Analysis of multivariate social science data:
Saved in:
| Previous Title: | Analysis and interpretation of multivariate data for scientists |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton [u.a.]
CRC Press
2008
|
| Edition: | second edition |
| Series: | Statistics in the social and behavioral sciences series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | 1. Aufl. u.d.T.: Analysis and interpretation of multivariate data for scientists |
| Physical Description: | xi, 371 S. Diagramme |
| ISBN: | 9781584889601 |
Staff View
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| 245 | 1 | 0 | |a Analysis of multivariate social science data |c David J. Bartholomew ... |
| 250 | |a second edition | ||
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2008 | |
| 300 | |a xi, 371 S. |b Diagramme | ||
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| adam_text | 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
8
1.4
The geometry of multivariate analysis
10
1.5
Use of examples
11
1.6
Data inspection, transformations, and missing data
13
1.7
Reading
14
2
Cluster Analysis
17
2.1
Classification in social sciences
17
2.2
Some methods of cluster analysis
20
2.3
Graphical presentation of results
25
2.4
Derivation of the distance matrix
29
2.5
Example on English dialects
33
2.6
Comparisons
39
2.7
Clustering variables
41
2.8
Additional examples and further work
41
2.9
Further reading
53
3
Multidimensional Scaling
55
3.1
Introduction
55
3.2
Examples
57
3.3
Classical, ordinal, and metrical multidimensional scaling
61
3.4
Comments on computational procedures
64
3.5
Assessing fit and choosing the number of dimensions
65
3.6
A worked example: dimensions of colour vision
66
3.7
Additional examples and further work
(58
3.8
Further reading
81
4
Correspondence Analysis
83
4.1
Aims of correspondence analysis
83
4.2
Carrying out, a correspondence analysis: a simple numerical
example
85
vi
CONTENTS
4.3
Carrying out a correspondence analysis: the general method
90
4.4
The biplot
93
4.5
Interpretation of dimensions
97
4.6
Choosing the number of dimensions
99
4.7
Example: confidence in purchasing from European
Community countries
101
4.8
Correspondence analysis of multiway tables
107
4.9
Additional examples and further work 111
4.10
Further reading
116
5
Principal Components Analysis
117
5.1
Introduction
117
5.2
Some potential applications
118
5.3
Illustration of PCA for two variables
119
5.4
An outline of PCA
122
5.5
Examples
125
5.6
Component scores
131
5.7
The link between PCA and multidimensional scaling, and
between PCA and correspondence analysis
134
5.8
Using principal component scores to replace the original
variables
137
5.9
Additional examples and further work
138
5.10
Further reading
144
6
Regression Analysis
145
6.1
Basic ideas
145
6.2
Simple linear regression
147
6.3
A probability model for simple linear regression
150
6.4
Inference for the simple linear regression model
151
6.0
Checking the assumptions
153
6.6
Multiple regression
154
6.7
Examples of multiple regression
156
6.8
Estimation and inference about the parameters
157
6.9
Interpretation of the regression coefficients
159
6.10
Selection of regressor variables
161
6.11
Transformations and interactions
163
6.12
Logistic regression
165
6.13
Path analysis
168
6.14
Additional examples and further work
171
6.1.5
Further reading
174
7
Factor Analysis
175
7.1
Introduction to latent variable models
175
7.2
The linear single-factor model
178
7.3
The general linear factor model
180
7.4
Interpretation
184
CONTENTS
vii
7.5
Adequacy of the model and
dioice
of the number of factors
186
7.6
Rotation
188
7.7
Factor scores
192
7.8
A worked example: the test anxiety inventory
194
7.9
How rotation helps interpretation
198
7.10
A comparison of factor analysis and principal components
analysis
199
7.11
Additional examples and further work
201
7.12
Software
207
7.13
Further reading
207
8
Factor
Analysis for Binary Data
209
8.1
Latent trait models
209
8.2
Why is the factor analysis model for metrical variables invalid
for binary responses?
212
8.3
Factor model for binary data using the Item Response Theory
approach
21
Л
8.4
Goodnesis-of-fit
218
8.5
Factor scores
222
8.6
Rotation
224
8.7
Underlying variable approach
224
8.8
Example: sexual attitudes
226
8.9
Additional examples and further work
231
8.10
Software
240
8.11
Fnrther reading
240
9
Factor Analysis for Ordered Categorical Variables
243
9.1
The practical background
243
9.2
Two approaches to modelling ordered categorical data
244
9.3
Item response function approach
245
9.4
Examples
252
9.5
The underlying variable approach
255
9.6
Unordered and partially ordered observed variables
260
9.7
Additional examples and further work
264
9.8
Software
270
9.9
Further reading
270
10
Latent Class Analysis for Binary Data
271
10.1
Introduction
271
10.2
The latent class model for binary data
272
10.3
Example: attitude to science and technology data
277
10.4
How can we distinguish the latent class model from the latent
trait model?
