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| quantitative social science an introduction in tidyverse: Quantitative Social Science Kosuke Imai, Nora Webb Williams, 2022-08-02 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 empirical analysis, showing students how to analyze and interpret data using the tidyverse family of R packages. Data sets taken directly from leading quantitative social science research illustrate how to use data analysis to answer important questions about society and human behavior. Emphasizes hands-on learning, not paper-and-pencil statisticsIncludes data sets from actual research for students to test their skills onCovers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical toolsFeatures a wealth of supplementary exercises, including additional data analysis exercises and programming exercisesOffers a solid foundation for further studyComes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides |
| quantitative social science an introduction in tidyverse: Quantitative Social Science Kosuke Imai, Lori D. Bougher, 2021-03-16 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 an introduction in tidyverse: Quantitative Social Science Kosuke Imai, 2018-02-09 An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics Includes more than forty data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides Looking for a more accessible introduction? Consider Data Analysis for Social Science by Elena Llaudet and Kosuke Imai, which teaches from scratch and step-by-step the fundamentals of survey research, predictive models, and causal inference. It covers descriptive statistics, the difference-in-means estimator, simple linear regression, and multiple linear regression. |
| quantitative social science an introduction in tidyverse: Data Analysis for Social Science Elena Llaudet, Kosuke Imai, 2022-11-29 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 knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors-- |
| quantitative social science an introduction in tidyverse: Thinking Clearly with Data Ethan Bueno de Mesquita, Anthony Fowler, 2021-11-16 An engaging introduction to data science that emphasizes critical thinking over statistical techniques An introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives. Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel. Above all else, Thinking Clearly with Data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking. An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields Introduces the basic toolkit of data analysis—including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity Uses real-world examples and data from a wide variety of subjects Includes practice questions and data exercises |
| quantitative social science an introduction in tidyverse: Data Visualization Kieran Healy, 2018-12-18 An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions |
| quantitative social science an introduction in tidyverse: Colonialism and Landscape Andrew Sluyter, 2002 Spurred by the dramatic landscape transformation associated with European colonization of the Americas, this work creates a prototype theory to explain relationships between colonialism and landscape. |
| quantitative social science an introduction in tidyverse: Humanities Data in R Taylor Arnold, |
| quantitative social science an introduction in tidyverse: Ethics in Cyberspace Thomas Ploug, 2009-04-21 Over the last few decades information and communication technology has come to play an increasingly prominent role in our dealings with other people. Computers, in particular, have made available a host of new ways of interacting, which we have increasingly made use of. In the wake of this development a number of ethical questions have been raised and debated. Ethics in Cyberspace focuses on the consequences for ethical agency of mediating interaction by means of computers, seeking to clarify how the conditions of certain kinds of interaction in cyberspace (for example, in chat-rooms and virtual worlds) differ from the conditions of interaction face-to-face and how these differences may come to affect the behaviour of interacting agents in terms of ethics. |
| quantitative social science an introduction in tidyverse: New Frontier In Evolutionary Algorithms: Theory And Applications Hitoshi Iba, Nasimul Noman, 2011-08-26 This book delivers theoretical and practical knowledge of Genetic Algorithms (GA) for the purpose of practical applications. It provides a methodology for a GA-based search strategy with the integration of several Artificial Life and Artificial Intelligence techniques, such as memetic concepts, swarm intelligence, and foraging strategies. The development of such tools contributes to better optimizing methodologies when addressing tasks from areas such as robotics, financial forecasting, and data mining in bioinformatics.The emphasis of this book is on applicability to the real world. Tasks from application areas - optimization of the trading rule in foreign exchange (FX) and stock prices, economic load dispatch in power system, exit/door placement for evacuation planning, and gene regulatory network inference in bioinformatics - are studied, and the resultant empirical investigations demonstrate how successful the proposed approaches are when solving real-world tasks of great importance. |
| quantitative social science an introduction in tidyverse: Data Science Tiffany Timbers, Trevor Campbell, Melissa Lee, 2022-07-15 Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course. |
