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This book explains the importance of using the probability that the hypothesis is correct (PHC), an intuitive measure that anyone can understand, as an alternative to the p-value. In order to overcome the “reproducibility crisis” caused by the misuse of significance tests, this book provides a detailed explanation of the mechanism of p-hacking using significance tests, and concretely shows the merits of PHC as an alternative to p-values. In March 2019, two impactful papers on statistics were published. One paper,'Moving to a World Beyond ‘p < 0.05'”, was featured in the scholarly journal The American Statistician, overseen by the American Statistical Association. The title of the first chapter is “Don't Say ‘Statistically Significant'”, and it uses the imperative form to clearly forbid the use of significance testing. Another paper, “Retire statistical significance”, was published in the prestigious scientific journal Nature. This commentary was endorsed by more than 800 scientists, advocating for the statement, “We agree, and call for the entire concept of statistical significance to be abandoned.” Consider a study comparing the duration of hospital stays between treatments A and B. Previously, research conclusions were typically stated as: “There was a statistically significant difference at the 5% level in the average duration of hospital stays.” This phrasing is quite abstract. Instead, we present the following conclusion as an example: (1) The average duration of hospital stays for Group A is at least half a day shorter than for Group B. (2) 71% of patients in Group A have shorter hospital stays than the average for Group B. (3) Group A has an average hospital stay that is, on average, no more than 94% of that of Group B. Then, the probability that the expression is correct is shown. That is the PHC curve.

This book covers the following subjects: growth curve modeling, directional dependence, dyadic data modeling, item response modeling (IRT), and other methods for the analysis of dependent data (e.g., approaches for modeling cross-section dependence, multidimensional scaling techniques, and mixed models). It presents contributions on handling data in which the postulate of independence in the data matrix is violated. When this postulate is violated and when the methods assuming independence are still applied, the estimated parameters are likely to be biased, and statistical decisions are very likely to be incorrect. Problems associated with dependence in data have been known for a long time, and led to the development of tailored methods for the analysis of dependent data in various areas of statistical analysis. These include, for example, methods for the analysis of longitudinal data, corrections for dependency, and corrections for degrees of freedom. Researchers and graduate students in the social and behavioral sciences, education, econometrics, and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful.

This textbook reconstructs the statistics curriculum from the perspective of posterior probability. In recent years, there have been several reports that the results of studies using significant tests cannot be reproduced. It is a problem called a “reproducibility crisis”. For example, suppose we could reject the null hypothesis that “the average number of days to recovery in patients who took a new drug was the same as that in the control group”. However, rejecting the null hypothesis is only a necessary condition for the new drug to be effective. Even if the necessary conditions are met, it does not necessarily mean that the new drug is effective. In fact, there are many cases where the effect is not reproduced. Sufficient conditions should be presented, such as “the average number of days until recovery in patients who take new drugs is sufficiently short compared to the control group, evaluated from a medical point of view”, without paying attention to necessary conditions. This book reconstructs statistics from the perspective of PHC, i.e., probability that a research hypothesis is correct. For example, the PHC curve shows the posterior probability that the statement “The average number of days until recovery for patients taking a new drug is at least θ days shorter than that of the control group” is correct as a function of θ. Using the PHC curve makes it possible to discuss the sufficient conditions rather than the necessary conditions for being an efficient treatment. The value of statistical research should be evaluated with concrete indicators such as “90% probability of being at least 3 days shorter”, not abstract metrics like the p-value.

This book addresses a wide range of recent methodological aspects, applications and best practices of statistics production. It comprises a selection of peer-reviewed contributions of methodological and applied interest presented at the 4th Conference of European Statistics Stakeholders, CESS 2022, held in Rome, Italy, on October 20-21, 2022. The first part discusses statistical methods with applications to environmental risk assessment, sentinels data, surveillance systems during the Covid-19 pandemic, healthcare risk management, the analysis of regional or structural changes of scale, household distributional accounts, regional rental prices on municipalities, the network topology of the Euro area interbank market, tourism statistics and big data, statistical literacy, and Sustainable Development Goal composite indicators for EU countries. The second part focuses on statistical methodologies for complex data analysis, namely the optimal number of clusters to rank a model-based index, clustering methods for asymmetric data using spectral approaches, a family of parsimonious matrix-variate mixture models for heavy-tailed data, the importance of robust second-stage regressions for financial data, and on perturbation methods. In view of the overarching theme “The European Data Ecosystem for the Statistical Information of the Digital Age” and the importance of statistical data for monitoring the progress of the United Nations'Sustainable Development Goals, the CESS 2022 meeting provided a forum for discussion on methodologies, results, challenges and best practices among methodologists, producers, and users of European Statistics from academia, the national statistical offices and the institutions of the European Union. Chapter Estimating regional rental prices on LAU 2 municipalities in North Rhine-Westphalia is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book includes presentations given at the 88th annual meeting of the Psychometric Society, held in Maryland, USA on July 24–28, 2023. The proceeding covers a diverse set of psychometric topics. The topics include, but are not limited to item response theory, cognitive diagnostic models, Bayesian estimation, validity and reliability issues, and several applications within different fields. The authors are from all over the world, they work in different psychometrics areas, as well as having diverse professional and academic experiences.

