The Analysis and Interpretation of Multivariate Data for Social Scientists

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>

Table of 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."