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. 2021 Aug 17:8:101492.
doi: 10.1016/j.mex.2021.101492. eCollection 2021.

Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis

Ivana Stankov  1   2 , Andres F Useche  3   4 , Jose D Meisel  4   5 , Felipe Montes  3   4 , Lidia Mo Morais  6   7 , Amelia Al Friche  6   7 , Brent A Langellier  8 , Peter Hovmand  9 , Olga L Sarmiento  10 , Ross A Hammond  11   12   13 , Ana V Diez Roux  1
Affiliations

Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis

Ivana Stankov et al. MethodsX. .

Abstract

Cross-impact balance (CIB) analysis leverages expert knowledge pertaining to the nature and strength of relationships between components of a system to identify the most plausible future 'scenarios' of the system. These scenarios, also referred to as 'storylines', provide qualitative insights into how the state of one factor can either promote or restrict the future state of one or multiple other factors in the system. This paper presents a novel, visually oriented questionnaire developed to elicit expert knowledge about the relationships between key factors in a system, for the purpose of CIB analysis. The questionnaire requires experts to make selections from a series of standardized cause-effect graphical profiles that depict a range of linear and non-linear relationships between factor pairs. The questionnaire and the process of translating the graphical selections into data that can be used for CIB analysis is described using an applied example which focuses on urban health in Latin American cities.

Keywords: Chronic disease; Complex Systems; Diet; Epidemiology; Food environment; Scenario analysis; Systems thinking; Transportation system; Urban Health.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image, graphical abstract
Graphical abstract
Fig 1
Fig. 1
The values entered in the CIB matrix if no direct relationship between physical activity and chronic disease prevalence is identified.
Fig 2
Fig. 2
List of all possible bivariate relationships from which experts selected the relationship they perceived best described the influence of one factor on another.
Fig 3
Fig. 3
Three examples showing how the graphs selected by experts are interpreted and encoded in numerical form in the CIB matrix.
See this image and copyright information in PMC

References

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