2. David Eccles School of Business, University of Utah, 1645 E. Campus Center Drive, Room 101, Salt Lake City, UT, 84112

    Kimberly J. Dodson & William L. Moore

Authors
  1. Madeleine E. Pullman
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  2. Kimberly J. Dodson
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  3. William L. Moore
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Pullman, M.E., Dodson, K.J. & Moore, W.L. A Comparison of Conjoint Methods When There Are Many Attributes. Marketing Letters 10, 125–138 (1999). https://doi.org/10.1023/A:1008036829555

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