This book provides the first accessible introduction to neural network analysis as a methodological strategy for social scientists. The author details numerous studies and examples which illustrate the advantages of neural network analysis over other quantitative and modelling methods in widespread use. Methods are presented in an accessible style for readers who do not have a background in computer science. The book provides a history of neural network methods, a substantial review of the literature, detailed applications, coverage of the most common alternative models and examples of two leading software packages for neural network analysis.

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`Garson's book would be a good buy for someone setting out to apply neural networks to their data. It takes a balanced approach, trying to make it clear where they would be applicable and where traditional statisitcs might be a better bet. It is certainly easy to read' - British Journal of Mathematical and Statisistical Psychology

`A useful reference source for terminology, mathematical background, possible application areas and pointers towards software use' - Statistical Methods in Medical Research

The much of the material within is timeless and the quality of its presentation allows it to remain a value-add contributor, even today. Overall this book needs to be taken off the storage shelf, dusted off, and placed on your lap. The book’s publication age is an advantage in this case as the all-important basics of neural networks are not skimmed over in this book as they often can be the books published today. This is a must-read for any computational modeler looking to a way to progress their technique.

(Terrill L. Frantz)

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Neural Networks: An Introductory Guide for Social Scientists (New Technologies for Social Research series)

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