Statistical Modeling and Inference for Social Science


Author: Sean Gailmard
Publisher: Cambridge University Press
ISBN: 1107003148
Category: Business & Economics
Page: 388
View: 4578
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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.

Statistical Modelling for Social Researchers

Principles and Practice
Author: Roger Tarling
Publisher: Routledge
ISBN: 1134061072
Category: Social Science
Page: 224
View: 2971
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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.

Counterfactuals and Causal Inference


Author: Stephen L. Morgan,Christopher Winship
Publisher: Cambridge University Press
ISBN: 1107065070
Category: Mathematics
Page: 524
View: 989
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This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.

A Mathematics Course for Political and Social Research


Author: Will H. Moore,David A. Siegel
Publisher: Princeton University Press
ISBN: 140084861X
Category: Political Science
Page: 456
View: 3699
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Political science and sociology increasingly rely on mathematical modeling and sophisticated data analysis, and many graduate programs in these fields now require students to take a "math camp" or a semester-long or yearlong course to acquire the necessary skills. Available textbooks are written for mathematics or economics majors, and fail to convey to students of political science and sociology the reasons for learning often-abstract mathematical concepts. A Mathematics Course for Political and Social Research fills this gap, providing both a primer for math novices in the social sciences and a handy reference for seasoned researchers. The book begins with the fundamental building blocks of mathematics and basic algebra, then goes on to cover essential subjects such as calculus in one and more than one variable, including optimization, constrained optimization, and implicit functions; linear algebra, including Markov chains and eigenvectors; and probability. It describes the intermediate steps most other textbooks leave out, features numerous exercises throughout, and grounds all concepts by illustrating their use and importance in political science and sociology. Uniquely designed and ideal for students and researchers in political science and sociology Uses practical examples from political science and sociology Features "Why Do I Care?" sections that explain why concepts are useful Includes numerous exercises Complete online solutions manual (available only to professors, email david.siegel at duke.edu, subject line "Solution Set") Selected solutions available online to students

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
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David A. Freedman presents a definitive synthesis of his approach to statistical modeling and causal inference in the social sciences.

Event History Modeling

A Guide for Social Scientists
Author: Janet M. Box-Steffensmeier,Bradford S. Jones
Publisher: Cambridge University Press
ISBN: 9780521546737
Category: Political Science
Page: 218
View: 5904
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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.

Handbook of Causal Analysis for Social Research


Author: Stephen L. Morgan
Publisher: Springer Science & Business Media
ISBN: 9400760949
Category: Social Science
Page: 424
View: 6246
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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.

Spatial Analysis for the Social Sciences


Author: David Darmofal
Publisher: Cambridge University Press
ISBN: 0521888263
Category: Political Science
Page: 258
View: 369
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This book shows how to model the spatial interactions between actors that are at the heart of the social sciences.

Bayesian Statistics for the Social Sciences


Author: David Kaplan
Publisher: Guilford Publications
ISBN: 1462516513
Category: Psychology
Page: 318
View: 1767
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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.

Theory-Based Data Analysis for the Social Sciences


Author: Carol S. Aneshensel
Publisher: SAGE
ISBN: 1412994357
Category: Social Science
Page: 446
View: 5234
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This book presents a method for bringing data analysis and statistical technique into line with theory. The author begins by describing the elaboration model for analyzing the empirical association between variables. She then introduces a new concept into this model, the focal relationship. Building upon the focal relationship as the cornerstone for all subsequent analysis, two analytic strategies are developed to establish its internal validity: an exclusionary strategy to eliminate alternative explanations, and an inclusive strategy which looks at the interconnected set of relationships predicted by theory. Using real examples of social research, the author demonstrates the use of this approach for two common forms of analysis, multiple linear regression and logistic regression. Whether learning data analysis for the first time or adding new techniques to your repertoire, this book provides an excellent basis for theory-based data analysis.

Computational Social Science

Discovery and Prediction
Author: R. Michael Alvarez
Publisher: Cambridge University Press
ISBN: 1316531287
Category: Political Science
Page: N.A
View: 8163
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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.

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: 8830
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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.

A Mathematical Primer for Social Statistics


Author: John Fox
Publisher: SAGE
ISBN: 1412960800
Category: Mathematics
Page: 170
View: 6476
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Beyond the introductory level, learning and effectively using statistical methods in the social sciences requires some knowledge of mathematics. This handy volume introduces the areas of mathematics that are most important to applied social statistics.

Monte Carlo Simulation and Resampling Methods for Social Science


Author: Thomas M. Carsey,Jeffrey J. Harden
Publisher: SAGE Publications
ISBN: 1483324923
Category: Social Science
Page: 304
View: 4984
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Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, Monte Carlo Simulation and Resampling Methods for Social Science, by Thomas M. Carsey and Jeffrey J. Harden, examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.

Big Data and Social Science

A Practical Guide to Methods and Tools
Author: Ian Foster,Rayid Ghani,Ron S. Jarmin,Frauke Kreuter,Julia Lane
Publisher: CRC Press
ISBN: 1498751431
Category: Mathematics
Page: 376
View: 9305
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Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

Identification Problems in the Social Sciences


Author: Charles F. Manski
Publisher: Harvard University Press
ISBN: 9780674442849
Category: Social Science
Page: 172
View: 429
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The author draws on examples from a range of disciplines to provide social and behavioural scientists with a toolkit for finding bounds when predicting behaviours based upon nonexperimental and experimental data.

Unifying Political Methodology

The Likelihood Theory of Statistical Inference
Author: Gary King
Publisher: University of Michigan Press
ISBN: 9780472085545
Category: Philosophy
Page: 274
View: 5785
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Argues that likelihood theory is a unifying approach to statistical modeling in political science

Bayesian Inference in the Social Sciences


Author: Ivan Jeliazkov,Xin-She Yang
Publisher: John Wiley & Sons
ISBN: 1118771125
Category: Mathematics
Page: 352
View: 8261
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Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.

Bayesian Analysis for the Social Sciences


Author: Simon Jackman
Publisher: John Wiley & Sons
ISBN: 9780470686638
Category: Mathematics
Page: 598
View: 2200
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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.

Learning While Governing

Expertise and Accountability in the Executive Branch
Author: Sean Gailmard,John W. Patty
Publisher: University of Chicago Press
ISBN: 0226924408
Category: Political Science
Page: 321
View: 4669
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Although their leaders and staff are not elected, bureaucratic agencies have the power to make policy decisions that carry the full force of the law. In this groundbreaking book, Sean Gailmard and John W. Patty explore an issue central to political science and public administration: How do Congress and the president ensure that bureaucratic agencies implement their preferred policies? The assumption has long been that bureaucrats bring to their positions expertise, which must then be marshaled to serve the interests of a particular policy. In Learning While Governing, Gailmard and Patty overturn this conventional wisdom, showing instead that much of what bureaucrats need to know to perform effectively is learned on the job. Bureaucratic expertise, they argue, is a function of administrative institutions and interactions with political authorities that collectively create an incentive for bureaucrats to develop expertise. The challenge for elected officials is therefore to provide agencies with the autonomy to do so while making sure they do not stray significantly from the administration’s course. To support this claim, the authors analyze several types of information-management processes. Learning While Governing speaks to an issue with direct bearing on power relations between Congress, the president, and the executive agencies, and it will be a welcome addition to the literature on bureaucratic development.