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.
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.
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