The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis.
Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of:
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Customer Reviews:
The one to have.......2006-08-26
If you want one book on Categorical Data analysis, this is the one. But there are others that are easier to read, if your math is not great (including the same author's book with an almost identical title)
Good reading, but how do I analyze my data?.......2006-05-04
In the theoretical sense, this book provides a very thorough overview of categorical data analysis. However, this book should not be used as a reference for the scientist needing to do the occasional number crunching of categorical data. The examples are vague and the tests are not well explained. If you want to derive the tests, this book is for you. If you're not a statistician at heart and just want the answer, I suggest looking at Conover's "Practical Nonparametric Statistics" for a good explanation of which tests to use and how to use them.
A classic, made even better.......2003-07-22
This is a very demanding, thorough, and clear description of just about everything anyone could want to know on the subject. The second edition is considerably more rigorous than the first. Agresti stresses that logistic models are one kind of generalized linear model. This offers solid connections to many other models, but places corresponding demands on the reader. In particular, Chapter 4 is difficult going, but might be skipped or skimmed on first reading.
Given the mathematical level and rigor, this is a remarkably clear book. Anyone who analyzes categorical data on a regular basis should read it and have it on his or her shelf.
some day should be a Wiley classic.......2001-06-13
When this book came out in 1990 it was the first book to provide a truely modern treatment of categorical data analysis for both ordinal and nominal data. It provides an excellent treatment of the asymptotic theory for binary and multinomial data. It is extremely well written and is still a favorite of statisticians and practitioners. Because of its popularity and continued value, it should soon be added to the Wiley Classic series.
This is the first book to take the regression approach to categorical data analysis tieing the subject to the methods and theory of the generalized linear models. It also was one of the first to show the modern practicality of exact permutation methods.
The only drawback of this book is that it is 11 years old and there have been many interesting and relevant research developments in computer-intensive methods, analysis of missing data and mixed effects linear models to make a revision useful. Some of the latest developments can be found in Lloyd's new book "Statistical Analysis of Categorical Data" that was recently published by Wiley.
Agresti provides clear advice and also gives a nice historical perspective on the development of the subject. The book is authoritative and includes numerous relevant references. Each chapter contains many exercises and a wealth of practical examples for illustration of the techniques. This is a good text from both practical and theoretical perspectives. It is excellent for a graduate level course on categorical data analysis.
Book Description
This popular text provides an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. The companion Web page offers data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.
Customer Reviews:
Excellent introduction to SEM.......2007-01-10
Kline's book provides a very readable introduction to and explanation of structural equation modeling. The book does not include statistical proofs, so it would not serve well as an advanced text. But if you are looking for a book that explains what SEM is and how it fits within the larger framework of inferential statistics, I recommend it.
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
Packed with concrete examples, Hatcher's book provides an introduction to more advanced statistical procedures and includes handy appendices that give basic instruction in using the SAS System. Novice SAS users will find all they need to master SAS basics and move into advanced statistical analyses in this one book. Featured is a simple, step-by-step approach to testing structural equation models with latent variables using the CALIS procedure. The following topics are explained in easy-to-understand terms: exploratory factor analysis, principal component analysis, and developing measurement models with confirmatory factor analysis. Other covered topics of note include "LISREL-type" analyses with the user-friendly PROC CALIS and solving problems encountered in real-world social science research.
Supports releases 6.09 and higher of SAS software.
Customer Reviews:
Very Good .......2007-08-06
More detailed explanation of every subject discussed. Nothing was assummed. The appendix include vital information for new SAS users.
Absolutely outstanding.......2007-05-23
This is the single best statistics text I've ever read. It's extremely easy to follow, very practical, and gives outstanding examples. Everything is so clearly described in the book that one might call it Factor analysis and SEM for Dummies. I only wish that the book covered nonrecursive SEM models.
Easy to read.......2006-11-03
Very easy to read. Examples are simple and effective. The book explains the statistical concepts as well as the SAS programming tricks. In fact, the book provides complete code - you will be able to get good amount of analysis done just by copying that code. However, you also need to buy the "The Little SAS Book" if you never used SAS before.
