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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 fully updated fourth edition explores the foundations of statistical reasoning, focusing on how to interpret psychological data and statistical results. This edition includes three important new features. First, the book is closely integrated with the free statistical analysis program JASP. Thus, students learn how to use JASP to help with tasks such as constructing grouped frequency distributions, making violin plots, conducting inferential statistical tests, and creating confidence intervals. Second, reflecting the growing use of Bayesian analyses in the professional literature, this edition includes a chapter with an introduction to Bayesian statistics (also using JASP). Third, the revised text incorporates adjunct questions, that is, questions that challenge the student's understanding, after each major section. Cognitive psychology has demonstrated how adjunct questions and related techniques such as self-explanation can greatly improve comprehension.Additional key features of the book include:• A user-friendly approach, with focused attention to explaining the more difficult concepts and the logic behind them. End of chapter tables summarize the hypothesis testing procedures introduced, and exercises support information recall and application.• The consistent use of a six-step procedure for all hypothesis tests that captures the logic of statistical inference.• Multiple examples of each of the major inferential statistical tests.• Boxed media reports illustrate key concepts and their relevance to real-world issues.• A focus on power, with a separate chapter, and power analysis procedures in each chapter.With comprehensive digital resources, including large data sets integrated throughout the textbook, and files for conducting analysis in JASP, this is an essential text for undergraduate or beginning graduate statistics courses in psychology, education, and other applied social and health sciences.

Adam Grant's “8 New Idea Books to Start Spring”Next Big Idea Club's Must-Read Books for May 2024'A wonderful litany of the myriad ways in which we can be deceived, and deceive ourselves.'—The Guardian“Highlights how first recognizing our biases and then taking small but intentional steps to overcome them can have an outsized effect on the quality of our decisions.'—Inc.How our biases cause us to fall for misinformation—and how to combat it. Our lives are minefields of misinformation. It ripples through our social media feeds, our daily headlines, and the pronouncements of politicians, executives, and authors. Stories, statistics, and studies are everywhere, allowing people to find evidence to support whatever position they want. Many of these sources are flawed, yet by playing on our emotions and preying on our biases, they can gain widespread acceptance, warp our views, and distort our decisions. In this eye-opening book, renowned economist Alex Edmans teaches us how to separate fact from fiction. Using colorful examples—from a wellness guru's tragic but fabricated backstory to the blunders that led to the Deepwater Horizon disaster to the diet that ensnared millions yet hastened its founder's death—Edmans highlights the biases that cause us to mistake statements for facts, facts for data, data for evidence, and evidence for proof. Armed with the knowledge of what to guard against, he then provides a practical guide to combat this tide of misinformation. Going beyond simply checking the facts and explaining individual statistics, Edmans explores the relationships between statistics—the science of cause and effect—ultimately training us to think smarter, sharper, and more critically. May Contain Lies is an essential read for anyone who wants to make better sense of the world and better decisions.

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.

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.

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 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.

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.

Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world's leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas.This Advanced Introduction provides a critical review and discussion of research concerning spatial statistics, differentiating between it and spatial econometrics, to answer a set of core questions covering the geographic-tagging-of-data origins of the concept and its theoretical underpinnings, conceptual advances, and challenges for future scholarly work. It offers a vital tool for understanding spatial statistics and surveys how concerns about violating the independent observations assumption of statistical analysis developed into this discipline.Key Features:A concise overview of spatial statistics theory and methods, looking at parallel developments in geostatistics and spatial econometrics, highlighting the eclipsing of centography and point pattern analysis by geostatistics and spatial autoregression, and the emergence of local analysisContemporary descriptions of popular geospatial random variables, emphasizing one- and two-parameter spatial autoregression specifications, and Moran eigenvector spatial filtering coupled with a broad coverage of statistical estimation techniquesA detailed articulation of a spatial statistical workflow conceptualizationThe helpful insights from empirical applications of spatial statistics in agronomy, criminology, demography, economics, epidemiology, geography, remotely sensed data, urban studies, and zoology/botany, will make this book a useful tool for upper-level students in these disciplines.

Statistics for the Terrified offers a clear and concise introduction to statistics. Perfect as a brief core or supplementary text for undergraduate courses in statistics and research methods, this seventh edition is also an ideal refresher for graduate students who have already taken a statistics course. Designed for students who may struggle with mathematical concepts, its informal and highly engaging narrative includes self-help strategies, numerous concrete examples, and a great deal of humor to encourage students from all backgrounds with the study of statistics.

There is no other source that provides in one place the wide range and depth of insight found in Vital Statistics on American Politics (VSAP), published since 1988. VSAP provides historical and statistical information on all aspects of American politics: Political parties Voter turnout Public opinion Campaign finance Media perspective and influence, congressional membership and voting patterns The presidency and executive branch Military policy and spending Supreme Court and federal court make-up and caseloads Foreign, social, and economic policy In over 230 tables and figures, students and professional researchers will find chapters devoted to key subject areas such as elections and political parties, public opinion and voting, the media, the three branches of U.S. government, foreign, military, social and economic policy, and much more. This book provides a vivid and multifaceted portrait of the broad spectrum of United States politics and policies. Along with updated and new data content, this edition offers brand new data literacy lessons that take a'guide on the side'approach to teach data researchers how to wade through the sea of data and do the difficult work of grappling for the meaning of the data on their own. Lessons include understanding descriptive representation data, comparing data over time, noticing gaps in data, unpacking dichotomies of public opinion, and more.

This book reveals the myriad aspects of Big Data collection and analysis, by defining and clarifying the meaning of Big Data and its unique characteristics in a non-technical and easy-to-follow way. Moreover, it discusses critical issues and problems related to the Big Data revolution and their implications for both Statistics as a discipline and for our everyday lives. The author identifies various problems and limitations in the quantitative analysis of Big Data, with regard to e.g. its volume, velocity and variety, as well as its reliability and veridicity. Dedicated chapters focus on the epistemological aspects of data-based knowledge and ethical aspects of the use of Big Data, while also addressing paradigmatic cases such as Cambridge Analytica and the use of data from social networks to influence election outcomes.

An experienced author in the field of data analytics and statistics, John Macinnes has produced a straight-forward text that breaks down the complex topic of inferential statistics with accessible language and detailed examples. It covers a range of topics, including: · Probability and Sampling distributions · Inference and regression · Power, effect size and inverse probability Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

This book provides an up-to-date and accessible introduction to causal inference in quantitative research. Featuring worked example datasets throughout, it clearly outlines the steps involved in carrying out various types of statistical causal analysis. In turn, helping you apply these methods to your own research. It contains guidance on: Selecting the most appropriate conditioning method for your data. Applying the Rubin's Causal Model to your analysis, a mathematical framework for understanding and ensuring accurate causation inferences. Utilising various techniques and designs, such as propensity scores, instrumental variables analysis, and regression discontinuity designs, to better synthesise and analyse different types of data. Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

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 book shows the capabilities of Microsoft Excel in teaching social science statistics effectively. Similar to the previously published Excel 2016 for Social Sciences Statistics, this book is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical social science problems. If understanding statistics isn't your strongest suit, you are not especially mathematically inclined, or 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 science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2019 for Social Science 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 science 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.

The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences. The data revolution in social science research has not only produced new business models, but has also provided policymakers with better decision-making support tools. In this volume, statisticians, computer scientists and experts on social research discuss the opportunities and challenges of the social data revolution in order to pave the way for addressing new research problems. The respective contributions focus on complex social systems and current methodological advances in extracting social knowledge from large data sets, as well as modern social research on human behavior and society using large data sets. Moreover, they analyze integrated systems designed to take advantage of new social data sources, and discuss quality-related issues. The papers were originally presented at the 2nd International Conference on Data Science and Social Research, held in Milan, Italy, on February 4-5, 2019.

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.

With the constant evolution of change in healthcare from both a technology and governmental perspective, it is imperative to take a step back and view the big picture. Relying on hunches or beliefs is no longer sustainable, so avoid jumping to conclusions and making decisions without thoroughly understanding the statistics being analyzed. The triple aim of statistics is a conceptual model laying the foundation for improving healthcare outcomes through statistics. This foundation is: know your numbers; develop behavioral interventions; and set goals to drive change.With the availability of electronic data sources, the quantity and quality of data have grown exponentially to the point of information overload. Translating all this data into words that tell a meaningful story is overwhelming. This book takes the reader on a journey that navigates through this data to tell a story that everyone can understand and use to drive improvement. Readers will learn to tell a narrative story based on data, to develop creative, innovative and effective solutions to improve processes and outcomes utilizing the authors'tools. Topics include mortality and readmission, patient experience, patient safety survey, governmental initiatives, CMS Star Rating and Hospital Compare.Storytelling with Data in Healthcare combines methodology and statistics in the same course material, making it coherent and easier to put into practice. It uses storytelling as a tool for knowledge acquisition and retention and will be valuable for courses in nursing schools, medical schools, pharmacy schools or any healthcare profession that has a research design or statistics course offered to students. The book will be of interest to researchers, academics, healthcare professionals, and students in the fields of healthcare management and operations as well as statistics and data visualization.

