Solutions to some problems in the implementation of conjoint analysis

  • Carol A. E. Nickerson
  • Gary H. McClelland
  • Doreen M. Petersen
Methods & Designs


Methodological problems encountered in implementing conjoint analysis include (1) the impractically large set of multiattribute choice alternatives created by the factorial combination of more than a few attributes, (2) the hypothetical nature of the alternatives in the choice set, and (3) the assumption that each individual’s preferences can be described by the same composition rule. The techniques of tailoring, belief matching, and axiom testing are suggested as solutions to these problems, and their use is demonstrated in a conjoint analysis study of individuals’ contraceptive preferences. It is noted that tailoring and belief matching can also be used as methodological enhancements in functional measurement studies.


  1. Abelson, R. P., &Levi, A. (1985). Decision making and decision theory. In G. Lindzey & E. Aronson (Eds.),Handbook of social psychology (3rd ed., Vol. 1, pp. 231–309). New York: Random House.Google Scholar
  2. Anderson, N. H. (1981).Foundations of information integration theory. New York: Academic Press.Google Scholar
  3. Anderson, N. H. (1982a). Cognitive algebra and social psychophysics. In B. Wegener (Ed.),Social attitudes and psychophysical measurement (pp. 123–148). Hillsdale, NJ: Eribaum.Google Scholar
  4. Anderson, N. H. (1982b).Methods of information integration theory. New York: Academic Press.Google Scholar
  5. Anderson, N. H., Krantz, D. H., &Tversky, A. (1971). An exchange on functional and conjoint measurement [Letters to the Editor].Psychological Review,78, 457–458.CrossRefGoogle Scholar
  6. Anderson, N. H., &Shanteau, J. (1977). Weak inference with linear models.Psychological Bulletin,84, 1155–1170.CrossRefGoogle Scholar
  7. Anscombe, F. J. (1973). Graphs in statistical analysis.American Statistician,27, 17–21.CrossRefGoogle Scholar
  8. Barron, F. H. (1977). Axiomatic conjoint measurement.Decision Sciences,8, 548–559.CrossRefGoogle Scholar
  9. Bettman, J. R., Capon, N., &Lutz, R. J. (1975a). Cognitive algebra in multiattribute attitude models.Journal of Marketing Research,12, 151–164.CrossRefGoogle Scholar
  10. Bettman, J. R., Capon, N., &Lutz, R. J. (1975b). Multiattribute measurement models and multiattribute attitude theory: A test of construct validity.Journal of Consumer Research,1, 1–15.CrossRefGoogle Scholar
  11. Birnbaum, M. H. (1973). The devil rides again: Correlation as an index of fit.Psychological Bullerin,79, 239–242.CrossRefGoogle Scholar
  12. Birnbaum, M. H. (1982). Controversies in psychological measurement. In B. Wegener (Ed.),Social attitudes and psychophysical measurement (pp. 401–485). Hillsdale, NJ: Eribaum.Google Scholar
  13. Boston Women’s Health Book Collective. (1976).Our bodies, ourselves. New York: Simon & Schuster.Google Scholar
  14. Busemeyer, J. R. (1980). Importance of measurement theory, error theory, and experimental design for testing the significance of interactions.Psychological Bulletin,88, 237–244.CrossRefGoogle Scholar
  15. Campbell, A. A., &Berelson, B. (1971). Contraceptive specifications: Report on a workshop.Studies in Family Planning,2, 14–19.PubMedCrossRefGoogle Scholar
  16. Carroll, J. D. (1973). Appendix B: Models and algorithms for multidimensional scaling, conjoint measurement, and related techniques. In P. E. Green & Y. Wind,Multiauribute decisions in marketing: A measurement approach (pp. 299–371). Hinsdale, IL: Dryden Press.Google Scholar
  17. Cattin, P., &Wittink, D. R. (1982). Commercial use of conjoint analysis: A survey.Journal of Marketing,46, 44–53.CrossRefGoogle Scholar
  18. Cliff, N. (1973). Scaling.Annual Review of Psychology,24, 473–506.CrossRefGoogle Scholar
  19. Cohen, J. B., Severy, L. J., &Ahtola, O. T. (1978). An extended expectancy-value approach to contraceptive alternatives.Journal of Population,1, 22–41.Google Scholar