Excellent book.......2003-05-01
This outstanding book was one of the mainstays for my Ph.D. dissertation. It explain factor analysis and SEM techniques in a clear and effective way, with an hands-on approach that guides the reader during all the data analysis and result interpretation phases.
Thank goodness for this book.......2002-10-21
I really needed this for my dissertation. As a computer scientist who is using statistically-based data mining techniques in my PhD dissertation, I needed a good "Quick Course" in factor analysis. More important than just "what is factor analysis", I needed to know how to implement my ideas in SAS. This book did the trick. Now let's hope my advisors think so. :)
Book Description
The first practical look at spatial statistics for the scientist and engineer. Designed for the scientific and engineering professional eager to exploit its enormous potential, Statistics for Spatial Data is a primer to the theory as well as the nuts-and-bolts of this influential technique. Focusing on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, and reveals how spatial statistical models can be used to solve a host of problems in science and engineering. Revised to reflect state-of-the-art developments, this edition also features many detailed examples, numerous illustrations, and over 1,000 references. The first fully comprehensive introduction, Statistics for Spatial Data is an essential guide for professionals in biology, earth science, civil, electrical, and agricultural engineering, geography, epidemiology, and ecology. "This excellent book, which presents the most important branches of modern spatial statistics in an exemplary manner, will become a basic reference book and will popularize many methods which have been used until now only by small circles of specialists." -Mathematical Reviews "The majority of geoscientists should make sure they have access to this volume, while for those who are actively involved in applying statistical methods to data (of all sorts) this book should be in their personal libraries. As a compendium of statistical procedures to use to analyze data, this volume will surely provide a good solid framework for some time to come." -Geophysics
Customer Reviews:
Excellent book to learn geostatistics.......2007-06-12
I am a beginner in geostatistics and this book helped me alot to learn and analyze my data for my thesis. Excellent reference book to keep.
Bible or Paperweight? I hope you are a mathematical guru!.......2000-11-05
This text is more of a formula-reference with examples and is written for persons with substantial statistical / mathematical background. While I am sure that the text represents substantial value for those with substantial backgrounds in statistics / mathematics/ calculus, the rest of us may be somewhat less enlightened by the text. Make sure you have read over your Statistics 101, 102, 201, 202, 301, 302, 401, 402, 501, 502, Calculus 101, 102, 201, 202, and committed these texts to memory before reading this text. If you never met a formula you didn't like...this text is for you. The author does not hold your hand through the math and if you lack the background required, the book may have better use as a paperweight.
the bible for spatial statistics.......2000-08-04
This is a wonderful source of information on methods for analyzing spatial data. It is still reasonably current for this rapidly developing area in statistics. You won't find some of the latest research on the joint modeling of space and time. But I expect that Cressie is working on a book that will cover it. It is an encyclopedia of references. The author covers the gamut of techniques. This explains why the book is so lengthy. I think that if he plans a revision he will be more selective and only cover those methods that have proved to be the most promising. Here you can finally learn what kriging is and even see how the bootstrap can be applied to spatial data.
The best book for Geostatistician and not only ..........2000-05-30
The book covers very wide spectrum of different simulation and prediction methods and techniques. One can really merit from this book but under good mathematical background. The large number of practical examples helps for better understanding. The most of them can be recommended to apply for the development and testing of own statistical and mathematical software.
The best book for Geostatistician and not only ..........2000-05-30
The book covers very wide spectrum of different simulation and prediction methods and techniques. One can really merit from this book but under good mathematical background. The large number of practical examples helps for better understanding. The most of them can be recommended to apply for the development and testing of own statistical and mathematical software.
Book Description
Praise for the First Edition
"This is a superb text from which to teach categorical data analysis, at a variety of levels. . . [t]his book can be very highly recommended."
—Short Book Reviews
"Of great interest to potential readers is the variety of fields that are represented in the examples: health care, financial, government, product marketing, and sports, to name a few."
—Journal of Quality Technology
"Alan Agresti has written another brilliant account of the analysis of categorical data."
