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Quantitative Social Science
  • Language: en
  • Pages: 464

Quantitative Social Science

"Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--

Quantitative Social Science
  • Language: en
  • Pages: 478

Quantitative Social Science

A tidyverse edition of the acclaimed textbook on data analysis and statistics for the social sciences and allied fields Quantitative analysis is an essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it. Quantitative Social Science is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, including business, economics, education, political science, psychology, sociology, public policy, and data science. Proven in classrooms around the world, this one-of-a-kind textbook engages directly with empiri...

Data Analysis for Social Science
  • Language: en
  • Pages: 256

Data Analysis for Social Science

"Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior ...

Data Analysis for Social Science
  • Language: en
  • Pages: 256

Data Analysis for Social Science

"Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior ...

Thinking Clearly with Data
  • Language: en
  • Pages: 400

Thinking Clearly with Data

"This is an intro-level text that teaches how to think clearly and conceptually about quantitative information, emphasizing ideas over technicality and assuming no prior exposure to data analysis, statistics, or quantitative methods. The books four parts present the foundation for quantiative reasoning: correlation and causation; statistical relationships; causal phenomena; and incorporating quantitative information into decision making. Within these parts it covers the array of tools used by social scientists, including regression, inference, experiments, research design, and more, all by explaining the rationale and logic behind such tools rather than focusing only on the technical calculations used for each. New concepts are presented simply, with the help of copious examples, and the books leans towards graphic rather than mathematical representation of data, with any technical material included in appendices"--

Handbook of Matching and Weighting Adjustments for Causal Inference
  • Language: en
  • Pages: 634

Handbook of Matching and Weighting Adjustments for Causal Inference

  • Type: Book
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  • Published: 2023-04-11
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  • Publisher: CRC Press

An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.

Quantitative Social Science Data with R
  • Language: en
  • Pages: 462

Quantitative Social Science Data with R

  • Type: Book
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  • Published: 2018-11-24
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  • Publisher: SAGE

"One of the few books that provide an accessible introduction to quantitative data analysis with R. A particular strength of the text is the focus on ′real world′ examples which help students to understand why they are learning these methods." - Dr Roxanne Connelly, University of York Relevant, engaging, and packed with student-focused learning features, this book provides the step-by-step introduction to quantitative research and data every student needs. Gradually introducing applied statistics and R, it uses examples from across the social sciences to show you how to apply abstract statistical and methodological principles to your own work. At a student-friendly pace, it enables you to: - Understand and use quantitative data to answer questions - Approach surrounding ethical issues - Collect quantitative data - Manage, write about, and share the data effectively Supported by incredible digital resources with online tutorials, videos, datasets, and multiple choice questions, this book gives you not only the tools you need to understand statistics, quantitative data, and R software, but also the chance to practice and apply what you have learned.

Divided Armies
  • Language: en
  • Pages: 530

Divided Armies

How do armies fight and what makes them victorious on the modern battlefield? In Divided Armies, Jason Lyall challenges long-standing answers to this classic question by linking the fate of armies to their levels of inequality. Introducing the concept of military inequality, Lyall demonstrates how a state's prewar choices about the citizenship status of ethnic groups within its population determine subsequent battlefield performance. Treating certain ethnic groups as second-class citizens, either by subjecting them to state-sanctioned discrimination or, worse, violence, undermines interethnic trust, fuels grievances, and leads victimized soldiers to subvert military authorities once war begi...

Theory on Gender
  • Language: en
  • Pages: 400

Theory on Gender

How do various social theories explain gender inequality? Are these theories infused with masculinist biases that need to be redressed with insights from feminist theory? To address these questions, this collection of original essays features prominent sociologists discussing the strengths and the limitations of the theoretical traditions within which they have worked. Among the theoretical perspectives included are those of Marxism, world system theory, macrostructural theories, rational choice theory, neofunctionalism, psychoanalysis, ethno-methodology, expectation states theory, poststructuralist symbolic interactionism, and network theory. Each of the chapter-length essays of the first two sections provides an overview of the theory, explains its implications for gender inequality, reviews empirical research, and comments upon sexist biases or other limitations of the perspective. The final section contains chapters on feminist debates over methodology, critical commentaries on the preceding papers by four feminist scholars, and replies by the original authors.

Causal Inference in Statistics, Social, and Biomedical Sciences
  • Language: en
  • Pages: 647

Causal Inference in Statistics, Social, and Biomedical Sciences

This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.