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Applied Multivariate Research

Applied Multivariate Research
Design and Interpretation

Second Edition

Multivariate concepts presented in an extremely applied manner, strongly emphasizing written results and offering SPSS examples for greater comprehension.

August 2012 | 1 104 pages | SAGE Publications, Inc
A companion website is available for this text

Today, through the sophistication of statistical software packages such as SPSS, virtually all graduate students across the social and behavioral sciences are exposed to the complex multivariate statistical techniques such as correlation and multiple regression, exploratory factor analysis, MANOVA, path analysis and structural equation modeling. This book is designed to provide full coverage of the wide range of multivariate topics in a conceptual, non-mathematical, approach. It is geared toward the needs, level of sophistication, and interest in multivariate methodology of students in applied programs in the social and behavioral sciencesáthat need to focus on design and interpretation rather than the intricacies of specific computations.

Part I. The Basics of Multivariate Design
Chapter 1. An Introduction to Multivariate Design
Chapter 2. Some Fundamental Research Design Concepts
Chapter 3A. Data Screening
Chapter 3B. Data Screening Using IBM SPSS
Part II. Comparisons of Means
Chapter 4A. Univariate Comparison of Means
Chapter 4B. Univariate Comparison of Means Using IBM SPSS
Chapter 5A. Multivariate Analysis of Variance (MANOVA)
Chapter 5B. Multivariate Analysis of Variance (MANOVA) Using IBM SPSS
Part III. Predicting the Value of a Single Variable
Chapter 6A. Bivariate Correlation and Simple Linear Regression
Chapter 6B. Bivariate Correlation and Simple Linear Regression Using IBM SPSS
Chapter 7A. Multiple Regression: Statistical Methods
Chapter 7B. Multiple Regression: Statistical Methods Using IBM SPSS
Chapter 8A. Multiple Regression: Beyond Statistical Regression
Chapter 8B. Multiple Regression: Beyong Statistical Regression Using IBM SPSS
Chapter 9A. Multilevel Modeling
Chapter 9B. Multilevel Modeling Using IBM SPSS
Chapter 10A. Binary and Multinomial Logistic Regression and ROC Analysis
Chapter 10B. Binary and Multinomial Logistic Regression and ROC Analysis Using IBM SPSS
Part IV. Analysis of Structure
Chapter 11A. Discriminant Function Analysis
Chapter 11B. Discriminant Function Analysis Using IBM SPSS
Chapter 12A. Principal Components and Exploratory Factor Analysis
Chapter 12B. Principal Components and Exploratory Factor Analysis Using IBM SPSS
Chapter 13A. Canonical Correlation Analysis
Chapter 13B. Canonical Correlation Analysis Using IBM SPSS
Chapter 14A. Multidimensional Scaling
Chapter 14B. Multidimensional Scaling Using IBM SPSS
Chapter 15A. Cluster Analysis
Chapter 15B. Cluster Analysis Using IBM SPSS
Part V. Fitting Models to Data
Chapter 16A. Confirmatory Factor Analysis
Chapter 16B. Confirmatory Factor Analysis Using Amos
Chapter 17A. Path Analysis: Multiple Regression
Chapter 17B. Path Analysis: Multiple Regression Using IBM SPSS
Chapter 18A. Path Analysis: Structural Modeling
Chapter 18B. Path Analysis: Structural Modeling Using Amos
Chapter 19A. Structural Equation Modeling
Chapter 19B. Structural Equation Modeling Using Amos
Chapter 20A. Model Invariance: Applying a Model to Different Groups
Chapter 20B. Assessing Model Invariance Using Amos

For me the comprehensive nature of the text is most important – even when I don’t cover topics in class students gain value by being able to read about cluster analysis or ROC analysis in enough detail that they can conduct their own analyses. Students appreciate the integration with SPSS. There is an appropriate balance of “practice” and background so that students learn what they need to know about the techniques but also learn how to implement and interpret the analysis.

E. Kevin Kelloway, Saint Mary's University
Saint Mary's University

The key strengths are its clearly written explanations of OLS regression and logistic regression as well as its treatment of path analysis.

Andrew Jorgenson, University of Utah
University of Utah

The comprehensive nature of the topics presented and the numerous figures and charts.

Marie Kraska, Ph.D., Auburn University
Auburn University

Organization is excellent.

Thomas J. Keil, Arizona State University
Arizona State University

Well written and accessible. I find the additional readings at the end of the chapters to be valuable and have tracked down several of the sources for my own personal use.

Glenn J. Hansen, University of Oklahoma
University of Oklahoma

My students think the book is well written and the language is easy for them to understand

Xiaofen Deng Keating, The University of Texas at Austin
The University of Texas at Austin

Concepts presented in an applied way, emphasizing written results using practical examples solved with SPSS.
Adopted for later courses with different focus.

Dr Celina Leao
Production and Systems, Minho University
April 19, 2013

very valuable source!

Professor Othmar Lehner
ACRN GmbH. Center for Research in Social Sciences, University of Applied Science
March 9, 2013

Indepth text suitable for MSc. students

Mr Andrew Palmer
School of Business and Social Sciences, Roehampton University
February 16, 2013

A very comprehensive and well written book - for the specific courses I am teaching (touching only on quantitive methods), too detailed. Will maybe be adopted for later courses with different focus.

Professor Eva Heidbreder
Political Science , Hertie School of Governance
January 2, 2013
Key features


  • Revisions in the order of content that take the reader smoothly from simpler concepts to more complex concepts, beginning with basics, then cover comparisons of means, followed by prediction, analyses of structure, and finally model fitting
  • Substantial additions to coverage of the IBM SPSS Missing Value Analysis module, advanced multiple regression, multilevel modeling, cluster analysis, multidimensional scaling, and binary logistic regression, as well as new SPSS Amos Appendix
  • Three group designs now included in both the logistic regression and discriminant analysis chapters
  • Updated sample reports of results including exact probability values and confidence intervals consistent with the new APA publication guidelines


  • Coverage of the most widely used multivariate designs: multiple regression, exploratory factor analysis, MANOVA, and structural equation modeling, multilevel modeling, multidimensional scaling, cluster analysis, Missing Value Analysis
  • Integrated SPSS examples for hands-on learning from one large study (for consistency of application throughout the text)
  • Examples of written results to enable students to learn how the results of these procedures are communicated
  • Practical application of the techniques using contemporary studies that will resonate with students

Despite retaining the general structure of providing pairs of chapters for each topic, this second edition represents a considerable revision of the first edition. Every chapter was extensively reviewed and, in most places, substantially rewritten to make the narrative more readable, complete, and/or current. We have also added a good deal of new material to cover topics not included in the earlier edition. The changes we have made include the following:

· We have revised somewhat the ordering and organizational schema of the chapters in an effort to more smoothly build from conceptually simpler to conceptually more complex procedures. In very global terms, we begin with basics, then cover comparisons of means, followed by prediction, analyses of structure, and finally model fitting.

· Our treatment of missing values with the IBM SPSS Missing Value Analysis module has been updated and expanded.

· We have collapsed the three pairs of MANOVA chapters down to a single pair.

· To supplement the basic statistical regression procedures, we added a pair of multiple regression chapters covering some advanced topics such as polynomial regression, mediation, interaction effects, and dummy and effect coding.

· We added a pair of chapters on multilevel modeling.

· Both the logistic regression and the discriminant analysis chapters now include three group designs as well as the two-group designs we presented in the first edition.

· We have now included within binary logistic regression the topic of ROC curves and ROC analysis applied to the classification decision criterion.

· We have expanded on the strategy to perform exploratory principal components/factor analysis, displaying the results from several extraction techniques. We have also added to that material the procedure for performing a reliability analysis based on the factor analysis results, how to compute subscales based on the reliability analysis, and how to use the computed scales in a subsequent (albeit simple demonstration) analysis.

· We have added a new pair of chapters on multidimensional scaling.

· We have added a new pair of chapters on cluster analysis, including both hierarchical clustering and k-means clustering.

· We separated the full information structural modeling material dealing with path analysis and structural equation modeling into separate pairs of chapters.

· We have updated the chapters on model invariance.

· We have included an Appendix covering many of the frequently used IBM SPSS Amos commands.

· Our sample reports of results now includes exact probability values and confidence intervals consistent with the new APA publication guidelines.

Sample Materials & Chapters


ch 1

ch 7b

Lawrence S. Meyers

Lawrence S. Meyers earned his doctorate in experimental psychology and has been a Professor in the Psychology Department at California State University, Sacramento, for a number of years. He supervises research students and teaches research design courses as well as history of psychology at both the undergraduate and graduate levels. His areas of expertise include test development and validation. More About Author

Glenn C. Gamst

Glenn Gamst is Professor and Chair of the Psychology Department at the University of La Verne, where he teaches the doctoral advanced statistics sequence. His research interests include the effects of multicultural variables on clinical outcome. Additional research interests focus on conversation memory and discourse processing. He received his PhD in experimental psychology from the University of Arkansas. More About Author

Anthony J. Guarino

A. J. Guarino is a professor of biostatistics at Massachusetts General Hospital, Institute of Health Professions. He is the statistician on numerous National Institutes of Health grants and a reviewer on several research journals. He received his BA from the University of California, Berkeley, and a PhD in statistics and research methodologies from the Department of Educational Psychology, the University of Southern California. More About Author

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ISBN: 9781412988117