Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis
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 h...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016121002855 |
| _version_ | 1819099434666950656 |
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| author | Ivana Stankov Andres F. Useche Jose D. Meisel Felipe Montes Lidia MO. Morais Amelia AL. Friche Brent A. Langellier Peter Hovmand Olga L. Sarmiento Ross A. Hammond Ana V. Diez Roux |
| author_facet | Ivana Stankov Andres F. Useche Jose D. Meisel Felipe Montes Lidia MO. Morais Amelia AL. Friche Brent A. Langellier Peter Hovmand Olga L. Sarmiento Ross A. Hammond Ana V. Diez Roux |
| author_sort | Ivana Stankov |
| collection | DOAJ |
| description | 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. • 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. |
| first_indexed | 2024-12-22T00:46:49Z |
| format | Article |
| id | doaj.art-60247da145cf4bbabff1327b56bc0792 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-12-22T00:46:49Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-60247da145cf4bbabff1327b56bc07922022-12-21T18:44:32ZengElsevierMethodsX2215-01612021-01-018101492Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysisIvana Stankov0Andres F. Useche1Jose D. Meisel2Felipe Montes3Lidia MO. Morais4Amelia AL. Friche5Brent A. Langellier6Peter Hovmand7Olga L. Sarmiento8Ross A. Hammond9Ana V. Diez Roux10Urban Health Collaborative, Dornsife School of Public Health, Drexel University, 3600 Market St, Philadelphia, PA 19104, USA; South Australian Health and Medical Research Institute, North Terrace, Adelaide, SA 5000, Australia; Corresponding author at: Urban Health Collaborative, Dornsife School of Public Health, Drexel University, 3600 Market St, Philadelphia, PA 19104, USA.Department of Industrial Engineering, Universidad de Los Andes, Bogotá, Colombia; Social and Health Complexity Center, Universidad de Los Andes, Bogotá, ColombiaSocial and Health Complexity Center, Universidad de Los Andes, Bogotá, Colombia; Facultad de Ingeniería, Universidad de Ibagué, Carrera 22 Calle 67, Ibagué 730001, ColombiaDepartment of Industrial Engineering, Universidad de Los Andes, Bogotá, Colombia; Social and Health Complexity Center, Universidad de Los Andes, Bogotá, ColombiaObservatory for Urban Health in Belo Horizonte, Belo Horizonte, Brazil; School of Medicine, Federal University of Minas Gerais, Belo Horizonte, BrazilObservatory for Urban Health in Belo Horizonte, Belo Horizonte, Brazil; School of Medicine, Federal University of Minas Gerais, Belo Horizonte, BrazilDepartment of Health Management and Policy, Dornsife School of Public Health, Drexel University, 3215 Market St, Philadelphia, PA 19104, USACenter for Community Health Integration, Case Western Reserve University, Cleveland, OH, USADepartment of Public Health, School of Medicine, Universidad de los Andes, Bogotá, ColombiaBrown School at Washington University in St. Louis, One Brookings Drive, St Louis, MO 36130, USA; Center on Social Dynamics and Policy, The Brookings Institution, 1775 Massachusetts Ave NW, Washington, DC 20036, USA; Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USAUrban Health Collaborative, Dornsife School of Public Health, Drexel University, 3600 Market St, Philadelphia, PA 19104, USACross-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.http://www.sciencedirect.com/science/article/pii/S2215016121002855Cross-impact balance (CIB) analysis |
| spellingShingle | Ivana Stankov Andres F. Useche Jose D. Meisel Felipe Montes Lidia MO. Morais Amelia AL. Friche Brent A. Langellier Peter Hovmand Olga L. Sarmiento Ross A. Hammond Ana V. Diez Roux Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis MethodsX Cross-impact balance (CIB) analysis |
| title | Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis |
| title_full | Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis |
| title_fullStr | Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis |
| title_full_unstemmed | Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis |
| title_short | Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis |
| title_sort | using cause effect graphs to elicit expert knowledge for cross impact balance analysis |
| topic | Cross-impact balance (CIB) analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2215016121002855 |
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