| quantitative social science an introduction in tidyverse: R for Political Data Science Francisco Urdinez, Andres Cruz, 2020-11-18 R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse, the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package, including all data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions. |
| quantitative social science an introduction in tidyverse: Political Economy for Public Policy Ethan Bueno de Mesquita, 2016-09-06 The ideal introductory textbook to the politics of the policymaking process This textbook uses modern political economy to introduce students of political science, government, economics, and public policy to the politics of the policymaking process. The book's distinct political economy approach has two virtues. By developing general principles for thinking about policymaking, it can be applied across a range of issue areas. It also unifies the policy curriculum, offering coherence to standard methods for teaching economics and statistics, and drawing connections between fields. The book begins by exploring the normative foundations of policymaking—political theory, social choice theory, and the Paretian and utilitarian underpinnings of policy analysis. It then introduces game theoretic models of social dilemmas—externalities, coordination problems, and commitment problems—that create opportunities for policy to improve social welfare. Finally, it shows how the political process creates technological and incentive constraints on government that shape policy outcomes. Throughout, concepts and models are illustrated and reinforced with discussions of empirical evidence and case studies. This textbook is essential for all students of public policy and for anyone interested in the most current methods influencing policymaking today. Comprehensive approach to politics and policy suitable for advanced undergraduates and graduate students Models unify policy curriculum through methodological coherence Exercises at the end of every chapter Self-contained appendices cover necessary game theory Extensive discussion of cases and applications |
| quantitative social science an introduction in tidyverse: Othermindedness Michael Joyce, 2001 Meditations on network culture, hypertext, the geography of cyberspace, and interactive film |
| quantitative social science an introduction in tidyverse: Quantitative Economics with R Vikram Dayal, 2020-02-03 This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills. |
| quantitative social science an introduction in tidyverse: Introduction to Public Policy Charles Wheelan, 2010-01-26 The first introductory public policy text with a strong economics perspective. A new textbook by Charles Wheelan, author of Naked Economics, Introduction to Public Policy uses economic principles to demonstrate that sound public policy occurs when unfettered private markets provide the greatest good for the greatest number. Only when it does not do this is government intervention needed. |
| quantitative social science an introduction in tidyverse: Statistics for Linguists: An Introduction Using R Bodo Winter, 2019-10-30 Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields, including Psychology, Cognitive Science, and Data Science. |
| quantitative social science an introduction in tidyverse: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice. |
| quantitative social science an introduction in tidyverse: Reproducible Research with R and RStudio Christopher Gandrud, 2020-02-21 Praise for previous editions: Gandrud has written a great outline of how a fully reproducible research project should look from start to finish, with brief explanations of each tool that he uses along the way... Advanced undergraduate students in mathematics, statistics, and similar fields as well as students just beginning their graduate studies would benefit the most from reading this book. Many more experienced R users or second-year graduate students might find themselves thinking, ‘I wish I’d read this book at the start of my studies, when I was first learning R!’...This book could be used as the main text for a class on reproducible research ... (The American Statistician) Reproducible Research with R and R Studio, Third Edition brings together the skills and tools needed for doing and presenting computational research. Using straightforward examples, the book takes you through an entire reproducible research workflow. This practical workflow enables you to gather and analyze data as well as dynamically present results in print and on the web. Supplementary materials and example are available on the author’s website. New to the Third Edition Updated package recommendations, examples, URLs, and removed technologies no longer in regular use. More advanced R Markdown (and less LaTeX) in discussions of markup languages and examples. Stronger focus on reproducible working directory tools. Updated discussion of cloud storage services and persistent reproducible material citation. Added discussion of Jupyter notebooks and reproducible practices in industry. Examples of data manipulation with Tidyverse tibbles (in addition to standard data frames) and pivot_longer() and pivot_wider() functions for pivoting data. Features Incorporates the most important advances that have been developed since the editions were published Describes a complete reproducible research workflow, from data gathering to the presentation of results Shows how to automatically generate tables and figures using R Includes instructions on formatting a presentation document via markup languages Discusses cloud storage and versioning services, particularly Github Explains how to use Unix-like shell programs for working with large research projects |
