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.
Publisher: Taylor & Francis Ltd
Number of pages: 794
Weight: 1480 g
Dimensions: 254 x 178 x 41 mm
Edition: 6th New edition
The new edition maintains the tradition of the great Stevens book (with clear examples using both SAS/SPSS) while incorporating important new information. I also appreciate that this new edition gives more attention to the APA-style write-ups. I look forward to using the 6th edition of this text in my class. -Chris Oshima, Georgia State University, USA
This new edition of Applied Multivariate Statistics for the Social Sciences is even more comprehensive; covering major advanced topics in the social sciences combined with excellent computer-based examples, in-depth discussions, and example write-ups. Yet, it is written in an accessible way that students will find useful and comprehensible. -Namok Choi, University of Louisville, USA
While Stevens has always been my "go to" book for multivariate statistics, the revised 6th Edition by Pituch and Stevens improves significantly on the previous version with key chapter revisions and additions, as well as new analysis summaries and results write-ups. -Philip Schatz, Saint Joseph's University, USA
The revised 6th edition by Pituch and Stevens is a very valuable and well-written text that provides students with all they need to understand and apply multivariate data analysis. The authors did a great job in revising the chapters, and the new coverage of binary logistic regression, multivariate multilevel modeling, and missing data analysis are a "must" for both applied researchers and graduate students. -Karin Schermelleh-Engel, Goethe University, Germany