Roping the reader in with humor and real-world case examples presented as mysteries to be solved, this engaging text has been updated with new cases, the latest version of SPSS, and new coverage of multivariate analysis of variance. Steven R. Terrell prepares students and practitioners to become informed consumers of statistics so that they can make decisions based on data, and understand decisions others have made. He identifies six simple steps and guides readers to master them--from identifying a researchable problem to stating a hypothesis; identifying independent and dependent variables; and selecting, computing, and interpreting appropriate statistical tests. All techniques are demonstrated both manually and with the help of SPSS software. New to This Edition •All software instructions and examples are updated to SPSS Version 25. •Expanded chapter on the analysis of variance (ANOVA)--now covers multivariate ANOVA. •New and revised examples and quiz items pertaining to a broader range of fields, such as business, information systems, and medical sciences, along with education and psychology. Pedagogical Features •Examples of SPSS screenshots used for analyzing data. •User-friendly cautionary notes,'Putting it All Together'recaps, and alerts, such as'notice the effect size'or'check the direction of the mean scores.'•End-of-chapter'Quiz Time'exercises that guide students to answer intriguing questions like whether working from home increases productivity, or whether age affects how long it takes to complete a doctoral degree. •Lists of key terms and formulas in each chapter, plus end-of-book glossary.

With a clear, engaging writing style and fascinating examples using a variety of real data, this text covers the contemporary statistical techniques that students will encounter in the world of social research. It covers these techniques at an introductory level and carefully guides students through increasingly complex examples without intimidating them. Recurrent examples using four timely topics—health, immigration, income inequality, and everyday harassment—help students understand how the techniques fit together, and how to use the techniques in combination with one another. A superb author-created web resource accompanies the text. How to make clear presentations of research results is also a feature of the text. New to this edition: New research shows how the techniques has changed over time in the academic literature, showing students that social scientists really do use the statistical techniques the book teaches and giving them ample motivation to learn the techniques. Examples throughout the book use the most recent data from the General Social Survey. Four timely topics are threaded throughout the book: immigration, health, income inequality, and everyday harassment. Linneman uses these topics recurrently with different statistical techniques to illustrate how the techniques are related to one another. The new edition more explicitly emphasizes that the various techniques the students are learning are often used in combination with one another. After introducing a new technique and showing how to use it on its own, Linneman then systematically offers examples of how to combine that technique with techniques students learned in previous chapters. Most of the literature examples that end each chapter are new and use very recent research from top academic journals (three quarters from 2015 or later, nearly half from 2019). They feature research that covers timely topics such as Black Lives Matter, transgender health, social media, police behavior, and climate change. The SPSS demonstrations are completely redone, both in the book and on the website's demonstration videos, using more recent data. Linneman applies his experience teaching his own students SPSS (knowing where students get confused) to clarify his explanations in these demonstrations.

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.

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 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.

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.

This book introduces demographic applications which employ current demographic concepts and theories and cutting-edge methods and findings, all of which have and will continue to have an impact in the broad area of social demography. Through providing an introduction to new and current developments in demography, methodological and statistical issues, data issues, issues of health, aging and mortality, and issues in social demography, this book gives new insights into data, substantive issues, and methodological approaches that will assist readers in their use of demography in their research. At the same time it shows demographers, sociologists, economists, statisticians, methodologists, planners, and marketers how they may learn and improve upon the quality and relevance of their demographic investigations now and in the future.

