Throughout the world, voters lack access to information about politicians, government performance, and public services. Efforts to remedy these informational deficits are numerous. Yet do informational campaigns influence voter behavior and increase democratic accountability? Through the first project of the Metaketa Initiative, sponsored by the Evidence in Governance and Politics (EGAP) research network, this book aims to address this substantive question and at the same time introduce a new model for cumulative learning that increases coordination among otherwise independent researcher teams. It presents the overall results (using meta-analysis) from six independently conducted but coordinated field experimental studies, the results from each individual study, and the findings from a related evaluation of whether practitioners utilize this information as expected. It also discusses lessons learned from EGAP's efforts to coordinate field experiments, increase replication of theoretically important studies across contexts, and increase the external validity of field experimental research.

An interdisciplinary look at interaction in the standardized survey interview

This volume presents a theoretical and empirical inquiry into the interaction between interviewers and respondents in standardized research interviews. The editors include a range of articles that showcase the perspectives of conversation analysts, ethnomethodologists, and survey methodologists, to gain a more complete picture of interaction in the standardized survey interview than was previously available.

This book is the first to focus solely on the interactional substrate or conversational architecture of interviewing. It offers a range of insights into standardized interviewing as interaction and forms a bridge between survey methodology and the study of interaction and tacit practices. The articles are arranged into four subject groups: theoretical orientations, survey recruitment, interaction during the substantive interview, and interaction and survey data quality. Articles include:

• Interactions in telephone surveys
• Recruitment of respondents
• Respondent laughter
• Interaction coding
• Impact of technology on interaction
• Occasions for interviewer intervention

Standardization and Tacit Knowledge serves as a one-of-a-kind reference for survey methodologists, linguists, and researchers and also as a postgraduate coursebook in survey interviewing.

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.

Distribution-free resampling methods permutation tests, decision trees, and the bootstrap are used today in virtually every research area. A Practitioner s Guide to Resampling for Data Analysis, Data Mining, and Modeling explains how to use the bootstrap to estimate the precision of sample-based estimates and to determine sample size, data permutations to test hypotheses, and the readily-interpreted decision tree to replace arcane regression methods.

Highlights

• Each chapter contains dozens of thought provoking questions, along with applicable R and Stata code
• Methods are illustrated with examples from agriculture, audits, bird migration, clinical trials, epidemiology, image processing, immunology, medicine, microarrays and gene selection
• Lists of commercially available software for the bootstrap, decision trees, and permutation tests are incorporated in the text
• Access to APL, MATLAB, and SC code for many of the routines is provided on the author s website
• The text covers estimation, two-sample and k-sample univariate, and multivariate comparisons of means and variances, sample size determination, categorical data, multiple hypotheses, and model building

Statistics practitioners will find the methods described in the text easy to learn and to apply in a broad range of subject areas from A for Accounting, Agriculture, Anthropology, Aquatic science, Archaeology, Astronomy, and Atmospheric science to V for Virology and Vocational Guidance, and Z for Zoology.

Practitioners and research workers and in the biomedical, engineering and social sciences, as well as advanced students in biology, business, dentistry, medicine, psychology, public health, sociology, and statistics will find an easily-grasped guide to estimation, testing hypotheses and model building.

This book has won the CHOICE Outstanding Academic Title award 2014. A century of education and education reform along with the last three decades of high-stakes testing and accountability reveals a disturbing paradox: Education has a steadfast commitment to testing and grading despite decades of research, theory, and philosophy that reveal the corrosive consequences of both testing and grading within an education system designed to support human agency and democratic principles. This edited volume brings together a collection of essays that confronts the failure of testing and grading and then offers practical and detailed examinations of implementing at the macro and micro levels of education teaching and learning free of the weight of testing and grading. The book explores the historical failure of testing and grading; the theoretical and philosophical arguments against testing and grading; the negative influence of testing and grading on social justice, race, class, and gender; and the role of testing and grading in perpetuating a deficit perspective of children, learning, race, and class. The chapters fall under two broad sections: Part I: "Degrading Learning, Detesting Education: The Failure of High-Stake Accountability in Education" includes essays on the historical, theoretical, and philosophical arguments against testing and grading; Part II: "De-Grading and De-Testing in a Time of High-Stakes Education Reform" presents practical experiments in de-testing and de-grading classrooms for authentic learning experiences.

