This article demonstrates how a digital environment offers new opportunities for transforming qualitative data into quantitative data in order to use data mining and information visualization for mixed methods research. The digital approach to mixed methods research is illustrated by a framework which combines qualitative methods of multimodal discourse analysis with quantitative methods of data mining and information visualization in a multilevel, contextual model that will result in an integrated, theoretically well-founded, and empirically evaluated technology for analyzing large data sets of multimodal texts. The framework is applicable to situations in which critical information needs to be extracted from geotagged public data: for example, in crisis informatics, where public reports of extreme events provide valuable data sources for disaster management.Keywords multimodal discourse analysis, social semiotics, data mining, information visualization, digital mixed methods design Mixed methods research is defined as:[a]n approach to research in the social, behavioural, and health sciences in which the investigator gathers both quantitative (closed-ended) and qualitative (open-ended) data, integrates the two, and then draws interpretations based on the combined strengths of both sets of data to [better] understand research problems. (Creswell, 2015, p. 2)

There is a growing interest in the use of visual thinking techniques for promoting conceptual thinking in problem solving tasks as well as for reducing the complexity of ideas expressed in scientific and technical formats. The products of visual thinking, such as sketchnotes, graphics and diagrams, consist of ‘multimodal complexes’ that combine language, images, mathematical symbolism and various other semiotic resources. This article adopts a social semiotic perspective, more specifically a Systemic Functional Multimodal Discourse Analysis approach, to study the underlying semiotic mechanisms through which visual thinking makes complex scientific content accessible. To illustrate the approach, the authors analyse the roles of language, images, and mathematical graphs and symbolism in four sketchnotes based on scientific literature in physics. The analysis reveals that through the process of resemiotization, where meanings are transformed from one semiotic system to another, the abstractness of specialized discourses such as physics and mathematics is reduced by multimodal strategies which include reformulating the content in terms of entities which participate in observable (i.e. tangible) processes and enhancing the reader/viewer’s engagement with the text. Moreover, the compositional arrangement creates clear stages in the development of the ideas and arguments that are presented. In this regard, visual thinking is a form of cultural communication through which abstract ideas are translated and explained using a multimodal outline or summary of essential parts by adapting resources (e.g. linguistic resources and mathematical graphs), using new resources (e.g. stick figures and other simple schematic drawings) and maintaining others from the original text (e.g. mathematical symbolic notation), resulting in a congruent (or concrete) depiction of abstract concepts and ideas for a non-specialist audience.

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until 2013. Her areas of research include multimodal analysis, social semiotics, mathematics discourse, and the development of interactive digital media technologies and mathematical and scientific visualization techniques for multimodal and sociocultural analytics.

This paper endogenizes the market structure of an economy with heterogeneous agents who want to form bilateral matches in the presence of search frictions and when utility is nontransferable. There exist infinitely many marketplaces, and each agent chooses which marketplace to be in: agents get to choose not only whom to match with but also whom they meet with. Perfect segmentation is obtained in equilibrium, where agents match with the first person they meet. All equilibria have the same matching pattern. Although perfect assortative matching is not obtained in equilibrium, the degree of assortativeness is greater than in standard models. (c) 2007 by The University of Chicago. All rights reserved..