Since the early days of performance assessment, human ratings have been subject to various forms of error and bias. Expert raters often come up with different ratings for the very same performance and it seems that assessment outcomes largely depend upon which raters happen to assign the rating. This book provides an introduction to many-facet Rasch measurement (MFRM), a psychometric approach that establishes a coherent framework for drawing reliable, valid, and fair inferences from rater-mediated assessments, thus answering the problem of fallible human ratings. Revised and updated throughout, the Second Edition includes a stronger focus on the Facets computer program, emphasizing the pivotal role that MFRM plays for validating the interpretations and uses of assessment outcomes.

This book occupies a unique position in the field of statistical analysis in the behavioural and social sciences in that it targets learners who would benefit from learning more conceptually and less computationally about statistical procedures and the software packages that can be used to implement them. This book provides a comprehensive overview of this important research skill domain with an emphasis on visual support for learning and better understanding. The primary focus is on fundamental concepts, procedures and interpretations of statistical analyses within a single broad illustrative research context. The book covers a wide range of descriptive, correlational and inferential statistical procedures as well as more advanced procedures not typically covered in introductory and intermediate statistical texts. It is an ideal reference for postgraduate students as well as for researchers seeking to broaden their conceptual exposure to what is possible in statistical analysis.

The importance of data analytics is well known, but how can you get end users to engage with analytics and business intelligence (BI) when adoption of new technology can be frustratingly slow or may not happen at all? Avoid wasting time on dashboards and reports that no one uses with this practical guide to increasing analytics adoption by focusing on people and process, not technology. Pulling together agile, UX and change management principles, Delivering Data Analytics outlines a step-by-step, technology agnostic process designed to shift the organizational data culture and gain buy-in from users and stakeholders at every stage of the project. This book outlines how to succeed and build trust with stakeholders amid the politics, ambiguity and lack of engagement in business. With case studies, templates, checklists and scripts based on the author's considerable experience in analytics and data visualisation, this book covers the full cycle from requirements gathering and data assessment to training and launch. Ensure lasting adoption, trust and, most importantly, actionable business value with this roadmap to creating user-centric analytics projects.

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.

Survey data are used in many disciplines including Social Sciences, Economics and Psychology. Interviewers' behaviour might affect the quality of such data. This book presents the results of new research on interviewers' motivation and behaviour. A substantial number of contributions address deviant behaviour, methods for assessing the impact of such behaviour on data quality and tools for detecting faked interviews. Further chapters discuss methods for preventing undesirable interviewer effects. Apart from specific methodological contributions, the chapters of the book also provide a unique collection of examples of deviant behaviour and its detection - a topic not overly present in literature despite its substantial prevalence in survey field work. The volume includes 13 peer reviewed papers presented at an international workshop in Rauischholzhausen in October 2011.

What is Big Data, and why should you care? Big data knows where you've been and who your friends are. It knows what you like and what makes you angry. It can predict what you'll buy, where you'll be the victim of crime and when you'll have a heart attack. Big data knows you better than you know yourself, or so it claims. But how well do you know big data? You've probably seen the phrase in newspaper headlines, at work in a marketing meeting, or on a fitness-tracking gadget. But can you understand it without being a Silicon Valley nerd who writes computer programs for fun? Yes. Yes, you can. Timandra Harkness writes comedy, not computer code. The only programmes she makes are on the radio. If you can read a newspaper you can read this book. Starting with the basics - what IS data? And what makes it big? - Timandra takes you on a whirlwind tour of how people are using big data today: from science to smart cities, business to politics, self-quantification to the Internet of Things. Finally, she asks the big questions about where it's taking us; is it too big for its boots, or does it think too small? Are you a data point or a human being? Will this book be full of rhetorical questions? No. It also contains puns, asides, unlikely stories and engaging people, inspiring feats and thought-provoking dilemmas. Leaving you armed and ready to decide what you think about one of the decade's big ideas: big data.

