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An Introduction to Models in the Social Sciences

An Introduction to Models in the Social Sciences Author Charles A. Lave
ISBN-10 0819183814
Release 1993
Pages 421
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What is a model? How do you construct one? What are some common models in the social sciences? How can models be applied in new situations? What makes a model good? Focusing on answers to these and related questions, this multidisciplinary introduction to model building in the social sciences formulates interesting problems that involve students in creative model building and the process of invention. The book describes models of individual choice, exchange, adaptation, and diffusion. Throughout, student participation in analytical thinking is encouraged. Originally published in 1975 by HarperCollins Publishers.



An Introduction to Models in the Social Sciences

An Introduction to Models in the Social Sciences Author Charles A. Lave
ISBN-10 9781461677239
Release 1993-09-27
Pages 432
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What is a model? How do you construct one? What are some common models in the social sciences? How can models be applied in new situations? What makes a model good? Focusing on answers to these and related questions, this multidisciplinary introduction to model building in the social sciences formulates interesting problems that involve students in creative model building and the process of invention.



An introduction to models in the social sciences

An introduction to models in the social sciences Author Charles A. Lave
ISBN-10 STANFORD:36105007534683
Release 1975
Pages 421
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An introduction to models in the social sciences has been writing in one form or another for most of life. You can find so many inspiration from An introduction to models in the social sciences also informative, and entertaining. Click DOWNLOAD or Read Online button to get full An introduction to models in the social sciences book for free.



An Introduction to Mathematical Models in the Social and Life Sciences

An Introduction to Mathematical Models in the Social and Life Sciences Author Michael Olinick
ISBN-10 0201054485
Release 1978
Pages 466
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An Introduction to Mathematical Models in the Social and Life Sciences has been writing in one form or another for most of life. You can find so many inspiration from An Introduction to Mathematical Models in the Social and Life Sciences also informative, and entertaining. Click DOWNLOAD or Read Online button to get full An Introduction to Mathematical Models in the Social and Life Sciences book for free.



Causal Models in the Social Sciences

Causal Models in the Social Sciences Author Hubert M. Blalock
ISBN-10 0202303136
Release 1985
Pages 448
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Causal models are formal theories stating the relationships between precisely defined variables, and have become an indispensable tool of the social scientist. This collection of articles is a course book on the causal modeling approach to theory construction and data analysis. H. M. Blalock, Jr. summarizes the then-current developments in causal model utilization in sociology, political science, economics, and other disciplines. This book provides a comprehensive multidisciplinary picture of the work on causal models. It seeks to address the problem of measurement in the social sciences and to link theory and research through the development of causal models. Organized into five sections (Simple Recursive Models, Path Analysis, Simultaneous Equations Techniques, The Causal Approach to Measurement Error, and Other Complications), this volume contains twenty-seven articles (eight of which were specially commissioned). Each section begins with an introduction explaining the concepts to be covered in the section and links them to the larger subject. It provides a general overview of the theory and application of causal modeling. Blalock argues for the development of theoretical models that can be operationalized and provide verifiable predictions. Many of the discussions of this subject that occur in other literature are too technical for most social scientists and other scholars who lack a strong background in mathematics. This book attempts to integrate a few of the less technical papers written by econometricians such as Koopmans, Wold, Strotz, and Fisher with discussions of causal approaches in the social and biological sciences. This classic text by Blalock is a valuable source of material for those interested in the issue of measurement in the social sciences and the construction of mathematical models.



Computational and Mathematical Modeling in the Social Sciences

Computational and Mathematical Modeling in the Social Sciences Author Scott de Marchi
ISBN-10 0521853621
Release 2005-08-15
Pages 197
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Offers an overview of mathematical modeling concentrating on game theory, statistics and computational modeling.



Agent Based Models

Agent Based Models Author Nigel Gilbert
ISBN-10 9781412949644
Release 2008
Pages 98
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Aimed at readers with minimal experience in computer programming, this brief book provides a theoretical and methodological rationale for using ABM in the social sciences. It goes on to describe some carefully chosen examples from different disciplines, illustrating different approaches to ABM. It concludes with practical advice about how to design and create ABM, a discussion of validation procedures, and some guidelines about publishing articles based on ABM.



Mathematical Models in the Social Sciences

Mathematical Models in the Social Sciences Author John G. Kemeny
ISBN-10 0262610302
Release 1978
Pages 145
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Mathematical Models in the Social Sciences investigates and teaches the formation and analysis of mathematical models with detailed interpretations of the results.