  20. Coombs, C. H., &Bowen, J. N. (1971). A test of VE-theories of risk and the effect of the central limit theorem.Acta Psychologica,35, 15–28.CrossRefGoogle Scholar
  21. Coombs, C. H., Coombs, L. C., &McClelland, G. H. (1975). Preference scales for number and sex of children.Population Studies,29, 273–298.CrossRefGoogle Scholar
  22. Coombs, C. H., Dawes, R. M., &Tversky, A. (1970).Mathematical psychology: An elementary introduction. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  23. Coombs, C. H., &Huang, L. C. (1970). Polynomial psychophysics of risk.Journal of Mathematical Psychology,7, 317–338.CrossRefGoogle Scholar
  24. Coombs, L. C. (1979). The measurement of commitment to work.Journal of Population,2, 203–223.Google Scholar
  25. Curley, S. P. (1990).Practical application of axiomatic conjoint measurement. Manuscript submitted for publication.Google Scholar
  26. Darlington, R. B. (1968). Multiple regression in psychological research and practice.Psychological Bulletin,69, 161–182.PubMedCrossRefGoogle Scholar
  27. Davidson, A. R., &Jaccard, J. J. (1975). Population psychology: A new look at an old problem.Journal of Personality & Social Psychology,31, 1073–1082.CrossRefGoogle Scholar
  28. Dawes, R. M., &Corrigan, B. (1974). Linear models in decision making.Psychological Bulletin,81, 95–106.CrossRefGoogle Scholar
  29. Dawes, R. M., &Smith, T. L. (1985). Attitude and opinion measurement. In G. Lindzey & E. Aronson (Eds.),Handbook of social psychology (3rd ed., Vol. 1, pp. 509–566). New York: Random House.Google Scholar
  30. de Leeuw, J., Young, F. W., &Takane, Y. (1976). Additive structure in qualitative data: An alternating least squares method with optimal scaling features.Psychometrika,41, 471–503.CrossRefGoogle Scholar
  31. Dixon, W. J., &Brown, M. B. (Eds.) (1977).BMDP-77: Biomedical computer programs, P series. Berkeley: University of California Press.Google Scholar
  32. Downs, P. E. (1977). Intrafamily decision making in family planning.Journal of Business Research,5, 63–74.PubMedCrossRefGoogle Scholar
  33. Einhorn, H. J., Kleinmuntz, D. N., &Kleinmuntz, B. (1979). Linear regressionand process-tracing models of judgment.Psychological Review,86, 465–485.CrossRefGoogle Scholar
  34. Emery, D. R. (1977a). DIST: A numerical conjoint measurement program designed to scale data to a distributive model in three dimensions.Journal of Marketing Research,14, 413–414.Google Scholar
  35. Emery, D. R. (1977b). DULST: A numerical conjoint measurement program designed to scale data to a dual-distributive model in three dimensions.Journal of Marketing Research,14, 558–559.Google Scholar
  36. Emery, D. R. (1978).Optimal scaling via ordinary least squares (Working Paper No. WP-31-78). Alberta, Canada: University of Calgary, School of Business, Faculty of Management.Google Scholar
  37. Emery, D. R., &Barron, F. H. (1979). Axiomatic and numerical conjoint measurement: An evaluation of diagnostic efficacy.Psychometrika,44, 195–210.CrossRefGoogle Scholar
  38. Falmagne, J.-C. (1976). Random conjoint measurement and loudness summation.Psychological Review,83, 65–79.CrossRefGoogle Scholar
  39. Fischer, G. W. (1976). Multidimensional utility models for risky and riskless choice.Organizational Behavior & Human Performance,17, 127–146.CrossRefGoogle Scholar
  40. Fishbein, M. (1967). A behavior theory approach to the relations between beliefs about an object and the attitude toward the object. In M. Fishbein (Ed.),Readings in attitude theory and measurement (pp. 389–400). New York: Wiley.Google Scholar
  41. Fishbein, M., &Jaccard, J. J. (1973). Theoretical and methodological considerations in the prediction of family planning intentions and behavior.Representative Research in Social Psychology,4 (1), 37–51.PubMedGoogle Scholar
  42. Goodman, L. A., &Kruskal, W. H. (1954). Measures of association for cross classifications.Journal of the American Statistical Association,49, 732–764.CrossRefGoogle Scholar