Categorical data-comprising counts of individuals, objects, or entities in different categories-emerge frequently from many areas of study, including medicine, sociology, geology, and education. They provide important statistical information that can lead to real-life conclusions and the discovery of fresh knowledge. Therefore, the ability to manipulate, understand, and interpret categorical data becomes of interest-if not essential-to professionals and students in a broad range of disciplines.

Although t-tests, linear regression, and analysis of variance are useful, valid methods for analysis of measurement data, categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students, A Course in Categorical Data Analysis presents the easiest, most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Internet, readers do not need full knowledge of a statistical software package.

In this unique text, the author chooses methods and an approach that nurtures intuitive thinking. He trains his readers to focus not on finding a model that fits the data, but on using different models that may lead to meaningful conclusions. The book offers some simple, innovative techniques not highighted in other texts that help make the book accessible to a broad, interdisciplinary audience. A Course in Categorical Data Analysis enables readers to quickly use its offering of tools for drawing scientific, medical, or real-life conclusions from categorical data sets.

Organizational surveys are widely recognized as a powerful tool for measuring and improving employee commitment. If poorly designed and administered, however, they can create disappointment and cynicism. There are many excellent books on sampling methodology and statistical analysis, but little has been written so far for those responsible for designing and implementing surveys in organizations. Now Allan H Church and Janine Waclawski have drawn on their extensive experience in this field to develop a seven-step model covering the entire process, from initiation to final evaluation. They explain in detail how to devise and administer different types of organizational surveys, leading the reader systematically through the various stages involved. Their text is supported throughout by examples, specimen documentation, work sheets and case studies from a variety of organizational settings. They pay particular attention to the political and human sensitivities concerned and show how to surmount the many potential barriers to a successful outcome. Designing and Using Organizational Surveys is a highly practical guide to one of the most effective methods available for organizational diagnosis and change.

This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists' preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice

Recent advances in sabermetrics have made it possible to assess the exact contribution of each player to the success of failure of his team. Using the simple metric Wins Above Average-the number of wins that the 2016 Red Sox, for example, added to their total because they had Mookie Betters in right field instead of an average player (5)-David Kaiser leads us on a fascinating tour through the history of major league baseball from 1901 through 2016, analyzing all the greatest players and teams of the past and showing exactly why they enjoyed the success that they did. Along the way, he identifies the 15 or 20 greatest players of every generation, using simple metrics that allow him to compare the impact of players from Ty Cobb through Ted Williams to Willie Mays, Rickey Henderson and Barry Bonds, and pitchers from Christy Mathewson to Roger Clemens. The book also says a great deal about short- and long-term strategies for organizational success. Along the way, Kaiser takes on a good many tenets of diamond faith.. The importance of pitching, he argues, has been vastly exaggerated since the beginning of baseball time, and great pitching has almost never been the key to a dynasty. Many Hall of Fame pitchers and some hitters as well, he finds, have reached Cooperstown almost entirely on the backs of their teammates. Accurate metrics also reveal that a few over-qualified players are still awaiting selection to Cooperstown. Last but hardly least, Kaiser shows that baseball is threatened by an unprecedented shortage of great players, and challenges MLB to do something about it.

This is a book about the scientific process and how you apply it to data in ecology. You will learn how to plan for data collection, how to assemble data, how to analyze data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program to carry out data handling as well as producing graphs. Statistical approaches covered include: data exploration; tests for difference - t-test and U-test; correlation - Spearman's rank test and Pearson product-moment; association including Chi-squared tests and goodness of fit; multivariate testing using analysis of variance (ANOVA) and Kruskal-Wallis test; and multiple regression. Key skills taught in this book include: how to plan ecological projects; how to record and assemble your data; how to use R and Excel for data analysis and graphs; how to carry out a wide range of statistical analyses including analysis of variance and regression; how to create professional looking graphs; and how to present your results. New in this edition: a completely revised chapter on graphics including graph types and their uses, Excel Chart Tools, R graphics commands and producing different chart types in Excel and in R; an expanded range of support material online, including; example data, exercises and additional notes & explanations; a new chapter on basic community statistics, biodiversity and similarity; chapter summaries and end-of-chapter exercises. Praise for the first edition: This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. - Sue Townsend, Biodiversity Learning Manager, Field Studies Council [M]akes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel - Mark Edwards, EcoBlogging A must for anyone getting to grips with data analysis using R and excel. - Amazon 5-star review It has been very easy to follow and will be perfect for anyone. - Amazon 5-star review A solid introduction to working with Excel and R. The writing is clear and informative, the book provides plenty of examples and figures so that each string of code in R or step in Excel is understood by the reader. - Goodreads, 4-star review

Business Intelligence Strategy and Big Data Analytics is written for business leaders, managers, and analysts - people who are involved with advancing the use of BI at their companies or who need to better understand what BI is and how it can be used to improve profitability. It is written from a general management perspective, and it draws on observations at 12 companies whose annual revenues range between $500 million and $20 billion. Over the past 15 years, my company has formulated vendor-neutral business-focused BI strategies and program execution plans in collaboration with manufacturers, distributors, retailers, logistics companies, insurers, investment companies, credit unions, and utilities, among others. It is through these experiences that we have validated business-driven BI strategy formulation methods and identified common enterprise BI program execution challenges. In recent years, terms like "big data" and "big data analytics" have been introduced into the business and technical lexicon. Upon close examination, the newer terminology is about the same thing that BI has always been about: analyzing the vast amounts of data that companies generate and/or purchase in the course of business as a means of improving profitability and competitiveness. Accordingly, we will use the terms BI and business intelligence throughout the book, and we will discuss the newer concepts like big data as appropriate. More broadly, the goal of this book is to share methods and observations that will help companies achieve BI success and thereby increase revenues, reduce costs, or both.

This title should enable clinicians and managers of stroke services to judge their effectiveness and efficiency. It may be useful for auditing the National Service Frameworks for older people and stroke patients. The work provides generic tools for collecting and interpreting data and assessing cost effectiveness, that can be applied to any chronic desease. This tool is practical for both running stroke services and doing comparative research on any chronic disease.

Traditionally, research impact has been measured by counting citations, and citation-based indicators, such as impact factors. But in the last few years there has been increasing pressure on research and higher education institutions to move beyond citation metrics, and look instead at different forms of impact - at real world impact.Scholarly impact expert Elaine Lasda brings together a cast of innovative contributors from a variety of sectors to look at how impact is measured in ways that go beyond citations in peer-reviewed journal articles. With case studies from publishers, museums, scientific centers and government agencies, the contributors show how using a different mix of traditional bibliometrics, newer altmetrics, and other new measures can provide vital information to support the mission and vision of their organizations. For librarians and information professionals, it is becoming increasingly more important to be able to provide expertise on research impact, influence, productivity and prestige. This exciting new book shows readers how to clarify the importance and relevance of organizational research output, and therefore increase their professional value. With the growing sophistication of research impact analysis, the need for "impact metric literacy" is rising, and this book is a helpful tool for those looking to improve their understanding of research impact.

This volume presents the latest advances in statistics and data science, including theoretical, methodological and computational developments and practical applications related to classification and clustering, data gathering, exploratory and multivariate data analysis, statistical modeling, and knowledge discovery and seeking. It includes contributions on analyzing and interpreting large, complex and aggregated datasets, and highlights numerous applications in economics, finance, computer science, political science and education. It gathers a selection of peer-reviewed contributions presented at the 16th Conference of the International Federation of Classification Societies (IFCS 2019), which was organized by the Greek Society of Data Analysis and held in Thessaloniki, Greece, on August 26-29, 2019.

