Average Reviews:
(More customer reviews)As someone who has tried to teach multivariate statistics to non-statistician graduate students for the past 5 years, I have found this to be a very valuable and clearly-written text. As advertised and as the previous reviewer noted, the text is largely free of complex statistical equations and instead has clear descriptions of each type of test as well as common applications of that test. It is a perfect introduction for students who are intimidated by numbers and equations yet need to know about multivariate statistics for their graduate studies.
The book has several weaknesses that I found require supplementing with other texts. For one, there is no tie-in with major computerized statistical applications like SPSS and SAS nor are there example exercises for students to run and interpret statistical tests for themselves. I have found such exercises to be invaluable in teaching the meaning and uses of multivariate tests. There also should have been a discussion of general issues that cut across the different multivariate tests such as data cleaning, data transformation, the role of correlation matrices and the like and so on. For coverage of these issues, I have found it helpful to use chapters from Tabachnik and Fidel's Using Multivariate Statistics text. Finally, a number of tests, such as survival analysis are not covered in this text, though a second volume by the same authors does cover survival analysis as well as other techniques and should be considered as a companion volume as well.
In sum, this is an excellent and unusually clearly written text that is ideal for non-statistician graduate students in the social sciences. More in-depth analysis of important issues related to multivariate statistics and classroom exercises using statistical computer applications requires augmenting this text with additional readings.
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The book presents an overview of multivariate statistics and their place in research. It describes the appropriate context for -- and the types of empirical questions that can best be addressed by -- each technique or family of techniques, as well as the distribution assumptions that must be met for the analysis to be meaningful. The most commonly used multivariate techniques are examined in detail: multiple regression and correlation, path analysis, principal-components analysis, exploratory and confirmatory factor analysis, multidimensional scaling, analysis of cross-classified data, logistic regression, multivariate an alysis of variance (MANOVA), discriminant analysis, and meta-analysis. Statistical notations are explained, underlying assumptions are described, and terms are defined clearly and understandably. Concepts and symbols are presented with minimal use of formulas and a generous use of real-world research examples. Each chapter also includes suggestions for additional reading and a glossary of statistical and related terms.
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