—The Statistician
The use of statistical methods for categorical data is ever increasing in today's world. An Introduction to Categorical Data Analysis, Second Edition provides an applied introduction to the most important methods for analyzing categorical data. This new edition summarizes methods that have long played a prominent role in data analysis, such as chi-squared tests, and also places special emphasis on logistic regression and other modeling techniques for univariate and correlated multivariate categorical responses.
This Second Edition features:
- Two new chapters on the methods for clustered data, with an emphasis on generalized estimating equations (GEE) and random effects models
- A unified perspective based on generalized linear models
- An emphasis on logistic regression modeling
- An appendix that demonstrates the use of SAS® for all methods
- An entertaining historical perspective on the development of the methods
- Specialized methods for ordinal data, small samples, multicategory data, and matched pairs
- More than 100 analyses of real data sets and nearly 300 exercises
Written in an applied, nontechnical style, the book illustrates methods using a wide variety of real data, including medical clinical trials, drug use by teenagers, basketball shooting, horseshoe crab mating, environmental opinions, correlates of happiness, and much more.
An Introduction to Categorical Data Analysis, Second Edition is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.
Customer Reviews:
A great reference! .......2007-08-01
Agresti's book 'Introduction to Categorical Data Analysis' has been very valuable for my research and understanding of logistics regression techniques. I am not a statistics major and so I greatly appreciate his use of examples to discuss the concepts. Rather than getting lost in equations with tons of vectors and matrices, Agresti's book focuses on the core concepts and methodologies you need to prepare your data, set up the models and interpret the results. The companion webpage has the SAS code for most of the examples so you can replicate what he did. Highly Recommended!
Informative and concise.......2006-02-25
Categorical Data Analysis covers the basics of categorical data in an informative and inclusive manner. It is understandable to most readers, and it follows in logical order.
An excellent introduction.......1999-11-24
The larger, older, more complex Agresti (to which many of the reviewers have referred) is written in formulas. His newer book, the subject of review, is written in words. For a large group of readers, "Introduction" is perfectly suitable. However, those who are really interested in the mathematical concepts, should buy the older Agresti title. My personal opinion is that they are good companions and that the introductory book is the place to start, unless you know that you will be unhappy with the light treatment of the mathematical issues.
Remove 1/3 of Agresti's big book, and you have this one........1999-09-10
Agresti took most of the material from his big book, _Categorical Data Analysis_, and took out all the justifications. What's left is _An Introduction to Categorical Data Analysis_, and no math. If you enjoy having results fed to you so that you can memorise then, read this book. Otherwise, I concur with the other reader: buy this book's big brother.
Clear, well written & dumbed-down for the math impaired........1999-06-19
Clear, concise,and extremely well written and edited ( in the 99% tail for Statistics books!). Great problems. Do them and you will learn quickly and easily. Contains a gem: A chapter on the modern history of Statistics including the egos, feuds, and angry debates and Pearson's great error, which he arrogantly refused to admit. But, a big disappointment: the book does NOT give enough formulas or guidance to the use the usual software. The author condescendingly underates the average user's math ability and understanding. Conclusion: The book is interesting, and can be read as a novel, but it is fluff --- you really can't use the book to use your software. Essentially an entertaining waste of time. Buy Agresti's industrial strength book, Categorial Data Analysis instead.