This study explores issues of biomass energy use in relation to household welfare and it assesses Ethiopia's future energy security with a focus on long-term model of the energy sector, and institutional arrangements required for decentralized energy initiatives. Data from Ethiopian rural households reveal negative welfare effects associated with traditional biomass energy utilization, while increases in the opportunity cost of fuelwood collection is associated negatively with allocation of labour to agriculture and fuelwood use. It appears that investment on integrated energy source diversification improves sustainability and resilience, but increases production cost. Innovations that improve alternative sources reduce production cost, improve energy security, and thus serve as an engine of economic growth.

A comprehensive introduction and teaching resource for state-of-the-art Qualitative Comparative Analysis (QCA) using R software. This guide facilitates the efficient teaching, independent learning, and use of QCA with the best available software, reducing the time and effort required when encountering not just the logic of a new method, but also new software. With its applied and practical focus, the book offers a genuinely simple and intuitive resource for implementing the most complete protocol of QCA. To make the lives of students, teachers, researchers, and practitioners as easy as possible, the book includes learning goals, core points, empirical examples, and tips for good practices. The freely available online material provides a rich body of additional resources to aid users in their learning process. Beyond performing core analyses with the R package QCA, the book also facilitates a close integration with the R package SetMethods allowing for a host of additional protocols for building a more solid and well-rounded QCA.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

This book presents an accessible introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, it offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners.

The massive volume of data generated in modern applications can overwhelm our ability to conveniently transmit, store, and index it. For many scenarios, building a compact summary of a dataset that is vastly smaller enables flexibility and efficiency in a range of queries over the data, in exchange for some approximation. This comprehensive introduction to data summarization, aimed at practitioners and students, showcases the algorithms, their behavior, and the mathematical underpinnings of their operation. The coverage starts with simple sums and approximate counts, building to more advanced probabilistic structures such as the Bloom Filter, distinct value summaries, sketches, and quantile summaries. Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.

Data has dramatically changed how our world works. Understanding and using data is now one of the most transferable and desirable skills out there - whether you're an entrepreneur wanting to boost your business, a job-seeker looking for that employable edge, or hoping to make the most of your current career. Learning how to work with data may seem intimidating or difficult - but don't worry, Confident Data Skills is here to help. This updated second edition takes you through the basics of data: from data mining and preparing and analyzing your data, to visualizing and communicating your insights, and now with exciting new content on neural networks and deep learning. Featuring in-depth international case studies from companies like Netflix, LinkedIn and Mike's Hard Lemonade Co., as well as easy-to understand language and inspiring advice and guidance, Confident Data Skills help you use your new-found data skills to give your career that cutting-edge boost. About the Confident series... From coding and web design to data, digital content and cyber security, the Confident books are the perfect beginner's resource for enhancing your professional life, whatever your career path..

Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.

Just Plain Data Analysis teaches students statistical literacy skills that they can use to evaluate and construct arguments about public affairs issues grounded in numerical evidence. The book addresses skills that are often not taught in introductory social science research methods courses and that are often covered sketchily in the research methods textbooks: where to find commonly used measures of political and social conditions; how to assess the reliability and validity of specific indicators; how to present data efficiently in charts and tables; how to avoid common misinterpretations and misrepresentations of data; and how to evaluate causal arguments based on numerical data. With a new chapter on statistical fallacies and updates throughout the text, the new edition teaches students how to find, interpret, and present commonly used social indicators in an even clearer and more practical way.

This book highlights some of the unique aspects of spatio-temporal graph data from the perspectives of modeling and developing scalable algorithms. The authors discuss in the first part of this book, the semantic aspects of spatio-temporal graph data in two application domains, viz., urban transportation and social networks. Then the authors present representational models and data structures, which can effectively capture these semantics, while ensuring support for computationally scalable algorithms. In the first part of the book, the authors describe algorithmic development issues in spatio-temporal graph data. These algorithms internally use the semantically rich data structures developed in the earlier part of this book. Finally, the authors introduce some upcoming spatio-temporal graph datasets, such as engine measurement data, and discuss some open research problems in the area. This book will be useful as a secondary text for advanced-level students entering into relevant fields of computer science, such as transportation and urban planning. It may also be useful for researchers and practitioners in the field of navigational algorithms.

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.