  43. Green, P. E. (1974). On the design of choice experiments involving multifactor alternatives.Journal of Consumer Research,1, 61–68.CrossRefGoogle Scholar
  44. Green, P. E., Carmone, F. J., &Wind, Y. (1972). Subjective evaluation models and conjoint measurement.Behavioral Science,17, 288–299.CrossRefGoogle Scholar
  45. Green, P. E., &Rao, V. R. (1971). Conjoint measurement for quantifying judgmental data.Journal of Marketing Research,8, 355–363.CrossRefGoogle Scholar
  46. Green, P. E., Rao, V. R., &DeSarbo, W. S. (1978). Incorporating group-level similarity judgments in conjoint analysis.Journal of Consumer Research,5, 187–193.CrossRefGoogle Scholar
  47. Green, P. E., &Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook.Journal of Consumer Research,5, 103–123.CrossRefGoogle Scholar
  48. Green, P. E., &Wind, Y. (1973).Multiattribute decisions in marketing: A measurement approach. Hinsdale, IL: Dryden Press.Google Scholar
  49. Green, P. E., &Wind, Y. (1975). New way to measure consumers’ judgments.Harvard Business Review,53 (4), 107–117.Google Scholar
  50. Hansen, F. (1969). Consumer choice behavior: An experimental approach.Journal of Marketing Research,6, 436–443.CrossRefGoogle Scholar
  51. Holbrook, M. B., Moore, W. L., Dodgen, G. N., &Havlena, W. J. (1985). Nonisomorphism, shadow features, and imputed preferences.Marketing Science,4, 215–233.CrossRefGoogle Scholar
  52. Holt, J. O., &Wallsten, T. S. (1974).A user’s manual for CONJOINT: A computer program for evaluating certain conjoint-measurement axioms (Tech. Rep. No. 42). Chapel Hill: University of North Carolina, L. L. Thurstone Psychometric Laboratory.Google Scholar
  53. Jaccard, J. J., &Becker, M. A. (1985). Attitudes and behavior: An information integration perspective.Journal of Experimental Social Psychology,21, 440–465.CrossRefGoogle Scholar
  54. Jaccard, J. J., &Davidson, A. R. (1972). Toward an understanding of family planning behaviors: An initial investigation.Journal of Applied Social Psychology,2, 228–235.CrossRefGoogle Scholar
  55. Johnson, R. M. (1975). A simple method for pairwise monotone regression.Psychometrika,40, 163–168.CrossRefGoogle Scholar
  56. Judd, C. M., &McClelland, G. H. (1989).Data analysis: A model-comparison approach. San Diego: Harcourt Brace Jovanovich.Google Scholar
  57. Kocur, G.,Hyman, W., &Aunet, B. (1982). Wisconsin work mode-choice models based on functional measurement and disaggregate behavioral data.Transportation Research Record, No. 895, 24–32.Google Scholar
  58. Krantz, D. H., Luce, R. D., Suppes, P., &Tversky, A. (1971).Foundations of measurement (Vol. 1). New York: Academic Press.Google Scholar
  59. Krantz, D. H., &Tversky, A. (1971). Conjoint-measurement analysis of composition rules in psychology.Psychological Review,78, 151–169.CrossRefGoogle Scholar
  60. Kruskal, J. B. (1965). Analysis of factorial experiments by estimating monotone transformations of the data.Journal of the Royal Statistical Society,27B, 251–263.Google Scholar
  61. Lerman, S. R., &Louviere, J. J. (1978). Using functional measurement to identify the form of utility functions in travel demand models.Transportation Research Record, No. 673, 78–86.Google Scholar
  62. Levin, I. P., &Herring, R. D. (1981). Functional measurement of qualitative variables in mode choice: Ratings of economy, safety, and desirability of flying versus driving.Transportation Research,15A, 207–214.Google Scholar
  63. Louviere, J. J. (1988).Analyzing decision making: Metric conjoint analysis. Newbury Park, CA: Sage Publications.Google Scholar
  64. McClelland, G. (1977). A note on Arbuckle and Larimer, “The number of two-way tables satisfying certain additivity axioms.”Journal of Mathematical Psychology,15, 292–295.CrossRefGoogle Scholar
  65. McClelland, G. H., &Coombs, C. H. (1975). ORDMET: A general algorithm for constructing all numerical solutions to ordered metric structures.Psychometrika,40, 269–290.CrossRefGoogle Scholar
  66. Meyer, R. J. (1982). A descriptive model of consumer information search behavior.Marketing Science,1, 93–121.CrossRefGoogle Scholar
