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Managing Delphi Surveys Using Nonparametric Statistical Techniques*

Schmidt, Roy C.·1997·Decision Sciences

Abstract

Information systems researchers have often turned to a variant of the Delphi survey technique to support their research of key issues in their field. Two particular weaknesses of past studies using this approach have been a lack of a definitive method for conducting the research and a lack of statistical support for the conclusions drawn by the researchers. In this paper, the author presents a method, based on nonparametric statistical techniques, to conduct ranking‐type Delphi surveys, perform analysis, and report results. The author takes this one step further by illustrating an actual analysis of a Delphi survey. The analysis is compared to results that were presented without the benefit of the author's approach. This paper shows that use of the advocated approach can streamline and strengthen studies, improve the validity of results, and thus better serve the consumers of the research findings. Since the ranking‐type Delphi is so popular among information systems researchers, a consistent method is needed to apply to their data collection, analysis, and reporting of results. This paper provides such a method in concise form and illustrates the use of the method in a manner affording comparison between it and previous practice.

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Metadata

Authors
Schmidt, R. C.
Venue
Decision Sciences
Publisher
Wiley
Published
1997-07
Volume
28
Issue
3
Pages
763-774
ISSN
  • 0011-7315 (ISSN-L)
  • 1540-5915
OpenAlex
W2004868876
Cited by
1,141
References
0
FWCI
7.64
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Managing Delphi Surveys Using Nonparametric Statistical Techniques*. https://app.cochat.ai/papers/10.1111/j.1540-5915.1997.tb01330.x
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Schmidt, R. C. (1997). Managing Delphi Surveys Using Nonparametric Statistical Techniques*. Decision Sciences. https://doi.org/10.1111/j.1540-5915.1997.tb01330.x

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.