© 2011 – Chapman and Hall/CRC

537 pages | 75 B/W Illus.

FREE Standard Shipping!

This new version of the bestselling *Computer-Aided Multivariate Analysis* has been appropriately renamed to better characterize the nature of the book. Taking into account novel multivariate analyses as well as new options for many standard methods,** Practical Multivariate Analysis, Fifth Edition** shows readers how to perform multivariate statistical analyses and understand the results. For each of the techniques presented in this edition, the authors use the most recent software versions available and discuss the most modern ways of performing the analysis.

**New to the Fifth Edition**

- Chapter on regression of correlated outcomes resulting from clustered or longitudinal samples
- Reorganization of the chapter on data analysis preparation to reflect current software packages
- Use of R statistical software
- Updated and reorganized references and summary tables
- Additional end-of-chapter problems and data sets

The first part of the book provides examples of studies requiring multivariate analysis techniques; discusses characterizing data for analysis, computer programs, data entry, data management, data clean-up, missing values, and transformations; and presents a rough guide to assist in choosing the appropriate multivariate analysis. The second part examines outliers and diagnostics in simple linear regression and looks at how multiple linear regression is employed in practice and as a foundation for understanding a variety of concepts. The final part deals with the core of multivariate analysis, covering canonical correlation, discriminant, logistic regression, survival, principal components, factor, cluster, and log-linear analyses.

While the text focuses on the use of R, S-PLUS, SAS, SPSS, Stata, and STATISTICA, other software packages can also be used since the output of most standard statistical programs is explained. Data sets and code are available for download from the book’s web page and CRC Press Online.

"I have come to know this book, and its several precursors, very well indeed. I have used it – in its various editions – for almost thirty years, as the principal text in my doctoral-level statistics course, S-052: Applied Data Analysis, at the Harvard University Graduate School of Education. My course served around 65-75 doctoral students annually, drawn from the social-science disciplines around Harvard and MIT. Over those years, students told me repeatedly what an excellent book they found it to be, as a resource both during and after their successful completion of the course. In my view, the book’s main strength is embodied in its title – "*Practical Multivariate Analysis*" – with a strong emphasis on the "practical."

In the book, a thoughtful set of powerful multivariate methods are presented and described. Explanations in the text are built upon real-world data-examples and their accompanying research questions. There is strong emphasis on perceptive data-display and good data-analysis, on the testing of assumptions and on the credible interpretation of results. These very things are modelled deeply and repeatedly in the book itself, so that it is an exemplar for the rigorous conduct and clear reporting of sophisticated quantitative analyses. The writing is clear and understandable, while remaining technically stringent. It is in these important roles – as informant, explainer and model – that the book has always shone, in my view. Consequently, it has been a critical companion to many cohorts of new scholars and been instrumental in insuring the quality and rigor of the work that they then went on to do. Who knows how far its influence stretches? Overall, I do not think that there many other books at this level – maybe none – that have these same qualities. I recommend it very highly indeed."

—**John B. Willett**, Charles William Eliot Research Professor, Harvard University Graduate School of Education

"First of all, it is very easy to read. … The authors manage to introduce and (at least partially) explain even quite complex concepts, e.g. eigenvalues, in an easy and pedagogical way that I suppose is attractive to readers without deeper statistical knowledge. The text is also sprinkled with references for those who want to probe deeper into a certain topic. Secondly, I personally find the book’s emphasis on practical data handling very appealing. … Thirdly, the book gives very nice coverage of regression analysis. … this is a nicely written book that gives a good overview of a large number of multivariate techniques and also tries to give the reader practical advice on how to perform statistical analyses."

—*Australian & New Zealand Journal of Statistics*, 56(4), 2014

"I found the text enjoyable and easy to read. The authors provide a sufficient description of all the methodology for practical use. Each chapter includes at least one real world dataset analysis and the software commands summary tables included at the end of every chapter should be particularly helpful to a practitioner of statistics. … I would recommend the text for practitioners of statistics looking for a handy reference, particularly those performing basic analysis in the health sciences."

—Thomas J. Fisher, *Journal of Biopharmaceutical Statistics*, Issue 6, 2012

**Praise for Previous Editions:**

For the past 20 years, whenever I had an occasion to review a multivariate method…this was the book that I grabbed first. These books kept the mathematical content to the minimally necessary material and used a wealth of nice examples. One of its attractions is that it is a practical text that works well with nonstatisticians who have had a decent statistics course. It also continues to be an excellent book for the statistician's bookshelf.

—*Technometrics*, November 2004

This book is an excellent presentation of computer-aided multivariate analysis. I believe that it will be a very useful addition to any scholarly library … it provides a comprehensive introduction to available techniques for analyzing data of this form, written in a style that should appeal to non-specialists as well as to statisticians.

