Applied Multivariate Research: Design and Interpretation / Edition 2

by Lawrence S. Meyers, Glenn C. Gamst, Anthony J. Guarino

Overview

This book provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter, using a conceptual, non-mathematical, approach. Addressing correlation, multiple regression, exploratory factor analysis, MANOVA, path analysis, and structural equation modeling, it is geared toward the needs, level of sophistication, and interest in multivariate methodology that serves students in applied programs in the social and behavioral sciences. Readers are encouraged to focus on design and interpretation rather than the intricacies of specific computations.

Product Details

ISBN-13:	9781412988117
Publisher:	SAGE Publications
Publication date:	08/17/2012
Edition description:	Second Edition
Pages:	1104
Product dimensions:	7.70(w) x 9.20(h) x 2.20(d)

About the Author

Larry 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 level. 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. He received his Ph.D. from the University of Arkansas in experimental psychology. His research interests include the effects of multicultural variables on clinical outcome. Additional research interests focus conversation memory and discourse processing.

A.J. Guarino received his B.A. from the University of California, Berkeley and a Ph.D. from the University of Southern California in statistics and research methodologies from the Department of Educational Psychology. He is professor of biostatistics at Massachusetts General Hospital, Institute of Health Professions. He is the statistician on numerous NIH grants and reviewer on several research journals.

Table of Contents

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

Editorial Reviews

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

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

Andrew Jorgenson

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

Marie Kraska

Organization is excellent.

Thomas J. Keil

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

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

Xiaofen Deng Keating

A major strength of this text is that it covers the new features of the most recent SPSS® edition. With the step-by-step tutorial on the new features, students and empirical researchers can use it as a handbook when they conduct data analysis.

Haiyan Bai