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Analysis of Multivariate Social Science Data, Second Edition : Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences - David J. Bartholomew

Analysis of Multivariate Social Science Data, Second Edition

Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

Paperback Published: 25th June 2008
ISBN: 9781584889601
Number Of Pages: 384

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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.

Prefacep. ix
Setting the Scenep. 1
Structure of the bookp. 1
Our limited use of mathematicsp. 4
Variablesp. 8
The geometry of multivariate analysisp. 10
Use of examplesp. 11
Data inspection, transformations, and missing datap. 13
Readingp. 14
Cluster Analysisp. 17
Classification in social sciencesp. 17
Some methods of cluster analysisp. 20
Graphical presentation of resultsp. 25
Derivation of the distance matrixp. 29
Example on English dialectsp. 33
Comparisonsp. 39
Clustering variablesp. 41
Additional examples and further workp. 41
Further readingp. 53
Multidimensional Scalingp. 55
Introductionp. 55
Examplesp. 57
Classical, ordinal, and metrical multidimensional scalingp. 61
Comments on computational proceduresp. 64
Assessing fit and choosing the number of dimensionsp. 65
A worked example: dimensions of colour visionp. 66
Additional examples and further workp. 68
Further readingp. 81
Correspondence Analysisp. 83
Aims of correspondence analysisp. 83
Carrying out a correspondence analysis: a simple numerical examplep. 85
Carrying out a correspondence analysis: the general methodp. 90
The biplotp. 93
Interpretation of dimensionsp. 97
Choosing the number of dimensionsp. 99
Example: confidence in purchasing from European Community countriesp. 101
Correspondence analysis of multiway tablesp. 107
Additional examples and further workp. 111
Further readingp. 116
Principal Components Analysisp. 117
Introductionp. 117
Some potential applicationsp. 118
Illustration of PCA for two variablesp. 119
An outline of PCAp. 122
Examplesp. 125
Component scoresp. 131
The link between PCA and multidimensional scaling, and between PCA and correspondence analysisp. 134
Using principal component scores to replace the original variablesp. 137
Additional examples and further workp. 138
Further readingp. 144
Regression Analysisp. 145
Basic ideasp. 145
Simple linear regressionp. 147
A probability model for simple linear regressionp. 150
Inference for the simple linear regression modelp. 151
Checking the assumptionsp. 153
Multiple regressionp. 154
Examples of multiple regressionp. 156
Estimation and inference about the parametersp. 157
Interpretation of the regression coefficientsp. 159
Selection of regressor variablesp. 161
Transformations and interactionsp. 163
Logistic regressionp. 165
Path analysisp. 168
Additional examples and further workp. 171
Further readingp. 174
Factor Analysisp. 175
Introduction to latent variable modelsp. 175
The linear single-factor modelp. 178
The general linear factor modelp. 180
Interpretationp. 184
Adequacy of the model and choice of the number of factorsp. 186
Rotationp. 188
Factor scoresp. 192
A worked example: the test anxiety inventoryp. 194
How rotation helps interpretationp. 198
A comparison of factor analysis and principal components analysisp. 199
Additional examples and further workp. 201
Softwarep. 207
Further readingp. 207
Factor Analysis for Binary Datap. 209
Latent trait modelsp. 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 approachp. 213
Goodness-of-fitp. 218
Factor scoresp. 222
Rotationp. 224
Underlying variable approachp. 224
Example: sexual attitudesp. 226
Additional examples and further workp. 231
Softwarep. 240
Further readingp. 240
Factor Analysis for Ordered Categorical Variablesp. 243
The practical backgroundp. 243
Two approaches to modelling ordered categorical datap. 244
Item response function approachp. 245
Examplesp. 252
The underlying variable approachp. 255
Unordered and partially ordered observed variablesp. 260
Additional examples and further workp. 264
Softwarep. 270
Further readingp. 270
Latent Class Analysis for Binary Datap. 271
Introductionp. 271
The latent class model for binary datap. 272
Example: attitude to science and technology datap. 277
How can we distinguish the latent class model from the latent trait model?p. 281
Latent class analysis, cluster analysis, and latent profile analysisp. 283
Additional examples and further workp. 284
Softwarep. 288
Further readingp. 288
Confirmatory Factor Analysis and Structural Equation Modelsp. 289
Introductionp. 289
Path diagramp. 291
Measurement modelsp. 292
Adequacy of the modelp. 298
Introduction to structural equation models with latent variablesp. 301
The linear structural equation modelp. 302
A worked examplep. 312
Extensionsp. 316
Additional examples and further workp. 317
Softwarep. 322
Further readingp. 323
Multilevel Modellingp. 325
Introductionp. 325
Some potential applicationsp. 326
Comparing groups using multilevel modellingp. 327
Random intercept modelp. 333
Random slope modelp. 335
Contextual effectsp. 339
Multilevel multivariate regressionp. 342
Multilevel factor analysisp. 348
Additional examples and further workp. 351
Further topicsp. 353
Estimation procedures and softwarep. 354
Further readingp. 355
Referencesp. 357
Indexp. 363
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781584889601
ISBN-10: 1584889608
Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Audience: Tertiary; University or College
Format: Paperback
Language: English
Number Of Pages: 384
Published: 25th June 2008
Publisher: Taylor & Francis Inc
Country of Publication: US
Dimensions (cm): 23.5 x 15.6  x 2.01
Weight (kg): 0.6
Edition Number: 2
Edition Type: New edition

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