Identifying superfluous survey items

Kylie Brosnan, Grün, Bettina and Sara Dolnicar

Journal of Retailing and Consumer Services, 2018, vol. 43, issue C, 39-45

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

Keywords: Survey length; Survey item reduction; Measurement (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:43:y:2018:i:c:p:39-45

DOI: 10.1016/j.jretconser.2018.02.007

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