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Identifying superfluous survey items

Author

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  • Brosnan, Kylie
  • Grün, Bettina
  • Dolnicar, Sara

Abstract

Surveys provide critical insights into consumer satisfaction and experience. Excessive survey length, however, can reduce data quality. We propose using constrained principle components analysis to shorten the survey length in a data-driven way by identifying optimal items with maximum information. The method allows assessing item elimination potential, and explicitly identifies which items provide maximum information for a specified number of items. We use artificial data to explain the method, provide two illustrations with empirical survey data, and make code freely available in an online tool

Suggested Citation

  • Brosnan, Kylie & Grün, Bettina & Dolnicar, Sara, 2018. "Identifying superfluous survey items," Journal of Retailing and Consumer Services, Elsevier, vol. 43(C), pages 39-45.
    • Handle: RePEc:eee:joreco:v:43:y:2018:i:c:p:39-45
    DOI: 10.1016/j.jretconser.2018.02.007
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    References listed on IDEAS

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    1. António Pedro Duarte Silva, 2002. "Discarding Variables in a Principal Component Analysis: Algorithms for All-Subsets Comparisons," Computational Statistics, Springer, vol. 17(2), pages 251-271, July.
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    3. Cadima, Jorge & Cerdeira, J. Orestes & Minhoto, Manuel, 2004. "Computational aspects of algorithms for variable selection in the context of principal components," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 225-236, September.
    4. Farah, Maya F. & Ramadan, Zahy B., 2017. "Disruptions versus more disruptions: How the Amazon dash button is altering consumer buying patterns," Journal of Retailing and Consumer Services, Elsevier, vol. 39(C), pages 54-61.
    5. Armstrong, J. Scott & Soelberg, Peer, 1968. "On the interpretation of factor analysis," MPRA Paper 81665, University Library of Munich, Germany.
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