Using Graphical Perception Principles to Improve the ST Tools' Data Visualization: Revisiting the Systems Dynamics Model

• Päätekijät: Sara Vaca (Tekijä), Pablo Vidueira (Tekijä)
• Aineistotyyppi: Kirja
• Julkaistu: The Evaluation Center at Western Michigan University, 2016-01-01T00:00:00Z.
• Aiheet: article
• Linkit: Connect to this object online.
• Notes: 10.56645/jmde.v12i26.441; 1556-8180
• Keywords: Education; L; Social Sciences; H
• Source: Journal of MultiDisciplinary Evaluation, Vol 12, Iss 26 (2016)

Background: Systems Thinking (ST) is the new paradigm in Evaluation. It represents a significant mind-set shift for the evaluation field and it is a powerful tool to tackle complex environments. Heir to the systems concepts of the engineering field, and especially regarding the hard systems tools, ST in evaluation uses the same visual tools that were created many years ago. All these tools already incorporate data visualization features: they depict ideas, relationships and concepts relying in shapes and figures more than a textual explanation. Revisiting these tools and applying the latest data visualization principles, they could be optimised in order to provide with more information within the same concept. Purpose: To provide ST practitioners with more informative tools in order to facilitate: - ST experts and users can optimise the application of the tools to real life models beyond the initial set up of their visual representations. - Audiences of evaluations using ST as part of the toolkit can find the outputs more apprehensible and easy to understand . Setting: Not applicable. Intervention: Not applicable. Research Design: Not applicable. Data Collection and Analysis: Not applicable. Findings: Improving ST representations of reality and systems can help both enhance ST applications and make it more accessible and comprehensible for evaluation practitioners' and users. Six ways for improving the understanding of the current stock and flow diagrams were identified. The tools proposed consist of: customizing the colours and shape of the variables and their relationships to make them more informative; highlighting the existing subsystems within the model; and providing the specific sequence for reading the main causal chains.