Leverage the power of Talent Intelligence (TI) to make evidence-informed decisions that drive business performance by using data about people, skills, jobs, business functions and geographies. Improved access to people and business data has created huge opportunities for the HR function. However, simply having access to this data is not enough. HR professionals need to know how to analyse the data, know what questions to ask of it and where and how the insights from the data can add the most value. Talent Intelligence is a practical guide that explains everything HR professionals need to know to achieve this. It outlines what Talent Intelligence (TI) is why it's important, how to use it to improve business results and includes guidance on how HR professionals can build the business case for it. This book also explains how and why talent intelligence is different from workforce planning, sourcing research and standard predictive HR analytics and shows how to assess where in the organization talent intelligence can have the biggest impact and how to demonstrate the results to all stakeholders. Most importantly, this book covers KPIs and metrics for success, short-term and long-term TI goals, an outline of what success looks like and the skills needed for effective Talent Intelligence. It also features case studies from organizations including Philips, Barclays and Kimberly-Clark.

Make any team or business data driven with this practical guide to overcoming common challenges and creating a data culture. Businesses are increasingly focusing on their data and analytics strategy, but a data-driven culture grounded in evidence-based decision making can be difficult to achieve. Be Data Driven outlines a step-by-step roadmap to building a data-driven organization or team, beginning with deciding on outcomes and a strategy before moving onto investing in technology and upskilling where necessary. This practical guide explains what it means to be a data-driven organization and explores which technologies are advancing data and analytics. Crucially, it also examines the most common challenges to becoming data driven, from a foundational skills gap to issues with leadership and strategy and the impact of organizational culture. With case studies of businesses who have successfully used data, Be Data Driven shows managers, leaders and data professionals how to address hurdles, encourage a data culture and become truly data driven.

As a result of the COVID-19 pandemic, medical statistics and public health data have become staples of newsfeeds worldwide, with infection rates, deaths, case fatality and the mysterious R figure featuring regularly. However, we don't all have the statistical background needed to translate this information into knowledge. In this lively account, Stephen Senn explains these statistical phenomena and demonstrates how statistics is essential to making rational decisions about medical care. The second edition has been thoroughly updated to cover developments of the last two decades and includes a new chapter on medical statistical challenges of COVID-19, along with additional material on infectious disease modelling and representation of women in clinical trials. Senn entertains with anecdotes, puzzles and paradoxes, while tackling big themes including: clinical trials and the development of medicines, life tables, vaccines and their risks or lack of them, smoking and lung cancer, and even the power of prayer.

Regulations based on animal diseases, demands for accountability in use of research funds, and pressure for economic justification of animal health regulatory and diagnostic activities call for a comprehensive text dealing with these issues. M.D. Salman answers the call in the book "Animal Disease Surveillance and Survey Systems: Methods and Applications."

"This valuable text addresses" a growing need in veterinary epidemiology and regulatory medicine. With contributions from more than a dozen scientists around the world, Salman offers readers practical information on monitoring, surveillance, control, and eradication of animal disease.

Technologies for conducting an animal disease surveillance program and developing an animal health monitoring system are thoroughly covered. Offering a "recipe book" for these systems by explaining modern techniques, this text emphasizes the fundamentals and principles behind these methods. The book targets epidemiologists and other animal health authorities who work in national, regional, and international programs, but also can be used as a text for professional and postgraduate training. The emphasis on fundamentals ensures that this book will not go out of date and is a valuable addition to regulatory medicine literature.

Value-Driven Data explains how data and business leaders can co-create and deploy data-driven solutions for their organizations. Value-Driven Data explores how organizations can understand their problems and come up with better solutions, aligning data storytelling with business needs. The book reviews the main challenges that plague most data-to-business interactions and offers actionable strategies for effective data value implementation, including methods for tackling obstacles and incentivizing change. Value-Driven Data is supported by tried-and-tested frameworks that can be applied to different contexts and organizations. It features cutting-edge examples relating to digital transformation, data strategy, resolving conflicts of interests, building a data P&L and AI value prediction methodology. Recognizing different types of data value, this book presents tangible methodologies for identifying, capturing, communicating, measuring and deploying data-enabled opportunities. This is essential reading for data specialists, business stakeholders and leaders involved in capturing and executing data value opportunities for organizations and for informing data value strategies.