281
10.5
Latent class analysis, cluster analysis, and latent profile
analysis
283
10.6
Additional examples and further work
284
viii CONTENTS
10.7
Software
288
10.8
Further reading
288
11
Confirmatory Factor Analysis and
Structural Equation Models
289
11.1
Introduction
289
11.2
Path diagram
291
11.3
Measurement models
292
11.4
Adequacy of the model
298
11.5
Introduction to structural equation models with latent
variables
301
11.6
The linear structural equation model
302
11.7
A worked example
312
11.8
Extensions
316
11.9
Additional examples and further work
317
11.10
Software
322
11.11
Further reading
323
12
Multilevel Modelling
325
12.1
Introduction
325
12.2
Some potential applications
326
12.3
Comparing groups using multilevel modelling
327
12.4
Random intercept model
333
12.5
Random slope model
335
12.6
Contextual effects
339
12.7
Multilevel multivariate regression
342
12.8
Multilevel factor analysis
348
12.9
Additional examples and further work
351
12.10
Further topics
353
12.11
Estimation procedures and software
354
12.12
Further reading
355
References
357
Index
363
|
| adam_txt |
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
8
1.4
The geometry of multivariate analysis
10
1.5
Use of examples
11
1.6
Data inspection, transformations, and missing data
13
1.7
Reading
14
2
Cluster Analysis
17
2.1
Classification in social sciences
17
2.2
Some methods of cluster analysis
20
2.3
Graphical presentation of results
25
2.4
Derivation of the distance matrix
29
2.5
Example on English dialects
33
2.6
Comparisons
39
2.7
Clustering variables
41
2.8
Additional examples and further work
41
2.9
Further reading
53
3
Multidimensional Scaling
55
3.1
Introduction
55
3.2
Examples
57
3.3
Classical, ordinal, and metrical multidimensional scaling
61
3.4
Comments on computational procedures
64
3.5
Assessing fit and choosing the number of dimensions
65
3.6
A worked example: dimensions of colour vision
66
3.7
Additional examples and further work
(58
3.8
Further reading
81
4
Correspondence Analysis
83
4.1
Aims of correspondence analysis
83
4.2
Carrying out, a correspondence analysis: a simple numerical
example
85
vi
CONTENTS
4.3
Carrying out a correspondence analysis: the general method
90
4.4
The biplot
93
4.5
Interpretation of dimensions
97
4.6
Choosing the number of dimensions
99
4.7
Example: confidence in purchasing from European
Community countries
101
4.8
Correspondence analysis of multiway tables
107
4.9
Additional examples and further work 111
4.10
Further reading
116
5
Principal Components Analysis
117
5.1
Introduction
117
5.2
Some potential applications
118
5.3
Illustration of PCA for two variables
119
5.4
An outline of PCA
122
5.5
Examples
125
5.6
Component scores
131
5.7
The link between PCA and multidimensional scaling, and
between PCA and correspondence analysis
134
5.8
Using principal component scores to replace the original
variables
137
5.9
Additional examples and further work
138
5.10
Further reading
144
6
Regression Analysis
145
6.1
Basic ideas
145
6.2
Simple linear regression
147
6.3
A probability model for simple linear regression
150
6.4
Inference for the simple linear regression model
151
6.0
Checking the assumptions
153
6.6
Multiple regression
154
6.7
Examples of multiple regression
156
6.8
Estimation and inference about the parameters
157
6.9
Interpretation of the regression coefficients
159
6.10
Selection of regressor variables
161
6.11
Transformations and interactions
163
6.12
Logistic regression
165
6.13
Path analysis
168
6.14
Additional examples and further work
171
6.1.5
Further reading
174
7
Factor Analysis
175
7.1
Introduction to latent variable models
175
7.2
The linear single-factor model
178
7.3
The general linear factor model
180
7.4
Interpretation
184
CONTENTS
vii
7.5
Adequacy of the model and
dioice
of the number of factors
186
7.6
Rotation
188
7.7
Factor scores
192
7.8
A worked example: the test anxiety inventory
194
7.9
How rotation helps interpretation
198
7.10
A comparison of factor analysis and principal components
analysis
199
7.11
Additional examples and further work
201
7.12
Software
207
7.13
Further reading
207
8
Factor
Analysis for Binary Data
209
8.1
Latent trait models
209
8.2
Why is the factor analysis model for metrical variables invalid
for binary responses?
