Book by Brian S Everitt
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Clarity and conciseness have always been the hallmarks of Everitt’s writing. This book is no exception. Anyone looking for a clearly written text on the subject that is also practitioner oriented needs to look no further.
―Chuck Chakrapani, Journal of the Royal Statistical Society, Series A, 2012
... a clear, well-orchestrated guide to multivariate statistics for the post-graduate and professional behavioural scientist who possesses basic statistical knowledge. ... Everitt successfully crafts a well-integrated introductory text that obviates potential difficulties by including real problems and their data sets. ... the book’s applied orientation introduces the behavioural scientist to both the use and rudimentary understanding of multivariate techniques. ... The book would also serve well as a training guide for the practitioner less experienced in multivariate techniques. ...
―Psychometrika, June 2010
... The first two chapters give a magnificent introduction before approaching the modeling issues. Especially the second chapter, which shows how to look at data, is among the best I have ever seen in books on multivariate methods. ... He also goes well beyond the typical graphs showing how to explore real insights of the data. ... the book is extremely easy to browse and read. ... Putting the R code in an appendix and on the website is an excellent choice. ... the huge experience of the author ... makes the presentation so clear and understandable. I’ll be happy to recommend this book to students and researchers.
―International Statistical Review, 2010
Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences shows students how to apply statistical methods to behavioral science data in a sensible manner. Assuming some familiarity with introductory statistics, the book analyzes a host of real-world data to provide useful answers to real-life issues.
The author begins by exploring the types and design of behavioral studies. He also explains how models are used in the analysis of data. After describing graphical methods, such as scatterplot matrices, the text covers simple linear regression, locally weighted regression, multiple linear regression, regression diagnostics, the equivalence of regression and ANOVA, the generalized linear model, and logistic regression. The author then discusses aspects of survival analysis, linear mixed effects models for longitudinal data, and the analysis of multivariate data. He also shows how to carry out principal components, factor, and cluster analyses. The final chapter presents approaches to analyzing multivariate observations from several different populations.
Through real-life applications of statistical methodology, this book elucidates the implications of behavioral science studies for statistical analysis. It equips behavioral science students with enough statistical tools to help them succeed later on in their careers. Solutions to the problems as well as all R code and data sets for the examples are available at www.crcpress.com
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