Statistical Modelling for Social Researchers:

This book explains the principles and theory of statistical modelling in an intelligible way for the non-mathematical social scientist looking to apply statistical modelling techniques in research. The book also serves as an introduction for those wishing to develop more detailed knowledge and skills in statistical modelling. Rather than present a limited number of statistical models in great depth, the aim is to provide a comprehensive overview of the statistical models currently adopted in social research, in order that the researcher can make appropriate choices and select the most suitable model for the research question to be addressed. To facilitate application, the book also offers practical guidance and instruction in fitting models using SPSS and Stata, the most popular statistical computer software which is available to most social researchers. Instruction in using MLwiN is also given. Models covered in the book include; multiple regression, binary, multinomial and ordered logistic regression, log-linear models, multilevel models, latent variable models (factor analysis), path analysis and simultaneous equation models and models for longitudinal data and event histories. An accompanying website hosts the datasets and further exercises in order that the reader may practice developing statistical models. An ideal tool for postgraduate social science students, research students and practicing social researchers in universities, market research, government social research and the voluntary sector.

Preface	p. ix
Statistical modelling: An overview	p. 1
Introduction	p. 1
Why model?	p. 1
The general linear statistical model	p. 3
Model development	p. 6
Model selection by computer	p. 8
Statistical software	p. 9
Research designs and data	p. 11
Units of analysis	p. 11
Research designs	p. 12
Sampling, surveys and weighting	p. 12
Variables and their measurement	p. 14
Missing data	p. 17
Data structures	p. 20
Statistical preliminaries	p. 21
Odds and odds ratios	p. 21
Logarithms	p. 24
z, t and chi-squared (X[superscript 2])	p. 25
Tests of significance	p. 30
Categorical explanatory variables; dummy variables; reference or base category	p. 31
Multicollinearity	p. 34
Interaction terms	p. 35
Incorporating nonlinear relationships	p. 37
Residuals	p. 37
Unobserved variables and unobserved heterogeneity	p. 39
Multiple regression for continuous response variables	p. 41
Introduction	p. 41
Example: Determinants of pay	p. 41
Fitting a multiple regression model in SPSS	p. 43
How good is the regression model?	p. 45
Expanding the model	p. 48
Adding interaction terms	p. 52
Automated model selection procedures	p. 54
Diagnostics: Analysis of residuals	p. 55
Fitting the regression model in Stata	p. 60
Tobit model	p. 61
Logistic regression for binary response variables	p. 63
Introduction	p. 63
Example: Victim of car crime	p. 64
Undertaking logistic regression in SPSS	p. 65
Expanding the model	p. 69
Automated model selection procedures	p. 73
Diagnostics: Analysis of residuals	p. 74
Fitting the logistic regression model in Stata	p. 75
Probit model	p. 75
Multinomial logistic regression for multinomial response variables	p. 77
Background	p. 77
Fitting the multinomial in SPSS to data on sentences awarded by the court	p. 78
Diagnostics: Analysis of residuals	p. 82
Expanding the model	p. 84
Relationship between SPSS logistic and SPSS multinomial	p. 87
Fitting the multinomial in Stata to voting data, incorporating complex design factors	p. 87
Loglinear models	p. 93
Introduction	p. 93
Three-factor model	p. 94
Expanding the model	p. 95
Loglinear models specifying a response variable	p. 96
Ordinal logistic regression for ordered categorical response variables	p. 98
Introduction	p. 98
Example: Ability to influence local political decisions	p. 98
Fitting the ordinal logistic regression model in SPSS	p. 101
Fitting the ordinal logistic regression model in Stata	p. 104
Multilevel modelling	p. 107
Introduction	p. 107
Methods of analysis and their deficiencies	p. 108
Multilevel models	p. 111
Statistical software	p. 116
Fitting models in MLwiN to employment data	p. 117
Latent variables and factor analysis	p. 125
Introduction	p. 125
How are latent variables constructed?	p. 126
Factor analysis	p. 127
Example: Fear of crime	p. 130
Interpreting factors and rotation	p. 136
Exploratory and confirmatory factor analysis	p. 139
Other latent variable models	p. 139
Causal modelling: simultaneous equation models	p. 141
Introduction	p. 141
Recursive models: Path analysis, determinants of pay	p. 141
Non-recursive models: Deterrent effect of solving crime	p. 147
Structural equation modelling (SEM)	p. 154
Longitudinal data analysis	p. 156
Introduction	p. 156
Time series analysis	p. 158
Repeated cross-sectional surveys	p. 159
Panel and cohort studies	p. 160
Statistical models for panel data	p. 161
Fitting panel data models in Stata	p. 165
Example from the British Household Panel Study, time spent on housework	p. 166
Relationship between panel models and multilevel models	p. 171
Event history models	p. 173
Time to an event: Survival models	p. 173
Cox's proportional hazards model	p. 177
Comparing survival times between two or more groups	p. 179
Fitting Cox's proportional hazards regression model to Training for work data	p. 182
Discrete time models	p. 184
Data structures	p. 184
Time to a single event	p. 189
Competing risk models	p. 190
Repeated events or multiple spells	p. 191
Multiple states	p. 192
Guidance on restructuring data to fit survival and discrete time models	p. 193
References	p. 197
The generalised linear model	p. 201
Handling tabular data	p. 203
Index	p. 207
Table of Contents provided by Ingram. All Rights Reserved.

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Statistical Modelling for Social Researchers: Principles and Practice

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