Written with the non-statistician in mind, Multivariate Data Analysis is an applications-oriented introduction to multivariate analysis that greatly reduces the amount of statistical notation and terminology used.  The fundamental concepts that affect the use of specific techniques are emphasized.  This text presents step-by-step explanations for exactly when and how to use each technique and includes guidelines on the interpretation of results.

Datasets, Command Syntax and Analysis Outputs: All Editions

   An important feature of Multivariate Data Analysis is the ability of users to replicate the analyses in the text.  In this section we provide downloads of all the necessary datasets, command syntax for the major software programs and even output files that can be used if the analyses can not be performed.  These are available for the most current edition (8th) as well as past editions (5th, 6th and 7th).

Supplemental Chapters 

    The expansion of content in the eighth edition of Multivariate Data Analysis required that several chapters from past editions be moved to an online resource. As a result, four techniques are now available as online files (Conjoint Analysis, Multidimensional Scaling, Correspondence Analysis and Canonical Correlation). The content is identical to chapters in the earlier editions and the other supplemental material (datasets, command syntax and analysis outputs) are still available.  


Supplementary Materials

    While Multivariate Data Analysis has grown over the years to provide expanded  coverage of the multivariate techniques used today, there are still some topics which can not be covered in the text.  In this section we provide coverage of additional topics (e.g., Basic Stats and SEM supplements), while several topics previously in this section (e.g., Advanced Regression Diagnostics) are now incorporated in the chapters of the eighth edition. 


Suggested Readings

    An important adjunct to Multivariate Data Analysis is access and use of readings as a means of illustrating innovative and well-conceived applications of multivariate techniques in as many disciplines of study as possible. We will continue to compile a listing of exemplary articles that may be of interest to student and educator alike.

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Multivariate Data Analysis
Hair, Black, Babin and Anderson