Making Sense of Multivariate Data Analysis

John Spicer

August 2004 | 256 pages | SAGE Publications, Inc

Making Sense of Multivariate Data Analysis is a short introduction to multivariate data analysis (MDA) for students and practitioners in the behavioral and social sciences. It provides a conceptual overview of the foundations of MDA and of a range of specific techniques including multiple regression, logistic regression, discriminant analysis, multivariate analysis of variance, factor analysis, and log-linear analysis. As a conceptual introduction, the book assumes no prior statistical knowledge, and contains very few symbols or equations. Its primary objective is to expose the conceptual unity of MDA techniques both in their foundations and in the common analytic strategies that lie at the heart of all of the techniques. Although introductory, the book encourages the reader to reflect critically on the general strengths and limitations of MDA techniques. Each chapter includes references for further reading accessible to the beginner.

This is an ideal text for advanced undergraduate and graduate courses across the social sciences. Practitioners who need to refresh their knowledge of MDA will also find this an invaluable resource.

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Preface

Part I. The Core Ideas

1. What Makes a Difference?

1. 1 Analyzing Data in the Form of Scores

1.2 Analyzing Data in the Form of Categories

1.3 Further Reading

2. Deciding Whether Differences Are Trustworthy

2.1 Sampling Issues

2.2 Measurement Issues

2.3 The Role of Chance

2.4 Statistical Assumptions

2.5 Further Reading

3. Accounting for Differences in a Complex World

3.1 Limitations of Bivariate Analysis

3.2 The Multivariate Strategy

3.3 Common Misinterpretations of Multivariate Analyses

3.4 Further Reading

Part II. The Techniques

4. Multiple Regression

4.1 The Composite Variable in Multiple Regression

4.2 Standard Multiple Regression in Action

4.3 Trustworthiness in Regression Analysis

4.4 Accommodating Other Types of Independent Variables

4.5 Sequential Regression Analysis

4.6 Further Reading

5. Logistic Regression and Discriminant Analysis

5.1 Logistic Regression

5.2 Discriminant Analysis

5.3 Further Reading

6. Multivariate Analysis of Variance

6.1 One-Way Analysis of Variance

6.2 Factorial Analysis of Variance

6.3 Multivariate Analysis of Variance

6.4 Within-Subjects ANOVA and MANOVA

6.5 Issues of Trustworthiness in MANOVA

6.6 Analysis of Covariance

6.7 Further Reading

7. Factor Analysis

7.1 The Composite Variable in Factor Analysis

7.2 Factor Analysis in Action

7.3 Issues of Trustworthiness in Factor Analysis

7.4 Confirmatory Factor Analysis

7.5 Further Reading

8. Log-Linear Analysis

8.1 Hierarchical Log-Linear Analysis

8.2 Trustworthiness in Log-Linear Analysis

8.3 Log-Linear Analysis With a Dependent Variable: Logit Analysis

8.4 Further Reading

Bibliography

Index

About the Author

“This book serves as a resource for readers who want to have an overall view of what encompasses multivariate analyses. The author has discussed some important issues rather philosophically (e.g., theory vs. data analysis). These points are valuable even for readers who have extensive training with multivariate analyses.”

Jenn-Yun Tein

Arizona State University

“This book is a helpful guide to reading and understanding multivariate data analysis results in social and psychological research.”

C. Y. Joanne Peng

University of Indiana at Bloomington

"Spicer's book is a superb overview of multivariate statistics, but without formulas. Even though he is trying to offer a nontechnical overview of multivariate analyses, he doesn't shortchange the reader in any way. As much as you might know about stat, you'll learn some more here."

William Dressler

University of Alabama

This book is easy to understand although multivariate analysis is complicated. Students find it very helpful especially when interpreting the analytic results.

Dr Yiyuan Sun

School Of Nursing, Adelphi University

February 20, 2011

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The approach of using published articles as illustrations and non-mathematical languages are definitely useful for social science students to understand the complexity of mutlivariate analysis in context.

Dr Wu Joseph

Department of Social Studies, City University of Hong Kong

December 5, 2010

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It is a straightforward presentation of analysis procedures included in this course. I have used a number of QASS series publications in teaching data analysis for almost 20 years. I had them as texts when I was a PhD student. Generally more accepted by students than more in-depth treatments.

Dr Joanne Youngblut

[DEPARTMENT NOT SPECIFIED], Florida International University - Miami

January 3, 2010

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Key features

KEY FEATURES:

Assumes no prior statistical knowledge
Relies mainly on verbal exposition rather than symbols and formulae
Carries the reader from basic to advanced ideas in a relatively short space to demonstrate their continuity
Highlights the underlying strategies that unite all common multivariate techniques
Encourages the reader to reflect critically on the limitations of multivariate analysis in the broad research context
Uses examples from contemporary research on subjective well-being drawn from a variety of areas in the behavioral and social sciences