This book is about the interplay between chance and order, but limited to mostly binary events, such as success/failure as they occur in a diversity of interesting applications. The goal is to entertain and instruct with topics that range from unexpected encounters with chance in everyday experiences, to significant “must know” insights regarding human health and other concerns in the social sciences. The first section provides the tools for being able to discuss random sequences with hints at what is to follow. This is followed by another surprising and, to some extent, bizarre result known as Stein's Paradox, which is applied to baseball. The troublesome topic of disease clusters, namely to decide whether the clumping of events is due to chance or some environmental cause, is treated using both the Poisson and normal approximations to the binomial distribution and this leads naturally into a discussion of the base rate fallacy and a case study of hospital performance. Next, another medical case study this time concerning some tricky questions about the effectiveness of colonoscopy and other medical interventions. A brief discussion of the mathematics of clinical trials, follows. Then, the book examines the error in random sampling, when polling for candidate preference with specific current examples. The essential tool here is covariance of random variables. The author follows this with a treatment of the spooky quality of coincidence using appropriate mathematical tools. After this, code breaking at Bletchley Park using Baye's theorem. It returns to Poisson events to discuss another unexpected result, followed by the use of spatial Poisson events in the delivery of emergency response services. Finally, an account of fluctuations that occur in a run of Bernoulli trials as a bookend to the very first section of the book. The probability theory involved is elementary using the binomial theorem and its extensions to Poisson and normal events in addition to conditional probability and covariance. The author provides an optional brief tutorial at the end, that covers the basic ideas in probability and statistics needed in the main text. Besides a list of references, several codes written in Matlab that were used to illustrate various topics in the text, as well as to support several figures that appear throughout, are provided.

This is an open access book. The 4th International Conference on Public Administration, Health and Humanity Development (PAHHD 2024), will be held on August 16-18 2024 in Shanghai, China. The basic function of public management is to provide public services for the public by creating a good social environment and order. Public administration must implement the principle of putting people first. The fundamental purpose of public management is to meet the public needs of the society. In the process of public administration, the rule of law and custom should be emphasized, and the humanistic spirit should be advocated. This conference brings together innovators and industry experts in the fields of public administration, health and human development in a common forum. The primary goal of the conference is to promote research and developmental activities in Public Administration, Health and Humanity Development and another goal is to promote scientific information interchange between researchers, developers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in Public Administration, Health and Humanity Development and related areas.

The authors of this book assert that Grid Square statistics, a method of aggregating data within a geographically defined Grid, may be an effective solution to approach geospatial data for big data integration. Grid Square statistics is a technique that allows us to collect and analyze data based on Grids and makes it easier to understand patterns and trends. Sustainability, a key concern for the future of our society, often involves balancing multiple independent objectives. These objectives and key performance indicators must be shared to solve multi-dimensional optimization problems with some constraints related to sustainability issues. However, many of these sustainability issues are challenges that are part of global systems, which are modeled as a complex system consisting of many components. Social, economic, and environmental aspects in our socio-economic systems require solutions designed for the sustainability of our society. These conditions imply that Big Data must contribute to evaluating our current situation, actions, and changes to continuously change our behavior and society based on a Plan-Do-Check-Action (PDCA) cycle. The PDCA cycle, also known as the Deming cycle, is a four-step management method used to improve processes and products continuously. We must understand how to construct a platform by using Big Data to do so. Thus, extracting meaningful information from Big Data is crucially important in the advanced information society. This book provides case studies based on Grid Square statistics. It shows their applications to socio-economic and environmental problems such as tourism, where we analyze visitor patterns to optimize resource allocation, SDGs indicator, where we monitor the sustainability of our socio-economic systems, and disaster management, where we face natural disaster to enhance preparedness. Finally, the book proposes a World Grid Square Statistics Reference Architecture for constructing data applications across organizations and domains and reports on design concepts, system architecture, and the implication of a data platform for World Grid Square data and statistics.

One of the strengths of this book is that it expresses occupational segregation from multidimensional viewpoints using correspondence analysis. Through a quantitative approach, the book examines occupational segregation by education and gender in response to industrial transformation in Japan and other countries. The transformation of industrial structure, such as post-industrialization, demands a reconsideration of traditional perspectives in sociology, especially in social stratification. In other words, it is a shift from the attribute to the achievement principle. Higher technological innovations will create higher levels of industries, and those industries will require jobs that need greater human capital. In short, the meritocracy will be promoted. Meritocracy is certainly considered persuasive. In fact, previous researchers have looked primarily at a person's occupation as a measure of social status. In Japan, jobs are normally acquired after completing education; thus, one's educational achievement plays an important role. Especially in recent years, however, education alone has not been enough to explain social status. This book, therefore, focuses on occupational segregation by gender in addition to education in post-industrial society. Can occupational segregation by gender be weakened in the highly educated group? Is this a universal story in modern society? Because post-industrialization is part of the larger story of modernization, international perspectives are needed to examine the linkage between education and gender occupational segregation. This book explores occupational segregation by gender in response to industrial transformation in Japan and other countries.

Scan statistics, one of the most active research areas in applied probability and statistics, has seen a tremendous growth during the last 25 years. Google Scholar lists about 3,500 hits to references of articles on scan statistics since the year 2020, resulting in over 850 hits to articles per year. This is mainly due to extensive and diverse areas of science and technology where scan statistics have been employed, including: atmospheric and climate sciences, business, computer science, criminology, ecology, epidemiology, finance, genetics and genomics, geographic sciences, medical and health sciences, nutrition, pharmaceutical sciences, physics, quality control and reliability, social networks and veterinary science. This volume of the Handbook of Scan Statistics is a collection of forty chapters, authored by leading experts in the field, outlines the research and the breadthof applications of scan statistics to the numerous areas of science and technology listed above. These chapters present an overview of the theory, methods and computational techniques, related to research in the area of scan statistics and outline future developments. It contains extensive references to research articles, books and relevant computer software. Handbook of Scan Statistics is an excellent reference for researchers and graduate students in applied probability and statistics, as well as for scientists in research areas where scan statistics are used. This volume may also be used as a textbook for a graduate level course on scan statistics.

This volume presents a selection of articles which explore methodological and applicative aspects of textual data analysis. Divided into four parts, it begins by focusing on statistical methods, and then moves on to problems in quantitative language processing. After discussing the challenging task of text mining in relation to emotional and sentiment analyses, the book concludes with a collection of studies in the social sciences and public health which apply textual data analysis methods.The refereed contributions were originally presented at the 16th International Conference on Statistical Analysis of Textual Data (JADT 2022), which took place in Naples, Italy, on July 6-8, 2022. The biennial JADT meeting discusses theories, problems, and practical uses of textual data analysis in various fields, sharing a quantitative approach to the study of lexical, textual, pragmatic or discursive features of information expressed in natural language.

This book analyses of public participation in the different development stages of urban renewal in Macao. Three types of participation are summarized from practice, analyzed their characteristics. And then develops a framework and a set of standards of participation quality evaluation that are applicable to Macao. Through a case study of public consultation meeting on Macao Urban Renewal Legal System, the participation quality of government-led public participation was evaluated. Finally, the applicability of the evaluation framework and standards was discussed. This book enriches the existing theories, and it provides new experience and the cases of public participation in urban renewal. Public participation in a new local context with multicultural and strong social connections was discussed. It builds the systems theoretical foundation on this field in Macao. It is a useful book for graduate students and the researcher in the academic field of urban renewal or urban governance to understand the public participation in urban renewal theory. This book can provide references for the government, associations, and private individuals to initiate and carry out public participation activities. It could provide decision-makers and researchers in Macao a reference for the quality evaluation of public participation.

This book offers a solution to one of film history's major controversies: the long-running dispute over Orson Welles'and Herman J. Mankiewicz's contributions to the Citizen Kane screenplay. It establishes the vital importance of computing and statistics to solving previously intractable puzzles in the arts and humanities. Citizen Kane (1941) is one of the most acclaimed films in the history of cinema. For 50 years it topped the Sight & Sound film critics'poll. Orson Welles directed the film and is credited with co-writing the screenplay with Herman J. Mankiewicz. But the co-writer credit generates furious disputes between those who argue Mankiewicz is the sole author of Citizen Kane and those who claim that Welles collaborated fully with its writing. The author employs computing and statistics to answer two questions: What are the distinguishing features of Welles'and of Mankiewicz's writing?And What did each contribute to the writing of the Citizen Kane screenplay? To answer these questions, the author bypasses opinions and impressions, and instead subjects the language of the Citizen Kane screenplay to a ‘forensic'examination. Employing linguistics, basic statistical tests, plus computer technology and software, the author identifies the stylistic signature of each author – the combination of consistent and regular linguistic habits that make each author's writing distinctive. This book replaces impressionistic discussions of Mankiewicz's and Welles'contributions to the Citizen Kane screenplay with a rigorous, experiment-driven statistical analysis. Earlier statistical studies of authorship have discovered that small, unassuming language features (such as punctuation, pronouns, and prepositions) in statistically significant quantities, constitute a screenwriter's distinctive writing habits. Only with the extensive experimentation carried out in this volume, did the author decide Mankiewicz's and Welles'specific habits and their contributions to Citizen Kane.

This book highlights research on the behavioral biases affecting judgmental accuracy in judgmental forecasting and showcases the state-of-the-art in judgment-based predictive analytics. In recent years, technological advancements have made it possible to use predictive analytics to exploit highly complex (big) data resources. Consequently, modern forecasting methodologies are based on sophisticated algorithms from the domain of machine learning and deep learning. However, research shows that in the majority of industry contexts, human judgment remains an indispensable component of the managerial forecasting process. This book discusses ways in which decision-makers can address human behavioral issues in judgmental forecasting. The book begins by introducing readers to the notion of human-machine interactions. This includes a look at the necessity of managerial judgment in situations where organizations commonly have algorithmic decision support models at their disposal.The remainder of the book is divided into three parts, with Part I focusing on the role of individual-level judgment in the design and utilization of algorithmic models. The respective chapters cover individual-level biases such as algorithm aversion, model selection criteria, model-judgment aggregation issues and implications for behavioral change. In turn, Part II addresses the role of collective judgments in predictive analytics. The chapters focus on issues related to talent spotting, performance-weighted aggregation, and the wisdom of timely crowds. Part III concludes the book by shedding light on the importance of contextual factors as critical determinants of forecasting performance. Its chapters discuss the usefulness of scenario analysis, the role of external factors in time series forecasting and introduce the idea of mindful organizing as an approach to creating more sustainable forecasting practices in organizations.

Diese Studie untersucht die dynamische Beziehung zwischen Wissenschaft, Zahlen und Politik. Was können wissenschaftliche Erkenntnisse realistischerweise in und für die Politik tun? Der Band leistet einen Beitrag zu dieser Debatte, indem er sich auf die Rolle von'Zahlen'als Mittel konzentriert, mit dem Wissen ausgedrückt wird und durch das dieses Wissen in den politischen Bereich übertragen werden kann. Ausgehend von der Annahme, dass Zahlen ständig aktiv geschaffen, übersetzt und verwendet werden und dass sie in ihren jeweiligen Kontexten interpretiert werden müssen, wird untersucht, wie Zahlen und Quantifizierungen'politisch nutzbar'gemacht werden, indem ihre Produktion, ihr Übergang in die Sphäre der Politik und ihre letztendliche Verwendung darin untersucht werden. Zu den zentralen Fragen, die behandelt werden, gehören: Auf welche Weise beeinflussen wissenschaftliche Erkenntnisse die politische Entscheidungsfindung in der heutigen Welt? Wie und warum hat Quantifizierung in der demokratischen Politik eine so wichtige Rolle gespielt? Was leisten wissenschaftliche Erkenntnisse und Zahlen in der Politik?

Dieses Buch zeigt anhand von journalistischen Fallbeispielen, warum und wie fortgeschrittene statistische Analysemethoden eingesetzt werden können, um aussagekräftige journalistische Informationen aus Daten zu extrahieren. Gleichzeitig setzt das Buch einen Anforderungsrahmen für die datenjournalistische Arbeit bezüglich Datenkompetenz und -visualisierung, dem Einsatz von Algorithmen sowie daten-ethischen Anforderungen und der Überprüfung externer Studien. Ziel ist es, die Qualität und Aussagekraft datenjournalistischer Arbeiten zu verbessern, welche, neben der angemessenen Erfassung und Aufbereitung von Daten, wesentlich von einer adäquaten Datenanalyse abhängen. Aber wie statistisch arbeiten Datenjournalist:innen heute eigentlich? Und wie statistisch können oder sollten sie arbeiten, um den Ansprüchen ihrer Leserschaft in Sachen Verständlichkeit gerecht zu werden, auch mit Blick auf deren unterschiedliches mathematisch-statisches Vorwissen? Das Buch zielt darauf ab, diese Fragen zu beantworten, indem es weiterführende statistische Methoden anhand von Fallstudien untersucht. Es verdeutlicht, warum diese Methoden auch im journalistischen Kontext oftmals problemangemessener sind und tiefer gehende Erkenntnisse liefern als vereinfachte Analysen und Basismethoden. Die Fallstudien decken dabei die wichtigsten statistischen Methoden ab: Verteilungen und Tests, Klassifikation, Regression, Zeitreihenanalyse, Clusteranalyse, Analyse von sequentiellen Daten ohne direkten Zeitbezug, Verwendung von Vorwissen und geplante Studien.

This book analyzes the development of medical big data projects in Japan.Japan is experiencing unprecedented population aging, and labor productivity has decreased accordingly. Big data analysis of the Japanese medical real-world database (RWD) has the potential to tackle this issue.To allow readers to gain an understanding of Japanese medical big data analysis, the book discusses the original Japanese system that generates medical RWDs in the hospital medical records system, the nationwide standardized health checkup system, and the public medical insurance system in Japan.After introducing four major big data projects in the healthcare–medical field in Japan, the book explains the importance of creating information standards to maintain data quality and to analyze medical big data. It enables readers to analyze which standards are installed in which RWDs, how the standards are maintained, and which issues are prevalent in Japan.This book also describes the ethical processes involved in big data projects involving medical RWDs in Japan.

This volume focuses on predicting users'attendance at a future event at specific time and location based on their common interests. Event attendance prediction has attracted considerable attention because of its wide range of potential applications. By predicting event attendance, events that better fit users'interests can be recommended, and personalized location-based or topic-based services related to the events can be provided to users. Moreover, it can help event organizers estimating the event scale, identifying conflicts, and help manage resources. This book first surveys existing techniques on event attendance prediction and other related topics in event-based social networks. It then introduces a context-aware data mining approach to predict the event attendance by learning how users are likely to attend future events. Specifically, three sets of context-aware attributes are identified by analyzing users'past activities, including semantic, temporal, and spatial attributes. This book illustrates how these attributes can be applied for event attendance prediction by incorporating them into supervised learning models, and demonstrates their effectiveness through a real-world dataset collected from event-based social networks.

Dieses Buch bewertet systematisch die politischen und sozialen Werte der mehr als 1,3 Milliarden Katholiken auf der ganzen Welt, der bei weitem größten Konfession des westlichen Christentums. Auf der Grundlage einer umfassenden Analyse von Daten aus dem World Values Survey und anderen globalen Meinungsumfragen wirft das Buch ein neues Licht auf die Wertesysteme und Meinungen der Katholiken. Die Autoren heben zentrale Probleme und Herausforderungen hervor, mit denen die Kirche derzeit bei der Anpassung an die moderne Welt konfrontiert ist, einschließlich des katholischen Antisemitismus, der religiösen und sexuellen Toleranz und der Meinungen zur Demokratie, und bieten gleichzeitig eine anthropologische Reflexion darüber, wie gut sich die Kirche an die Anforderungen einer offenen Gesellschaft anpasst oder nicht.

This volume presents a collection of twenty-five peer-reviewed articles carefully selected from the contributions presented at the XXV Congress of the Portuguese Statistical Society (2021). Containing state-of-the-art developments in theoretical and applied statistics, the book will be accessible to readers with a background in mathematics and statistics, but will also be of interest to researchers from other scientific disciplines (e.g., biology, economics, medicine), who will find a broad range of relevant applications.

The book explores, through a reflection on food, the complexity of the concept of well-being. It starts from the consideration that food is a fundamental element for human well-being, and for well-being of the planet as a whole. Not only does food guarantee the survival of human beings, it is also a cultural expression. With regard to the Italian socio-cultural context, the contributors explore how food relates to aspects such as history, tradition, new food styles, health, and the old and new technologies used to produce food. The studies in the book do not simply analyse indicators to illustrate the Italian situation in the'here and now'. As part of the tradition of studies on social indicators, they provide valid and well-founded indications to contribute to an improvement in the quality of life for years to come.This work on the theme of food represents a very useful contribution to the general reflection on well-being and its statistical, sociological, and multidisciplinary study, due to the importance historically given to food in Italy and the socio-cultural implications of food in various life contexts.

This proceedings volume highlights the latest research and developments in psychometrics and statistics. It represents selected and peer-reviewed presentations given at the 85th Annual International Meeting of the Psychometric Society (IMPS), held virtually on July 13-17, 2020. The IMPS is one of the largest international meetings on quantitative measurement in education, psychology and the social sciences. It draws approximately 500 participants from around the world, featuring paper and poster presentations, symposiums, workshops, keynotes, and invited presentations. Leading experts and promising young researchers have written the included chapters. The chapters address a wide variety of topics including but not limited to item response theory, adaptive testing, Bayesian estimation, propensity scores, and cognitive diagnostic models. This volume is the 9th in a series of recent works to cover research presented at the IMPS.

This text is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical social work problems. If understanding statistics isn't your strongest suit, you are not especially mathematically inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in social work courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2019 for Social Work Statistics: A Guide to Solving Practical Problems capitalizes on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. In this new edition, each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand social work problems. Practice problems are provided at the end of each chapter with their solutions in an Appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.

Design Research uses scientific methods to evaluate designs and build design theories. This book starts with recognizable questions in Design Research, such as A/B testing, how users learn to operate a device and why computer-generated faces are eerie. Using a broad range of examples, efficient research designs are presented together with statistical models and many visualizations. With the tidy R approach, producing publication-ready statistical reports is straight-forward and even non-programmers can learn this in just one day. Hundreds of illustrations, tables, simulations and models are presented with full R code and data included. Using Bayesian linear models, multi-level models and generalized linear models, an extensive statistical framework is introduced, covering a huge variety of research situations and yet, building on only a handful of basic concepts. Unique solutions to recurring problems are presented, such as psychometric multi-level models, beta regression for rating scales and ExGaussian regression for response times. A “think-first” approach is promoted for model building, as much as the quantitative interpretation of results, stimulating readers to think about data generating processes, as well as rational decision making. New Statistics for Design Researchers: A Bayesian Workflow in Tidy R targets scientists, industrial researchers and students in a range of disciplines, such as Human Factors, Applied Psychology, Communication Science, Industrial Design, Computer Science and Social Robotics. Statistical concepts are introduced in a problem-oriented way and with minimal formalism. Included primers on R and Bayesian statistics provide entry point for all backgrounds. A dedicated chapter on model criticism and comparison is a valuable addition for the seasoned scientist.

This book takes a unique approach to explaining permutation statistics by integrating permutation statistical methods with a wide range of classical statistical methods and associated R programs. It opens by comparing and contrasting two models of statistical inference: the classical population model espoused by J. Neyman and E.S. Pearson and the permutation model first introduced by R.A. Fisher and E.J.G. Pitman. Numerous comparisons of permutation and classical statistical methods are presented, supplemented with a variety of R scripts for ease of computation. The text follows the general outline of an introductory textbook in statistics with chapters on central tendency and variability, one-sample tests, two-sample tests, matched-pairs tests, completely-randomized analysis of variance, randomized-blocks analysis of variance, simple linear regression and correlation, and the analysis of goodness of fit and contingency. Unlike classical statistical methods, permutation statistical methods do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity, depend only on the observed data, and do not require random sampling. The methods are relatively new in that it took modern computing power to make them available to those working in mainstream research. Designed for an audience with a limited statistical background, the book can easily serve as a textbook for undergraduate or graduate courses in statistics, psychology, economics, political science or biology. No statistical training beyond a first course in statistics is required, but some knowledge of, or some interest in, the R programming language is assumed.

This book offers a new look at well-established quantification theory for categorical data, referred to by such names as correspondence analysis, dual scaling, optimal scaling, and homogeneity analysis. These multiple identities are a consequence of its large number of properties that allow one to analyze and visualize the strength of variable association in an optimal solution. The book contains modern quantification theory for analyzing the association between two and more categorical variables in a variety of applicative frameworks. Visualization has attracted much attention over the past decades and given rise to controversial opinions. One may consider variations of plotting systems used in the construction of the classic correspondence plot, the biplot, the Carroll-Green-Schaffer scaling, or a new approach in doubled multidimensional space as presented in the book. There are even arguments for no visualization at all. The purpose of this book therefore is to shed new light on time-honored graphical procedures with critical reviews, new ideas, and future directions as alternatives. This stimulating volume is written with fresh new ideas from the traditional framework and the contemporary points of view. It thus offers readers a deep understanding of the ever-evolving nature of quantification theory and its practice. Part I starts with illustrating contingency table analysis with traditional joint graphical displays (symmetric, non-symmetric) and the CGS scaling and then explores logically correct graphs in doubled Euclidean space for both row and column variables. Part II covers a variety of mathematical approaches to the biplot strategy in graphing a data structure, providing a useful source for this modern approach to graphical display. Part II is also concerned with a number of alternative approaches to the joint graphical display such as bimodal cluster analysis and other statistical problems relevant to quantification theory.

This textbook is a step-by-step guide for high school, community college, and undergraduate students who are taking a course in applied statistics and wish to learn how to use Excel to solve statistical problems. All of the statistics problems in this book come from the following fields of study: business, education, psychology, marketing, engineering and advertising. Students will learn how to perform key statistical tests in Excel without being overwhelmed by statistical theory. Each chapter briefly explains a topic and then demonstrates how to use Excel commands and formulas to solve specific statistics problems. The book offers guidance in using Excel in two different ways: (1) writing formulas (e.g., confidence interval about the mean, one-group t-test, two-group t-test, correlation) and (2) using Excel's drop-down formula menus (e.g., simple linear regression, multiple correlations and multiple regression, and one-way ANOVA). Three practice problems are provided at the end of each chapter, along with their solutions in an appendix. An additional practice test allows readers to test their understanding of each chapter by attempting to solve a specific statistics problem using Excel; the solution to each of these problems is also given in an appendix. This book is a tool that can be used either by itself or along with any good statistics book.​

This volume gathers peer-reviewed contributions on data analysis, classification and related areas presented at the 28th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2019, held in Szczecin, Poland, on September 18–20, 2019. Providing a balance between theoretical and methodological contributions and empirical papers, it covers a broad variety of topics, ranging from multivariate data analysis, classification and regression, symbolic (and other) data analysis, visualization, data mining, and computer methods to composite measures, and numerous applications of data analysis methods in economics, finance and other social sciences. The book is intended for a wide audience, including researchers at universities and research institutions, graduate and doctoral students, practitioners, data scientists and employees in public statistical institutions.

This edited volume offers state-of-the-art research on the dynamics of contemporary fertility by examining the implications of the economic and social forces that are driving the rapid change in fertility behavior, and the changing context, determinants, and measurement of contemporary human reproduction. The volume explores new theoretical avenues that seek to incorporate uncertainty, examine social contagion effects, and explain the rise in childlessness. Reproductive attitudes are re-examined in chapters that deal with models of parenthood and with the persistence of race-ethnic-nativity differences. A new and important subject of multi-partner fertility is also described by examining it in the context of total fertility and from the usually neglected perspective of men. The impact of divorce on fertility, the measurement of childlessness and the postponement of first births, developments in assortative mating and fertility, and current patterns of interracial fertility are also addressed in this volume. By combining up-to-date research spanning the entire field to illuminate contemporary developments, the book is a valuable source for demographers, sociologists, economists, and all those interested in understanding fertility in today's world.

This book provides an in-depth view on Bourdieu's empirical work, thereby specially focusing on the construction of the social space and including the concept of the habitus. Themes described in the book include amongst others: • the theory and methodology for the construction of “social spaces”, • the relation between various “fields” and “the field of power”, • formal construction and empirical observation of habitus, • the formation, accumulation, differentiation of and conversion between different forms of capital, • relations in geometric data analysis.The book also includes contributions regarding particular applications of Bourdieu's methodology to traditional andnew areas of research, such as the analysis of institutional, international and transnational fields. It further provides a systematic introduction into the empirical construction of the social space.

Diese Festschrift zu Ehren Steffen M. Kühnels versammelt eine Vielzahl von Beiträgen zu verschiedenen Themengebieten, mit denen sich der renommierte Soziologe in den vergangenen Jahrzehnten beschäftigt hat. In rund 35 Artikeln diskutieren deutsche und internationale Expertinnen und Experten zentrale Fragen der empirischen Sozialforschung. Die inhaltliche Vielfalt der Beiträge spiegelt dabei das breite Forschungsinteresse und langjährige Schaffen von Steffen M. Kühnel wider. Der erste Teil der Festschrift setzt sich mit der (Weiter-)Entwicklung von statistischen Verfahren und empirischen Forschungsmethoden auseinander. Hierbei wird auch wissenschaftstheoretischen Fragestellungen nachgegangen. Der zweite Teil behandelt Themen der angewandten Sozialforschung und untersucht aktuelle Fragen, insbesondere aus den Bereichen der politischen Wahlen und Partizipation sowie der Einstellungs- und Vorurteilsforschung.Dr. Anja Mays, Institut für Soziologie, TU Darmstadt.Dr. André Dingelstedt, Institut für Qualitätssicherung und Transparenz im Gesundheitswesen (IQTIG), Berlin.Verena Hambauer, Methodenzentrum Sozialwissenschaften, Universität Göttingen.Stephan Schlosser, Methodenzentrum Sozialwissenschaften, Universität Göttingen.Florian Berens, Methodenzentrum Sozialwissenschaften, Universität Göttingen.Dr. Jürgen Leibold, Methodenzentrum Sozialwissenschaften, Universität Göttingen.Jan-Karem Höhne, Universität Mannheim.

This book offers a comprehensible overview of the statistical approach called the person-centered method. Instead of analyzing means, variances and covariances of scale scores as in the common variable-centered approach, the person-centered approach analyzes persons or objects grouped according to their characteristic patterns or configurations in contingency tables. This second edition explores the relationship between two statistical methods: log-linear modeling (LLM) and configural frequency analysis (CFA). Both methods compare expected frequencies with observed frequencies. However, while LLM searches for the underlying dependencies of the involved variables in the data (model-fitting), CFA examines significant residuals in non-fitting models. New developments in the second edition include: Configural Mediation Models, CFA with covariates, moderator CFA, and CFA modeling branches in tree-based methods. The new developments enable the use of categorical together with continuous variables, which makes CFA a very powerful statistical tool. This new edition continues to utilize R-package confreq (derived from Configural Frequency Analysis), much updated since the first edition and newly adjusted to the new R base program 4.0. An electronic supplement is now available with 18 R-scripts and many datasets.

This book briefly analyzes the performance of selected Italian universities during a pre-crisis period and an economic crisis period, on the basis of graduate characteristics and graduate placement statistics. The Electre model is used to produce eighteen university rankings according to three different scenarios (Neutral, University, and Job), three different roles ascribed to the key criteria (overeducation and mismatching), and two years corresponding to two postgraduation placement sampling surveys carried out in a pre-crisis period (2006) and during a crisis period (2011). The eighteen rankings are based on Economics/Statistics and Political & Social Sciences graduates. The rankings vary according to both the scenarios considered and graduate characteristics. Some differences are noted between the two fields of study. The book will be of interest for statisticians interested in evaluation issues, policymakers concerned with university comparisons and rankings, and future students and graduates wishing to make the best choice when selecting their university course. In addition, the new methodology adopted will be relevant for scholars in Statistics and Engineering.

This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions.

Focusing on methodologies, applications and challenges of textual data analysis and related fields, this book gathers selected and peer-reviewed contributions presented at the 14th International Conference on Statistical Analysis of Textual Data (JADT 2018), held in Rome, Italy, on June 12-15, 2018. Statistical analysis of textual data is a multidisciplinary field of research that has been mainly fostered by statistics, linguistics, mathematics and computer science. The respective sections of the book focus on techniques, methods and models for text analytics, dictionaries and specific languages, multilingual text analysis, and the applications of text analytics. The interdisciplinary contributions cover topics including text mining, text analytics, network text analysis, information extraction, sentiment analysis, web mining, social media analysis, corpus and quantitative linguistics, statistical and computational methods, and textual data in sociology, psychology, politics, law and marketing.

Newly revised for Excel 2019, this text is a step-by-step guide for students taking a first course in statistics for advertising and for advertising managers and practitioners who want to learn how to use Excel to solve practical statistics problems in the workplace, whether or not they have taken a course in statistics.Excel 2019 for Advertising Statistics explains statistical formulas and offers practical examples for how students can solve real-world advertising statistics problems. Each chapter offers a concise overview of a topic, and then demonstrates how to use Excel commands and formulas to solve specific advertising statistics problems. This book demonstrates how to use Excel 2019 in two different ways: (1) writing formulas (e.g., confidence interval about the mean, one-group t-test, two-group t-test, correlation) and (2) using Excel's drop-down formula menus (e.g., simple linear regression, multiple correlation and multiple regression, andone-way ANOVA). Three practice problems are provided at the end of each chapter, along with their solutions in an appendix. An additional practice test allows readers to test their understanding of each chapter by attempting to solve a specific practical advertising statistics problem using Excel; the solution to each of these problems is also given in an appendix. This latest edition features a wealth of new end-of-chapter problems and an update of the chapter content throughout. ​

This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions.Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis.The book begins by explainingfundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra. Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.

