Data Analysis For Politics And Policy

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

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

This book focuses on two key ways to improve the literature surrounding policy analysis. Firstly, it explores the implications of new developments in policy process research, on the role of psychology in communication and the multi-centric nature of policymaking. This is particularly important since policy analysts engage with policymakers who operate in an environment over which they have limited understanding and even less control. Secondly, it incorporates insights from studies of power, co-production, feminism, and decolonisation, to redraw the boundaries of policy-relevant knowledge. These insights help raise new questions and change expectations about the role and impact of policy analysis.

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.

The first available textbook on the rapidly growing and increasingly important field of government analytics This first textbook on the increasingly important field of government analytics provides invaluable knowledge and training for students of government in the synthesis, interpretation, and communication of big data, which is now an integral part of governance and policy making. Integrating all the major components of this rapidly growing field, this invaluable text explores the intricate relationship of data analytics to governance while providing innovative strategies for the retrieval and management of information.

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