| quantitative social science an introduction in tidyverse: Development Research in Practice Kristoffer Bjärkefur, Luíza Cardoso de Andrade, Benjamin Daniels, Maria Ruth Jones, 2021-07-16 Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University |
| quantitative social science an introduction in tidyverse: International Trade Theory and Policy Giancarlo Gandolfo, 2013-08-13 In the present text the author deals with both conventional and new approaches to trade theory and policy, treating all important research topics in international economics and clarifying their mathematical intricacies. The textbook is intended for undergraduates, graduates and researchers alike. It addresses undergraduate students with extremely clear language and illustrations, making even the most complex trade models accessible. In the appendices, graduate students and researchers will find self-contained treatments in mathematical terms. The new edition has been thoroughly revised and updated to reflect the latest research on international trade. |
| quantitative social science an introduction in tidyverse: Learning Statistics with R Daniel Navarro, 2013-01-13 Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com |
| quantitative social science an introduction in tidyverse: Data Science with R for Psychologists and Healthcare Professionals Christian Ryan, 2021-12-23 This introduction to R for students of psychology and health sciences aims to fast-track the reader through some of the most difficult aspects of learning to do data analysis and statistics. It demonstrates the benefits for reproducibility and reliability of using a programming language over commercial software packages such as SPSS. The early chapters build at a gentle pace, to give the reader confidence in moving from a point-and-click software environment, to the more robust and reliable world of statistical coding. This is a thoroughly modern and up-to-date approach using RStudio and the tidyverse. A range of R packages relevant to psychological research are discussed in detail. A great deal of research in the health sciences concerns questionnaire data, which may require recoding, aggregation and transformation before quantitative techniques and statistical analysis can be applied. R offers many useful and transparent functions to process data and check psychometric properties. These are illustrated in detail, along with a wide range of tools R affords for data visualisation. Many introductory statistics books for the health sciences rely on toy examples - in contrast, this book benefits from utilising open datasets from published psychological studies, to both motivate and demonstrate the transition from data manipulation and analysis to published report. R Markdown is becoming the preferred method for communicating in the open science community. This book also covers the detail of how to integrate the use of R Markdown documents into the research workflow and how to use these in preparing manuscripts for publication, adhering to the latest APA style guidelines. |
| quantitative social science an introduction in tidyverse: Conversational Coherence Robert T. Craig, Karen Tracy, 1983-10-01 Fourteen contributors present different approaches to conversational analysis. Their analyses of the B-K conversation, which appears as an appendix, will present a challenge to the reader who enjoys weighing the relative merits of each, and who seeks creative solutions to the problems of face-to-face interactions. The book also serves a useful function in getting an extensively transcribed and analyzed conversation into the public domain. `...an excellent resource for scholars interested in the growing area of conversation and coherence as a part of the total study of human communication.' -- Journal of Communication, Winter 1985 |
| quantitative social science an introduction in tidyverse: Markets in the Making Michel Callon, 2021-12-07 Slicing through blunt theories of supply and demand, Callon presents a rigorously researched but counterintuitive model of how everyday market activity gets produced. If you’re convinced you know what a market is, think again. In his long-awaited study, French sociologist and engineer Michel Callon takes us to the heart of markets, to the unsung processes that allow innovations to become robust products and services. Markets in the Making begins with the observation that stable commercial transactions are more enigmatic, more elusive, and more involved than previously described by economic theory. Slicing through blunt theories of supply and demand, Callon presents a rigorously researched but counterintuitive model of market activity that emphasizes what people designing products or launching startups soon discover—the inherent difficulties of connecting individuals to things. Callon’s model is founded upon the notion of “singularization,” the premise that goods and services must adapt and be adapted to the local milieu of every individual whose life they enter. Person by person, thing by thing, Callon demonstrates that for ordinary economic transactions to emerge en masse, singular connections must be made. Pushing us to see markets as more than abstract interfaces where pools of anonymous buyers and sellers meet, Callon draws our attention to the exhaustively creative practices that market professionals continuously devise to entangle people and things. Markets in the