The Analysis and Interpretation of Multivariate Data for Social Scientists (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Galbraith, J.I.

ISBN 10: 1584882956 / ISBN 13: 9781584882954

Editore: Chapman and Hall/CRC, 2002

Riguardo questo articolo

The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Codice articolo GOR005288922

Dati bibliografici

• Titolo: The Analysis and Interpretation of Multivariate Data for Social Scientists (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
• Editore: Chapman and Hall/CRC
• Data di pubblicazione: 2002
• Lingua: Inglese
• ISBN 10: 1584882956
• ISBN 13: 9781584882954
• Rilegatura: Paperback
• Condizione: Very Good
• Peso: 204 grammi

Descrizione articolo

Riassunto

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>

Contenuti

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."