NEURAL NETWORK AND MULTIVARIATE ANALYSES: PATTERN RECOGNITION IN ACADEMIC AND SOCIAL RESEARCH.
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 emp...
| Publicado en: | Behavior & Social Issues Vol. 22; no. 1; pp. 49 - 64 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
May2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=136197399&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 136197399 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10649506 G26 jtl: Behavior & Social Issues issn: 10649506 maglogo: N pubinfo: dt: May2013 vid: 22 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 136197399 10.5210/bsi.v22i0.4450 ppf: 49 ppct: 15 formats: fmt: @attributes: type: P size: 2.5MB tig: atl: NEURAL NETWORK AND MULTIVARIATE ANALYSES: PATTERN RECOGNITION IN ACADEMIC AND SOCIAL RESEARCH. aug: au: Ninness, Chris Rumph, Marilyn Clary, Logan Lawson, David Lacy, John-Thomas Halle, Sarah McAdams, Ranleigh Parker, Sonya Forney, Diane affil: Stephen F. Austin State University keyword: multivariate analysis neural network pattern recognition probability self-organizing map multivariate analysis neural network pattern recognition probability self-organizing map ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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