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Boca Raton : Chapman & Hall, c2002

xi,263 s. : il.

ISBN 1-58488-295-6 (váz.)

Texts in statistical science

Obsahuje tabulky, grafy, předmluvu, rejstřík, údaje o autorech

Bibliografie: s. 253-256

Sociografie - pojednání

000004067

Contents // Preface ix // 1 Setting the Scene 1 // 1.1 Structure of the book 1 // 1.2 Our limited use of mathematics 4 // 1.3 Variables 7 // 1.4 The geometry of multivariate analysis 9 // 1.5 Use of examples 10 // 1.6 Data inspection, transformations, and missing data 12 // 1.7 A final word 13 // 1.8 Reading 13 // 2 Cluster Analysis 15 // 2.1 Classification in social sciences 15 // 2.2 Some methods of cluster analysis 18 // 2.3 Graphical presentation of results 23 // 2.4 Derivation of the distance matrix 27 // 2.5 Example on English dialects 31 // 2.6 Comparisons 37 // 2.7 Clustering variables 39 // 2.8 Further examples and suggestions for further work 39 // 2.9 Further reading 51 // 3 Multidimensional Scaling 53 // 3.1 Introduction 53 // 3.2 Examples 55 // 3.3 Classical, ordinal, and metrical multidimensional scaling 59 // 3.4 Comments on computational procedures 62 // 3.5 Assessing fit and choosing the number of dimensions 63 // 3.6 A worked example: dimensions of colour vision 64 // 3.7 Further examples and suggestions for further work 68 // 3.8 Further reading 79 // 4 Correspondence Analysis 81 // 4.1 Aims of correspondence analysis 81 // vi // CONTENTS // 4.2 Carrying out a correspondence analysis: a simple numerical // example 83 // 4.3 Carrying out a correspondence analysis: the general method 88 // 4.4 The biplot 91 // 4.5 Interpretation of dimensions 95 // 4.6 Choosing the number of dimensions 97 // 4.7 Example: confidence in purchasing from European Community // countries

99 // 4.8 Correspondence analysis of multi-way tables 105 // 4.9 Further examples and suggestions for further work 109 // 4.10 Further reading 114 // 5 Principal Components Analysis 115 // 5.1 Introduction 115 // 5.2 Some potential applications 116 // 5.3 Illustration of PC A for two variables 117 // 5.4 An outline of PCA 120 // 5.5 Examples 122 // 5.6 Component scores 129 // 5.7 The link between PCA and multidimensional scaling, and // between PCA and correspondence analysis 132 // 5.8 Using principal component scores to replace original variables 134 // 5.9 Further examples and suggestions for further work 136 // 5.10 Further reading 141 // 6 Factor Analysis 143 // 6.1 Introduction to latent variable models 143 // 6.2 The linear single-factor model 146 // 6.3 The general linear factor model 149 // 6.4 Interpretation 152 // 6.5 Adequacy of the model and choice of the number of factors 154 // 6.6 Rotation 156 // 6.7 Factor scores 160 // 6.8 A worked example: the test anxiety inventory 161 // 6.9 How rotation helps interpretation 166 // 6.10 A comparison of factor analysis and principal component analysis 167 // 6.11 Further examples and suggestions for further work 169 // 6.12 Further reading 174 // 7 Factor Analysis for Binary Data 175 // 7.1 Latent trait models 175 // 7.2 Why is the factor analysis model for metrical variables invalid // for binary responses? 178 // 7.3 Factor model for binary data 179 // 7.4 Goodness-of-fit 184 // CONTENTS vii // 7.5 Factor scores 188 // 7.6

Rotation 190 // 7.7 Underlying variable approach 190 // 7.8 Example: sexual attitudes 192 // 7.9 Further examples and suggestions for further work 197 // 7.10 Software 206 // 7.11 Further reading 206 // 8 Factor Analysis for Ordered Categorical Variables 207 // 8.1 The practical background 207 // 8.2 Two approaches to modelling ordered categorical data 208 // 8.3 Item response function approach 209 // 8.4 Examples 216 // 8.5 The underlying variable approach 219 // 8.6 Unordered and partially ordered observed variables 224 // 8.7 Further examples and suggestions for further work 228 // 8.8 Software 234 // 8.9 Further reading 234 // 9 Latent Class Analysis for Binary Data 235 // 9.1 Introduction 235 // 9.2 The latent class model for binary data 236 // 9.3 Example: attitude to science and technology data 241 // 9.4 How can we distinguish the latent class model from the latent // trait model? 245 // 9.5 Latent class analysis, cluster analysis, and latent profile analysis 247 // 9.6 Further examples and suggestions for further work 248 // 9.7 Software 252 // 9.8 Further reading 252 // References 253 // Index // 257