Book Description
With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field.
Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book:
* Provides a thorough criticism and an up-to-date survey of meta-analysis methods
* Emphasises the practical approach, and illustrates the methods by numerous examples
* Describes the use of Bayesian methods in meta-analysis
* Includes discussion of appropriate software for each analysis
* Includes numerous references to more advanced treatment of specialist topics
* Refers to software code used in the examples available on the authors' Web site
Practising statisticians, statistically-minded clinicians and health research professionals will benefit greatly from the clear presentation and numerous examples. Medical researchers will grasp the basic principles of meta-analysis, and learn how to apply the various methods.
Customer Reviews:
A personal review.......2007-05-13
This book has two big advantages: In my personal view it is written well for someone who has at least some basic statistical understanding and for me there are quite enough practical examples and illustrations of the chosen methodology. The second advantage in my opinion is its wealth of references at the end of every chapter.
Overall it covers almost all of the relevant methods and thus can be considered as a reference for the experienced statistician and as an easily comprehensible introduction to the beginner.
I think to recommend this book warmly to any reader who does not need the theoretical foundations of probability in meta-analysis.
Book Description
Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique.
Provides an overview of several techniques and approaches that are available to analysts today — e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide
a practical, logical progression of the phases of analysis and to
group similar types of techniques applicable to most situations.
Customer Reviews:
Best general Multivariate stats book.......2007-06-07
This is without question THE BEST introduction to Multivariate Statistics book currently available. It is designed for the user of the techniques, not someone who wants to examine the math underlying the techniques. I have created a collection of the various editions of this book and I have all of them going from the 1st edition to the current one. Personally I really likely the 2nd and 3rd editions but the current one is also very good. Whether you are interested in Exploratory Factor Analysis, Multiple Regression Analysis, Discriminant Analysis [I think that there should have been more on classification analysis in this section], Logistic regression, multivariate analysis of variance, conjoint analysis, cluster analysis, multidimensional scaling, Confirmatory Factor Analysis or Structural Equation Modeling, this book provides a good broad overview as to how to use and interpret the techniques. The key terms for each technique are defined clearly technique by technique. Having taught faculty how to teach multivariate statistics this is the book that I chose to use. It is important to remember that it is BROAD overview and if you are going to do serious analyses that you'd likely want to get additional books about the specific technique or techniques that you are going to use.
Probably the best advanced stats book ever written...GOD bless the authors!.......2007-05-12
Over the course of my undergrad, grad, and post grad, I have read a variety of statistics books. Without a doubt, Hair's Multivariate Data Analysis is THE BEST book of them all. Here is a brief outline of the awesome features of the book:
1. The book itself is very well organized - chapter order and the order within each chapter helps the reader in knowing what is coming next and provides a sense of direction. I think this is a very important feature for any book to have especially when the topics are complex and are discussed over 800 odd pages.
2. The HBAT data set that comes along with the book (or that is provided by the instructor of the course depending on the version of the book you purchase) is really a very good resource. All multivariate techniques in the book can be carried out using this data set. The data set is clearly explained at the end of the first chapter.
3. Tables of examples, the 'Rules of thumb" after each important concept discussion prove invaluable. This is akin to the managerial implication written at the end of lenghty academic articles. This is almost like saying - Here is the deal folks.....Much precise than the summary section, in bullet points, these rules of thumb acts as quick referece that captures the content of the discussion.
4. From chapter 4 onwards till the very end of the book, each chapter is divided into two halves - the first half is the concept dicsussion - in detail, with examples and in very simple and understandable language. The second half is the illustration of the discussed concept through a very elaborate example using the HBAT data set. This arrangement not only helps the reader in better understanding the complex concepts, but also allows the reader to get their hands dirty by actually working out.
5. Keywords at the begining of each chapter provides a list of all the 'jargon' that would be used in that chapter. This list provides a detail definition of each term. Many times while reading the chapter, you would come across a confusing term and in those times the keyword list can prove invaluable.
All in all, this is an invaluable book. If you are a taking stats and you have not read this book, you are missing something. In spite of all the above great things, the best feature of this book is the writing style. I have not come across a book that explains concepts is such easy to understand language but at the same time not over simplifying the subject matter.
My advanced stats became enjoyable because of this book. Really may GOD bless these authors for writing this book!!
