Analysis of Multivariate Social Science Data,

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	p. ix
Setting the Scene	p. 1
Structure of the book	p. 1
Our limited use of mathematics	p. 4
Variables	p. 8
The geometry of multivariate analysis	p. 10
Use of examples	p. 11
Data inspection, transformations, and missing data	p. 13
Reading	p. 14
Cluster Analysis	p. 17
Classification in social sciences	p. 17
Some methods of cluster analysis	p. 20
Graphical presentation of results	p. 25
Derivation of the distance matrix	p. 29
Example on English dialects	p. 33
Comparisons	p. 39
Clustering variables	p. 41
Additional examples and further work	p. 41
Further reading	p. 53
Multidimensional Scaling	p. 55
Introduction	p. 55
Examples	p. 57
Classical, ordinal, and metrical multidimensional scaling	p. 61
Comments on computational procedures	p. 64
Assessing fit and choosing the number of dimensions	p. 65
A worked example: dimensions of colour vision	p. 66
Additional examples and further work	p. 68
Further reading	p. 81
Correspondence Analysis	p. 83
Aims of correspondence analysis	p. 83
Carrying out a correspondence analysis: a simple numerical example	p. 85
Carrying out a correspondence analysis: the general method	p. 90
The biplot	p. 93
Interpretation of dimensions	p. 97
Choosing the number of dimensions	p. 99
Example: confidence in purchasing from European Community countries	p. 101
Correspondence analysis of multiway tables	p. 107
Additional examples and further work	p. 111
Further reading	p. 116
Principal Components Analysis	p. 117
Introduction	p. 117
Some potential applications	p. 118
Illustration of PCA for two variables	p. 119
An outline of PCA	p. 122
Examples	p. 125
Component scores	p. 131
The link between PCA and multidimensional scaling, and between PCA and correspondence analysis	p. 134
Using principal component scores to replace the original variables	p. 137
Additional examples and further work	p. 138
Further reading	p. 144
Regression Analysis	p. 145
Basic ideas	p. 145
Simple linear regression	p. 147
A probability model for simple linear regression	p. 150
Inference for the simple linear regression model	p. 151
Checking the assumptions	p. 153
Multiple regression	p. 154
Examples of multiple regression	p. 156
Estimation and inference about the parameters	p. 157
Interpretation of the regression coefficients	p. 159
Selection of regressor variables	p. 161
Transformations and interactions	p. 163
Logistic regression	p. 165
Path analysis	p. 168
Additional examples and further work	p. 171
Further reading	p. 174
Factor Analysis	p. 175
Introduction to latent variable models	p. 175
The linear single-factor model	p. 178
The general linear factor model	p. 180
Interpretation	p. 184
Adequacy of the model and choice of the number of factors	p. 186
Rotation	p. 188
Factor scores	p. 192
A worked example: the test anxiety inventory	p. 194
How rotation helps interpretation	p. 198
A comparison of factor analysis and principal components analysis	p. 199
Additional examples and further work	p. 201
Software	p. 207
Further reading	p. 207
Factor Analysis for Binary Data	p. 209
Latent trait models	p. 209
Why is the factor analysis model for metrical variables invalid for binary responses?	p. 212
Factor model for binary data using the Item Response Theory approach	p. 213
Goodness-of-fit	p. 218
Factor scores	p. 222
Rotation	p. 224
Underlying variable approach	p. 224
Example: sexual attitudes	p. 226
Additional examples and further work	p. 231
Software	p. 240
Further reading	p. 240
Factor Analysis for Ordered Categorical Variables	p. 243
The practical background	p. 243
Two approaches to modelling ordered categorical data	p. 244
Item response function approach	p. 245
Examples	p. 252
The underlying variable approach	p. 255
Unordered and partially ordered observed variables	p. 260
Additional examples and further work	p. 264
Software	p. 270
Further reading	p. 270
Latent Class Analysis for Binary Data	p. 271
Introduction	p. 271
The latent class model for binary data	p. 272
Example: attitude to science and technology data	p. 277
How can we distinguish the latent class model from the latent trait model?	p. 281
Latent class analysis, cluster analysis, and latent profile analysis	p. 283
Additional examples and further work	p. 284
Software	p. 288
Further reading	p. 288
Confirmatory Factor Analysis and Structural Equation Models	p. 289
Introduction	p. 289
Path diagram	p. 291
Measurement models	p. 292
Adequacy of the model	p. 298
Introduction to structural equation models with latent variables	p. 301
The linear structural equation model	p. 302
A worked example	p. 312
Extensions	p. 316
Additional examples and further work	p. 317
Software	p. 322
Further reading	p. 323
Multilevel Modelling	p. 325
Introduction	p. 325
Some potential applications	p. 326
Comparing groups using multilevel modelling	p. 327
Random intercept model	p. 333
Random slope model	p. 335
Contextual effects	p. 339
Multilevel multivariate regression	p. 342
Multilevel factor analysis	p. 348
Additional examples and further work	p. 351
Further topics	p. 353
Estimation procedures and software	p. 354
Further reading	p. 355
References	p. 357
Index	p. 363
Table of Contents provided by Ingram. All Rights Reserved.

What is included with this book?

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

FREE SHIPPING

Amazon no longer offers textbook rentals. We do!

Analysis of Multivariate Social Science Data, Second Edition

Summary

Table of Contents

Supplemental Materials