Analysis of multivariate social science data

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Title:

Series:

Chapman & Hall/CRC statistics in the social and behavioral sciences series

Edition:

2nd ed.

Publication Information:

Boca Raton : CRC Press, 2008

Physical Description:

xi, 371 p. : ill. ; 24 cm.

ISBN:

9781584889601

Subject Term:

Social sciences -- Statistical methods

Multivariate analysis

Added Author:

Bartholomew, David J.

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010218998	HA29 A52 2008	Open Access Book	Book	Searching... Unknown
Searching... PSZ JB	30000003503681	HA29 A52 2008	Open Access Book	Book	Searching... Unknown
Searching... SPACE Kuching Library	30000003503640	HA29 A52 2008	Open Access Book	Book	Searching... Unknown

Drawing on the authors' varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data , Second Edition enables 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
1 Setting the Scene	p. 1
1.1 Structure of the book	p. 1
1.2 Our limited use of mathematics	p. 4
1.3 Variables	p. 8
1.4 The geometry of multivariate analysis	p. 10
1.5 Use of examples	p. 11
1.6 Data inspection, transformations, and missing data	p. 13
1.7 Reading	p. 14
2 Cluster Analysis	p. 17
2.1 Classification in social sciences	p. 17
2.2 Some methods of cluster analysis	p. 20
2.3 Graphical presentation of results	p. 25
2.4 Derivation of the distance matrix	p. 29
2.5 Example on English dialects	p. 33
2.6 Comparisons	p. 39
2.7 Clustering variables	p. 41
2.8 Additional examples and further work	p. 41
2.9 Further reading	p. 53
3 Multidimensional Scaling	p. 55
3.1 Introduction	p. 55
3.2 Examples	p. 57
3.3 Classical, ordinal, and metrical multidimensional scaling	p. 61
3.4 Comments on computational procedures	p. 64
3.5 Assessing fit and choosing the number of dimensions	p. 65
3.6 A worked example: dimensions of colour vision	p. 66
3.7 Additional examples and further work	p. 68
3.8 Further reading	p. 81
4 Correspondence Analysis	p. 83
4.1 Aims of correspondence analysis	p. 83
4.2 Carrying out a correspondence analysis: a simple numerical example	p. 85
4.3 Carrying out a correspondence analysis: the general method	p. 90
4.4 The biplot	p. 93
4.5 Interpretation of dimensions	p. 97
4.6 Choosing the number of dimensions	p. 99
4.7 Example: confidence in purchasing from European Community countries	p. 101
4.8 Correspondence analysis of multiway tables	p. 107
4.9 Additional examples and further work	p. 111
4.10 Further reading	p. 116
5 Principal Components Analysis	p. 117
5.1 Introduction	p. 117
5.2 Some potential applications	p. 118
5.3 Illustration of PCA for two variables	p. 119
5.4 An outline of PCA	p. 122
5.5 Examples	p. 125
5.6 Component scores	p. 131
5.7 The link between PCA and multidimensional scaling, and between PCA and correspondence analysis	p. 134
5.8 Using principal component scores to replace the original variables	p. 137
5.9 Additional examples and further work	p. 138
5.10 Further reading	p. 144
6 Regression Analysis	p. 145
6.1 Basic ideas	p. 145
6.2 Simple linear regression	p. 147
6.3 A probability model for simple linear regression	p. 150
6.4 Inference for the simple linear regression model	p. 151
6.5 Checking the assumptions	p. 153
6.6 Multiple regression	p. 154
6.7 Examples of multiple regression	p. 156
6.8 Estimation and inference about the parameters	p. 157
6.9 Interpretation of the regression coefficients	p. 159
6.10 Selection of regressor variables	p. 161
6.11 Transformations and interactions	p. 163
6.12 Logistic regression	p. 165
6.13 Path analysis	p. 168
6.14 Additional examples and further work	p. 171
6.15 Further reading	p. 174
7 Factor Analysis	p. 175
7.1 Introduction to latent variable models	p. 175
7.2 The linear single-factor model	p. 178
7.3 The general linear factor model	p. 180
7.4 Interpretation	p. 184
7.5 Adequacy of the model and choice of the number of factors	p. 186
7.6 Rotation	p. 188
7.7 Factor scores	p. 192
7.8 A worked example: the test anxiety inventory	p. 194
7.9 How rotation helps interpretation	p. 198
7.10 A comparison of factor analysis and principal components analysis	p. 199
7.11 Additional examples and further work	p. 201
7.12 Software	p. 207
7.13 Further reading	p. 207
8 Factor Analysis for Binary Data	p. 209
8.1 Latent trait models	p. 209
8.2 Why is the factor analysis model for metrical variables invalid for binary responses?	p. 212
8.3 Factor model for binary data using the Item Response Theory approach	p. 213
8.4 Goodness-of-fit	p. 218
8.5 Factor scores	p. 222
8.6 Rotation	p. 224
8.7 Underlying variable approach	p. 224
8.8 Example: sexual attitudes	p. 226
8.9 Additional examples and further work	p. 231
8.10 Software	p. 240
8.11 Further reading	p. 240
9 Factor Analysis for Ordered Categorical Variables	p. 243
9.1 The practical background	p. 243
9.2 Two approaches to modelling ordered categorical data	p. 244
9.3 Item response function approach	p. 245
9.4 Examples	p. 252
9.5 The underlying variable approach	p. 255
9.6 Unordered and partially ordered observed variables	p. 260
9.7 Additional examples and further work	p. 264
9.8 Software	p. 270
9.9 Further reading	p. 270
10 Latent Class Analysis for Binary Data	p. 271
10.1 Introduction	p. 271
10.2 The latent class model for binary data	p. 272
10.3 Example: attitude to science and technology data	p. 277
10.4 How can we distinguish the latent class model from the latent trait model?	p. 281
10.5 Latent class analysis, cluster analysis, and latent profile analysis	p. 283
10.6 Additional examples and further work	p. 284
10.7 Software	p. 288
10.8 Further reading	p. 288
11 Confirmatory Factor Analysis and Structural Equation Models	p. 289
11.1 Introduction	p. 289
11.2 Path diagram	p. 291
11.3 Measurement models	p. 292
11.4 Adequacy of the model	p. 298
11.5 Introduction to structural equation models with latent variables	p. 301
11.6 The linear structural equation model	p. 302
11.7 A worked example	p. 312
11.8 Extensions	p. 316
11.9 Additional examples and further work	p. 317
11.10 Software	p. 322
11.11 Further reading	p. 323
12 Multilevel Modelling	p. 325
12.1 Introduction	p. 325
12.2 Some potential applications	p. 326
12.3 Comparing groups using multilevel modelling	p. 327
12.4 Random intercept model	p. 333
12.5 Random slope model	p. 335
12.6 Contextual effects	p. 339
12.7 Multilevel multivariate regression	p. 342
12.8 Multilevel factor analysis	p. 348
12.9 Additional examples and further work	p. 351
12.10 Further topics	p. 353
12.11 Estimation procedures and software	p. 354
12.12 Further reading	p. 355
References	p. 357
Index	p. 363

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