Sure it's good, and a good price by the pund too!.......2005-10-26
A pretty good overview and a lot of in-depth material on Multivariate data analysis. Not quite a bed time read though.
I recommend this book as part of your analytical library.
If you liked this book, another good book on multivariate data analysis you may want to check out as well is Sharma, S.; Applied Multivariate Techniques, New York: John Wiley & Sons, Inc., 1996.
If you want something easier to read/more practical, and you prefer SPSS over SAS you may want to check out either `Discovering Statistics using SPSS for Windows' by Andy Field, or probably even better/simpler `SPSS Survival Manual' by Pallant.
Tom Anderson
Anderson Analytics, LLC
(...)
Good for a second stats course & reference.......2005-08-03
We used this book for our Stats 2 course in grad school, and although our professor was good enough to eclipse Hair, et al. (he had written his own Stats text), I am most pleased at how much mileage I have drawn from Hair, et al. in the years since I took the course. In my subsequent career, reviewers have often sent me back to Hair, et al. when they have questions about something I'm doing with a data analysis, so it has become an invaluable reference for that reason alone.
The chapters on structural equation modeling, MANOVA, and factor analysis are particularly useful and well-written. I recommend this book without reservations for graduate students and others who work with advanced statistics as part of their daily work.
simple but great!!.......2005-06-02
I used this book as a reference on the topics while I was working on a research project at the university. Because the book does not use complex mathematics to explain the multivariate statistics, it is easy for social sciences students to understand. Each chapter starts with an overview, step-by step procedures and ends with an example from the data set that is used throughout the book.
In addition to the common topics in multivariate, the book also includes the new analysis techniques as CHAID, neural network, and data mining.
Book Description
The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering datasets in which the variables are continuous, categorical, or both. The focus is applied, where necessary, to help readers thoroughly understand the statistical properties of those methods, and the behavior of the accompanying algorithms. All techniques are illustrated with real data examples, with extended discussion and practical advice. All of the algorithms described in this book have been implemented by the author for general use in the statistical languages S and S Plus. The software is available free of charge on the Internet.
Customer Reviews:
A nice book.......2006-03-23
The ideas are presented neatly. It is a good introduction for those who step into imcomplete data for the first time.
Book Description
Provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are unusual or inordinately influential, and measure the presence and intensity of collinear relations among the regression data and help to identify variables involved in each and pinpoint estimated coefficients potentially most adversely affected. Emphasizes diagnostics and includes suggestions for remedial action.
Download Description
Provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are unusual or inordinately influential, and measure the presence and intensity of collinear relations among the regression data and help to identify variables involved in each and pinpoint estimated coefficients potentially most adversely affected. Emphasizes diagnostics and includes suggestions for remedial action.
Book Description
What do juggling, old bones, criminal careers and human growth patterns have in common? They all give rise to functional data, that come in the form of curves or functions rather than the numbers, or vectors of numbers, that are considered in conventional statistics. The authors' highly acclaimed book Functional Data Analysis (1997) presented a thematic approach to the statistical analysis of such data. By contrast, the present book introduces and explores the ideas of functional data analysis by the consideration of a number of case studies, many of them presented for the first time. The two books are complementary but neither is a prerequisite for the other. The case studies are accessible to research workers in a wide range of disciplines. Every reader, whether experienced researcher or graduate student, should gain not only a specific understanding of the methods of functional data analysis, but more importantly a general insight into the underlying patterns of thought. Some of the studies demand the development of novel aspects of the methodology of functional data analysis, but technical details aimed at the specialist statistician are confined to sections which the more general reader can safely omit. There is an associated web site with MATLAB and S-PLUS implementations of the methods discussed, together with all the data sets that are not proprietary. Jim Ramsay is Professor of Psychology at McGill University, and is an international authority on many aspects of multivariate analysis. He was elected President of the Statistical Society of Canada for the term 2002-3 and is a holder of the Society's Gold Medal for his work in functional data analysis. His statistical work draws on his collaborations with researchers in speech articulation, biomechanics, economics, human biology, meteorology and psychology. Bernard Silverman is Professor of Statistics at Bristol University. He was President of the Institute of Mathematical Statistics in 2000-1 and has held various offices in the Royal Statistical Society. He is a Fellow of the Royal Society and a member of Academia Europaea. His main specialty is computational statistics, and he is the author or editor of several highly regarded books in this area. He has also published widely in theoretical and applied statistics, and in many other fields, including law, human and veterinary medicine, earth sciences and engineering.
