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The Analysis and Interpretation of Multivariate Data for Social Scientists

By J.I. Galbraith, Irini Moustaki, David J. Bartholomew, Fiona Steele

Series Editor: Chris Chatfield, Jim Zidek, Martin A. Tanner, Jim Lindsey

Chapman and Hall/CRC – 2002 – 280 pages

Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

Purchasing Options:

  • Paperback:
    978-1-58488-295-4
    February 25th 2002
    Out-of-print

Description

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>

Reviews

"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."

-Technometrics, 2003

"..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)

Contents

SETTING THE SCENE

Structure of the Book

Our Limited Use of Mathematics

Variables

The Geometry of Multivariate Analysis

Use of Examples

Data Inspection, Transformations, and Missing Data

A Final Word

Reading

CLUSTER ANALYSIS

Classification in Social Sciences

Some Methods of Cluster Analysis

Graphical Presentation of Results

Derivation of the Distance Matrix

Example on English Dialects

Comparisons

Clustering Variables

MULTIDIMENSIONAL SCALING

Introduction

Examples

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

CORRESPONDENCE ANALYSIS

Aims of Correspondence Analysis

Carrying Out a Correspondence Analysis : A Simple Numerical Example

Carrying Out a Correspondence Analysis: The General Method

The Biplot

Interpretation of Dimensions

Choosing the Number of Dimensions

Example: Purchasing from European Community Countries

Correspondence Analysis of Multi-Way Tables

PRINCIPAL COMPONENTS ANALYSIS

Introduction

Some Potential Applications

Illustration of PCA for Two Variables

An Outline of PCA

Examples

Component Scores

The Link Between PCA and Multidimensional Scaling and Between PCA and Correspondence Analysis

Using Principal Component Scores to Replace Original Variables

FACTOR ANALYSIS

Introduction to Latent Variable Models

The Linear Single-Factor Model

The General Linear Factor Model

Interpretation

Adequacy of the Model and Choice of the Number of Factors

Rotation

Factor Scores

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

Goodness-of-Fit

Factor Scores

Rotation

Underlying Variable Approach

Example: Sexual Attitudes

Software

FACTOR ANALYSIS FOR ORDERED CATEGORICAL VARIABLES

The Practical Background

Two Approaches to Modelling Ordered Categorical Data

Item Response Function Approach

Examples

The Underlying Variable Approach

Unordered and Partially Ordered Observed Variables

Software

LATENT CLASS ANALYSIS FOR BINARY DATA

Introduction

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

Software

REFERENCES

INDEX

Each chapter also contains sections of "Further Examples and Suggestions for Further Wor" and "Further Reading."

Name: The Analysis and Interpretation of Multivariate Data for Social Scientists (eBook)Chapman and Hall/CRC 
Description: By J.I. Galbraith, Irini Moustaki, David J. Bartholomew, Fiona SteeleSeries Editor: Chris Chatfield, Jim Zidek, Martin A. Tanner, Jim Lindsey. 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...
Categories: CRC/IHC Default Subject Code, Regression Analysis and Multivariate Statistics