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The Validity of Conjoint Analysis: An Investigation of Commercial Studies Over Time

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Data Analysis, Machine Learning and Knowledge Discovery

Abstract

Due to more and more online questionnaires and possible distraction—e.g. by mails, social network messages, or news reading during the processing in an uncontrolled environment—one can assume that the (internal and external) validity of conjoint analyses lowers. We test this assumption by comparing the (internal and external) validity of commercial conjoint analyses over the last years. Research base are (disguised) recent commercial conjoint analyses of a leading international marketing research company in this field with about 1.000 conjoint analyses per year. The validity information is analyzed w.r.t. research objective, product type, period, incentives, and other categories, also w.r.t. other outcomes like interview length and response rates. The results show some interesting changes in the validity of these conjoint analyses. Additionally, new procedures to deal with this setting will be shown.

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Authors and Affiliations

  1. Marketing and Innovation Management, Institute of Business Administration and Economics, Brandenburg University of Technology Cottbus, 101344, 03013, Cottbus, Germany

    Sebastian Selka (Chair) & Daniel Baier (Chair)

  2. TNS Infratest GmbH, Arnulfstrasse 205, Munich, Germany

    Peter Kurz

Authors
  1. Sebastian Selka
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  2. Daniel Baier
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  3. Peter Kurz
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Corresponding author

Correspondence to Sebastian Selka .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

    Myra Spiliopoulou

  2. Institute of Computer Science, University of Hildesheim, Hildesheim, Germany

    Lars Schmidt-Thieme

  3. Institute of Computer Science, University of Hildesheim, Hildesheim, Germany

    Ruth Janning

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© 2014 Springer International Publishing Switzerland

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Selka, S., Baier, D., Kurz, P. (2014). The Validity of Conjoint Analysis: An Investigation of Commercial Studies Over Time. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_25

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