—*Zentralblatt MATH* 105

This is a text for a broad spectrum of researchers … who may find it very useful as it stresses the importance of understanding the concepts and methods through useful real life illustrations.

—*Journal of the RSS*, Vol. 168, 2005

A key feature of this book is that it can be used in conjunction with any or all of the following very well-known software tools: S-Plus, SAS, SPSS, STATA, and STATISTICA.

—Pat Altham, University of Cambridge, UK, *Statistics in Medicine*, 2005

PREPARATION FOR ANALYSIS

What Is Multivariate Analysis?

Defining multivariate analysis

Examples of multivariate analyses

Multivariate analyses discussed in this book

Organization and content of the book

Characterizing Data for Analysis

Variables: their definition, classification, and use

Defining statistical variables

Stevens’s classification of variables

How variables are used in data analysis

Examples of classifying variables

Other characteristics of data

Preparing for Data Analysis

Processing data so they can be analyzed

Choice of a statistical package

Techniques for data entry

Organizing the data

Example: depression study

Data Screening and Transformations

Transformations, assessing normality and independence

Common transformations

Selecting appropriate transformations

Assessing independence

Selecting Appropriate Analyses

Which analyses to perform?

Why selection is often difficult

Appropriate statistical measures

Selecting appropriate multivariate analyses

APPLIED REGRESSSION ANALYSIS

Simple Regression and Correlation

Chapter outline

When are regression and correlation used?

Data example

Regression methods: fixed-X case

Regression and correlation: variable-X case

Interpretation: fixed-X case

Interpretation: variable-X case

Other available computer output

Robustness and transformations for regression

Other types of regression

Special applications of regression

Discussion of computer programs

What to watch out for

Multiple Regression and Correlation

Chapter outline

When are regression and correlation used?

Data example

Regression methods: fixed-X case

Regression and correlation: variable-X case

Interpretation: fixed-X case

Interpretation: variable-X case

Regression diagnostics and transformations

Other options in computer programs

Discussion of computer programs

What to watch out for

Variable Selection in Regression

Chapter outline

When are variable selection methods used?

Data example

Criteria for variable selection

A general F test

Stepwise regression

Subset regression

Discussion of computer programs

Discussion of strategies

What to watch out for

Special Regression Topics

Chapter outline

Missing values in regression analysis

Dummy variables

Constraints on parameters

Regression analysis with multicollinearity

Ridge regression

MULTIVARIATE ANALYSIS

Canonical Correlation Analysis

Chapter outline

When is canonical correlation analysis used?

Data example

Basic concepts of canonical correlation

Other topics in canonical correlation

Discussion of computer program

What to watch out for

Discriminant Analysis

Chapter outline

When is discriminant analysis used?

Data example

Basic concepts of classification

Theoretical background

Interpretation

Adjusting the dividing point

How good is the discrimination?

Testing variable contributions

Variable selection

Discussion of computer programs

What to watch out for

Logistic Regression

Chapter outline

When is logistic regression used?

Data example

Basic concepts of logistic regression

Interpretation: Categorical variables

Interpretation: Continuous variables

Interpretation: Interactions

Refining and evaluating logistic regression

Nominal and ordinal logistic regression

Applications of logistic regression

Poisson regression

Discussion of computer programs

What to watch out for

Regression Analysis with Survival Data

Chapter outline

When is survival analysis used?

Data examples

Survival functions

Common survival distributions

Comparing survival among groups

The log-linear regression model

The Cox regression model

Comparing regression models

Discussion of computer programs

What to watch out for

Principal Components Analysis

Chapter outline

When is principal components analysis used?

Data example

Basic concepts

Interpretation

Other uses

Discussion of computer programs

What to watch out for

Factor Analysis

Chapter outline

When is factor analysis used?

Data example

Basic concepts

Initial extraction: principal components

Initial extraction: iterated components

Factor rotations

Assigning factor scores

Application of factor analysis

Discussion of computer programs

What to watch out for

Cluster Analysis

Chapter outline

When is cluster analysis used?

Data example

Basic concepts: initial analysis

Analytical clustering techniques

Cluster analysis for financial data set

Discussion of computer programs

What to watch out for

Log-Linear Analysis

Chapter outline

When is log-linear analysis used?

Data example

Notation and sample considerations

Tests and models for two-way tables

Example of a two-way table

Models for multiway tables

Exploratory model building

Assessing specific models

Sample size issues

The logit model

Discussion of computer programs

What to watch out for

Correlated Outcomes Regression

Chapter outline

When is correlated outcomes regression used?

Data example

Basic concepts

Regression of clustered data

Regression of longitudinal data

Other analyses of correlated outcomes

Discussion of computer programs

What to watch out for

Appendix

References

Index

A Summary and Problems appear at the end of each chapter.

- MAT029000
- MATHEMATICS / Probability & Statistics / General