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Determining what constitutes a causal relationship between two or more concepts, and how to infer causation, are fundamental concepts in statistics and all the sciences. Causation becomes especially difficult in the social sciences where there is a myriad of different factors that are not always easily observed or measured that directly or indirectly influence the dynamic relationships between independent variables and dependent variables. This paper proposes a procedure for helping researchers explicitly understand what their underlying assumptions are, what kind of data and methodology are needed to understand a given relationship, and how to develop explicit assumptions with clear alternatives, such that researchers can then apply a process of probabilistic elimination. The procedure borrows from Pearl&rsquo ; s concept of &ldquo ; causal diagrams&rdquo ; and concept mapping to create a repeatable, step-by-step process for systematically researching complex relationships and, more generally, complex systems. The significance of this methodology is that it can help researchers determine what is more probably accurate and what is less probably accurate in a comprehensive fashion for complex phenomena. This can help resolve many of our current and future political and policy debates by eliminating that which has no evidence in support of it, and that which has evidence against it, from the pool of what can be permitted in research and debates. By defining and streamlining a process for inferring truth in a way that is graspable by human cognition, we can begin to have more productive and effective discussions around political and policy questions.

El mapeo causal (mejor conocido como mapeo cognitivo) surgió a fines de 1970 como un método innovador para capturar y analizar políticos que toman decisiones y patrones fenomenológicos organizacionales y de creencias causales. Desde entonces, en particular el mapeo causal comparativo (MCC) ha sido muy utilizado especialmente en la gestión y organización y en los estudios IT en el Reino Unido y en Estados Unidos. Este artículo brinda un panorama de los fundamentos conceptuales y las principales variedades del MCC para presentar el enfoque a los investigadores cualitativos interesados en nuevos métodos y herramientas de investigación. Después, se presenta el nuevo software no comercial CMAP3 basado en Windows, diseñado para estudios de MCC en N pequeños y medianos. Finalmente, para describir la versatilidad y potencia de los métodos MCC, se discuten seis tipos de estudios MCC que representan diferentes concepciones de fenómenos clave y objetivos de investigación y aplican a diferentes modificaciones de la plataforma básica de MCC.URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1202133 ; Kausales (kognitives) Mapping ist in den späten 1970er Jahren als innovative Methodik entstanden, um die organisationsbezogenen Überzeugungen von politischen Entscheidungsträger/innen zu erheben und zu analysieren. Seit dieser Zeit hat insbesondere das komparativ-kausale Mapping (KKM) in Management-, Organisations- und IT-Studien in Großbritannien und den USA breiten Einsatz gefunden. In diesem Beitrag gebe ich einen Überblick über die konzeptuellen Grundlagen und wesentlichen Varianten des KKM, um diesen Ansatz qualitativ Forschenden vorzustellen, die an neuen Verfahren und Werkzeugen interessiert sind. Zusätzlich informiere ich über eine neue, nicht-kommerzielle Windows-Software, CMAP3, die für kleinere und mittlere Fallzahlen entwickelt wurde. Am Ende veranschauliche ich die Vielseitigkeit und Potenz des CCM an sechs Typen von Untersuchungen, mit denen jeweils unterschiedliche Zielsetzungen und Forschungsfragen verbunden sind ...

Evaluators are interested in capturing how things causally influence one another. They are also interested in capturing how stakeholders think things causally influence one another. Causal mapping – the collection, coding and visualisation of interconnected causal claims – has been used widely for several decades across many disciplines for this purpose. It makes the provenance or source of such claims explicit and provides tools for gathering and dealing with this kind of data and for managing its Janus-like double-life: on the one hand, providing information about what people believe causes what, and on the other hand, preparing this information for possible evaluative judgements about what causes what. Specific reference to causal mapping in the evaluation literature is sparse, which we aim to redress here. In particular, the authors address the Janus dilemma by suggesting that causal maps can be understood neither as models of beliefs about causal pathways nor as models of causal pathways per se but as repositories of evidence for those pathways.

