The Delphi Tehnique
The Delphi Tehnique
Delphi Method is a technique for determining the likelihood of future events based upon past experience. The Delphi method assembles a panel of experts from different disciplines to comment upon the research of others in their own and different fields. It is typically used to arrive at high-level predictions, especially in relation to economics and politics. The aim is to account for the complex factors that affect long-range forecasting, or situations in which unknowns might play a major part, by generating a wide range of possible future scenarios. The method also claims to safeguard against the tendency of group discussions on these kinds of matters to arrive at a consensus. With the Delphi method experts respond to questionnaires at a distance.
The Delphi Method was developed at the RAND Corporation in the early 1950s and 1960w as a spin-off of an Air Force-sponsored research project, “Project Delphi.” The first Delphi applications were in the area of technological forecasting and aimed to forecast likely inventions, new technologies, and the social and economic impact of those changes. Since that time it has been refined further and applied to gain information in a wide range of fields. These fields are as diverse as regional economic development, health care policy, sociology, environmental risks, prediction of fruit prices, tourism and recreation, forestry and advanced manufacturing techniques. The Delphi technique may be particularly useful in situations where strictly objective data are scarce. The original project, however, was designed to anticipate an optimal targeting of U.S. industries by a hypothetical Soviet strategic planner. Delphi was first brought before a wider audience in a 1963 RAND study “Report on a Long-Range Forecasting Study,” by Olaf Helmer and T. J. Gordon (Linstone and Turoff, 2002). It is undoubtedly the best-known method of eliciting and synthesizing expert opinion.
In the middle 1960s and early 1970s the Delphi method found a wide variety of applications, and by 1974 the number of Delphi studies had exceeded 10,000 (Arkansas Administrative Office of the Courts, 2002). Although most applications are concerned with technology forecasting, the method has also been applied to many types of policy analysis. Policy Delphis differ from technology forecasting Delphis with respect to both purpose and method. In technology forecasting, the team conducting the Delphi study seeks experts who are most knowledgeable on the issues in question, and seeks to achieve a high degree of consensus regarding predicted developments. Policy Delphis on the other hand seek to incorporate the views of the entire spectrum of “stakeholders” and seek to communicate the spread of their opinions to the decision maker (Linstone and Turoff, 2002). This discussion of the Delphi Technique will focus on the classic Delphi technique.
The Delphi method is based on group communication among a panel of experts who are potentially all over the world. The technique is designed to elicit and develop individual responses to the problem posed, but enable the experts to refine their views as the panel’s work continues. A major point is the group work is anonymous. Ideas are presented to the panel in a way in which the identity of the specific panellist is hidden. The major characteristics of the Delphi method are anonymity, controlled feedback, and statistical response.
In a classic Delphi survey, the first round is unstructured, allowing panelists to identify freely and elaborate on the issues that they consider important. These are consolidated into a single set by the monitors, who then produce a structured questionnaire designed to elicit the views, opinions and judgments of the panelists in a quantitative form. The consolidated list of scenarios is presented to the panelists in the second round, at which time they place estimates on key variables, such as the time an event will occur. These responses are then summarized and the summary information is presented to the panelists, who are invited to reassess their original opinions in the light of anonymous individual responses. In addition, if panelists assessments fall outside the upper or lower quartiles, they may be asked to provide justification of why they consider their estimates are more accurate than the median values. Further rounds of collection of estimates, compiling summary information and inviting revisions continue until there is no further convergence of expert opinion. Experience reveals this usually occurs after two rounds, or at the most four rounds (Sharp, 2006). The responses on the final round generally show a much smaller spread than the responses from the first round, and this is taken to indicate that the experts have reached a degree of consensus. The median values on the final round are taken as the best predictions (Ali, 2005).
The Delphi