This volume provides an overview of the ways the Italian school of quality of life studies addresses well-being and quality of life, from both a substantive and a methodological point of view. It discusses various topics such as those of equitable and sustainable wellbeing, lifestyles, the organization of economy and welfare, as well as aspects related to the measurement of quality of life in small towns, institutional transparency and corruption prevention indicators. Chapters presented in this volume are drawn from papers presented at the conferences of the Italian Association for Quality of Life Studies (AIQUAV) held in Florence, Italy, in 2015 and 2016. The volume is organised into three parts. The first part is devoted to methods and indicators for research on quality of life, the second part to social sustainability, lifestyles, cultural aspects and local applications, and the third to economy, welfare and quality of life. The volume hosts contributions that are interdisciplinary in scope and mirror the complexity of the globalized world.

This book introduces readers to advanced statistical methods for analyzing survival data involving correlated endpoints. In particular, it describes statistical methods for applying Cox regression to two correlated endpoints by accounting for dependence between the endpoints with the aid of copulas. The practical advantages of employing copula-based models in medical research are explained on the basis of case studies. In addition, the book focuses on clustered survival data, especially data arising from meta-analysis and multicenter analysis. Consequently, the statistical approaches presented here employ a frailty term for heterogeneity modeling. This brings the joint frailty-copula model, which incorporates a frailty term and a copula, into a statistical model. The book also discusses advanced techniques for dealing with high-dimensional gene expressions and developing personalized dynamic prediction tools under the joint frailty-copula model. To help readers apply the statistical methods to real-world data, the book provides case studies using the authors'original R software package (freely available in CRAN). The emphasis is on clinical survival data, involving time-to-tumor progression and overall survival, collected on cancer patients. Hence, the book offers an essential reference guide for medical statisticians and provides researchers with advanced, innovative statistical tools. The book also provides a concise introduction to basic multivariate survival models.

This book demonstrates the importance of computer-generated statistical analyses in behavioral science research, particularly those using the R software environment. Statistical methods are being increasingly developed and refined by computer scientists, with expertise in writing efficient and elegant computer code. Unfortunately, many researchers lack this programming background, leaving them to accept on faith the black-box output that emerges from the sophisticated statistical models they frequently use. Building on the author's previous volume, Linear Models in Matrix Form, this text bridges the gap between computer science and research application, providing easy-to-follow computer code for many statistical analyses using the R software environment. The text opens with a foundational section on linear algebra, then covers a variety of advanced topics, including robust regression, model selection based on bias and efficiency, nonlinear models and optimization routines, generalized linear models, and survival and time-series analysis. Each section concludes with a presentation of the computer code used to illuminate the analysis, as well as pointers to packages in R that can be used for similar analyses and nonstandard cases. The accessible code and breadth of topics make this book an ideal tool for graduate students or researchers in the behavioral sciences who are interested in performing advanced statistical analyses without having a sophisticated background in computer science and mathematics.

The intention behind this book is to illustrate the deep relation among human behavior, data-centric science, and social design. In fact, these three issues have been independently developing in different fields, although they are, of course, deeply interrelated to one another. Specifically, fundamental understanding of human behavior should be employed for investigating our human society and designing social systems. Insights and both quantitative and qualitative understandings of collective human behavior are quite useful when social systems are designed. Fundamental principles of human behavior, theoretical models of human behavior, and information cascades are addressed as aspects of human behavior. Data-driven investigation of human nature, social behavior, and societal systems are developed as aspects of data-centric science. As design aspects, how to design social systems from heterogeneous memberships is explained. There is also discussion of these three aspects—human behavior, data-centric science, and social design—independently and with regard to the relationships among them.

This book focuses on experimental research in two disciplines that have a lot of common ground in terms of theory, experimental designs used, and methods for the analysis of experimental research data: education and psychology. Although the methods covered in this book are also frequently used in many other disciplines, including sociology and medicine, the examples in this book come from contemporary research topics in education and psychology. Various statistical packages, commercial and zero-cost Open Source ones, are used.The goal of this book is neither to cover all possible statistical methods out there nor to focus on a particular statistical software package. There are many excellent statistics textbooks on the market that present both basic and advanced concepts at an introductory level and/or provide a very detailed overview of options in a particular statistical software programme. This is not yet another book in that genre. Core theme of this book is a heuristic called the question-design-analysis bridge: there is a bridge connecting research questions and hypotheses, experimental design and sampling procedures, and common statistical methods in that context. Each statistical method is discussed in a concrete context of a set of research question with directed (one-sided) or undirected (two-sided) hypotheses and an experimental setup in line with these questions and hypotheses. Therefore, the titles of the chapters in this book do not include any names of statistical methods such as ‘analysis of variance'or ‘analysis of covariance'. In a total of seventeen chapters, this book covers a wide range of topics of research questions that call for experimental designs and statistical methods, fairly basic or more advanced.

This introductory book enables researchers and students of all backgrounds to compute interrater agreements for nominal data. It presents an overview of available indices, requirements, and steps to be taken in a research project with regard to reliability, preceded by agreement. The book explains the importance of computing the interrater agreement and how to calculate the corresponding indices. Furthermore, it discusses current views on chance expected agreement and problems related to different research situations, so as to help the reader consider what must be taken into account in order to achieve a proper use of the indices.The book offers a practical guide for researchers, Ph.D. and master students, including those without any previous training in statistics (such as in sociology, psychology or medicine), as well as policymakers who have to make decisions based on research outcomes in which these types of indices are used.