Book Description
Computer-Aided Multivariate Analysis, Fourth Edition enables researchers and students with limited mathematical backgrounds to understand the concepts underlying multivariate statistical analysis, perform analysis using statistical packages, and understand the output. New topics include Loess and Poisson regression, nominal and ordinal logistic regression, interpretation of interactions in logistic and survival analysis, and imputation for missing values. This book includes new exercises and references, and updated options in the latest versions of the statistical packages. All data sets and codebooks are available for download. The authors explain the assumptions made in performing each analysis and test, how to determine if your data meets those assumptions, and what to do if they do not. What to Watch out for sections in each chapter warn of common difficulties. By reading this text, you will know what method to use with your data set, how to get the results, and how to interpret them and explain them to others. New in the Fourth Edition: · Expanded explanation of checking for goodness of fit in logistic regression and survival analysis · Kaplan-Meier estimates of survival curves, formal tests for comparing survival between groups, interactions and the use of time-dependent covariates in survival analysis · Expanded discussion of how to handle missing values · Latest features of the S-PLUS package in addition to SAS, SPSS, STATA, and STATISTICA for multivariate analysis · Data sets for the problems are available at the CRC web site: http://www.crcpress.com/e_products/downloads/ · Commands and output for examples used in the text for each statistical package are available at the UCLA web site: http://www.ats.ucla.edu/stat/examples/cama4/
Customer Reviews:
Perspective from the Get-Go: Technical Names for Multivariate Analysis Methods Decoded for Your Concrete Understanding.......2006-07-06
I'm writing about the 2nd ed. (1990), but if you landed here, I think what I have is of value for later editions too. The book is fantastic. There are a myriad of technical terms for various kinds of multivariate analyses. The average person has no idea what the names mean -- or how to choose between them! To be able to DO anything (right, anyway) you need a CONCRETE understanding. Such is what this book provides. Right off the bat, the book tells you -- not only what kind of data each technique requires -- but what questions you might ask (and want answered) for such data. So, in a mere 4 pages -- near the beginning! -- you get 1 or 2 paragraphs on each of these: factor analysis, discriminant analysis, cluster analysis, multiple linear regression, principal components analysis, and more. You'll know what these techniques require as input (what kinds of data situations) and what they assume the question(s) is/are that you want answered -- already concrete, already good understanding of the differences of the formerly cryptic technique names in a mere 4 pages! I have seen no other book do this. The rest of the book is nearly as effective in the decoding of other details of the multivariate analyses. In short, start with this book, because therein you will be way way ahead in acquiring a perspective on multivariate analyses with little effort. Go to any other book(s) first and you may eventually develope a perspective -- only by accident, that is with little author help -- after a long hard road. Get your perspective from the get-go instead -- with this book.
Only a limited knowledge of statistics is assumed!.......1998-08-22
Often, students and researchers need to perform multivariate statistical analyses on their data. Unfortunately, a lack of mathematical training prevents many from taking advantage of advanced techniques, in part, because books focus on the theory and neglect explaining how to perform and interpret multivariate analyses on real-life data. Computer-Aided Multivariate Analysis is a welcome exception, helping students choose the appropriate analyses for their data, carry them out and interpret the results. Only a limited knowledge of statistics is assumed and geometrical and graphical explanations are used to explain what the analyses do. However the basic model is always given and assumptions are discussed. In this edition the computer emphasis is enhanced by the inclusion of three additional statistical packages written for the personal computer. The authors also discuss data entry, database management, data screening, data transformations, as well as multivariate data analysis. This third edition contains a new chapter on log-linear analysis of multi-way frequency tables.
Excellent, low-key intro to many techniques.......1997-03-29
While this book will not teach you all you need to know about any of the techniques to be an expert, it WILL teach you enough to read research, and to learn what the various techniques do.
The mathematical level is moderate, and the emphasis is on practical applications.
Highly recommended for grad students in the social sciences
Average customer rating:
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Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis
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Geometric Data Analysis (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by Benzécri as Correspondence Analysis, an approach that has become more and more used and appreciated over the years. This book presents the full formalization of GDA in terms of linear algebra - the most original and far-reaching consequential feature of the approach - and shows also how to integrate the standard statistical tools such as Analysis of Variance, including Bayesian methods. Chapter 9, Research Case Studies, is nearly a book in itself; it presents the methodology in action on three extensive applications, one for medicine, one from political science, and one from education (data borrowed from the Stanford computer-based Educational Program for Gifted Youth ). Thus the readership of the book concerns both mathematicians interested in the applications of mathematics, and researchers willing to master an exceptionally powerful approach of statistical data analysis.
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Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on both general and hierarchical model specifications, develop the model-building process step-by-step, and demonstrate the estimation, testing, and interpretation of fixed-effect parameters and covariance parameters associated with random effects. These concepts are illustrated through examples using real-world data sets that enable comparisons of model fitting options and results across the software procedures. The book also gives an overview of important options and features available in each procedure. Making popular software procedures for fitting LMMs easy-to-use, this valuable resource shows how to perform LMM analyses and provides a clear explanation of mixed modeling techniques and theories.
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