Statistical modelling for social researchers: principles and practice
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London
Routledge
2009
|
| Schriftenreihe: | Social research today
|
| Schlagworte: | |
| Online-Zugang: | Table of contents only Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | x, 210 S. ill., plans 26 cm |
| ISBN: | 0415448379 0415448409 9780415448376 9780415448406 |
Internformat
MARC
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents
Preface ix
1 Statistical modelling: An overview 1
1.1 Introduction 1
1.2 Why model? 1
1.3 The general linear statistical model 3
1.4 Model development 6
1.5 Model selection by computer 8
1.6 Statistical software 9
esear ch designs and data 11
2.1 Units of analysis 11
2.2 Research designs 12
2.3 Sampling, surveys and weighting 12
2.4 Variables and their measurement 14
2.5 Missing data 17
2.6 Data structures 20
tatist ical preliminaries 21
3.1 Odds and odds ratios 21
3.2 Logarithms 24
3.3 z, t and chi-squared (x2) 25
3.4 Tests of significance 30
3.5 Categorical explanatory variables; dummy variables;
reference or base category 31
3.6 Multicollinearity 34
3.7 Interaction terms 35
3.8 Incorporating nonlinear relationships 37
3.9 Residuals 37
3.10 Unobserved variables and unobserved heterogeneity 39
vi Contents
4 Multiple regression for continuous response variables 41
4.1 Introduction 41
4.2 Example: Determinants of pay 41
4.3 Fitting a multiple regression model in SPSS 43
4.4 How good is the regression model? 45
4.5 Expanding the model 48
4.6 Adding interaction terms 52
4.7 Automated model selection procedures 54
4.8 Diagnostics: Analysis of residuals 55
4.9 Fitting the regression model in Stata 60
4.10 Tobit model 61
5 Logistic regression for binary response variables 63
5.1 Introduction 63
5.2 Example: Victim of car crime 64
5.3 Undertaking logistic regression in SPSS 65
5.4 Expanding the model 69
5.5 Automated model selection procedures 73
5.6 Diagnostics: Analysis of residuals 74
5.7 Fitting the logistic regression model in Stata 75
5.8 Probit model 75
6 Multinomial logistic regression for multinomial response variables 77
6.1 Background 77
6.2 Fitting the multinomial in SPSS to data on sentences awarded
by the court 78
6.3 Diagnostics: Analysis of residuals 82
6.4 Expanding the model 84
6.5 Relationship between SPSS logistic and SPSS multinomial 87
6.6 Fitting the multinomial in Stata to voting data, incorporating
complex design factors 87
7 Loglinear models 93
7.1 Introduction 93
7.2 Three-factor model 94
7.3 Expanding the model 95
7.4 Loglinear models specifying a response variable 96
8 Ordinal logistic regression for ordered categorical
response variables 98
8.1 Introduction 98
8.2 Example: Ability to influence local political decisions 98
Contents vii
8.3 Fitting the ordinal logistic regression model in SPSS 101
8.4 Fitting the ordinal logistic regression model in Stata 104
9 Multilevel modelling 107
9.1 Introduction 107
9.2 Methods of analysis and their deficiencies 108
9.3 Multilevel models 111
9.4 Statistical software 116
9.5 Fitting models in MLwiN to employment data 117
10 Latent variables and factor analysis 125
10.1 Introduction 125
10.2 How are latent variables constructed? 126
10.3 Factor analysis 127
10.4 Example: Fear of crime 130
10.5 Interpreting factors and rotation 136
10.6 Exploratory and confirmatory factor analysis 139
10.7 Other latent variable models 139
11 Causal modelling: simultaneous equation models 141
11.1 Introduction 141
11.2 Recursive models: Path analysis, determinants of pay 141
11.3 Non-recursive models: Deterrent effect of solving crime 147
11.4 Structural equation modelling (SEM) 154
12 Longitudinal data analysis 156
12.1 Introduction 156
12.2 Time series analysis 158
12.3 Repeated cross-sectional surveys 159
12.4 Panel and cohort studies 160
12.5 Statistical models for panel data 161
12.6 Fitting panel data models in Stata 165
12.7 Example from the British Household Panel Study, time spent
on housework 166
12.8 Relationship between panel models and multilevel models 171
13 Event history models 173
13.1 Time to an event: Survival models 173
13.2 Cox s proportional hazards model 177
13.3 Comparing survival times between two or more groups 179
13.4 Fitting Cox s proportional hazards regression model to Training
for work data 182
viii Contents
13.5 Discrete time models 184
13.6 Data structures 184
13.7 Time to a single event 189
13.8 Competing risk models 190
13.9 Repeated events or multiple spells 191
13.10 Multiple states 192
13.11 Guidance on restructuring data to fit survival and discrete
time models 193
References 197
Appendix 1 The generalised linear model 201
Appendix 2 Handling tabular data 203
Index 207
|
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| id | DE-604.BV035830209 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:05:36Z |
| institution | BVB |
| isbn | 0415448379 0415448409 9780415448376 9780415448406 |
| language | English |
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| physical | x, 210 S. ill., plans 26 cm |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Routledge |
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| series2 | Social research today |
| spelling | Tarling, Roger Verfasser (DE-588)137382472 aut Statistical modelling for social researchers principles and practice Roger Tarling London Routledge 2009 x, 210 S. ill., plans 26 cm txt rdacontent n rdamedia nc rdacarrier Social research today Includes bibliographical references and index SPSS swd Sozialwissenschaften swd Statistik swd Sozialwissenschaften Social sciences Research Statistical methods Social sciences Statistical methods Erscheint auch als Online-Ausgabe 0-203-92948-9 Erscheint auch als Online-Ausgabe 978-0-203-92948-3 http://www.loc.gov/catdir/toc/ecip0812/2008008888.html Table of contents only HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018688757&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Tarling, Roger Statistical modelling for social researchers principles and practice SPSS swd Sozialwissenschaften swd Statistik swd Sozialwissenschaften Social sciences Research Statistical methods Social sciences Statistical methods |
| title | Statistical modelling for social researchers principles and practice |
| title_auth | Statistical modelling for social researchers principles and practice |
| title_exact_search | Statistical modelling for social researchers principles and practice |
| title_full | Statistical modelling for social researchers principles and practice Roger Tarling |
| title_fullStr | Statistical modelling for social researchers principles and practice Roger Tarling |
| title_full_unstemmed | Statistical modelling for social researchers principles and practice Roger Tarling |
| title_short | Statistical modelling for social researchers |
| title_sort | statistical modelling for social researchers principles and practice |
| title_sub | principles and practice |
| topic | SPSS swd Sozialwissenschaften swd Statistik swd Sozialwissenschaften Social sciences Research Statistical methods Social sciences Statistical methods |
| topic_facet | SPSS Sozialwissenschaften Statistik Social sciences Research Statistical methods Social sciences Statistical methods |
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