Analytical Methods for Social Research

95 折

1.

Causal Mediation Analysis

出版日：2026/01/31 作者：Geoffrey T. Wodtke (University of Chicago); Xiang (Harvard University Zhou Massachusetts) 出版社：Cambridge University Press 裝訂：精裝

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75 折

2.

Formal Models of Domestic Politics

出版日：2021/09/30 作者：Scott Gehlbach 出版社：Cambridge Univ Pr 裝訂：平裝

Formal Models of Domestic Politics offers a unified and accessible approach to canonical and important new models of politics. Intended for political science and economics students who have already taken a course in game theory, this new edition retains the widely appreciated pedagogic approach of the first edition. Coverage has been expanded to include a new chapter on nondemocracy; new material on valance and issue ownership, dynamic veto and legislative bargaining, delegation to leaders by imperfectly informed politicians, and voter competence; and numerous additional exercises. Political economists, comparativists, and Americanists will all find models in the text central to their research interests. This leading graduate textbook assumes no mathematical knowledge beyond basic calculus, with an emphasis placed on clarity of presentation. Political scientists will appreciate the simplification of economic environments to focus on the political logic of models; economists will discov

優惠:Cambridge 書展優惠價： 75 1394

庫存：1

Multiverse Analysis：Computational Methods for Robust Results

95 折

3.

Multiverse Analysis：Computational Methods for Robust Results

出版日：2024/11/30 作者：Cristobal (Cornell University Young New York); Erin (Stanford University Cumberworth California) 出版社：Cambridge University Press 裝訂：平裝

優惠價： 95 1605

無庫存

90 折

4.

Multiverse Analysis：Computational Methods for Robust Results

出版日：2024/11/30 作者：Cristobal (Cornell University Young New York); Erin (Stanford University Cumberworth California) 出版社：Cambridge University Press 裝訂：精裝

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90 折

5.

Formal Models of Domestic Politics

出版日：2021/09/30 作者：Scott Gehlbach 出版社：Cambridge Univ Pr 裝訂：精裝

Formal Models of Domestic Politics offers a unified and accessible approach to canonical and important new models of politics. Intended for political science and economics students who have already taken a course in game theory, this new edition retains the widely appreciated pedagogic approach of the first edition. Coverage has been expanded to include a new chapter on nondemocracy; new material on valance and issue ownership, dynamic veto and legislative bargaining, delegation to leaders by imperfectly informed politicians, and voter competence; and numerous additional exercises. Political economists, comparativists, and Americanists will all find models in the text central to their research interests. This leading graduate textbook assumes no mathematical knowledge beyond basic calculus, with an emphasis placed on clarity of presentation. Political scientists will appreciate the simplification of economic environments to focus on the political logic of models; economists will discov

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90 折

6.

Inferential Network Analysis

出版日：2020/11/30 作者：Skyler J. Cranmer 出版社：Cambridge Univ Pr 裝訂：精裝

This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical formulation of methods, review examples, and real-world applications, as well as provide data and code in the R environment that can be customised. Inferential network analysis transcends fields, and examples from across the social sciences are discussed (from management to electoral politics), which can be adapted and applied to a panorama of research. From scholars to undergraduates, spanning the social, mathematical, computational and physical sciences, readers will be introduced to inferential network models and their extensions. The exponential random graph model and latent space network model are paid particular attention and, fundamentally, the reader is given the tools to independently conduct their own analyses.

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滿額折

7.

Inferential Network Analysis

出版日：2020/11/30 作者：Skyler J. Cranmer 出版社：Cambridge Univ Pr 裝訂：平裝

This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical formulation of methods, review examples, and real-world applications, as well as provide data and code in the R environment that can be customised. Inferential network analysis transcends fields, and examples from across the social sciences are discussed (from management to electoral politics), which can be adapted and applied to a panorama of research. From scholars to undergraduates, spanning the social, mathematical, computational and physical sciences, readers will be introduced to inferential network models and their extensions. The exponential random graph model and latent space network model are paid particular attention and, fundamentally, the reader is given the tools to independently conduct their own analyses.

優惠價： 9 2456

無庫存

90 折

8.

Regression and Other Stories

出版日：2020/08/31 作者：Andrew Gelman 出版社：Cambridge Univ Pr 裝訂：精裝

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

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滿額折

9.