Making exemplifies how prototypes, fragile curiosities that have only just been imagined, are gradually honed into predictable objects and practices. Once these are active enough to create a desired effect, yet passive enough to be transferred from one place to another without disruption, they will have successfully achieved the status of “goods” or “services.” The output of this more ample process of innovation, as redefined by Callon, is what we recognize as “the market”—commercial activity, at scale. The capstone of an influential research career at the forefront of science and technology studies, Markets in the Making coherently integrates the empirical perspective of product engineering with the values of the social sciences. After masterfully redescribing how markets are made, Callon culminates with a strong empirical argument for why markets can and should be harnessed to enact social change. His is a theory of markets that serves social critique. |
| quantitative social science an introduction in tidyverse: Applied Statistics Using R Mehmet Mehmetoglu, Matthias Mittner, 2021-11-13 Drawing on real world data to showcase different techniques, this practical book helps you use R for data analysis in your own research. |
| quantitative social science an introduction in tidyverse: Perspectives in Sustainable Equity Investing Guillaume Coqueret, 2022-03-08 Sustainable investing has recently gained traction throughout the world. This trend has multiple sources, which span from genuine ethical concerns to hopes of performance boosting, and also encompass risk mitigation. The resulting appetite for green assets is impacting the decisions of many investors. Perspectives in Sustainable Equity Investing is an up-to-date review of the academic literature on sustainable equity investing. It covers more than 800 academic sources grouped into six thematic chapters. Designed for corporate sustainability and financial management professionals, this is an ideal reference for ESG-driven financiers (both retail and institutional). Students majoring in finance or economics with some background or interest in ESG concerns would also find this compact overview useful. Key Features: Introduces the reader to terms and nomenclature used in the field. Surveys the link between sustainability and performance (including risk). Details the integration of sustainable criteria in complex portfolio optimization. Reviews the financial liabilities induced by climate change. |
| quantitative social science an introduction in tidyverse: Research Design John W. Creswell, 2014 The bestseller that pioneered the comparison of qualitative, quantitative, and mixed methods research design continues in its Fourth Edition to help students and researchers prepare their plan or proposal for a scholarly journal article, dissertation or thesis. |
| quantitative social science an introduction in tidyverse: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| quantitative social science an introduction in tidyverse: American Foreign Policy and Political Ambition James Lee Ray, 2013-08-20 In his eagerly-awaited second edition of American Foreign Policy and Political Ambition, James Ray revisits his deceptively simple premise that the highest priority of leaders is to stay in power. Looking at how political ambition and domestic pressures impact foreign policymaking is the key to understanding how and why foreign policy decisions are made. The text begins by using this analytic approach to look at the history of foreign policymaking and then examines how various parties inside and outside government influence decision making. In a unique third section, the book takes a regional approach, not only covering trends other books tend to miss, but giving students the opportunity to think comprehensively about how issues intersect around the globe—from human security and democratization, to globalization and pollution. Guided by input from adopters and reviewers, Ray has thoroughly re-organized the book and streamlined some coverage to better consolidate the historical, institutional, regional, and topical chapters and focus the thematic lens of the book. Ray has also brought the book fully up-to-date, addressing the latest events in American foreign policy, including the wars in Afghanistan and Iraq, the killing of Bin Laden, the WikiLeaks scandal and its aftermath, the impact of social media on foreign policy and world affairs, nuclear proliferation, developments in U.S.-Russian relations, climate change, and more. |
| quantitative social science an introduction in tidyverse: Hate in the Homeland Cynthia Miller-Idriss, 2022-01-11 A startling look at the unexpected places where violent hate groups recruit young people Hate crimes. Misinformation and conspiracy theories. Foiled white-supremacist plots. The signs of growing far-right extremism are all around us, and communities across America and around the globe are struggling to understand how so many people are being radicalized and why they are increasingly attracted to violent movements. Hate in the Homeland shows how tomorrow's far-right nationalists are being recruited in surprising places, from college campuses and mixed martial arts gyms to clothing stores, online gaming chat rooms, and YouTube cooking channels. Instead of focusing on the how and why of far-right radicalization, Cynthia Miller-Idriss seeks answers in the physical and virtual spaces where hate is cultivated. Where does the far right do its recruiting? When do young people encounter extremist messaging in their everyday lives? Miller-Idriss shows how far-right groups are swelling their ranks and developing their cultural, intellectual, and financial capacities in a variety of mainstream settings. She demonstrates how young people on the margins of our communities are targeted in these settings, and how the path to radicalization is a nuanced process of moving in and out of far-right scenes throughout adolescence and adulthood. Hate in the Homeland is essential for understanding the tactics and underlying ideas of modern far-right extremism. This eye-opening book takes readers into the mainstream places and spaces where today's far right is engaging and ensnaring young people, and reveals innovative strategies we can use to combat extremist radicalization. |
