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Comments on Neural Networks

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  • HAEJUNG PAIK

    (University of Oklahoma)

Abstract

Although neural networks do offer a few advantages over some other nonlinear methods, in certain situations these advantages are difficult to utilize. However, many neural network applications in the social sciences are flawed in ways that obfuscate such effects. In this article, the neural network methodology is reviewed, some common flaws are pointed out, and a rather commonplace data set—dealing with school delinquency—is analyzed for illustrative purposes.

Suggested Citation

  • Haejung Paik, 2000. "Comments on Neural Networks," Sociological Methods & Research, , vol. 28(4), pages 425-453, May.
    • Handle: RePEc:sae:somere:v:28:y:2000:i:4:p:425-453
    DOI: 10.1177/0049124100028004002
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    References listed on IDEAS

    as
    1. Rees, Daniel I. & Argys, Laura M. & Brewer, Dominic J., 1996. "Tracking in the United States: Descriptive statistics from NELS," Economics of Education Review, Elsevier, vol. 15(1), pages 83-89, February.
    2. Artyushkin, V. F. & Belyayev, A. V. & Sandler, Y. M. & Sergeyev, V. M., 1990. "Neural network ensembles as models of interdependence in collective behavior," Mathematical Social Sciences, Elsevier, vol. 19(2), pages 167-177, April.
    3. Haejung Paik, 1998. "The Effect of Prior Probability on Skill in Two-Group Discriminant Analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 32(2), pages 201-211, May.
    Full references (including those not matched with items on IDEAS)

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