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Harris R.J. Lawrence Erbaum Associates; 2001; 609стр.; ISBN: 0805832106 Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why one should consider diving into more detailed treatments of computer-modeling and latent-variable techniques, such as non-recursive path analysis, confirmatory factor analysis, and hierarchical linear modeling. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. From Book News, Inc. As he was looking over materials for his multivariate course, Harris (U. of New Mexico) realized that the course had outstripped the current edition of his own textbook. He decided to revise it rather than use someone else's because he finds them veering too much toward math avoidance, and not paying enough attention to emergent variables or to structural equation modeling. He has updated the 1997 second edition with new coverage of structural equation modeling and various aspects of it, new demonstrations of the properties of the various techniques, and computer applications integrated into each chapter rather than appended.Copyright © 2004 Book News, Inc., Portland, OR |
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http://rapidshare.de/files/3841407/A_Prime...istics.pdf.html |
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http://rapidshare.de/files/6304488/A_Prime...istics.pdf.html |
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