This study explores the dynamic relationship between science, numbers and politics. What can scientific evidence realistically do in and for politics? The volume contributes to that debate by focusing on the role of “numbers” as a means by which knowledge is expressed and through which that knowledge can be transferred into the political realm. Based on the assumption that numbers are constantly being actively created, translated, and used, and that they need to be interpreted in their respective and particular contexts, it examines how numbers and quantifications are made ‘politically workable', examining their production, their transition into the sphere of politics and their eventual use therein. Key questions that are addressed include: In what ways does scientific evidence affect political decision-making in the contemporary world? How and why did quantification come to play such an important role within democratic politics? What kind of work do scientific evidenceand numbers do politically?

This volume presents a compilation of composite indicators created in order to measure important aspects of the quality of European societies. It examines three main questions: do Europeans live in good societies and enjoy good lives; are European societies becoming better as time passes, or is their quality slowly deteriorating; is the quality of life of Europe's citizens improving over time or is it gradually and irrecoverably getting worse. The volume uses a precise and rigorous system of information to answer these questions and to assess the current situation and monitor the quality of European societies. It describes and discusses fourteen key domains, and per chapter, presents five rankings of EU countries based on composite indicators, which are used as one of the best instruments social science has to synthesize a large amount of information, and they are especially well suited to measure multidimensional social phenomena. The new System of Indices on the Quality of European Societies (SIQES) presented in this volume offers a very broad and rich empirical overview of more than 70 social composite indicators and their nearly 300 dimensions. One of the key findings coming out from the SIQES is that, according to the societal quality of European countries, there exist five different “Europes” inside Europe.

This book is devoted to Corrado Gini, father of the Italian statistical school. It celebrates the 50th anniversary of his death by bearing witness to the continuing extraordinary scientific relevance of his interdisciplinary interests. The book comprises a selection of the papers presented at the conference of the Italian Statistical Society, Statistics and Demography – the Legacy of Corrado Gini, held in Treviso in September 2015. The work covers many topics linked to Gini's scientific legacy, ranging from the theory of statistical inference to multivariate statistical analysis, demography and sociology. In this volume, readers will find many interesting contributions on entropy measures, permutation procedures for the heterogeneity test, robust estimation of skew-normal parameters, S-weighted estimator, measures of multidimensional performance using Gini's delta, small-sample confidence intervals for Gini's gamma index, Bayesian estimation of the Gini-Simpson index, spatial residential patterns of selected foreign groups, minority segregation processes, dynamic time warping to study cruise tourism, and financial stress spill over. This book will appeal to all statisticians, demographers, economists, and sociologists interested in the field.

This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.

This textbook is a step-by-step guide for high school, community college, or undergraduate students who are taking a course in applied statistics and wish to learn how to use Excel to solve statistical problems. All of the statistics problems in this book will come from the following fields of study: business, education, psychology, marketing, engineering and advertising. Students will learn how to perform key statistical tests in Excel without being overwhelmed by statistical theory. Each chapter briefly explains a topic and then demonstrates how to use Excel commands and formulas to solve specific statistics problems. This book gives practice in using Excel in two different ways: (1) writing formulas (e.g., confidence interval about the mean, one-group t-test, two-group t-test, correlation) and (2) using Excel's drop-down formula menus (e.g., simple linear regression, multiple correlations and multiple regression, and one-way ANOVA). Three practice problems are provided at the end of each chapter, along with their solutions in an Appendix. An additional Practice Test allows readers to test their understanding of each chapter by attempting to solve a specific statistics problem using Excel; the solution to each of these problems is also given in an Appendix. This book is a tool that can be used either by itself or along with any good statistics book. Includes 166 illustrations in color Suitable for high school and community college students.

This book offers a relatively self-contained presentation of the fundamental results in categorical data analysis, which plays a central role among the statistical techniques applied in the social, political and behavioral sciences, as well as in marketing and medical and biological research. The methods applied are mainly aimed at understanding the structure of associations among variables and the effects of other variables on these interactions. A great advantage of studying categorical data analysis is that many concepts in statistics become transparent when discussed in a categorical data context, and, in many places, the book takes this opportunity to comment on general principles and methods in statistics, addressing not only the “how” but also the “why.” Assuming minimal background in calculus, linear algebra, probability theory and statistics, the book is designed to be used in upper-undergraduate and graduate-level courses in the field and in more general statistical methodology courses, as well as a self-study resource for researchers and professionals. The book covers such key issues as: higher order interactions among categorical variables; the use of the delta-method to correctly determine asymptotic standard errors for complex quantities reported in surveys; the fundamentals of the main theories of causal analysis based on observational data; the usefulness of the odds ratio as a measure of association; and a detailed discussion of log-linear models, including graphical models. The book contains over 200 problems, many of which may also be used as starting points for undergraduate research projects. The material can be used by students toward a variety of goals, depending on the degree of theory or application desired.

This book highlights the quantitative methods of data mining and information visualization and explores their use in relation to the films and writings of the Russian director, Dziga Vertov. The theoretical basis of the work harkens back to the time when a group of Russian artists and scholars, known as the “formalists,” developed new concepts of how art could be studied and measured. This book brings those ideas to the digital age. One of the central questions the book intends to address is, “How can hypothetical notions in film studies be supported or falsified using empirical data and statistical tools?” The first stage involves manual and computer-assisted annotation of the films, leading to the production of empirical data which is then used for statistical analysis but more importantly for the development of visualizations. Studies of this type furthermore shed light on the field of visual presentation of time-based processes; an area which has its origin in the Russian formalist sphere of the 1920s and which has recently gained new relevance due to technological advances and new possibilities for computer-assisted analysis of large and complex data sets. In order to reach a profound understanding of Vertov and his films, the manual or computer-assisted data analysis must be combined with film-historical knowledge and a study of primary sources. In addition, the status of the surviving film materials and the precise analysis of these materials combined with knowledge of historical film technology provide insight into archival policy and political culture in the Soviet Union in the 1920s and 30s.