Customer Reviews:
functional data analysis by example.......2002-08-30
In 1997 Ramsay and Silverman wrote a standard text that stands out as a classic book teaching the functional approach. With this new book they take the approach of teaching by example. Each of chapters 2 -12 present a case study first. The one on childhood growth is standard fair but they also present unusual cases such as modeling a juggler and analyzing handwriting. In eachcase they show how special characteristics of the data are best portrayed by treating curves or functions as individual data points. The application is presented first. Then the authors show how as consultants they formuated a functional data approach to the problem. Sometimes transformations are applied first. In another case principal component curves are constructed. After each problem is presented with the approach taken, the authors go back and present a "what we have seen" section that reviews the key points and lessons learned from the examples.
This is a wonderful way to make the material very accessible to practitioners as well as statisticians and it illustrates the variety of problems and that can be handle by this approach to data analysis.
functional data analysis by example.......2002-08-30
In 1997 Ramsay and Silverman wrote a standard text that stands out as a classic book teaching the functional approach. With this new book they take the approach of teaching by example. Each of chapters 2 -12 present a case study first. The one on childhood growth is standard fair but they also present unusual cases such as modeling a juggler and analyzing handwriting. In eachcase they show how special characteristics of the data are best portrayed by treating curves or functions as individual data points. The application is presented first. Then the authors show how as consultants they formuated a functional data approach to the problem. Sometimes transformations are applied first. In another case principal component curves are constructed. After each problem is presented with the approach taken, the authors go back and present a "what we have seen" section that reviews the key points and lessons learned from the examples.
This is a wonderful approach that makes the material very accessible to practitioners as well as statisticians and illustrates the variety of problems and that can be handle by this approach.
Average customer rating:
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Applied Multivariate Data Analysis: Volume II: Categorical and Multivariate Methods (Springer Texts in Statistics)
J. D. Jobson
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Applied Multivariate Data Analysis: Volume 1: Regression and Experimental Design (Springer Texts in Statistics)
ASIN: 0387978046 |
Book Description
This books presents an easy to read and wide-ranging introduction to techniques in multivariate analysis. It covers all the traditional topics of multivariate analysis including multidimensional contingency tables, logistic regression, cluster analysis, multidimensional scaling, and correspondence analysis. It is the companion volume to Volume I: Regression and Experimental Design published in 1991. The emphasis on the practicalities of the subject, and the author has included numerous analyses of real data sets drawn from a wide range of business, social sciences, and biological sciences settings. There are also many exercises which are designed to extend the analyses of the data sets including the use of statistical computing packages, and to cover further theoretical results relevant to the book. As a result, any student whose work uses these techniques will find this to be an excellent introduction to the subject.
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Applied Multivariate Data Analysis: Volume 1: Regression and Experimental Design (Springer Texts in Statistics)
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Applied Multivariate Data Analysis: Volume II: Categorical and Multivariate Methods (Springer Texts in Statistics)
ASIN: 0387976604 |
Book Description
An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.
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- Understand clusters and clustering deeply
- different methods for finding clusters
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Classification, Clustering and Data Analysis
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Cluster Analysis
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Classification, Clustering, and Data Mining Applications: Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), ... Data Analysis, and Knowledge Organization)
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Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics)
ASIN: 354043691X |
Book Description
This book deals with recent developments in classification and data analysis and presents new topics which are of central interest to modern statistics. In particular, these include: classification models and clustering methods, multivariate data analysis, symbolic data, neural networks and learning devices, phylogeny and bioinformatics, new software systems for classification and data analysis, as well as applications in social, economic, biological, medical and other sciences. The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.
Customer Reviews:
Understand clusters and clustering deeply.......2006-08-19
This is a good and broad approach about cluster and clustering. It is better for those who want to understand deeply the theme. Is has lot of formulas and mathmatics.
different methods for finding clusters.......2005-01-13
The book has a nice treatment of the problem of finding, in some sense, clusters in data. Several papers point out that there is often some subjectivity here, as to which data sits in a particular cluster. Fuzziness in the boundary of a cluster. It can depend on what your underlying model is.