Be Data Analytical is the book organizations and individuals need to understand how to truly use analytics to turn data into valuable insights and drive smarter decision making. Data needs analytics to turn it into value and for organizations to be truly data-driven, they need to use analytics correctly. However, most organizations do not move beyond the first, most rudimentary stage of analytics. They miss out on the powerful insights and opportunities available with all the four levels of analytics: descriptive, diagnostic, predictive and prescriptive. Be Data Analytical reveals how to supercharge data value through all the four levels of analytics, bringing data to life and enhancing data-driven decision making. Be Data Analytical examines each of these four levels of analytics in-depth: what they are, why they matter, how they can be used strategically and how they can be implemented. The book also explores how individuals and organizations can improve their skills and performance in each of these areas. Written by a global trailblazer in the world of data literacy, the book shows professionals, managers, leaders and organizations how to use analytics for the successful and strategic conversion of data into value, insight and action.

Multivariate data analysis is a central tool whenever several variables need to be considered at the same time. The present book explains a powerful and versatile way to analyse data tables, suitable also for researchers without formal training in statistics.

This method for extracting useful information from data is demonstrated for various types of quality assessment, ranging from human quality perception via industrial quality monitoring to health quality and its molecular basis.

Key features include:

• Minimum mathematics; all technical details in appendix form
• Very accessible style with cartoons, self assessment questions and wide range of practical examples
• All data sets available FREE online on Chemometrics World (http://www.wiley.co.uk/wileychi/chemometrics).

The book is written with ISO certified businesses and laboratories in mind, to enhance Total Quality Management (TQM). As yet there are no clear guidelines for realistic data analysis of quality in complex systems - this volume bridges the gap.

Human error is implicated in nearly all aviation accidents, yet most investigation and prevention programs are not designed around any theoretical framework of human error. Appropriate for all levels of expertise, the book provides the knowledge and tools required to conduct a human error analysis of accidents, regardless of operational setting (i.e. military, commercial, or general aviation). The book contains a complete description of the Human Factors Analysis and Classification System (HFACS), which incorporates James Reason's model of latent and active failures as a foundation. Widely disseminated among military and civilian organizations, HFACS encompasses all aspects of human error, including the conditions of operators and elements of supervisory and organizational failure. It attracts a very broad readership. Specifically, the book serves as the main textbook for a course in aviation accident investigation taught by one of the authors at the University of Illinois. This book will also be used in courses designed for military safety officers and flight surgeons in the U.S. Navy, Army and the Canadian Defense Force, who currently utilize the HFACS system during aviation accident investigations. Additionally, the book has been incorporated into the popular workshop on accident analysis and prevention provided by the authors at several professional conferences world-wide. The book is also targeted for students attending Embry-Riddle Aeronautical University which has satellite campuses throughout the world and offers a course in human factors accident investigation for many of its majors. In addition, the book will be incorporated into courses offered by Transportation Safety International and the Southern California Safety Institute. Finally, this book serves as an excellent reference guide for many safety professionals and investigators already in the field.

The construction industry is vital to any national economy; it is also one of the industries most susceptible to workplace incidents. The unacceptably high rates of incidents in construction have huge socio-economic consequences for the victims, their families and friends, co-workers, employers and society at large. Construction safety researchers have introduced numerous strategies, models and tools through scientific inquiries involving primary data collection and analyses. While these efforts are commendable, there is a huge potential to create new knowledge and predictive models to improve construction safety by utilising already existing data about workplace incidents. In this new book, Imriyas Kamardeen argues that more sophisticated approaches need to be deployed to enable improved analyses of incident data sets and the extraction of more valuable insights, patterns and knowledge to prevent work injuries and illnesses. The book aims to apply data mining and analytic techniques to past workplace incident data to discover patterns that facilitate the development of innovative models and strategies, thereby improving work health, safety and well-being in construction, and curtailing the high rate of incidents. It is essential reading for researchers and professionals in construction, health and safety and anyone interested in data analytics.

