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Title: Practical Nonparametric Statistics, 3rd Edition by W. J. Conover ISBN: 0-471-16068-7 Publisher: Wiley Text Books Pub. Date: 14 December, 1998 Format: Hardcover Volumes: 1 List Price(USD): $109.95 |
Average Customer Rating: 4.83 (6 reviews)
Rating: 5
Summary: Clear and practical
Comment: Standard statistics make assumptions about how the data are distributed, then give results based on the assumed distribution. Two big problems are that the distribution buried in the analysis may not be the right one, and that the assumption might not even be visible in the analysis. "Nonparametric statistics" (NPS) make no assumptions about the distribution. They work no matter how the data are distributed. Even better, they sometimes work to determine whether the standard techniques have any hope of giving answers.
For the practitioner, this book is the broadest catalog I know of how-to for NPS: when each analysis applies and how to apply it. Even more, it gives insight into how some of the tests work. That gives the reader a better chance to understand each technique's strengths, weaknesses, and applicability. For the student, including self-taught, it's a clear and well-organized textbook. The exercises are varied and generally meaningful, and half have answers (though little discussion of how the answers were derived).
I wish the book gave more background, including how some of the distributions are derived. Most times, seeing more of the derivation gives me more confidence in using an analysis. Face it, almost every real-life situation needs to be bashed a bit to fit the format expected by a test. Knowing more of the background gives me more assurance that my machinations don't break any important assumptions. Still, it's the author's choice to emphasize practice over theory and I have to respect that.
More seriously, I would like to see the bootstrapping section enlarged. Many modern applications, particularly in biology, deal with data so complex that they define analysis or even real understanding. Bootstrapping is just one of many randomization and resampling techniques used for such data. More discussion on the design and analysis of resampling techniques would have been very useful.
The book meets its goals, though, and does so admirably. I'm not a stat specialist, but this is the book I'll recommend for heavy users who want a little more than rote recitation of analytic techniques.
Rating: 4
Summary: Some excellent features and some glaring omissions
Comment: At first glance this textbook appears to be a well-written and thorough introduction to nonparametric statistics. The range of research studies presented and the list of references is far more comprehensive than what is found in other familiar texts, such as Siegel and Castellan. Upon further inspection, however, one discovers large gaps in Conover's treatment. A vast number of research studies that have contributed to current knowledge of nonparametric methods are missing. For example, I have been doing Monte Carlo studies the area for about twenty years, and some fifteen or twenty of the studies that I consult frequently in connection with my own work, and which I am sure many other investigators find necessary to be familiar with, are nowhere cited. This major oversight cries out for explanation.
A feature of the text that is attractive upon first reading is the discussion of the so-called rank transformation and its relation to familiar nonparametric tests. The fact that certain nonparametric methods, such as the Wilcoxon-Mann-Whitney test, produce the same results as parametric methods like the Student t test performed on ranks replacing scores, is valuable information for researchers as well as theorists. Conover deserves credit for introducing these interesting and important findings at a time when they were either overlooked or swept under the rug by other authors of widely-used introductory textbooks.
Unfortunately, however, Conover does not delve into the implications of the rank transformation for the classification of scales of measurement into nominal, ordinal, interval, and ratio scales and its significance for practical use of statistical methods. The above mentioned equivalence raises serious problems for this venerable classification. Curiously, Conover adheres to the old scheme. Further and somewhat more venturesome discussion could have added substantially to the worth of the text. It remains for other investigators to explore more fully the inevitable implications of the rank transformation for statistics education as well as statistical theory and practice.
Rating: 5
Summary: this is a review of third edition
Comment: As I just got a copy of the third edition I can now say that many of my comments on the second edition still hold. The book is authoritative, clearly written and very much applications oriented. Conover has done a good job of updating it with recent developments. He provides a nice introductory treatment of bootstrap among other things.
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Title: Nonparametric Statistical Methods, 2nd Edition by Myles Hollander, Douglas A. Wolfe ISBN: 0471190454 Publisher: Wiley-Interscience Pub. Date: 11 January, 1999 List Price(USD): $115.00 |
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Title: Handbook of Parametric and Nonparametric Statistical Procedures, Third Edition by David Sheskin ISBN: 1584884401 Publisher: Chapman & Hall/CRC Pub. Date: 27 August, 2003 List Price(USD): $139.95 |
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Title: Applied Nonparametric Statistical Methods, Third Edition by Peter Sprent, N. C. Smeeton, P. Sprent ISBN: 1584881453 Publisher: Chapman & Hall/CRC Pub. Date: 07 September, 2000 List Price(USD): $69.95 |
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Title: Categorical Data Analysis (Wiley Series in Probability and Statistics) by Alan Agresti ISBN: 0471360937 Publisher: Wiley-Interscience Pub. Date: 12 July, 2002 List Price(USD): $98.50 |
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Title: Nonparametric Statistics for The Behavioral Sciences by Sidney Siegel, N. John Castellan Jr. ISBN: 0070573573 Publisher: McGraw-Hill Humanities/Social Sciences/Languages Pub. Date: 01 January, 1988 List Price(USD): $121.10 |
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