Regression and Other Stories

出版日：2020/08/31 作者：Andrew Gelman 出版社：Cambridge Univ Pr 裝訂：平裝

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

優惠價： 9 2047

無庫存

滿額折

10.

Maximum Likelihood for Social Science

出版日：2018/11/30 作者：Michael D. Ward 出版社：Cambridge Univ Pr 裝訂：平裝

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.

優惠價： 9 1637

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Maximum Likelihood for Social Science ― Strategies for Analysis

90 折

11.

Maximum Likelihood for Social Science ― Strategies for Analysis

出版日：2018/11/22 作者：Michael D. Ward 出版社：Cambridge Univ Pr 裝訂：精裝

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.

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Statistical Modeling and Inference for Social Science

滿額折

12.

Statistical Modeling and Inference for Social Science

出版日：2017/04/06 作者：Sean Gailmard 出版社：Cambridge Univ Pr 裝訂：平裝

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 will also gain the ability to create, read and critique statistical applications in their fields of inte

優惠價： 9 1345

無庫存

Computational Social Science ─ Discovery and Prediction

90 折

13.

Computational Social Science ─ Discovery and Prediction

出版日：2016/01/31 作者：R. Michael Alvarez 出版社：Cambridge Univ Pr 裝訂：精裝

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.

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滿額折

14.

Computational Social Science ─ Discovery and Prediction

出版日：2016/01/31 作者：R. Michael Alvarez 出版社：Cambridge Univ Pr 裝訂：平裝

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.

優惠價： 9 1520

無庫存

15.

Spatial Analysis for the Social Sciences

出版日：2015/10/31 作者：David Darmofal 出版社：Cambridge Univ Pr 裝訂：精裝

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滿額折

16.

Spatial Analysis for the Social Sciences

出版日：2015/10/31 作者：David Darmofal 出版社：Cambridge Univ Pr 裝訂：平裝

"Many theories in the social sciences predict spatial dependence or the similarity of behaviors at neighboring locations. Spatial Analysis for the Social Sciences demonstrates how researchers can diag

優惠價： 1 2144

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Counterfactuals and Causal Inference ― Methods and Principles for Social Research

90 折

17.

Counterfactuals and Causal Inference ― Methods and Principles for Social Research

出版日：2014/11/30 作者：Stephen L. Morgan 出版社：Cambridge Univ Pr 裝訂：精裝

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.

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滿額折

18.

Counterfactuals and Causal Inference ─ Methods and Principles for Social Research

出版日：2014/11/30 作者：Stephen L. Morgan 出版社：Cambridge Univ Pr 裝訂：平裝

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.

優惠價： 9 1754

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Time Series Analysis for the Social Sciences

滿額折

19.

Time Series Analysis for the Social Sciences

出版日：2014/08/31 作者：Janet M. Box-Steffensmeier; John R. Freeman; Jon C. W. Pevehouse; Matthew Perry Hitt 出版社：Cambridge Univ Pr 裝訂：平裝

優惠價： 1 2144

無庫存

20.

Time Series Analysis for the Social Sciences

出版日：2014/08/31 作者：Janet M. Box-Steffensmeier; John R. Freeman; Jon C. W. Pevehouse; Matthew Perry Hitt 出版社：Cambridge Univ Pr 裝訂：精裝

"Time-series, or longitudinal, data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time-series properties of their data as a nuisance rather than a substantively meanin

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滿額折

21.

Statistical Modeling and Inference for Social Science

出版日：2014/05/31 作者：Sean Gailmard 出版社：Cambridge Univ Pr 裝訂：精裝

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 will also gain the ability to create, read and critique statistical applications in their fields of inte

優惠價： 9 2483

無庫存

90 折

22.

Formal Models of Domestic Politics

出版日：2013/04/30 作者：Scott Gehlbach 出版社：Cambridge Univ Pr 裝訂：平裝

Formal Models of Domestic Politics offers the first unified and accessible treatment of canonical and important new formal models of domestic politics. Intended for students in political science and e

優惠價： 9 1080

無庫存

90 折

23.

Counterfactuals and Causal Inference：Methods and Principles for Social Research

出版日：2007/07/30 作者：Christopher Winship Stephen L. Morgan 出版社：Cambridge University Press 裝訂：平裝

In this book, the essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics. The importan

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90 折

24.