Throughout the physical and social sciences, researchers face the challenge of fitting statistical distributions to their data. Although the study of statistical modelling has made great strides in recent years, the number and variety of distributions to choose from-all with their own formulas, tables, diagrams, and general properties-continue to create problems. For a specific application, which of the dozens of distributions should one use? What if none of them fit well?

Fitting Statistical Distributions helps answer those questions. Focusing on techniques used successfully across many fields, the authors present all of the relevant results related to the Generalized Lambda Distribution (GLD), the Generalized Bootstrap (GB), and Monte Carlo simulation (MC). They provide the tables, algorithms, and computer programs needed for fitting continuous probability distributions to data in a wide variety of circumstances-covering bivariate as well as univariate distributions, and including situations where moments do not exist.

Regardless of your specific field-physical science, social science, or statistics, practitioner or theorist-Fitting Statistical Distributions is required reading. It includes wide-ranging applications illustrating the methods in practice and offers proofs of key results for those involved in theoretical development. Without it, you may be using obsolete methods, wasting time, and risking incorrect results.

In the fast moving world of the fourth industrial revolution not everyone needs to be a data scientist but everyone should be data literate, with the ability to read, analyze and communicate with data. It is not enough for a business to have the best data if those using it don't understand the right questions to ask or how to use the information generated to make decisions. Be Data Literate is the essential guide to developing the curiosity, creativity and critical thinking necessary to make anyone data literate, without retraining as a data scientist or statistician. With learnings to show development and real-world examples from industries implementing data literacy skills, this book explains how to confidently read and speak the 'language of data' in the modern business environment and everyday life. Be Data Literate is a practical guide to understanding the four levels of analytics, how to analyze data and the key steps to making smarter, data-informed decisions. Written by a founding pioneer and worldwide leading expert on data literacy, this book empowers professionals with the skills they need to succeed in the digital world.

THE NEW YORK TIMES BESTSELLER

AN ECONOMIST BOOK OF THE YEAR 2017

Insightful, surprising and with ground-breaking revelations about our society, Everybody Lies exposes the secrets embedded in our internet searches, with a foreword by bestselling author Steven Pinker

Everybody lies, to friends, lovers, doctors, pollsters - and to themselves. In Internet searches, however, people confess their secrets - about sexless marriages, mental health problems, even racist views. Seth Stephens-Davidowitz, an economist and former Google data scientist, shows that this could just be the most important dataset ever collected.

This huge database of secrets - unprecedented in human history - offers astonishing, even revolutionary, insights into humankind. Anxiety, for instance, does not increase after a terrorist attack. Crime levels drop when a violent film is released. And racist searches are no higher in Republican areas than in Democrat ones.

Stephens-Davidowitz reveals information we can use to change our culture, and the questions we're afraid to ask that might be essential to our health - both emotional and physical. Insightful, funny, and always surprising, Everybody Lies exposes the biases and secrets embedded deeply within us, at a time when things are harder to predict than ever.

What is the most effective way for a researcher to read and analyze life-story materials? Using a new model for the classification of types of readings, Narrative Research shows readers how to read, analyze and interpret life story materials. The authors first introduce readers to four models of reading and interpreting a text: the holistic-content reading, the holistic-form reading, categorical-content reading, and categorical-form reading. They next present two complete narratives so that readers can compare the author?s interpretations against the actual text as well as try to analyze the stories on their own. The subsequent chapters provide readings, interpretations, and analyses of the narrative data from the four models. Each of the examples is highlighted by sharing with the reader some of the processes of the researcher? work, including various considerations, doubts, self-criticism, and dialogues about the narratives. Readers wanting to learn more about life story research will not only learn the tools for doing this analysis, but will be inspired to contribute their own creativity to this developing field.

Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models. Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy. Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank. There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

Organize, plan, and build an exceptional data analytics team within your organization In Minding the Machines: Building and Leading Data Science and Analytics Teams, AI and analytics strategy expert Jeremy Adamson delivers an accessible and insightful roadmap to structuring and leading a successful analytics team. The book explores the tasks, strategies, methods, and frameworks necessary for an organization beginning their first foray into the analytics space or one that is rebooting its team for the umpteenth time in search of success. In this book, you'll discover: A focus on the three pillars of strategy, process, and people and their role in the iterative and ongoing effort of building an analytics team Repeated emphasis on three guiding principles followed by successful analytics teams: start early, go slow, and fully commit The importance of creating clear goals and objectives when creating a new analytics unit in an organization Perfect for executives, managers, team leads, and other business leaders tasked with structuring and leading a successful analytics team, Minding the Machines is also an indispensable resource for data scientists and analysts who seek to better understand how their individual efforts fit into their team's overall results.

