Statistical Modeling and Inference for Social Science


Author: Sean Gailmard
Publisher: Cambridge University Press
ISBN: 1107003148
Category: Business & Economics
Page: 388
View: 5670
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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. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes 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 - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students gain the ability to create, read and critique statistical applications in their fields of interest.

Counterfactuals and Causal Inference

Methods and Principles for Social Research
Author: Stephen L. Morgan,Christopher Winship
Publisher: Cambridge University Press
ISBN: 1316165159
Category: Social Science
Page: N.A
View: 5053
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In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.

Statistical Models and Causal Inference

A Dialogue with the Social Sciences
Author: David A. Freedman,David Collier,Jasjeet S. Sekhon
Publisher: Cambridge University Press
ISBN: 0521195004
Category: Mathematics
Page: 399
View: 8289
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David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.

Theory-Based Data Analysis for the Social Sciences


Author: Carol S Aneshensel
Publisher: Pine Forge Press
ISBN: 9780761987369
Category: Social Science
Page: 254
View: 1151
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The advent of complex and powerful computer-generated statistical models has greatly eroded the former prominence of social theory in data analysis, replacing it with an emphasis on statistical technique. To correct this trend, Carol S. Aneshensel presents a method for bringing data analysis and statistical technique into line with theory.

Statistical Models for Data Analysis


Author: Paolo Giudici,Salvatore Ingrassia,Maurizio Vichi
Publisher: Springer Science & Business Media
ISBN: 3319000322
Category: Mathematics
Page: 419
View: 300
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The papers in this book cover issues related to the development of novel statistical models for the analysis of data. They offer solutions for relevant problems in statistical data analysis and contain the explicit derivation of the proposed models as well as their implementation. The book assembles the selected and refereed proceedings of the biannual conference of the Italian Classification and Data Analysis Group (CLADAG), a section of the Italian Statistical Society. ​

Bayesian Statistics for the Social Sciences


Author: David Kaplan
Publisher: Guilford Publications
ISBN: 1462516513
Category: Psychology
Page: 318
View: 4702
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Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an "evidence-based" framework for the practice of Bayesian statistics. User-Friendly Features *Includes worked-through, substantive examples, using large-scale educational and social science databases, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth). *Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs. *Shows readers how to carefully warrant priors on the basis of empirical data. *Companion website features data and code for the book's examples, plus other resources.

Exponential Random Graph Models for Social Networks

Theory, Methods, and Applications
Author: Dean Lusher,Johan Koskinen,Garry Robins
Publisher: Cambridge University Press
ISBN: 0521193567
Category: Business & Economics
Page: 336
View: 2419
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This book provides an account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs).

Computational Social Science

Discovery and Prediction
Author: R. Michael Alvarez
Publisher: Cambridge University Press
ISBN: 1316531287
Category: Political Science
Page: N.A
View: 7306
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Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy.

Statistical Modelling for Social Researchers

Principles and Practice
Author: Roger Tarling
Publisher: Routledge
ISBN: 1134061072
Category: Social Science
Page: 224
View: 3539
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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.

Dictionary of Statistics & Methodology

A Nontechnical Guide for the Social Sciences
Author: W. Paul Vogt
Publisher: SAGE
ISBN: 9780761988557
Category: Social Science
Page: 355
View: 9485
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A complete sourcebook of simple definitions & explanations of statistical & statistics-related concepts, this book is aimed at providing students with access to the methodological skills of social science research.

Handbook of Causal Analysis for Social Research


Author: Stephen L. Morgan
Publisher: Springer Science & Business Media
ISBN: 9400760949
Category: Social Science
Page: 424
View: 1360
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What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development.

Event History Modeling

A Guide for Social Scientists
Author: Janet M. Box-Steffensmeier,Janet M.. Box-Steffensmeier,Bradford S. Jones
Publisher: Cambridge University Press
ISBN: 9780521546737
Category: Political Science
Page: 218
View: 4839
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Event History Modeling provides an accessible, up-to-date guide to event history analysis for researchers and advanced students in the social sciences. The authors explain the foundational principles of event-history analysis, and analyse numerous examples which they estimate and interpret using standard statistical packages, such as STATA and S-Plus. They review recent and critical innovations in diagnostics, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. They also discuss common problems encountered with time-to-event data, and make recommendations regarding the implementation of duration modeling methods.

Causal Inference for Statistics, Social, and Biomedical Sciences

An Introduction
Author: Guido W. Imbens,Donald B. Rubin
Publisher: Cambridge University Press
ISBN: 1316094391
Category: Mathematics
Page: N.A
View: 1188
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Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

Models for Social Networks With Statistical Applications


Author: Suraj Bandyopadhyay,A R. Rao,Bikas Kumar Sinha
Publisher: SAGE
ISBN: 1412941687
Category: Social Science
Page: 235
View: 6067
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The study of social networks is a new but fast widening multidisciplinary area involving social, mathematical, statistical and computer sciences for application in diverse social environments; in the latter sciences, and specially for the field of Economics. It has its own parameters and methodological tools. In 'Models for Social Networks with Statistical Applications', the authors show how graph-theoretic and statistical techniques can be used to study some important parameters of global social networks and illustrate their use in social science studies with some examples in real life survey data.

Introduction to Applied Bayesian Statistics and Estimation for Social Scientists


Author: Scott M. Lynch
Publisher: Springer Science & Business Media
ISBN: 0387712658
Category: Social Science
Page: 359
View: 3600
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This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.

Handbook of Statistical Modeling for the Social and Behavioral Sciences


Author: G. Arminger,Clifford C. Clogg,M.E. Sobel
Publisher: Springer Science & Business Media
ISBN: 9780306448058
Category: Mathematics
Page: 592
View: 3262
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Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems.

Data Analysis Using Regression and Multilevel/Hierarchical Models


Author: Andrew Gelman,Jennifer Hill
Publisher: Cambridge University Press
ISBN: 9780521686891
Category: Mathematics
Page: 625
View: 4487
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This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

The SAGE Handbook of Quantitative Methodology for the Social Sciences


Author: David Kaplan
Publisher: SAGE
ISBN: 0761923594
Category: Social Science
Page: 511
View: 826
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The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource.

Bayesian Analysis for the Social Sciences


Author: Simon Jackman
Publisher: John Wiley & Sons
ISBN: 9780470686638
Category: Mathematics
Page: 598
View: 2631
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Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS – the most-widely used Bayesian analysis software in the world – and R – an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.

Maximum Likelihood for Social Science

Strategies for Analysis
Author: Michael D. Ward,John S. Ahlquist
Publisher: Cambridge University Press
ISBN: 1316946657
Category: Political Science
Page: N.A
View: 4505
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This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques.