Applied Multivariate Research : Design and Interpretation
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 sciencesathat need to focus on design and interpretation rather than the intricacies of specific computations.
- Hardback | 1104 pages
- 198.12 x 233.68 x 53.34mm | 1,905.08g
- 23 Oct 2012
- SAGE Publications Inc
- Thousand Oaks, United States
- 2nd Revised edition
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 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 Organization is excellent. -- Thomas J. Keil, Arizona State University The comprehensive nature of the topics presented and the numerous figures and charts. -- Marie Kraska, Ph.D., Auburn 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 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
Table of contents
Part I. The Basics of Multivariate DesignChapter 1. An Introduction to Multivariate DesignChapter 2. Some Fundamental Research Design ConceptsChapter 3A. Data ScreeningChapter 3B. Data Screening Using IBM SPSSPart II. Comparisons of MeansChapter 4A. Univariate Comparison of MeansChapter 4B. Univariate Comparison of Means Using IBM SPSSChapter 5A. Multivariate Analysis of Variance (MANOVA)Chapter 5B. Multivariate Analysis of Variance (MANOVA) Using IBM SPSSPart III. Predicting the Value of a Single VariableChapter 6A. Bivariate Correlation and Simple Linear RegressionChapter 6B. Bivariate Correlation and Simple Linear Regression Using IBM SPSSChapter 7A. Multiple Regression: Statistical MethodsChapter 7B. Multiple Regression: Statistical Methods Using IBM SPSSChapter 8A. Multiple Regression: Beyond Statistical RegressionChapter 8B. Multiple Regression: Beyong Statistical Regression Using IBM SPSSChapter 9A. Multilevel ModelingChapter 9B. Multilevel Modeling Using IBM SPSSChapter 10A. Binary and Multinomial Logistic Regression and ROC AnalysisChapter 10B. Binary and Multinomial Logistic Regression and ROC Analysis Using IBM SPSSPart IV. Analysis of StructureChapter 11A. Discriminant Function AnalysisChapter 11B. Discriminant Function Analysis Using IBM SPSSChapter 12A. Principal Components and Exploratory Factor AnalysisChapter 12B. Principal Components and Exploratory Factor Analysis Using IBM SPSSChapter 13A. Canonical Correlation AnalysisChapter 13B. Canonical Correlation Analysis Using IBM SPSSChapter 14A. Multidimensional ScalingChapter 14B. Multidimensional Scaling Using IBM SPSSChapter 15A. Cluster AnalysisChapter 15B. Cluster Analysis Using IBM SPSSPart V. Fitting Models to DataChapter 16A. Confirmatory Factor AnalysisChapter 16B. Confirmatory Factor Analysis Using AmosChapter 17A. Path Analysis: Multiple RegressionChapter 17B. Path Analysis: Multiple Regression Using IBM SPSSChapter 18A. Path Analysis: Structural ModelingChapter 18B. Path Analysis: Structural Modeling Using AmosChapter 19A. Structural Equation ModelingChapter 19B. Structural Equation Modeling Using AmosChapter 20A. Model Invariance: Applying a Model to Different GroupsChapter 20B. Assessing Model Invariance Using Amos
About 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. 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. 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.