Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12313/2668
Title: Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis
Authors: Stankova, Ivana
Useche, Andres F.
Meisel, Jose D.
Montes, Felipe
Morais, Lidia MO.
Friche, Amelia AL.
Langellier, Brent A.
Hovman, Peter
Sarmiento, Olga L.
Hammond, Ross A.
Diez Roux, Ana V.
Keywords: Complex Systems
Systems thinking
Scenario analysis
Epidemiology
Urban Health
Chronic disease
Food environment
Diet
Transportation system
Issue Date: 17-Aug-2021
Publisher: MethodsX
Citation: tankov, I., Useche, A. F., Meisel, J. D., Montes, F., Morais, L. M., Friche, A. A., . . . Diez Roux, A. V. (2021). Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis. MethodsX, 8 doi:10.1016/j.mex.2021.101492
Abstract: Ross-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. • A questionnaire featuring a set of standardized cause-effect profiles was developed. • Cause-effect profiles were used to elicit information about the strength of linear and non-linear bivariate relationships. • The questionnaire represents an intuitive visual means for collecting data required for the conduct of CIB analysis.
URI: https://www.sciencedirect.com/science/article/pii/S2215016121002855
ISSN: 2215-0161
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