Statistics for Social Understanding: With Stata and SPSS introduces students to the way statistics is used in the social sciences--as a tool for advancing understanding of the social world. Written in an engaging and clear voice and based on the latest research on the teaching and learning of quantitative material, the text is geared to introductory students in the social sciences, including those with little quantitative background. It covers the conceptual aspects of statistics even when the mathematical details are minimized. Informed by research on teaching and learning in statistics, the book takes a universal design approach to accommodate diverse learning styles. With an early chapter on cross-tabulation, a focus on comparisons between groups throughout, and a unique chapter on causality, the text shows students the power of statistics for answering important real-world questions. By providing thorough coverage of social science statistical topics, a balanced approach to calculation, and step-by-step directions on how to use statistical software, authors Nancy Whittier, Tina Wildhagen, and Howard J. Gold give students the ability to analyze data and explore and answer exciting questions. To accommodate changing undergraduate courses, the text incorporates examples from both Stata and SPSS in every chapter andprovides practice problems of every type as well as readily available datasets for classroom use, including the General Social Survey, American National Election Study, and more. Each chapter concludes with a chapter summary, a section on using Stata, a section on using SPSS, and practice problems.Statistics for Social Understanding: With Stata and SPSS is accompanied by a learning package, written entirely by Tina Wildhagen, that is designed to enhance the experience of both instructors and students. Author-written ancillaries ensure that supplementary materials match the text in voice, language, and content.

Newly revised to specifically address Microsoft Excel 2019, this book shows the capabilities of Excel in teaching educational and psychological statistics effectively. Similar to the previously published Excel 2016 for Educational and Psychological Statistics, it is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical education and psychology problems.Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in education and psychology courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. Excel 2019 for Educational and Psychological Statistics capitalizes on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work.Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand educational and psychological 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. This new edition features a wealth of new chapter problems and solutions, as well as updated chapter content throughout.

This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture. The third edition considers significant advances in recent years, among which are: the development of overarching, conceptual frameworks for statistical learning;the impact of “big data” on statistical learning;the nature and consequences of post-model selection statistical inference;deep learning in various forms;the special challenges to statistical inference posed by statistical learning;the fundamental connections between data collection and data analysis;interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy. This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.

This book focuses on performing hands-on meta-analysis using MetaXL, a free add-on to MS Excel. The illustrative examples are taken mainly from medical and health sciences studies, but the generic methods can be used to perform meta-analysis on data from any other discipline. The book adopts a step-by-step approach to perform meta-analyses and interpret the results. Stata codes for meta-analyses are also provided. All popularly used meta-analytic methods and models – such as the fixed effect model, random effects model, inverse variance heterogeneity model, and quality effect model – are used to find the confidence interval for the effect size measure of independent primary studies and the pooled study. In addition to the commonly used meta-analytic methods for various effect size measures, the book includes special topics such as meta-regression, dose-response meta-analysis, and publication bias. The main attraction for readers is the book's simplicity and straightforwardness in conducting actual meta-analysis using MetaXL. Researchers would easily find everything on meta-analysis of any particular effect size in one specific chapter once they could determine the underlying effect measure. Readers will be able to see the results under different models and also will be able to select the correct model to obtain accurate results.

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.

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.

Neil J. Salkind's bestselling Statistics for People Who (Think They) Hate Statistics has been helping ease student anxiety around an often intimidating subject since it first published in 2000. Now the bestselling SPSS® and Excel® versions are joined by a text for use with the R software, Statistics for People Who (Think They) Hate Statistics Using R. New co-author Leslie A. Shaw carries forward Salkind's signature humorous, personable, and informative approach as the text guides students in a grounding of statistical basics and R computing, and the application of statistics to research studies. The book covers various basic and advanced statistical procedures, from correlation and graph creation to analysis of variance, regression, non-parametric tests, and more.

You can′t get anywhere in your statistics course without grasping statistical significance. it′s often seen as difficult but is actually a straightforward concept everyone can—and should—understand. Do your results mean something—or not? How can you measure it? Breaking it down into three building blocks, this Little Quick Fix shows students how to master: hypothesis testing normal distribution p values Students will learn how to understand the concept and also how to explain it for maximum effect in their essays and lab reports. Good for results—this is also a secret weapon for critical thinking. Little Quick Fix titles provide quick but authoritative answers to the problems, hurdles, and assessment points students face in the research course, project proposal, or design—whatever their methods learning is. Lively, ultra-modern design; full-colour, each page a tailored design. An hour′s read. Easy to dip in and out of with clear navigation enables the reader to find what she needs—quick. Direct written style gets to the point with clear language. Nothing needs to be read twice. No fluff. Learning is reinforced through a 2-minute overview summary; 3-second summaries with super-quick Q&A DIY tasks create a work plan to accomplish a task, do a self-check quiz, solve a problem, get students to what they need to show their supervisor. Checkpoints in each section make sure students are nailing it as they go and support self-directed learning. How do I know I'm done? Each Little Quick Fix wraps up with a final checklist that allows the reader to self-assess they've got what they need to progress, submit, or ace the test or task.

