7 edition of **Statistical modelling for social researchers** found in the catalog.

Statistical modelling for social researchers

Roger Tarling

- 125 Want to read
- 6 Currently reading

Published
**2009**
by Routledge in London, New York
.

Written in English

- Social sciences -- Research -- Statistical methods,
- Social sciences -- Statistical methods

**Edition Notes**

Includes bibliographical references and index.

Statement | Roger Tarling. |

Series | Social research today, Social research today (Routledge (Firm)) |

Classifications | |
---|---|

LC Classifications | HA29 .T28 2009 |

The Physical Object | |

Pagination | x, 210 p. : |

Number of Pages | 210 |

ID Numbers | |

Open Library | OL23675893M |

ISBN 10 | 0415448379, 0415448409, 0203929489 |

ISBN 10 | 9780415448376, 9780415448406, 9780203929483 |

LC Control Number | 2008008888 |

This book is intended as required reading material for my course, Experimen-tal Design for the Behavioral and Social Sciences, a second level statistics course for undergraduate students in the College of Humanities and Social Sciences at Carnegie Mellon University. This course is also cross-listed as a graduate level. Political representation Trench warfare Rational voting Candidate positioning Recap Themes I Mathematical models in social science are cool I But they tend to give qualitative rather than quantitative predictions I Statistical modeling as an alternative I Collaborations with Hayward Alker, Aaron Edlin, Noah Kaplan, Gary King, and Jonathan Katz Andrew Gelman Mathematical vs. statistical.

Statistical models are mathematical simplifications of the real world. The goal of statistical modeling is to explain complex social facts as simply as possible. A statistical model might be as simple as a graph showing that richer parents have richer children, as depicted in Figure statistics and modern parameter estimation via MCMC simulation methods in great depth. Throughout the book, I painstakingly show the modeling process from model development, through development of an MCMC algorithm to es-timate its parameters, through model evaluation, and .

The Institute for Digital Research and Education (IDRE) has a collection of books on statistics and statistical computing available for UCLA researchers to borrow on a short term basis to help with research. Many of the books have web pages associated with them that have the data files for the book and web pages showing how to perform the. This book is designed to introduce doctoral and graduate students to the process of scientific research in the social sciences, business, education, public health, and related disciplines. This book is based on my lecture materials developed over a decade of teaching the doctoral-level class on Research Methods at the University of South Florida. The target audience for this book includes Ph.D /5(34).

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Language! Student Mastery Book A

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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 Cited by: 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 5/5(1).

This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph.D. students. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social by: 8.

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.

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 Cited by: 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. This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph.D.

students. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists/5(3).

Models in Statistical Social Research provides a comprehensive insight of models used in statistical social research based on statistical data and methods. While traditionally understood statistical models relate to data generating processes which presuppose facts, this book focuses on analytical models which relate to substantial processes generating social by: 6.

"Statistical models: theory and practice is lucid, helpful, insightful and a joy to read. It focuses on the most common tools of applied statistics with a clear and simple presentation.

This revised edition organizes the chapters differently, making reading much easier. Moreover, it includes many new examples and by: Book Description. Models and likelihood are the backbone of modern statistics and data analysis.

Anthony Davison here blends theory and practice to provide an integrated text for advanced undergraduate and graduate students, researchers and practitioners. The coverage is unrivaled, with sections on survival analysis, missing data, Markov chains, /5(3). STATISTICAL METHODS FOR THE SOCIAL SCIENCES.

The purpose of this book is to acquaint the reader with the increasing number of applications of statistics in engineering and the social sciences.

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.

Multilevel Modeling Using R (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences) W. Holmes Finch. out of 5 stars 1. Kindle Edition. $ has never studied or engaged in this type of data analysis or research design before.

You may need to proceed to thicker treatises to make a thorough analysis and find out how to /5(17). SPSS, standing for Statistical Package for the Social Sciences, is a powerful, user-friendly software package for the manipulation and statistical analysis of data.

The package is particularly useful for students and researchers in psychology, sociology, psychiatry, and other behavioral sciences, contain. If you have not reset your password sinceplease use the 'forgot password' link below to reset your password and access your SAGE online account.

Making sense of statistical methods in social research, Yang () is an easy-to-follow book will be extremely useful to students taking PhD, statistics and/or quantitative research methods course because of the detailed formulas that are provided.

I also recommend it to instructors in statistics and quantitative methods. Abstract. Recent developments in statistical models for social networks reflect an increasing theoretical focus in the social and behavioral sciences on the interdependence of social actors in dynamic, network-based social settings (e.g., Abbott, ; White,).Cited by: 2.

STATISTICAL METHODS 1 STATISTICAL METHODS Arnaud Delorme, Swartz Center for Computational Neuroscience, INC, University of San Diego California, CA, La Jolla, USA. Email: [email protected] Keywords: statistical methods, inference, models, clinical, software, bootstrap, resampling, PCA, ICA Abstract: Statistics represents that body of methods by which characteristics.

Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data.

This book is of value to sociologists, applied and statistical mathematicians, and researchers. Show less Quantitative Sociology: International Perspective on Mathematical and Statistical Modeling presents diverse mathematical modeling procedures involving different strategies for. Researchers who use quantitative research convert their data into numbers for the purpose of analysis, and the numbers are then analyzed by statistical methods.

Recommend this book Email your librarian or administrator to recommend adding this book to your organisation's collection.Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit.Statistical Modeling and Inference for Social Science This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph.D.

students. Focusing on the connection between statistical proce-dures and social science theory, Sean Gailmard develops core statistical.