  67. Moore, W. L., &Holbrook, M. B. (1982). On the predictive validity of joint-space models in consumer evaluations of new concepts.Journal of Consumer Research,9, 206–210.CrossRefGoogle Scholar
  68. Morrison, D. M. (1985). Adolescent contraceptive behavior: A review.Psychological Bulletin,98, 538–568.PubMedCrossRefGoogle Scholar
  69. Nickerson, C. A., &McClelland, G. H. (1984). Scaling distortion in numerical conjoint measurement.Applied Psychological Measurement,8, 183–198.CrossRefGoogle Scholar
  70. Nickerson, C. A., &McClelland, G. H. (1988). Extended axiomatic conjoint measurement: A solution to a methodological problem in studying fertility-related behaviors.Applied Psychological Measurement,12, 129–153.CrossRefGoogle Scholar
  71. Nisbett, R. E., &Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes.Psychological Review,84, 231–259.CrossRefGoogle Scholar
  72. Norman, K. L. (1977). Attributes in bus transportation: Importance depends on trip purpose.Journal of Applied Psychology,62, 164–170.CrossRefGoogle Scholar
  73. Norman, K. L., &Louviere, J. J. (1974). Integration of attributes in bus transportation: Two modeling approaches.Journal of Applied Psychology,59, 753–758.CrossRefGoogle Scholar
  74. Nygren, T. E. (1980). Limitations of additive conjoint scaling procedures: Detecting nonadditivity when additivity is known to be violated.Applied Psychological Measurement,4, 367–383.CrossRefGoogle Scholar
  75. Nygren, T. E. (1985). An examination of conditional violations of axioms for additive conjoint measurement.Applied Psychological Measurement,9, 249–264.CrossRefGoogle Scholar
  76. Nygren, T. E. (1986). A two-stage algorithm for assessing violations of additivity via axiomatic and numerical conjoint analysis.Psychometrika,51, 483–491.CrossRefGoogle Scholar
  77. Orkin, F. K., &Greenhow, D. E. (1978). A study of decision making: How faculty define competence.Anesthesiology,48, 267–271.PubMedCrossRefGoogle Scholar
  78. Parker, B. R., &Srinivasan, V. (1976). A consumer preference approach to the planning of rural primary health-care facilities.Operations Research,24, 991–1025.CrossRefGoogle Scholar
  79. Polgar, S., &Marshall, J. F. (1976). The search for culturally acceptable fertility regulating methods. In J. F. Marshall & S. Polgar (Eds.),Culture, natality, and family planning (pp. 204–218). Chapel Hill, NC: Carolina Population Center.Google Scholar
  80. Roskam, E. E. (1974).Unidimensional conjoint measurement (UNICON) for multi-faceted designs. Nijmegen, The Netherlands: University of Nijmegen, Psychologisch Laboratorium.Google Scholar
  81. Sachs, N. J., &Pitz, G. F. (1981).Choosing the best method of contraception: Application of decision analysis to contraceptive counseling and selection. Unpublished manuscript, Southern Illinois University, Carbondaie.Google Scholar
  82. Salyer, S. L., &Bausch, J. J. (1978).Toward safe, convenient, and effective contraceptives: A policy perspective. New York: The Population Council.Google Scholar
  83. Schmitt, N., &Levine, R. L. (1977). Statistical and subjective weights: Some problems and proposals.Organizational Behavior & Human Performance,20, 15–30.CrossRefGoogle Scholar
  84. Schuler, H. J., &Prosperi, D. C. (1978). A conjoint measurement model of consumer spatial behavior.Regional Science Perspectives,8, 122–134.Google Scholar
  85. Shanteau, J. (1977). Correlation as a deceiving measure of fit.Bulletin of the Psychonomic Society,10, 134–136.Google Scholar
  86. Slovic, P., &Lichtenstein, S. (1971). Comparison of Bayesian and regression approaches to the study of information processing in judgment.Organizational Behavior & Human Performance,6, 649–744.CrossRefGoogle Scholar
  87. Srinivasan, V., &Shocker, A. D. (1973). Linear programming techniques for multidimensional analysis of preferences.Psychometrika,38, 337–369.CrossRefGoogle Scholar
