TY  - BOOK
AU  - Tarling,Roger
TI  - Statistical modelling for social researchers: principles and practice 
T2  - Social research today
SN  - 0415448379
AV  - HA29 .T28 2009
U1  - 519.5 22
PY  - 2009///
CY  - London, New York
PB  - Routledge
KW  - Social sciences
KW  - Research
KW  - Statistical methods
N1  - Includes bibliographical references and index; 1; Statistical modelling: An overview --; 2; Research designs and data --; 3; Statistical preliminaries --; 4; Multiple regression for continuous response variables --; 5; Logistic regression for binary response variables --; 6; Multinomial logistic regression for multinomial response variables --; 7; Loglinear models --; 8; Ordinal logistic regression for ordered categorical response variables --; 9; Multilevel modelling --; 10; Latent variables and factor analysis --; 11; Causal modelling: simultaneous equation models --; 12; Longitudinal data analysis --; 13; Event history models --; Appendix 1; The generalised linear model --; Appendix 2; Handling tabular data
N2  - "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."--Publisher's website
ER  -