Neural Network and Multivariate Analyses: Pattern Recognition in Academic and Social Research

Chris Ninness, Marilyn Rumph, Logan Clary, David Lawson, John-Thomas Lacy, Sarah Halle, Ranleigh McAdams, Sonya Parker, Diane Forney


Neural networks are the modern tools that focus most heavily on the logical structure of measurement/assessment, as well as the actual results we attempt to identify by way of scientific inquiry. Employing the Self-Organizing Map (SOM) neural network, we reexamined a well-recognized and commonly employed dataset from a popular applied multivariate statistics text by Stevens (2009). Using this textbook dataset as an exemplar, we provide a preliminary guide to neural networking approaches to the analysis of behavioral outcomes. When employing conventional multivariate procedures only, the sample dataset demonstrated significant familywise error rates; however, these outcomes did not provide sufficient information for identifying the curvilinear patterns that existed within these records. When converted to natural logs and reanalyzed by the SOM, the exemplar dataset showed the actual best fit performance patterns exhibited by all members of the experimental and control groups. The SOM and related neural network algorithms appear to have unique potential in the recognition of nonlinear but unified data patterns frequently exhibited within academic and social outcomes. In particular, the SOM allows the researcher to conduct a "finer grain" analysis identifying critically important similarities and differences that can inform treatment well beyond the probability values derived from conventional statistical techniques.


neural network, self-organizing map, pattern recognition, probability, multivariate analysis

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Published by the University of Illinois at Chicago Library

And Behaviorists for Social Responsibility