An Introduction to Exponential Random Graph Modeling

An Introduction to Exponential Random Graph Modeling Author Jenine K. Harris
ISBN-10 9781483322056
Release 2013-12-23
Pages 136
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This volume introduces the basic concepts of Exponential Random Graph Modeling (ERGM), gives examples of why it is used, and shows the reader how to conduct basic ERGM analyses in their own research. ERGM is a statistical approach to modeling social network structure that goes beyond the descriptive methods conventionally used in social network analysis. Although it was developed to handle the inherent non-independence of network data, the results of ERGM are interpreted in similar ways to logistic regression, making this a very useful method for examining social systems. Recent advances in statistical software have helped make ERGM accessible to social scientists, but a concise guide to using ERGM has been lacking. An Introduction to Exponential Random Graph Modeling, by Jenine K. Harris, fills that gap, by using examples from public health, and walking the reader through the process of ERGM model-building using R statistical software and the statnet package.



Complexity Theory and the Social Sciences

Complexity Theory and the Social Sciences Author David Byrne
ISBN-10 9781134714735
Release 2002-01-04
Pages 224
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Chaos and complexity are the new buzz words in both science and contemporary society. The ideas they represent have enormous implications for the way we understand and engage with the world. Complexity Theory and the Social Sciences introduces students to the central ideas which surround the chaos/complexity theories. It discusses key concepts before using them as a way of investigating the nature of social research. By applying them to such familiar topics as urban studies, education and health, David Byrne allows readers new to the subject to appreciate the contribution which complexity theory can make to social research and to illuminating the crucial social issues of our day.



Simulation For The Social Scientist

Simulation For The Social Scientist Author Gilbert, Nigel
ISBN-10 0335216005
Release 2005-02-01
Pages 295
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Social sciences -- Simulation methods. Social interaction -- Computer simulation. Social sciences -- Mathematical models. (publisher)



Dynamic Modeling

Dynamic Modeling Author R. Robert Huckfeldt
ISBN-10 OCLC:59673321
Release 1982
Pages
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Dynamic Modeling has been writing in one form or another for most of life. You can find so many inspiration from Dynamic Modeling also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Dynamic Modeling book for free.



An Introduction to Generalized Linear Models

An Introduction to Generalized Linear Models Author George H. Dunteman
ISBN-10 0761920846
Release 2006
Pages 72
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Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts to GLM and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets. The book provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation; includes discussion on checking model adequacy and description on how to use SAS to fit GLM; and describes the connection between survival analysis and GLM. It is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.



Chaos Theory in the Social Sciences

Chaos Theory in the Social Sciences Author L. Douglas Kiel
ISBN-10 0472084720
Release 1997
Pages 349
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Applications of chaos theory in political science, economics, and sociology



Data Mining for the Social Sciences

Data Mining for the Social Sciences Author Paul Attewell
ISBN-10 9780520280977
Release 2015-05-01
Pages 264
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"We live, today, in world of big data. The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and by huge corporations are they fine-tune their advertising strategies. And yet social scientists continue mainly to employ a set of analytical tools developed in an earlier era when data was sparse and difficult to come by. In this timely book, Paul Attewell and David Monaghan provide a simple and accessible introduction to Data Mining geared towards social scientists. They discuss how the data mining approach differs substantially, and in some ways radically, from that of conventional statistical modeling familiar to most social scientists. They demystify data mining, describing the diverse set of techniques that the term covers and discussing the strengths and weaknesses of the various approaches. Finally they give practical demonstrations of how to carry out analyses using data mining tools in a number of statistical software packages. It is the hope of the authors that this book will empower social scientists to consider incorporating data mining methodologies in their analytical toolkits"--Provided by publisher.



Introduction to Computational Social Science

Introduction to Computational Social Science Author Claudio Cioffi-Revilla
ISBN-10 9783319501314
Release 2017-06-22
Pages 618
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This textbook provides a comprehensive and reader-friendly introduction to the field of computational social science (CSS). Presenting a unified treatment, the text examines in detail the four key methodological approaches of automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. This updated new edition has been enhanced with numerous review questions and exercises to test what has been learned, deepen understanding through problem-solving, and to practice writing code to implement ideas. Topics and features: contains more than a thousand questions and exercises, together with a list of acronyms and a glossary; examines the similarities and differences between computers and social systems; presents a focus on automated information extraction; discusses the measurement, scientific laws, and generative theories of social complexity in CSS; reviews the methodology of social simulations, covering both variable- and object-oriented models.



Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

Introduction to Applied Bayesian Statistics and Estimation for Social Scientists Author Scott M. Lynch
ISBN-10 9780387712659
Release 2007-06-30
Pages 359
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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.