Ratings-/Rankings-Based Versus Choice-Based Conjoint Analysis for Predicting Choices

  • Daniel BaierEmail author
  • Marcin Pełka
  • Aneta Rybicka
  • Stefanie Schreiber
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Nowadays, for market simulation in consumer markets with multi-attributed products, choice-based conjoint analysis (CBC) is most popular. The popularity stems—on one side—from the possibility to use online-panels for affordable data collection and—on the other side—from the possibility to estimate part worths at the respondent level using only few observations. However, a still open question is, whether this money- and time-saving approach provides the same or even better results than ratings-/rankings-based alternatives. An experiment with 787 students from Poland and Germany is used to answer this question: Cola preferences are measured using CBC as well as ratings-/rankings-based alternatives. The results are compared using the Multitrait-Multimethod Matrix for the estimated part worths and first choice hit rates for holdout choice sets. The experiment shows a superiority of CBC, but also important differences between Polish and German cola consumers that outweigh methodological differences.


Conjoint Analysis Polish Student Conjoint Experiment Data Collection Task Enrich Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Ansari, W. E., Stock, C., & Mikolajczyk, T. (2012). Relationships between food consumption and living arrangements among university students in four European countries: A cross-sectional study. Nutrition Journal, 28, 1–7.CrossRefGoogle Scholar
  2. Baier, D. (1999). Methoden der Conjointanalyse in der Marktforschungs- und Marketingpraxis. In W. Gaul & M. Schader (Eds.), Mathematische Methoden der Wirtschaftswissenschaften (pp. 197–206). Heidelberg: Physica.CrossRefGoogle Scholar
  3. Campbell, D. R., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105.CrossRefGoogle Scholar
  4. Churchill, G. A. (1979). A paradigm for developing better measures for marketing constructs. Journal of Marketing Research, 16(1), 64–73.CrossRefMathSciNetGoogle Scholar
  5. Elrod, T., Louviere, J., & Davey, K. (1992). An empirical comparison of ratings-based and choice-based conjoint models. Journal of Marketing Research, 24(3), 368–377.CrossRefGoogle Scholar
  6. Green, P. E., Krieger, A. M., & Wind, Y. (2001). Thirty years of conjoint analysis: Reflections and prospects. Interfaces, 31(3b), 56–73.CrossRefGoogle Scholar
  7. Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.CrossRefGoogle Scholar
  8. Karniouchina, E. V., Moore, W. L., Van der Rhee, B., & Verma, R. (2009). Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research. European Journal of Operational Research, 197(1), 340–348.CrossRefzbMATHGoogle Scholar
  9. Louviere, J. J., & Woodworth, G. (1983). Design and analysis of simulated consumer choice or allocation experiments: An approach based on aggregate data. Journal of Marketing Research, 20(4), 350–367.CrossRefGoogle Scholar
  10. Moore, W. L. (2004). A cross-validity comparison of ratings-based and choice-based conjoint analysis models. International Journal of Research in Marketing, 21(3), 299–312.CrossRefGoogle Scholar
  11. Moore, W. L., Gray-Lee J., & Louviere, J. J. (1998). A cross-validity comparison of conjoint analysis and choice models at different levels of aggregation. Marketing Letters, 9(2), 195–208.CrossRefGoogle Scholar
  12. Oliphant, K., Eagle, T. G., Louviere, J. J., & Anderson, D. (1992). Cross-task comparison of ratings-based and choice-based conjoint. In M. Metegrano (Ed.), Sawtooth Software Conference Proceedings (pp. 383–404).Google Scholar
  13. Sattler, H., & Hartmann, A. (2008). Commercial use of conjoint analysis. In K. I. Hoeck & M. Voigt (Eds.), Operations management in theorie und praxis (Vol. 1, pp. 103–119). Wiesbaden: Gabler.CrossRefGoogle Scholar
  14. Sawtooth Software. (2013). The CBC system for choice-based conjoint analysis version 8. Orem, UT: Sawtooth Software Inc.Google Scholar
  15. Selka, S., & Baier, D. (2014). Kommerzielle Anwendung auswahlbasierter Verfahren der Conjointanalyse: Eine empirische Untersuchung zur Validitätsentwicklung. Marketing ZFP, 36(1), 54–64.CrossRefGoogle Scholar
  16. Selka, S., Baier, D., & Kurz, P. (2014). The validity development of conjoint analysis over time: An investigation of commercial studies. Studies in Classification, Data Analysis, and Knowledge Organization, 48, 227–234.CrossRefGoogle Scholar
  17. Vriens, M., Oppewal, H., & Wedel, M. (1998). Rating-based versus choice-based latent class conjoint models: An empirical comparison. Journal of the Market Research Society, 40(3), 237–248.Google Scholar
  18. Wittink, D. R., & Cattin, P. (1989). Commercial use of conjoint analysis: An update. Journal of Marketing, 53(3), 91–96.CrossRefGoogle Scholar
  19. Wittink, D. R., Vriens, M., & Burhenne, W. (1994). Commercial use of conjoint analysis in Europe: Results and critical reflections. International Journal of Research in Marketing, 11(1), 41–52.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Daniel Baier
    • 1
    Email author
  • Marcin Pełka
    • 2
  • Aneta Rybicka
    • 2
  • Stefanie Schreiber
    • 1
  1. 1.Brandenburg University of Technology Cottbus-SenftenbergChair of Marketing and Innovation ManagementCottbusGermany
  2. 2.Department of Econometrics and Computer ScienceWroclaw University of EconomicsJelenia GóraPoland

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