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Grounded Neural Networking: Modeling Complex Quantitative Data
Author(s) -
Castellani Brian,
Castellani John,
Spray S. Lee
Publication year - 2003
Publication title -
symbolic interaction
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.874
H-Index - 47
eISSN - 1533-8665
pISSN - 0195-6086
DOI - 10.1525/si.2003.26.4.577
Subject(s) - grounded theory , computer science , artificial neural network , qualitative property , artificial intelligence , qualitative research , data science , management science , machine learning , sociology , social science , economics
The latest advances in artificial intelligence software (neural networking) have finally made it possible for qualitative researchers to apply the grounded theory method to the study of complex quantitative databases in a manner consistent with the postpositivistic, neopragmatic assumptions of most symbolic interactionists. The strength of neural networking for the study of quantitative data is twofold: it blurs the boundaries between qualitative and quantitative analysis, and it allows grounded theorists to embrace the complexity of quantitative data. The specific technique most useful to grounded theory is the Self‐Organizing Map (SOM). To demonstrate the utility of the SOM we (1) provide a brief review of grounded theory, focusing on how it was originally intended as a comparative method applicable to both quantitative and qualitative data; (2) examine how the SOM is compatible with the traditional techniques of grounded theory; and (3) demonstrate how the SOM assists grounded theory by applying it to an example based on our research.