| quantitative social science an introduction in tidyverse: The Government of Emergency Stephen J. Collier, Andrew Lakoff, 2021-11-30 In the middle decades of the twentieth century, in the wake of economic depression, war, and in the midst of the Cold War, an array of technical experts and government officials developed a substantial body of expertise to contain and manage the disruptions to American society caused by unprecedented threats. Today the tools invented by these mid-twentieth century administrative reformers are largely taken for granted, assimilated into the everyday workings of government. As Stephen Collier and Andrew Lakoff argue in this book, the American government's current practices of disaster management can be traced back to this era. Collier and Lakoff argue that an understanding of the history of this initial formation of the emergency state is essential to an appreciation of the distinctive ways that the U.S. government deals with crises and emergencies-or fails to deal with them-today. This book focuses on historical episodes in emergency or disaster planning and management. Some of these episodes are well-known and have often been studied, while others are little-remembered today. The significance of these planners and managers is not that they were responsible for momentous technical innovations or that all their schemes were realized successfully. Their true significance lies in the fact that they formulated a way of understanding and governing emergencies that has come to be taken for granted-- |
| quantitative social science an introduction in tidyverse: New Statistics with R Andy Hector, 2015 An introductory level text covering linear, generalized linear, linear mixed-effects, and generalized mixed models implemented in R and set within a contemporary framework. |
| quantitative social science an introduction in tidyverse: Regression and Other Stories Andrew Gelman, Jennifer Hill, Aki Vehtari, 2021 A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference. |
| quantitative social science an introduction in tidyverse: Images as Data for Social Science Research Nora Webb Williams, Andreu Casas, John D. Wilkerson, 2020-08-31 Images play a crucial role in shaping and reflecting political life. Digitization has vastly increased the presence of such images in daily life, creating valuable new research opportunities for social scientists. We show how recent innovations in computer vision methods can substantially lower the costs of using images as data. We introduce readers to the deep learning algorithms commonly used for object recognition, facial recognition, and visual sentiment analysis. We then provide guidance and specific instructions for scholars interested in using these methods in their own research. |
| quantitative social science an introduction in tidyverse: Introduction to Small Area Estimation Techniques Asian Development Bank, 2020-05-01 This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways. It explains small area estimation techniques with examples of how the easily accessible R analytical platform can be used to implement them, particularly to estimate indicators on poverty, employment, and health outcomes. The guide is intended for staff of national statistics offices and for other development practitioners. It aims to help them to develop and implement targeted socioeconomic policies to ensure that the vulnerable segments of societies are not left behind, and to monitor progress toward the Sustainable Development Goals. |
| quantitative social science an introduction in tidyverse: Text as Data Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart, 2022-03-29 A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry |
| quantitative social science an introduction in tidyverse: Text Analysis with R Matthew L. Jockers, Rosamond Thalken, 2020-03-30 Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R. R is an extremely popular programming language, used throughout the sciences; due to its accessibility, R is now used increasingly in other research areas. In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of computational text analysis at both the micro and the macro scale. Each chapter builds on its predecessor as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying. Text Analysis with R is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological toolkit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that readers simply cannot gather using traditional qualitative methods of close reading and human synthesis. This new edition features two new chapters: one that introduces dplyr and tidyr in the context of parsing and analyzing dramatic texts to extract speaker and receiver data, and one on sentiment analysis using the syuzhet package. It is also filled with updated material in every chapter to integrate new developments in the field, current practices in R style, and the use of more efficient algorithms. |