Digital transformation is a vital practice for organizations trying to keep up with competitors, but with new digital approaches constantly promising to revolutionize the workplace it can feel impossible to keep up. Cut through the hype with this accessible guide to making end-to-end digital transformation happen. While technology offers the possibility for business improvement, successful digital transformation also requires an effective strategy, the right culture, change management, the ability to stimulate innovation and the knowledge of where to upskill and where to bring in new talent. The Practical Guide to Digital Transformation covers each of these factors and more by breaking the process down to 17 easy-to-follow and practical steps. Each chapter includes a case study of an organization getting it right, along with advice on putting the principle into action, key tips and tricks, and what you might say in your next meeting. This book also outlines how to start with the foundations of 'doing digital' and build from there, including data science, cyber security, workable technology, minimised stack duplication, data registers and good user experience. Quickly build confidence and make change happen with this actionable guide to the essentials of digital transformation.

Who is most likely to buy and what is the best way to target them? How can I use both consumer analytics and modelling to improve the impact of marketing campaigns? Marketing Analytics takes you step-by-step through these areas and more. Marketing Analytics enables you to leverage predictive techniques to measure and improve marketing performance. By exploring real-world marketing challenges, it provides clear, jargon-free explanations on how to apply different analytical models for each purpose. From targeted list creation and data segmentation, to testing campaign effectiveness, pricing structures and forecasting demand, it offers a complete resource for how statistics, consumer analytics and modelling can be put to optimal use. This revised and updated third edition of Marketing Analytics contains new material on forecasting, customer touchpoints modelling, and a new focus on customer loyalty. With accessible language throughout, methodologies are simplified to ensure the more complex aspects of data and analytics are fully accessible for any level of application. Supported by a glossary of key terms and supporting resources consisting of datasets, presentation slides for each chapter and a test bank of self-test question, this book supplies a concrete foundation for optimizing marketing analytics for day-to-day business advantage.

In recent years, popular media have inundated audiences with sensationalised headlines recounting data breaches, new forms of surveillance and other dangers of our digital age. Despite their regularity, such accounts treat each case as unprecedented and unique. This book proposes a radical rethinking of the history, present and future of our relations with the digital, spatial technologies that increasingly mediate our everyday lives. From smartphones to surveillance cameras, to navigational satellites, these new technologies offer visions of integrated, smooth and efficient societies, even as they directly conflict with the ways users experience them. Recognising the potential for both control and liberation, the authors argue against both acquiescence to and rejection of these technologies. Through intentional use of the very systems that monitor them, activists from Charlottesville to Hong Kong are subverting, resisting and repurposing geographic technologies. Using examples as varied as writings on the first telephones to the experiences of a feminist collective for migrant women in Spain, the authors present a revolution of everyday technologies. In the face of the seemingly inevitable dominance of corporate interests, these technologies allow us to create new spaces of affinity, and a new politics of change.

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

Leverage the power of Talent Intelligence (TI) to make evidence-informed decisions that drive business performance by using data about people, skills, jobs, business functions and geographies. Improved access to people and business data has created huge opportunities for the HR function. However, simply having access to this data is not enough. HR professionals need to know how to analyse the data, know what questions to ask of it and where and how the insights from the data can add the most value. Talent Intelligence is a practical guide that explains everything HR professionals need to know to achieve this. It outlines what Talent Intelligence (TI) is why it's important, how to use it to improve business results and includes guidance on how HR professionals can build the business case for it. This book also explains how and why talent intelligence is different from workforce planning, sourcing research and standard predictive HR analytics and shows how to assess where in the organization talent intelligence can have the biggest impact and how to demonstrate the results to all stakeholders. Most importantly, this book covers KPIs and metrics for success, short-term and long-term TI goals, an outline of what success looks like and the skills needed for effective Talent Intelligence. It also features case studies from organizations including Philips, Barclays and Kimberly-Clark.