Data Analysis For Politics And Policy

Introduction to data analysis; Predictions and projections: some issues of research design; Two-variable linear regression; Multiple regression.

A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government.

This is a work on "hostile" data and the conditions under which they are accepted and rejected. What is the place of data in politics and organization? Why are politicians and administrators so often hostile to research data, or why do they tend to perceive data as hostile to them? How can data become relevant to policy? These questions are the focus of this book. In answer I try to show how political and administrative institutions cope with "hostile" data; how they seek to maintain closedness to disconfirming data, and how they are led, in a free society, to change their policies despite the epistemological bias in favor of the already known and the initial inclination to resist change. At the same time, I demonstrate that data producers must learn that while their research findings may be subjected to science's own standards of verifiability, such data must also meet standards of contestability by the various interests involved in political and administrative decisions. The production and "appropriate" publication of a research report may at best buy one an admission ticket to participate in political and administrative contests, but not the power nor the justification to determine the outcomes of the contest. I begin with two hypotheses: Hypothesis 1: Politicians or administrators reject data that do not coincide with behavior they are unwilling to change. Hypothesis II: Politicians or administrators change behavior that does not coincide with data they are unwilling to reject.

This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R.

Cover -- Half-title -- Title -- Copyright -- Contents -- Introduction -- PART I: ENGAGING THE DATA -- 1 Measuring Political and Policy Preferences Using Item Response Scaling -- 2 Causal Inference with Observational Data -- 3 Causal Inference with Experimental Data -- PART II: EMERGING DATA SOURCES AND TECHNIQUES -- 4 Descriptive Network Analysis: Interest Group Lobbying Dynamics Around Immigration Policy -- 5 Learning from Place in the Era of Geolocation -- 6 Text Analysis: Estimating Policy Preferences from Written and Spoken Words -- 7 Machine Learning and Governance -- PART III: IMPLICATIONS FOR GOVERNANCE -- 8 Governing a Data-Driven Society -- 9 Big Data and Privacy -- 10 Reflections on Analytics: Knowledge and Power -- List of Contributors -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Y

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

This book presents a theory of information justice that subsumes the question of control and relates it to other issues that influence just social outcomes. ​Data does not exist by nature. Bureaucratic societies must provide standardized inputs for governing algorithms, a problem that can be understood as one of legibility. This requires, though, converting what we know about social objects and actions into data, narrowing the many possible representations of the objects to a definitive one using a series of translations. Information thus exists within a nexus of problems, data, models, and actions that the social actors constructing the data bring to it. This opens information to analysis from social and moral perspectives, while the scientistic view leaves us blind to the gains from such analysis—especially to the ways that embedded values and assumptions promote injustice. Toward Information Justice answers a key question for the 21st Century: how can an information-driven society be just? Many of those concerned with the ethics of data focus on control over data, and argue that if data is only controlled by the right people then just outcomes will emerge. There are serious problems with this control metaparadigm, however, especially related to the initial creation of data and prerequisites for its use. This text is suitable for academics in the fields of information ethics, political theory, philosophy of technology, and science and technology studies, as well as policy professionals who rely on data to reach increasingly problematic conclusions about courses of action.​

Big Data, gathered together and re-analysed, can be used to form endless variations of our persons - so-called ‘data doubles’. Whilst never a precise portrayal of who we are, they unarguably contain glimpses of details about us that, when deployed into various routines (such as management, policing and advertising) can affect us in many ways. How are we to deal with Big Data? When is it beneficial to us? When is it harmful? How might we regulate it? Offering careful and critical analyses, this timely volume aims to broaden well-informed, unprejudiced discourse, focusing on: the tenets of Big Data, the politics of governance and regulation; and Big Data practices, performance and resistance. An interdisciplinary volume, The Politics of Big Data will appeal to undergraduate and postgraduate students, as well as postdoctoral and senior researchers interested in fields such as Technology, Politics and Surveillance.

The SAGE Handbook of Research Methods in Political Science and International Relations offers a comprehensive overview of research processes in social science — from the ideation and design of research projects, through the construction of theoretical arguments, to conceptualization, measurement, & data collection, and quantitative & qualitative empirical analysis — exposited through 65 major new contributions from leading international methodologists. Each chapter surveys, builds upon, and extends the modern state of the art in its area. Following through its six-part organization, undergraduate and graduate students, researchers and practicing academics will be guided through the design, methods, and analysis of issues in Political Science and International Relations: Part One: Formulating Good Research Questions & Designing Good Research Projects Part Two: Methods of Theoretical Argumentation Part Three: Conceptualization & Measurement Part Four: Large-Scale Data Collection & Representation Methods Part Five: Quantitative-Empirical Methods Part Six: Qualitative & "Mixed" Methods