Identifying superfluous survey items

Identifying superfluous survey items 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Retailing and Consumer Services Elsevier

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Publisher
Elsevier
Copyright
Copyright © 2018 The Authors
ISSN
0969-6989
D.O.I.
10.1016/j.jretconser.2018.02.007
Publisher site
See Article on Publisher Site

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

Journal

Journal of Retailing and Consumer ServicesElsevier

Published: Jul 1, 2018

References

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