© 2002 – Chapman and Hall/CRC
280 pages | 100 B/W Illus.
Multivariate analysis is an important tool for social researchers, but the subject is broad and can be quite technical for those with limited mathematical and statistical backgrounds. To effectively acquire the tools and techniques they need to interpret multivariate data, social science students need clear explanations, a minimum of mathematical detail, and a wide range of exercises and worked examples.
Classroom tested for more than 10 years, The Analysis and Interpretation of Multivariate Data for Social Scientists describes and illustrates methods of multivariate data analysis important to the social sciences. The authors focus on interpreting the pattern of relationships among many variables rather than establishing causal linkages, and rely heavily on numerical examples, visualization, and on verbal , rather than mathematical exposition. They present methods for categorical variables alongside the more familiar method for continuous variables and place particular emphasis on latent variable techniques.
Ideal for introductory, senior undergraduate and graduate-level courses in multivariate analysis for social science students, this book combines depth of understanding and insight with the practical details of how to carry out and interpret multivariate analyses on real data. It gives them a solid understanding of the most commonly used multivariate methods and the knowledge and tools to implement them.
Datasets, the SPSS syntax and code used in the examples, and software for performing latent variable modelling are available at http://www.mlwin.com/team/aimdss.html>
"The authors interpret the pattern of relationships among several variables rather than establishing casual linkages, and place particular emphasis on latent variable techniques in their presentation. The authors' easy style makes reading this book a great pleasure. …A very useful and interesting book, excellent for researchers and students for analyzing and interpreting multivariate data in social sciences."
- CHOICE, September 2002
"This book …is a good source for learning how to use multivariate methods with data and how to interpret the results."
"..an exceptionally clear account of the alternative methods available… commendable level of clarity and user-friendliness…with a wide range of worked examples. This book would provide a valuable addition to the collection of any researcher who uses or plans to use multivariate methods. Although the level is introductory, readers who are familiar with some or all of the techniques discussed will nonetheless find that it delivers new insights into how these methods work and what the results mean. Novices will find an unusually gentle lead-in to what is generally thought of as a difficult area."
- ELIZABETH AUSTIN (Department of Psychology, University of Edinburgh)
SETTING THE SCENE
Structure of the Book
Our Limited Use of Mathematics
The Geometry of Multivariate Analysis
Use of Examples
Data Inspection, Transformations, and Missing Data
A Final Word
Classification in Social Sciences
Some Methods of Cluster Analysis
Graphical Presentation of Results
Derivation of the Distance Matrix
Example on English Dialects
Classical, Ordinal and Metrical Multidimensional Scaling
Comments on Computational Procedures
Assessing Fit and Choosing the Number of Dimensions
A Worked Example: Dimensions of Colour Vision
Aims of Correspondence Analysis
Carrying Out a Correspondence Analysis : A Simple Numerical Example
Carrying Out a Correspondence Analysis: The General Method
Interpretation of Dimensions
Choosing the Number of Dimensions
Example: Purchasing from European Community Countries
Correspondence Analysis of Multi-Way Tables
PRINCIPAL COMPONENTS ANALYSIS
Some Potential Applications
Illustration of PCA for Two Variables
An Outline of PCA
The Link Between PCA and Multidimensional Scaling and Between PCA and Correspondence Analysis
Using Principal Component Scores to Replace Original Variables
Introduction to Latent Variable Models
The Linear Single-Factor Model
The General Linear Factor Model
Adequacy of the Model and Choice of the Number of Factors
A Worked Example: The Test Anxiety Inventory
How Rotation Helps Interpretation
A Comparison of Factor Analysis and Principal Component Analysis
FACTOR ANALYSIS FOR BINARY DATA
Latent Trait Models
Why is the Factor Analysis Model for Metrical Variables Invalid for Binary Responses?
Factor Model for Binary Data
Underlying Variable Approach
Example: Sexual Attitudes
FACTOR ANALYSIS FOR ORDERED CATEGORICAL VARIABLES
The Practical Background
Two Approaches to Modelling Ordered Categorical Data
Item Response Function Approach
The Underlying Variable Approach
Unordered and Partially Ordered Observed Variables
LATENT CLASS ANALYSIS FOR BINARY DATA
The Latent Class Model for Binary Data
Example: Attitude to Science and Technology Data
How can we Distinguish the Latent Class Model from the Latent Trait Model?
Latent Class Analysis, Cluster Analysis, and Latent Profile Analysis
Each chapter also contains sections of "Further Examples and Suggestions for Further Wor" and "Further Reading."