This open access book shows how to use sensitivity analysis in demography. It presents new methods for individuals, cohorts, and populations, with applications to humans, other animals, and plants. The analyses are based on matrix formulations of age-classified, stage-classified, and multistate population models. Methods are presented for linear and nonlinear, deterministic and stochastic, and time-invariant and time-varying cases. Readers will discover results on the sensitivity of statistics of longevity, life disparity, occupancy times, the net reproductive rate, and statistics of Markov chain models in demography. They will also see applications of sensitivity analysis to population growth rates, stable population structures, reproductive value, equilibria under immigration and nonlinearity, and population cycles. Individual stochasticity is a theme throughout, with a focus that goes beyond expected values to include variances in demographic outcomes. The calculations are easily and accurately implemented in matrix-oriented programming languages such as Matlab or R. Sensitivity analysis will help readers create models to predict the effect of future changes, to evaluate policy effects, and to identify possible evolutionary responses to the environment. Complete with many examples of the application, the book will be of interest to researchers and graduate students in human demography and population biology. The material will also appeal to those in mathematical biology and applied mathematics.

This Study Guide for introductory statistics courses in psychology departments is designed to accompany Neil J. Salkind and Bruce B. Frey's best-selling Statistics for People Who (Think They) Hate Statistics, Seventh Edition. Extra exercises; activities; and true/false, multiple choice, and essay questions (with answers to all questions) feature psychology-specific content to help further student mastery of text concepts. Two additional appendix items in this guide include: Practice with Real Data!, which outlines four experiments and provides students with the datasets to run the analyses, plus Writing Up Your Results – Guidelines based on APA style.

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.

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 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 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 open access book provides innovative methods and original applications of sequence analysis (SA) and related methods for analysing longitudinal data describing life trajectories such as professional careers, family paths, the succession of health statuses, or the time use. The applications as well as the methodological contributions proposed in this book pay special attention to the combined use of SA and other methods for longitudinal data such as event history analysis, Markov modelling, and sequence network. The methodological contributions in this book include among others original propositions for measuring the precarity of work trajectories, Markov-based methods for clustering sequences, fuzzy and monothetic clustering of sequences, network-based SA, joint use of SA and hidden Markov models, and of SA and survival models. The applications cover the comparison of gendered occupational trajectories in Germany, the study of the changes in women market participation in Denmark, the study of typical day of dual-earner couples in Italy, of mobility patterns in Togo, of internet addiction in Switzerland, and of the quality of employment career after a first unemployment spell. As such this book provides a wealth of information for social scientists interested in quantitative life course analysis, and all those working in sociology, demography, economics, health, psychology, social policy, and statistics.

