The rapid growth of databases in many disciplines has overwhelmed the traditional, interactive approaches to data analysis and created a new generation of tools critical to intelligent and automated data discovery. Education researchers are beginning to investigate using these data mining techniques for applying knowledge-discovery principles and data mining in the field of special education. For special educators, data mining and knowledge discovery have great promise for providing anticipatory guidance in teaching and learning in the development of models critical for promoting student achievement. This monograph explores how knowledge-discovery applications can empower educators with the information they need to (a) provide anticipatory guidance for teaching and learning, (b) forecast school and district needs, and (c) find critical markers for making the best program decisions for children and youth with disabilities.
The new computational algorithms emerging in the data mining literature--in particular, the self-organizing map (SOM) and decision tree analysis (DTA)--offer qualitative researchers a unique set of tools for analyzing health informatics data. The uniqueness of these tools is that although they can be used to find meaningful patterns in large, complex quantitative databases, they are qualitative in orientation. To illustrate the utility of these tools, the authors review the two most popular: the SOM and DTA. They provide a basic definition of health informatics, focusing on how data mining assists this field, and apply the SOM and DTA to a hypothetical example to demonstrate what these tools are and how qualitative researchers can use them.
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.A new moment has arrived in the tradition of grounded theory (Charmaz 2000): grounded theory is no longer limited to the methodological formalities of its original users (e.g., Glaser and Strauss 1967) but is instead open to new ideas. Examples are Charmaz's (2000) constructivist-contra objectivist-approach to grounded theory; Soulliere, Britt, and Maines's (2001) grounded conceptual modeling; Strübing's (1998) simulated grounded theory; and computer-assisted grounded theory (Richards and Richards 1994). Even Strauss andCorbin (1990, 1998) offer a new postpositivistic version of grounded theory.
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