** Preface **

**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

**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

Further examples and suggestions for further work

**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 color vision

Further examples and suggestions for further work

**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: confidence in purchasing from European Community countries

Correspondence analysis of multiway tables

Further examples and suggestions for further work

**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 the original variables

Further examples and suggestions for further work

*NEW! ***Regression Analysis **

Basic ideas

Simple linear regression

A probability model for simple linear regression

Inference for the simple linear regression model

Checking the assumptions

Multiple regression

Examples of multiple regression

Estimation and inference about the parameters

Interpretation of the regression coefficients

Selection of regressor variables

Transformations and interactions

Logistic regression

Path analysis

Further examples and suggestions for further work

**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 components analysis

Further examples and suggestions for further work

Software

**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 using the item response theory approach

Goodness-of-fit

Factor scores

Rotation

Underlying variable approach

Example: sexual attitudes

Further examples and suggestions for further work

Software

**Factor Analysis for Ordered Categorical Variables **

The practical background

Two approaches to modeling ordered categorical data

Item response function approach

Examples

The underlying variable approach

Unordered and partially ordered observed variables

Further examples and suggestions for further work

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

Further examples and suggestions for further work

Software

*NEW!*** Confirmatory Factor Analysis and Structural Equation Models **

Introduction

Path diagram

Measurement models

Adequacy of the model

Introduction to structural equation models with latent variables

The linear structural equation model

A worked example

Extensions

Further examples

Software

*NEW! ***Multilevel Modeling **

Introduction

Some potential applications

Comparing groups using multilevel modeling

Random intercept model

Random slope model

Contextual effects

Multilevel multivariate regression

Multilevel factor analysis

Further examples and suggestions for further work

Further topics

Estimation procedures and software

**References**

**Index**

*Further reading sections appear at the end of each chapter.*