The goal of this book is to put an array of tools at the fingertips of students, practitioners, and researchers by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This volume serves at least three audiences: (1) students of applied sampling techniques; 2) practicing survey statisticians applying concepts learned in theoretical or applied sampling courses; and (3) social scientists and other survey practitioners who design, select, and weight survey samples. The text thoroughly covers fundamental aspects of survey sampling, such as sample size calculation (with examples for both single- and multi-stage sample design) and weight computation, accompanied by software examples to facilitate implementation. Features include step-by-step instructions for calculating survey weights, extensive real-world examples and applications, and representative programming code in R, SAS, and other packages. Since the publication of the first edition in 2013, there have been important developments in making inferences from nonprobability samples, in address-based sampling (ABS), and in the application of machine learning techniques for survey estimation. New to this revised and expanded edition: • Details on new functions in the PracTools package • Additional machine learning methods to form weighting classes • New coverage of nonlinear optimization algorithms for sample allocation • Reflecting effects of multiple weighting steps (nonresponse and calibration) on standard errors • A new chapteron nonprobability sampling • Additional examples, exercises, and updated references throughout Richard Valliant, PhD, is Research Professor Emeritus at the Institute for Social Research at the University of Michigan and at the Joint Program in Survey Methodology at the University of Maryland. He is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has been an Associate Editor of the Journal of the American Statistical Association, Journal of Official Statistics, and Survey Methodology. Jill A. Dever, PhD, is Senior Research Statistician at RTI International in Washington, DC. She is a Fellow of the American Statistical Association, Associate Editor for Survey Methodology and the Journal of Official Statistics, and an Assistant Research Professor in the Joint Program in Survey Methodology at the University of Maryland. She has served on several panels for the National Academy of Sciences and as a task force member for the American Association of Public Opinion Research's report on nonprobability sampling. Frauke Kreuter, PhD, is Professor and Director of the Joint Program in Survey Methodology at the University of Maryland, Professor of Statistics and Methodology at the University of Mannheim, and Head of the Statistical Methods Research Department at the Institute for Employment Research (IAB) in Nürnberg, Germany. She is a Fellow of the American Statistical Association and has been Associate Editor of the Journal of the Royal Statistical Society, Journal of Official Statistics, Sociological Methods and Research, Survey Research Methods, Public Opinion Quarterly, American Sociological Review, and the Stata Journal. She is founder of the International Program for Survey and Data Science and co-founder of

Late in a career of more than sixty years, Thomas Burch, an internationally known social demographer, undertook a wide-ranging methodological critique of demography. This open access volume contains a selection of resulting papers, some previously unpublished, some published but not readily accessible. Rejecting the idea that demography is simply a branch of applied statistics, his work views it as an autonomous and complete scientific discipline. When viewed from the perspective of modern philosophy of science, specifically the semantic or model-based school, demography is a balanced discipline, with a rich body of techniques and data, but also with more and better theories than generally recognized. As demonstrated in this book, some demographic techniques can also be seen as theoretical models, and some substantive/behavioral models, commonly rejected as theory because of inconsistent observations, are now seen as valuable theoretical models, for example demographic transition theory. This book shows how demography can build a strong theoretical edifice on its broad and deep empirical foundation by adoption of the model-based approach to science. But the full-fruits of this approach will require demographers to make greater use of computer modeling in the statement and manipulation of theoretical ideas, as well as for numerical computation.

Statistics for the Terrified offers a clear and concise introduction to statistics. Perfect as a brief core or supplementary text for undergraduate courses in statistics and research methods, this Sixth Edition is also an ideal refresher for graduate students who have already taken a statistics course. Designed for students who may struggle with mathematical concepts, its informal and highly engaging narrative includes self-help strategies, numerous concrete examples, and a great deal of humor to encourage students from all backgrounds to the study of statistics.

How does the United States compare when objectively measured shoulder-to-shoulder against the world's two most influential autocracies? This full-color book provides a solid foundation to enable the reader to create informed opinions about China, Russia, and the United States through comparative examination of their global status and the quality of their peoples'lives. Data resources—created by many respected organizations including the World Bank, the United Nations, the Center for Disease Control (CDC), and Freedom House, to name a few—have been mined to provide direct comparisons between many key characteristics including health, wealth, poverty, education, employment, crime, imprisonments, freedoms, happiness, natural resources, infrastructure, debt, taxes, trade, military assets, and nuclear warheads.It is the author's mission to present meaningful data—all with attributed sources—in an inviting, graphic format to convey much more information than would be possible in tabular form. By directly displaying data the usual biases and filters are bypassed enhancing your ability to draw your own conclusions. This visual approach very effectively reveals trends and makes differences between nations and their people self-evident.In the United States (2016):64,100 people died of drug overdose and 2.2 million people were in prisonsThe top twenty percent of households received 51.5 percent of all income1.2 trillion dollars were added to debt and 241 billion was paid in interestForeigners held one-third of federal debt including 1.1 trillion by ChinaIn comparison with the United States Russia had: 12.5 years lower life expectancy for males born in 2016 (only 65.0 years)Double the homicide rate and 40 percent higher suicide rate60 percent higher alcohol consumption per capitaAn economy one-fifth as large, measured by GDP in international dollarsEquivalent number of nuclear warheads (approximately 7,000)Double the crude oil reserves and five times the natural gas reservesRepressive government—rated within the worst 10 percent by Freedom House14 times as many residents (67,000) seeking asylum In comparison with the United States China had: An economy 15 percent larger, measured by GDP in international dollarsThree times as many patent applications filed by residentsOne-fifth the homicide rate and one-half the poverty rate58 percent more outbound international tourists61 points higher scores in mathematics literacy for students aged 15 yearsDouble the total carbon dioxide emissions Repressive government—rated within the worst 10 percent by Freedom House44 times as many residents (212,000) seeking asylum

