The SAGE Handbook of Regression Analysis and Causal Inference PDF Download

Author: Peter Congdon
Publisher: John Wiley & Sons
ISBN: 0470035935
Category : Mathematics
Languages : en
Pages : 596

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Book Description
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology

Author: Henning Best
Publisher: SAGE
ISBN: 1473914388
Category : Social Science
Languages : en
Pages : 577

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Book Description
′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.

Author: J. Scott Long
Publisher: Stata Press
ISBN: 1597180114
Category : Computers
Languages : en
Pages : 559

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Book Description
The goal of the book is to make easier to carry out the computations necessary for the full interpretation of regression nonlinear models for categorical outcomes usign Stata.

Author: Roberto Mariano
Publisher: Cambridge University Press
ISBN: 9780521591126
Category : Business & Economics
Languages : en
Pages : 488

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Book Description
This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.

Author: Peter Congdon
Publisher: John Wiley & Sons
ISBN: 1118895061
Category : Mathematics
Languages : en
Pages : 684

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Book Description
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.

Author: Myoung-jae Lee
Publisher: Springer Science & Business Media
ISBN: 0387688412
Category : Business & Economics
Languages : en
Pages : 789

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Book Description
Up-to-date coverage of most micro-econometric topics; first half parametric, second half semi- (non-) parametric Many empirical examples and tips in applying econometric theories to data Essential ideas and steps shown for most estimators and tests; well-suited for both applied and theoretical readers

Author: David Melamed
Publisher: CRC Press
ISBN: 1000907864
Category : Mathematics
Languages : en
Pages : 239

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Book Description
This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it’s ability to act as a practitioners guide. Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy calculator. Our programs will enable users to derive quantities that they can use in their work Timely- many in the social sciences are currently transitioning to R or are learning it now. Our book will be a useful resource Versatile- we will write functions into an R package that can be applied to all of the regression models we will cover in the book Aesthetically pleasing- one advantage of R relative to other software packages is that graphs are fully customizable. We will leverage this feature to yield high-end graphical displays of results Affordability- R is free. R packages are free. There is no need to purchase site licenses or updates.

Author: Ben Klemens
Publisher: Princeton University Press
ISBN: 1400828740
Category : Mathematics
Languages : en
Pages : 471

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Book Description
Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results. Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date. Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

Author: Nawazuddin Ahmed
Publisher: Taylor & Francis
ISBN: 1000901009
Category : Social Science
Languages : en
Pages : 173

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Book Description
This book analyses the magnitude of the relationship between family background and adult occupational and educational outcomes and provides a comprehensive view of intergenerational mobility in the context of religious and caste dynamics in India. Based on nationally representative data sets, the book tracks educational and occupational mobility experiences of different socio-religious groups in contemporary India. Examining primary and secondary data to comprehend the macro picture and the micro details, the book offers insights into intra-generational occupational mobility and the perceptions and expectations of Muslim households. The book presents a classification of jobs and mobility analysis that is built on solid foundations of stratification theories. Moreover, it identifies data and presents evidence on the neighborhood effects in India. Offering an analysis of intergenerational advancement, this book is aimed at researchers in the field of economics, sociology, labor studies, development studies, minority and subaltern studies as well as those interested in the socio-economic issues of disadvantaged socio-religious groups in India.

Author: Michael Mödl
Publisher: Springer Nature
ISBN: 3658315903
Category : Business & Economics
Languages : en
Pages : 207

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Book Description
Michael Mödl examines the impact and signaling effects of crowd-based start-up financing on subsequent venture capital funding rounds. The digital era has substantially expanded the entrepreneurial financing landscape and crowdfunding is emerging as a novel way for young innovative firms to secure scarce early-stage funding. As the evaluations of venture capitalists and business angels remain to be consequential for securing critical resources in later stages, questions on the interactions between new and traditional forms of venture financing arise. Drawing on choice experimental research designs the author provides causal empirical evidence that while “the crowd” is generally seen as a negative signal, it can generate certain positive signals which increase the likelihood of professional venture investors to consider crowdfunded start-ups for investment. The presented findings yield important implications for capital-seeking entrepreneurs, investors and public policy. Dr. Michael Maximilian Mödl completed his dissertation under supervision of Prof. Dietmar Harhoff, Ph.D. at Ludwig-Maximilians-Universität München and at the Max Planck Institute for Innovation and Competition.