Applied Multivariate Research

Design and Interpretation
Second Edition
Lawrence S. Meyers
Glenn C. Gamst
Anthony J. Guarino
Applied Multivariate Research
August 2012 | 1104 pages | Sage US
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Description

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.



 

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

Additional materials

Description

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.



 

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

Additional materials

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Applied Multivariate Research

Design and Interpretation

Lawrence S. Meyers , Glenn C. Gamst , Anthony J. Guarino
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August 2012 | 1104 pages | Sage US

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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.



 


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

Recent Product Reviews:

The comprehensive nature of the topics presented and the numerous figures and charts.
Marie Kraska, Ph.D., Auburn University, Auburn University
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
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

Recommendations