AbstractA central argument for non-reductive accounts of group agency is that complex social entities (states, companies, churches, political parties) are capable of exerting causal influence independently of and superseding the causal efficacy of the individuals constituting them. A prominent counter is that non-reductionists run into an insuperable dilemma between identity and redundancy – with identity undermining independent higher-level efficacy and redundancy leading to overdetermination or exclusion. This paper argues that critics of non-reductionism can manage with a simpler and more persuasive reductio strategy calledmapping: allow that group agents are causally efficacious in their own right and chart (a) how their causal efficacy is carried out; (b) how it relates to the causal efficacy of individual determiners; (c) how it connects to the causal relevance of background structural factors. The focus exclusively on whether groupsareorare notcausally efficacious black-boxes implementation, while close attention to how causation is wired increases the visibility of individualist arguments and countenances structure-oriented explanations.

"The causal mapping method has been used in a variety of research areas. The purpose of this book is to provide an introduction to causal mapping for IS researchers and practitioners, providing them everything they need to use causal mapping for both research and application"--Provided by publisher

"Kausales (kognitives) Mapping ist in den späten 1970er Jahren als innovative Methodik entstanden, um die organisationsbezogenen Überzeugungen von politischen Entscheidungsträger/innen zu erheben und zu analysieren. Seit dieser Zeit hat insbesondere das komparativ-kausale Mapping (KKM) in Management-, Organisations- und IT-Studien in Großbritannien und den USA breiten Einsatz gefunden. In diesem Beitrag gibt der Autor einen Überblick über die konzeptuellen Grundlagen und wesentlichen Varianten des KKM, um diesen Ansatz qualitativ Forschenden vorzustellen, die an neuen Verfahren und Werkzeugen interessiert sind. Zusätzlich informiert er über eine neue, nicht-kommerzielle Windows-Software, CMAP3, die für kleinere und mittlere Fallzahlen entwickelt wurde. Am Ende veranschauliche ich die Vielseitigkeit und Potenz des CCM an sechs Typen von Untersuchungen, mit denen jeweils unterschiedliche Zielsetzungen und Forschungsfragen verbunden sind und in denen unterschiedliche Modifikationen des CCM verwendet werden." (Autorenreferat)

In the papers of G.E. Archie and V.N. Dakhnov were presented formulas of dependence between resistivity (or conductivity) of soil and that of porous water. Such dependence shows that we need to know water resistivity, that means its study at each fieldwork area. In some areas places for groundwater are absent or restricted (boreholes, wells, springs) as places of surface water (rivers, streams, ponds, lakes). Can we measure water resistivity in pools? Immediately after rain such water has no relation with soil resistivity. The purpose of our study consists in determination the rules of ionic exchange between soil and rain water in time. In the paper [Brunet et al., 2010] the authors demonstrated results of water resistivity measurements at contact with soil in time. We wanted to check results of that experiment.

The expanding application of Concept Mapping includes its role in knowledge elicitation, institutional memory preservation, and ideation. With the advent of the CmapTools knowledge modeling software kit, Concept Mapping is being applied with increased frequency and success to address a variety of problems in the workplace. Supported by business application case studies, Applied Concept Mapping: Capturing, Analyzing, and Organizing Knowledge offers an accessible introduction to the theory, methods, and application of Concept Mapping in business and government. The case studies illustrate applications across a range of industries—including engineering, product development, defense, and healthcare. The authors provide access to a free download of CmapTools, courtesy of the Institute for Human and Machine Cognition, to enable readers to create and share their own Concept Maps. Offering examples from the United States, Canada, Australia, Spain, Brazil, Scotland, and The Netherlands, they highlight a global perspective of this dynamic tool. The text is organized into three sections: Practitioners' Views —supplies narratives, guidance, and reviews of applications from career Concept Mappers Recent Case Studies and Results —presents in-depth examinations of specific applications and their results Pushing the Boundaries —explores what's possible and where the boundary conditions lie Applied Concept Mapping facilitates the fundamental understanding needed to harness the power of Concept Mapping to develop viable solutions to a virtually unlimited number of real-world problems.