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- Structure of the book
- Our limited use of mathematics
- Variables
- The geometry of multivariate analysis
- Use of examples
- Data inspection, transformations, and missing data

- 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
- Additional examples and further work

- Sample chapter: 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
- Additional examples and further work

- 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
- Additional examples and further work

- 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
- Additional examples and further work

- 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
- Additional examples and further work

- 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
- Additional examples and further work
- Software

- Sample chapter: 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
- Additional examples and further work
- Software

- 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
- Additional examples and further work
- Software

- 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
- Additional examples and further work
- Software

- 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
- Additional examples
- Software

- Introduction
- Some potential applications
- Comparing groups using multilevel modelling
- Random intercept model
- Random slope model
- Contextual effects
- Multilevel multivariate regression
- Multilevel factor analysis
- Additional examples and further work
- Further topics
- Estimation procedures and software

**Index**

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

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