Grounded Neural Networking: Modeling Complex Quantitative Data

Castellani, Brian; Castellani, John; Spray, S. Lee

doi:10.1525/si.2003.26.4.577

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…This is also closely related to earlier research on prototype theory and the embodied nature of knowledge (Varela, Thomson, & Rosch, 1991). In its emphasis on the grounded nature of knowledge, the SOM approach aligns well with the central epistemological presuppositions of traditional and revised grounded theory (Castellani et al, 2003;Clarke, 2005;Glaser & Strauss, 1967;Locke, 1996;Strauss & Corbin, 1990).…”

Section: Methodological Comparisonsmentioning

confidence: 84%

“…We approach this issue by turning to one modern revision of grounded theory: the one formulated by Adele Clarke (2005) in her Situational Analysis: Grounded Theory After the Postmodern Turn. Clarke argued that the recent shifts in the intellectual landscape away from positivist presuppositions toward a recognition of the utmost complexity of social life, and of the analysis of it, means that we can no longer pretend to be theoretically innocent in the way represented by much of traditional grounded theory (e.g., Castellani et al, 2003;Clarke, 2005). We also have to avoid succumbing to the opposite danger, that is, that of performing premature theoretical or analytical closure (we would say theory-driven in its confirmatory mode).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Consequently, also the traditional grounded theory approach as conceived and elaborated by Glaser and Strauss has witnessed major reconfigurations (Clarke, 2005;Locke, 1996Locke, , 2001. In turn, the latter development has revealed exciting linkages between grounded theory and a particular quantitative method, that of the self-organizing map (SOM; e.g., Castellani, Castellani, & Spray, 2003).…”

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confidence: 99%

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Text Mining in Qualitative Research

Janasik¹,

Honkela²,

Bruun³

2008

Organizational Research Methods

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The article provides an introduction to and a demonstration of the self-organizing map (SOM) method for organizational researchers interested in the use of qualitative data. The SOM is a versatile quantitative method very commonly used across many disciplines to analyze large data sets. The outcome of the SOM analysis is a map in which entities are positioned according to similarity. The authors' argument is that text mining using the SOM is particularly effective in improving inference quality within qualitative research. SOM creates multiple well-grounded perspectives on the data and thus improves the quality of the concepts and categories used in the analysis.

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Section: Methodological Comparisonsmentioning

confidence: 84%

Section: Conclusion and Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Text Mining in Qualitative Research

Janasik¹,

Honkela²,

Bruun³

2008

Organizational Research Methods

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“…In its emphasis on the grounded nature of knowledge, the SOM approach aligns well with the central epistemological presuppositions of traditional and revised grounded theory (see e.g. [15]). In grounded theory, the concepts and conclusions are drawn from the research material rather than determined beforehand as hypotheses to be tested.…”

Section: Methodsmentioning

confidence: 84%

Text Mining and Qualitative Analysis of an IT History Interview Collection

Paju

Malmi

Honkela

2011

IFIP Advances in Information and Communication Technology

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Abstract. In this paper, we explore the possibility of applying a text mining method on a large qualitative source material concerning the history of information technology in one nation. This data was collected in the Swedish documentation project "From Computing Machines to IT." We apply text mining on the interview transcripts of this Swedish documentation project. Specifically, we seek to group the interviews according to their central themes and affinities and pinpoint the most relevant interviews for specific research questions. In addition, we search for interpersonal links between the interviews. We apply a method called the "self-organizing map" that can be used to create a similarity diagram of the interviews. We then discuss the results in several contexts including the possible future uses of text mining in researching history.

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“…Our discussion, mostly focused at the level of method, is intended to contribute to ongoing conversations (e.g., this special issue, as well as Castellani, Castellani, & Spray, 2003;Crossley, 2010;Latour, 2005, Latour et al, 2012Markham, 2012aMarkham, , 2012b Vis & Thewell, 2012) stressing the need to build conceptual and methodological frameworks that resonate more closely with the complexity of networked, technologically-mediated social contexts. Along with the editors of this special issue, we contend that symbolic interactionist concepts remain stymied by ill-fitting but taken-for-granted methods.…”

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confidence: 99%

Undermining ‘data’: A critical examination of a core term in scientific inquiry

Markham

2013

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Network analysis, especially as it has been understood in relation to the specific methodological approaches of Social Network Analysis (SNA), is not a tool that is naturally employed by interpretive sociologists, social constructionists or symbolic interactionists. To state it simply, SNA focuses on the presence of connections between people and the structural dimensions of relations whereas Symbolic Interactionism (SI) focuses on the content or meaning of these relationships as they are enacted and constantly negotiated in everyday communicative interactions. The approaches are seemingly incompatible.In spite of our issues with the positivist premises and ontological assumptions of standard SNA, we have both been drawn to some of the possibilities offered by a network sensibility. On the surface, network maps and visualizations are compelling. They speak to -and make concrete -patterns that we have already started to piece together in our minds. Scratching the surface of their visual impact, we have found that visualizations emerging from a network thinking prompt a range of sensibilities not available through a simple analysis of text. When the tools are separated from the disciplinary parameters for which they were developed, they offer a beguiling method for extending certain approaches, such as grounded theory, symbolic interactionism, or ethnography, and specifying other approaches, such as discourse analysis, actor network theory or practice theory.In this essay, we make a case for embracing and critically developing network sensibilities as a way to grapple with the complexity of contemporary social media interactions. Our discussion, mostly focused at the level of method, is intended to contribute to ongoing conversations (e.g., this special issue, as well as Castellani, Castellani, & Spray, 2003;Crossley, 2010;Latour, 2005, Latour et al, 2012Markham, 2012aMarkham, , 2012b Vis & Thewell, 2012) stressing the need to build conceptual and methodological frameworks that resonate more closely with the complexity of networked, technologically-mediated social contexts. Along with the editors of this special issue, we contend that symbolic interactionist concepts remain stymied by ill-fitting but taken-for-granted methods. Most of our methods were designed for and still assume relatively stable, local contexts wherein units of analysis such as individual, speech act, and interaction could be identified and isolated. Textbooks still encourage new scholars to draw boundaries around the object of inquiry at very early stages, 2 based on the assumption that the individual and the social are unequivocally linked together as part to whole.To begin, we clarify the distinction between network analysis and network sensibility. We then discuss three key strengths of using network sensibilities to study the nuances of social media: (1) network practices can generate data and add complexity by producing multiple renderings of potential meaning emerging through social media; (2) the practice of creating and then juxtaposi...

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Grounded Neural Networking: Modeling Complex Quantitative Data

Cited by 9 publications

References 12 publications

Text Mining in Qualitative Research

Text Mining in Qualitative Research

Text Mining and Qualitative Analysis of an IT History Interview Collection

Undermining ‘data’: A critical examination of a core term in scientific inquiry

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