Possibly of interest to some is work on high dimensionality data, and trying to find clusters in these. Even visualisations might be non-trivial.
The book has value in letting you see a variety of ideas for finding clusters. Perhaps some of these might prove germane to your research.
Book Description
Description: Real-world problems and data sets are the backbone of this book, which provides a unique approach to the topic, integrating statistical methods, data analysis, and applications. Now extensively revised, the book includes new information about mixed effects models, applications of the MIXED procedure, regression diagnostics with the corresponding IML procedure code, and covariance structures. The authors' approach to the information will aid professors, researchers, and students in a variety of disciplines and industries. Extensive SAS code and the corresponding high-resolution output accompany sample problems, and clear explanations of SAS procedures are included. Emphasis is on correct interpretation of the output to draw meaningful conclusions. Featuring both the theoretical and the practical, topics covered include multivariate analysis of experimental data and repeated measures data, graphical representation of data including biplots, and multivariate regression. In addition, a quick introduction to the IML procedure with special reference to multivariate data is available in an appendix. SAS programs and output integrated with the text make it easy to read and follow the examples.
Customer Reviews:
Very Nice Introduction to Multivariate Analysis using SAS.......2000-02-11
The authors gave a short tutorial on the book at the Deming conference last year. I enjoyed the presentation and the book which is not expensive was discounted. I met both authors. They are experts in multivariate analysis having studied under C. R. Rao at Pittsburgh. The book provides lots of SAS code and does a good job of explaining how to set-up the analysis in SAS which would not be straightforward from the SAS manuals. The authors discuss diagnostics and with most concepts they explain them first for univariate problems and then show you how it is extended to the multivariate case. It is also one of the few books that deals with multivariate outliers. Outliers can be difficult to detect in regression and high dimensional multivariate data. I have some research experience in the area of outlier detection and can appreciate the way the authors deal with it.
Book Description
Statisticians and nonstatisticians alike will appreciate this modern and comprehensive new book on multivariate statistical methods that utilizes statistical computing packages throughout. Author Dallas Johnson uses real-life examples and explains the "when to," "why to," and "how to" of numerous multivariate methods, stressing the importance and practical application of each. Technical details are kept to a minimum, making the book accessible to readers.
Customer Reviews:
An excellent book for practitioners of data analysis.......2002-09-27
If you are in the business of analyzing data, and do not have a degree in multivariate statistics, this book provides you the tools you need to get started right away. The author seems to have deep hands-on experience with the methods he is presenting and gives insights on their use in practice, often through well chosen and developed examples, that a pure theoretician would simply lack. I recommend it wholeheartedly to everyone who earns a living by analyzing data.
I second the reviewer from phili 's review.......2002-06-12
I hav e a stat background so like to see some theory, but this aside, I think most users will find it lacks in a decent level of backgound as to what is really going on. Works fine if you want a quick guide to how to get certain multivariate stat procedures done in SAS or soome other stat package
Light on understanding, heavy on computer output.......1999-07-27
A great text to learn the mechanics of multivariate analysis. Unfortunately, a poor one to learn what exactly you're doing!
The author and Duxbury Press disappoint the reader with many instances of improper writing and editing. Occasionally, misused prepositions and descriptors couple with notation errors in formulae to leave the reader totally clueless. For a 1st edition, this may be expected.
The first chapter's lame attempt to introduce the field tries to accomplish more than is possible with a student just launching into multivariate analysis.
Many derivations do not appear; the text provides only a reference. The author could have included them in the appendix for the interested student. Instead, much of the appendix explains a huge social science dataset. However, a part of the appendix reviewing linear algebra proved useful for this student.
Most "figures" are computer printouts. The text frequently references parts of the printout far away, requiring the reader to flip pages too much. The chapter exercises also emphasize computer analysis, with nearly all requiring statistical computing packages such as SAS, SPSS, etc. Although these reinforce the mechanics of analysis, they neither challenge the student to understand the fundamentals of the techniques nor hone critical thinking skills. For example, some exercises are more amenable to short answers. Yet the text contains no "answers to exercises" section anywhere.
For the reader who needs some theory and a lot of "hands on" information, this text may work. But it misses the mark for aspiring statistical consultants by diluting statistical concepts with reams of computer output.
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