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Content Analysis Using Fuzzy Cognitive Map (FCM): A Guide to Capturing Causal Relationships from Secondary Sources of Data

机译:使用模糊认知图(FCM)进行内容分析:从辅助数据源捕获因果关系的指南

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Cognitive mapping was introduced as a method to model complex systems that reflects how experts or stakeholders understand cause-and-effect relationships. Later, fuzzy cognitive mapping (FCM) combined cognitive mapping with artificial neural networks (ANN), resulting in the unique capacity to capture and use qualitative data to perform quantitative analysis and study system behavior in response to changes of system elements. However, when it comes to building FCM models from qualitative data, particularly from secondary data sources, guidance for modelers is scarce. This article introduces a step by step guideline for building FCM that not only deals with causal relationships but also offers techniques to adjust the inconsistencies, and tune the granularities through parent-child relationships. It also proposes two techniques, isolated graph analysis, and receiver-only-concept analysis to investigate the completeness of the final FCM and hypothesize new connections to fill the gaps.
机译:引入了认知映射作为模拟复杂系统的方法,这些方法反映了专家或利益相关者如何理解原因和效应关系的方式。后来,模糊认知映射(FCM)组合与人工神经网络(ANN)的认知映射,导致捕获和使用定性数据的独特容量,以响应系统元素的变化来执行定量分析和研究系统行为。但是,在从定性数据构建FCM模型方面,特别是来自二级数据源,建模者的指导稀缺。本文介绍了建立FCM的步骤指导,不仅要处理因果关系,还提供了调整不一致的技术,并通过亲子关系调整粒度。它还提出了两种技术,孤立的图分析和接收概念分析,以研究最终FCM的完整性,并假设新的连接以填补空白。

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