| quantitative social science an introduction in tidyverse: An Introduction to the American Legal System John M. Scheb (II), Hemant Sharma, 2015 The Fourth Edition of An Introduction to the American Legal System provides both historical context and thoroughly up-to-date coverage of all aspects of American law and the legal system. Vivid examples, on-point case summaries, and hot-button issues make this text an obvious choice for paralegal, criminal justice, political science, or legal studies courses. Key New Features Cases in Point that concisely illustrate how the law applies in the real world Questions for discussion in every chapter that point to high-interest issues for debate Discussions of recent U.S. Supreme Court decisions such as the Obamacare decisions, the Defense of Marriage Act decision, and key rulings on recess appointments and First Amendment Rights Contemporary topical coverage, such as the national security legislation and whistleblowers Updated discussions of justifiable use of force, intellectual property, abortion rights, capital punishment, and affirmative action A well-crafted design that includes learning objectives and chapter outlines A convenient Glossary of Legal Terms and The Constitution of the United States of America in the Appendices |
| quantitative social science an introduction in tidyverse: Microeconomics Glenn Hubbard, Anthony O'Brien, 2018-01-08 The relevance of microeconomics shown through real-world business examples. One of the challenges of teaching principles of microeconomics is fostering interest in concepts that may not seem applicable to students' lives. Microeconomics makes this topic relevant by demonstrating how real businesses use microeconomics to make decisions every day. With ever-changing US and world economies, the 7th Edition has been updated with the latest developments using new real-world business and policy examples. Regardless of their future career path -- opening an art studio, trading on Wall Street, or bartending at the local pub, students will benefit from understanding the economic forces behind their work. |
Qualitative vs Quantitative Research: What's the Difference?
May 16, 2025 · Quantitative data involves measurable numerical information used to test hypotheses and identify patterns, while qualitative data is descriptive, capturing …
QUANTITATIVE Definition & Meaning - Merriam-Webster
The meaning of QUANTITATIVE is of, relating to, or expressible in terms of quantity. How to use quantitative in a sentence.
Qualitative vs. Quantitative Research | Differences, Examples …
Apr 12, 2019 · Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. This type of research can be used to establish …
What Is Quantitative Research? An Overview and Guidelines
Aug 1, 2024 · In summary, quantitative research offers a structured, objective framework geared for hypothesis testing and generalizable insights, while non …
What is Quantitative Data? [Definition, Examples & FAQ] - Care…
May 11, 2023 · Quantitative data is, quite simply, information that can be quantified. It can be counted or measured, and given a numerical value—such as length in …
Qualitative vs Quantitative Research: What's the Difference?
May 16, 2025 · Quantitative data involves measurable numerical information used to test hypotheses and identify patterns, while qualitative data is descriptive, capturing phenomena …
QUANTITATIVE Definition & Meaning - Merriam-Webster
The meaning of QUANTITATIVE is of, relating to, or expressible in terms of quantity. How to use quantitative in a sentence.
Qualitative vs. Quantitative Research | Differences, Examples
Apr 12, 2019 · Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. This type of research can be used to establish …
What Is Quantitative Research? An Overview and Guidelines
Aug 1, 2024 · In summary, quantitative research offers a structured, objective framework geared for hypothesis testing and generalizable insights, while non-quantitative research provides a …
What is Quantitative Data? [Definition, Examples & FAQ]
May 11, 2023 · Quantitative data is, quite simply, information that can be quantified. It can be counted or measured, and given a numerical value—such as length in centimeters or revenue …
Quantitative research - Wikipedia
Quantitative research is a research strategy that focuses on quantifying the collection and analysis of data. [1] It is formed from a deductive approach where emphasis is placed on the …
Quantitative Research - Methods, Types and Analysis
Mar 26, 2024 · Quantitative research is a research approach that seeks to quantify data and generalize results from a sample to a larger population. It relies on structured data collection …
QUANTITATIVE | English meaning - Cambridge Dictionary
Our employees receive a quantitative rating based on the dollar value of their sales. quantitative analysis/researchPricing anomalies in the market could be picked up with the help of …
Understanding Quantitative Research - methods.sagepub.com
3 days ago · Quantitative research encompasses numerous statistical and analytic approaches, including surveys, experiments, and computational methods. Studies may aim to describe the …
What Is Qualitative vs. Quantitative Study? - National University
Qualitative research focuses on understanding concepts and experiences through non-numerical data like interviews and observations. Quantitative research, on the other hand, measures …
