Analysis of Multivariate Social Science Data (Second edition): 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

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
  • Additional examples and further work

Multidimensional scaling

  • 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

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
  • Additional examples and 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
  • Additional examples and further work

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
  • Additional examples and 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
  • Additional examples and further work
  • Software

Factor analysis for binary data

  • 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

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
  • Additional examples and 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
  • Additional examples and further work
  • Software

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
  • Additional examples
  • Software

Multilevel modelling

  • 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




Further reading sections appear at the end of each chapter.