384 Pages 87 B/W Illustrations
    by Chapman & Hall

    384 Pages
    by Chapman & Hall

    Drawing on the authors’ varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic understanding of how to use key multivariate methods in the social sciences. With updates in every chapter, this edition expands its topics to include regression analysis, confirmatory factor analysis, structural equation models, and multilevel models.

    After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two halves of the book, signaling the move from descriptive to inferential methods and from interdependence to dependence. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.

    Relying heavily on numerical examples, the authors provide insight into the purpose and working of the methods as well as the interpretation of data. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional exercises, encouraging readers to explore new ground in social science research.

    Requiring minimal mathematical and statistical knowledge, this book shows how various multivariate methods reveal different aspects of data and thus help answer substantive research questions.

    Setting the Scene
    Structure of the book
    Our limited use of mathematics
    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
    Clustering variables
    Further examples and suggestions for further work
    Multidimensional Scaling
    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
    Some potential applications
    Illustration of PCA for two variables
    An outline of PCA
    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
    Adequacy of the model and choice of the number of factors
    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
    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
    Factor scores
    Underlying variable approach
    Example: sexual attitudes
    Further examples and suggestions for further work
    Factor Analysis for Ordered Categorical Variables
    The practical background
    Two approaches to modeling ordered categorical data
    Item response function approach
    The underlying variable approach
    Unordered and partially ordered observed variables
    Further examples and suggestions for further work
    Latent Class Analysis for Binary Data
    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
    NEW! Confirmatory Factor Analysis and Structural Equation Models
    Path diagram
    Measurement models
    Adequacy of the model
    Introduction to structural equation models with latent variables
    The linear structural equation model
    A worked example
    Further examples
    NEW! Multilevel Modeling
    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
    Further reading sections appear at the end of each chapter.


    David J. Bartholomew, Fiona Steele, Fiona Steele, Irini Moustaki

    "… Written by some of the leaders in the field, the second edition expands the horizon of the first edition by three new chapters. The new edition enabled the authors to deal with two equally important types of methods—those for data summarization and those that are model based. … The book should provide a superb introduction to these methods for graduate students who are without substantial statistical or mathematical training … Good examples abound [and] … so do worked-out applications. … I also like the authors’ effort to compare related methods across the chapters … The website is a treasure trove … the book is essential to read … ."
    —Tim Futing Liao, University of Illinois, Journal of the Royal Statistical Society, Series A, 2010

    "The strength of this book lies in the right mixture of simple mathematical expressions, comprehensive non-mathematical descriptions of various multivariate approaches, numerous interesting real-life data examples, and detailed interpretation of the results. … The comprehensive web resource the authors provide is also commendable. … Overall, this is an outstanding book on multivariate statistics in the field of social sciences, with a strong focus on categorical data. It can be recommended without reservations for quantitative graduate courses in psychology, sociology, education, and related areas. …"
    Journal of Statistical Software, February 2009

    "…I am pleased that the authors emphasise that the book is in no sense a cookbook. … the presentation is well matched to its intended audience, relying on only the minimal necessary mathematics and driving the development with examples, figures, and verbal descriptions. …This is the sort of book from which I would have liked to have learnt multivariate statistics."
    International Statistical Review, 2008