  88. Takane, Y. (1978). A maximum likelihood method for nonmetric multidimensional scaling: I. The case in which all empirical pairwise orderings are independent—Theory.Japanese Psychological Research,20, 7–17.Google Scholar
  89. Timmermans, H. (1980). Unidimensional conjoint measurement models and consumer decision-making.Area (Publication of the Institute of British Geographers).12, 291–300.Google Scholar
  90. Troutman, C. M., &Shanteau, J. (1976). Do consumers evaluate products by adding or averaging attribute information?Journal of Consumer Research,3, 101–106.CrossRefGoogle Scholar
  91. Tukey, J. W. (1977).Exploratory data analysis. Reading, MA: Addison-Wesley.Google Scholar
  92. Ullrich, J. R., &Cummins, D. E. (1973). PCJM: A program for conjoint measurement analysis of polynomial composition rules.Behavioral Science,18, 226–227.Google Scholar
  93. Ullrich, J. R., Cummins, D. E., &Welkenbach, J. (1978). PCJM2: A program for the axiomatic conjoint measurement analysis of polynomial composition rules.Behavior Research Methods & Instrumentation,10, 89–90.Google Scholar
  94. Ullrich, J. R., &Painter, J. R. (1974). A conjoint-measurement analysis of human judgment.Organizational Behavior & Human Performance,12, 50–61.CrossRefGoogle Scholar
  95. Wall, E. M. (1985). Development of a decision aid for women choosing a method of birth control.Journal of Family Practice,21, 351–355.PubMedGoogle Scholar
  96. Wallsten, T. S. (1972). Conjoint-measurement framework for the study of probabilistic information processing.Psychological Review,79, 245–260.CrossRefGoogle Scholar
  97. Wallsten, T. S. (1976). Using conjoint-measurement models to investigate a theory about probabilistic information processing.Journal of Mathematical Psychology,14, 144–185.CrossRefGoogle Scholar
  98. Wallsten, T. S., &Budescu, D. V. (1981). Additivity and nonadditivity in judging MMPI profiles.Journal of Experimental Psychology: Human Perception & Performance,7, 1096–1109.CrossRefGoogle Scholar
  99. Weitz, B., &Wright, P. (1979). Retrospective self-insight on factors considered in product evaluation.Journal of Consumer Research,6, 280–294.CrossRefGoogle Scholar
  100. Werner, P. D., &Middlestadt, S. E. (1979). Factors in the use of oral contraceptives by young women.Journal of Applied Social Psychology,9, 537–547.CrossRefGoogle Scholar
  101. Wilkie, W. L., &Weinreich, R. P. (1972). Effects of the number and type of attributes included in an attitude model: More isnot better. In M. Venkatesan (Ed.),Proceedings of the Third Annual Conference of the Association for Consumer Research (pp. 325–340). College Park, MD: Association for Consumer Research.Google Scholar
  102. Wittink, D. R., &Montgomery, D. B. (1979). Predictive validity of trade-off analysis for alternative segmentation schemes. In N. Beckwith, M. Houston, R. Mittelstaedt, K. B. Monroe, & S. Ward (Eds.).1979 Educators’ Conference Proceedings (pp. 69–73). Chicago: American Marketing Association.Google Scholar
  103. Wright, P., &Weitz, B. (1977). Time horizon effects on product evaluation strategies.Journal of Marketing Research,14, 429–443.CrossRefGoogle Scholar
  104. Yntema, D. B., &Torgerson, W. S. (1961). Man-computer cooperation in decisions requiring common sense.IRE Transactions on Human Factors in Electronics,HFE-2[1], 20–26.CrossRefGoogle Scholar
  105. Young, F. W. (1972). A model for polynomial conjoint analysis algorithms. In R. N. Shepard, A. K. Romney, & S. B. Nerlove (Eds.),Multidimensional scaling: Theory and applications in the behavioral sciences (Vol. 1, pp. 69–104). New York: Seminar Press.Google Scholar
  106. Zeleny, M. (1976). On the inadequacy of the regression paradigm used in the study of human judgment.Theory & Decision,7, 57–65.CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 1990

Authors and Affiliations

  • Carol A. E. Nickerson
    • 1
  • Gary H. McClelland
    • 1
  • Doreen M. Petersen
    • 1
  1. 1.Publications Librarian, Center for Research on Judgment and PolicyUniversity of ColoradoBoulder

Personalised recommendations