This book visualizes mortality dynamics in the Lexis diagram. While the standard approach of plotting death rates is also covered, the focus in this book is on the depiction of rates of mortality improvement over age and time. This rather novel approach offers a more intuitive understanding of the underlying dynamics, enabling readers to better understand whether period- or cohort-effects were instrumental for the development of mortality in a particular country. Besides maps for single countries, the book includes maps on the dynamics of selected causes of death in the United States, such as cardiovascular diseases or lung cancer. The book also features maps for age-specific contributions to the change in life expectancy, for cancer survival and for seasonality in mortality for selected causes of death in the United States. The book is accompanied by instructions on how to use the freely available R Software to produce these types of surface maps. Readers are encouraged to use the presented tools to visualize other demographic data or any event that can be measured by age and calendar time, allowing them to adapt the methods to their respective research interests. The intended audience is anyone who is interested in visualizing data by age and calendar time; no specialist knowledge is required.This book is open access under a CC BY license.

In the light of better and more detailed administrative databases, this open access book provides statistical tools for evaluating the effects of public policies advocated by governments and public institutions. Experts from academia, national statistics offices and various research centers present modern econometric methods for an efficient data-driven policy evaluation and monitoring, assess the causal effects of policy measures and report on best practices of successful data management and usage. Topics include data confidentiality, data linkage, and national practices in policy areas such as public health, education and employment. It offers scholars as well as practitioners from public administrations, consultancy firms and nongovernmental organizations insights into counterfactual impact evaluation methods and the potential of data-based policy and program evaluation.

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.

The composition of the American population is rapidly changing from a white, male dominated society to one that is so diverse it will soon be without any single, dominant race, ethnicity, or gender. The dramatic demographic shifts in American society have provoked many false claims and distortions of facts that have fueled demagoguery, as occurred during the 2016 presidential campaign. Access to unvarnished facts about people different than you—but who are becoming your neighbors—is more critical now than ever.This book, in full-color, was created to provide a single source of easily accessible facts—obtained primarily from U.S. government agencies—comparing characteristics of race, ethnicity, and gender in graphic format to enhance comprehension, as only visual presentations can achieve. Virtually all major socio-economic topics are covered, including geographic distribution of populations, birth rates, health, wealth, poverty, income, employment, crime, incarcerations, social behaviors, education, and political preferences. Included are past and future trends for many characteristics, as are comparisons between foreign-born, natural citizens, legal and undocumented immigrants. Special Features:Socio-economic characteristics between races, ethnicities, and genders in AmericaComparisons include: health, education, wealth, poverty, income, employment, crime, incarcerations, social behaviors, geographic distributions, and political preferencesIncludes foreign-born and natural citizens, lawful and undocumented immigrantsAll data are graphically displayed for easy visualization and comprehensionAttributed sources for all data include web addresses to enable additional research Only factual data are presented without editorial comments or opinionsInteresting facts found in America's Diverse Population include:More than one-in-eight persons residing in the U.S. in 2015 were born elsewhere.Approximately one-in-four persons with'Green Cards'resided in California in 2013.Over three million temporary workers were admitted into the U.S. in 2014.In 2009 over two-thirds of convictions of undocumented immigrants were for violations of immigration law, and two percent for crimes against persons.Approximately one-of-three Black or African American children live in households with both married parents, compared with ninety-five percent of Asian households.In 2013 one-of-eight high school age Hispanic or Latino females reported they were forced to have sex.In 2015 ninety-seven percent of kindergarten teachers were women.Approximately one-half of all maids and housekeeping cleaners in 2015 were Hispanic or Latino women.In 2015, almost one-in-four Asian females held a Master's degree, the highest rate of any race or ethnicity.In 2013, the number of Non-Hispanic White children in the U.S., grades K–8, fell below fifty percent of students for the first time.