Counterfactuals and Causal Inference ─ Methods and Principles for Social Research

出版日：2007/07/30 作者：Stephen L. Morgan; Christopher Winship 出版社：Cambridge University Press 裝訂：平裝

Did mandatory busing programs in the 1970s increase the school achievement of disadvantaged minority youth? Does obtaining a college degree increase an individual's labor market earnings? Did the use

優惠價： 9 1296

無庫存

90 折

25.

Political Game Theory：An Introduction

出版日：2007/01/08 作者：Nolan McCarty 出版社：Cambridge Univ Pr 裝訂：精裝

Political Game Theory is a self-contained introduction to game theory and its applications to political science. The book presents choice theory, social choice theory, static and dynamic games of complete information, static and dynamic games of incomplete information, repeated games, bargaining theory, mechanism design and a mathematical appendix covering, logic, real analysis, calculus and probability theory. The methods employed have many applications in various disciplines including comparative politics, international relations and American politics. Political Game Theory is tailored to students without extensive backgrounds in mathematics, and traditional economics, however there are also many special sections that present technical material that will appeal to more advanced students. A large number of exercises are also provided to practice the skills and techniques discussed.

優惠價： 9 3041

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Data Analysis Using Regression and Multilevel/Hierarchical Models

90 折

26.

出版日：2004/09/27 作者：Gary King 出版社：Cambridge Univ Pr 裝訂：精裝

Drawing upon the explosion of research in the field, a diverse group of scholars surveys strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays, first published in 2004, offers many important contributions to the study of ecological inference.

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90 折

33.

Ecological Inference：New Methodological Strategies

出版日：2004/09/13 作者：Gary King 出版社：Cambridge Univ Pr 裝訂：平裝

Drawing upon the explosion of research in the field, a diverse group of scholars surveys strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays, first published in 2004, offers many important contributions to the study of ecological inference.

優惠價： 9 1813

無庫存

Event History Modeling：A Guide for Social Scientists

90 折

34.

Event History Modeling：A Guide for Social Scientists

出版日：2004/04/05 作者：Janet M. Box-Steffensmeier 出版社：Cambridge Univ Pr 裝訂：精裝

Event History Modeling, first published in 2004, provides an accessible guide to event history analysis for researchers and advanced students in the social sciences. The substantive focus of many social science research problems leads directly to the consideration of duration models, and many problems would be better analyzed by using these longitudinal methods to take into account not only whether the event happened, but when. The foundational principles of event history analysis are discussed and ample examples are estimated and interpreted using standard statistical packages, such as STATA and S-Plus. Critical innovations in diagnostics are discussed, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. The treatment of complicated events includes coverage of unobserved heterogeneity, repeated events, and competing risks models. The authors point out common problems in the analysis of time-to-event data in the social sciences and make

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新基本小六法(2月最新版)

Analytical Methods for Social Research

Causal Mediation Analysis

Formal Models of Domestic Politics

Multiverse Analysis：Computational Methods for Robust Results

Multiverse Analysis：Computational Methods for Robust Results

Formal Models of Domestic Politics

Inferential Network Analysis

Inferential Network Analysis

Regression and Other Stories

Regression and Other Stories

Maximum Likelihood for Social Science

Maximum Likelihood for Social Science ― Strategies for Analysis

Statistical Modeling and Inference for Social Science

Computational Social Science ─ Discovery and Prediction

Computational Social Science ─ Discovery and Prediction

Spatial Analysis for the Social Sciences

Spatial Analysis for the Social Sciences

Counterfactuals and Causal Inference ― Methods and Principles for Social Research

Counterfactuals and Causal Inference ─ Methods and Principles for Social Research

Time Series Analysis for the Social Sciences

Time Series Analysis for the Social Sciences

Statistical Modeling and Inference for Social Science

Formal Models of Domestic Politics

Counterfactuals and Causal Inference：Methods and Principles for Social Research

Counterfactuals and Causal Inference ─ Methods and Principles for Social Research

Political Game Theory：An Introduction

Data Analysis Using Regression and Multilevel/Hierarchical Models

Data Analysis Using Regression And Multilevel/Hierarchical Models

Essential Mathematics for Political and Social Research

Essential Mathematics for Political and Social Research

Spatial Models of Parliamentary Voting

Spatial Models Of Parliamentary Voting

Ecological Inference：New Methodological Strategies

Ecological Inference：New Methodological Strategies

Event History Modeling：A Guide for Social Scientists