212
8.3
Factor model for binary data using the Item Response Theory
approach
21
Л
8.4
Goodnesis-of-fit
218
8.5
Factor scores
222
8.6
Rotation
224
8.7
Underlying variable approach
224
8.8
Example: sexual attitudes
226
8.9
Additional examples and further work
231
8.10
Software
240
8.11
Fnrther reading
240
9
Factor Analysis for Ordered Categorical Variables
243
9.1
The practical background
243
9.2
Two approaches to modelling ordered categorical data
244
9.3
Item response function approach
245
9.4
Examples
252
9.5
The underlying variable approach
255
9.6
Unordered and partially ordered observed variables
260
9.7
Additional examples and further work
264
9.8
Software
270
9.9
Further reading
270
10
Latent Class Analysis for Binary Data
271
10.1
Introduction
271
10.2
The latent class model for binary data
272
10.3
Example: attitude to science and technology data
277
10.4
How can we distinguish the latent class model from the latent
trait model?
281
10.5
Latent class analysis, cluster analysis, and latent profile
analysis
283
10.6
Additional examples and further work
284
viii CONTENTS
10.7
Software
288
10.8
Further reading
288
11
Confirmatory Factor Analysis and
Structural Equation Models
289
11.1
Introduction
289
11.2
Path diagram
291
11.3
Measurement models
292
11.4
Adequacy of the model
298
11.5
Introduction to structural equation models with latent
variables
301
11.6
The linear structural equation model
302
11.7
A worked example
312
11.8
Extensions
316
11.9
Additional examples and further work
317
11.10
Software
322
11.11
Further reading
323
12
Multilevel Modelling
325
12.1
Introduction
325
12.2
Some potential applications
326
12.3
Comparing groups using multilevel modelling
327
12.4
Random intercept model
333
12.5
Random slope model
335
12.6
Contextual effects
339
12.7
Multilevel multivariate regression
342
12.8
Multilevel factor analysis
348
12.9
Additional examples and further work
351
12.10
Further topics
353
12.11
Estimation procedures and software
354
12.12
Further reading
355
References
357
Index
363 |
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| edition | second edition |
| format | Book |
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| id | DE-604.BV035004540 |
| illustrated | Not Illustrated |
| index_date | 2024-07-02T21:41:37Z |
| indexdate | 2024-07-09T21:19:59Z |
| institution | BVB |
| isbn | 9781584889601 |
| language | English |
| lccn | 2008005638 |
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| physical | xi, 371 S. Diagramme |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Statistics in the social and behavioral sciences series |
| spelling | Analysis of multivariate social science data David J. Bartholomew ... second edition Boca Raton [u.a.] CRC Press 2008 xi, 371 S. Diagramme txt rdacontent n rdamedia nc rdacarrier Statistics in the social and behavioral sciences series 1. Aufl. u.d.T.: Analysis and interpretation of multivariate data for scientists Sozialwissenschaften Social sciences Statistical methods Multivariate analysis Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Multivariate Analyse (DE-588)4040708-1 s b DE-604 Bartholomew, David J. 1931- Sonstige (DE-588)121635864 oth 1. Auflage Analysis and interpretation of multivariate data for scientists Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016673881&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Analysis of multivariate social science data Sozialwissenschaften Social sciences Statistical methods Multivariate analysis Multivariate Analyse (DE-588)4040708-1 gnd |
| subject_GND | (DE-588)4040708-1 (DE-588)4151278-9 |
| title | Analysis of multivariate social science data |
| title_auth | Analysis of multivariate social science data |
| title_exact_search | Analysis of multivariate social science data |
| title_exact_search_txtP | Analysis of multivariate social science data |
| title_full | Analysis of multivariate social science data David J. Bartholomew ... |
| title_fullStr | Analysis of multivariate social science data David J. Bartholomew ... |
| title_full_unstemmed | Analysis of multivariate social science data David J. Bartholomew ... |
| title_old | Analysis and interpretation of multivariate data for scientists |
| title_short | Analysis of multivariate social science data |
| title_sort | analysis of multivariate social science data |
| topic | Sozialwissenschaften Social sciences Statistical methods Multivariate analysis Multivariate Analyse (DE-588)4040708-1 gnd |
| topic_facet | Sozialwissenschaften Social sciences Statistical methods Multivariate analysis Multivariate Analyse Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016673881&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bartholomewdavidj analysisofmultivariatesocialsciencedata |