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DELPHI METHOD IN QUANTITATIVE RESEARCH

A graduate-level illustrated introduction to and tutorial the Delphi method, discussing quantitative applications and explaining fuzzy math approaches to Delphi networks.

Why we think it's important: The Delphi method has been used primarily as a qualitative approach in the social sciences. While the previous edition highlighted many of the quantitative uses of Delphi, the 2014 edition provides an illustrated tutorial on its applied mathematics, giving students the basis for undertaking their own quantitative Delphi analyses.

New in the 2014 edition:

• Now includes an extended illustrated tutorial on how to use fuzzy mathematics to analyze Delphi networks.
• Covers measures of central tendency, agreement network analysis, and mean network potency.
• Nearly double the length of the previous edition with expanded discussion throughout.
• Retains coverage of traditional Delphi procedures as well. The full content is now available from Statistical Associates Publishers. Click here.

```THE DELPHI METHOD IN QUANTITATIVE RESEARCH
Overview	5
Key Concepts and Terms	6
Appropriateness	6
Rounds	6
Forecasts vs. reasons	6
Legitimation and Selection of Experts	6
Anonymity	7
Examples of Delphi in quantitative research	8
Defining constructs	8
Developing and selecting indicators	8
Creating a multidimensional instrument	9
Creating a typological framework	10
Coding	10
Establishing the construct validity of factor weightings	11
Integrating factor eigenvalues and Delphi weights for purposes of optimal scoring for selection among alternatives	11
Using fuzzy mathematics in Delphi research	13
Overview	13
Example	14
Triangular fuzzy numbers (TFN)	16
Distances	17
Overview	17
The geometric method	17
The vertex method	18
Analysis	20
Overview	20
Central tendency	20
Agreement network	21
Mean network potency	25
Project planning networks	25
Assumptions	27
Representative selection of experts	27
Expert knowledge	27
Expert Motivation	27
Bias toward the mean	27
Avoidance of researcher bias	28
Intermediate level of predictability	28