This book was easy to understand and read, but left a LOT out. If you want a comprehensive understanding and multiple (more than two) methods for multivariate analysis, make sure to back this book up with other sources. I personally learned more doing my research online and toying with SPSS than I did from reading this book. Unfortunately, my professor decided to take from this book the lesson on matrices and waste two weeks of lecture on something that I think is outdated and unnecessary to learn for modern analysis with software packages. To waste a chapter teaching how to compute addition, subtraction, and multiplication of matrices but not cover cluster analysis is bad prioritizing I think. I understand trying to teach the theory behind linear combinations, but I don't think matrices are absolutely necessary to understand linear combinations. In other words, it's 2013. No need to learn how to hand calculate anything anymore. Waste of time!
Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies. With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally.
Features new to this edition include:
•NEW chapter on Logistic Regression (Ch. 11) that helps readers understand and use this very flexible and widely used procedure
•NEW chapter on Multivariate Multilevel Modeling (Ch. 14) that helps readers understand the benefits of this "newer" procedure and how it can be used in conventional and multilevel settings
•NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles
•NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data
•Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results
•NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach
•Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3)
•A free online resources site at www.routledge.com/9780415836661 with data sets and syntax from the text, additional data sets, and instructor’s resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises).
Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.
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