The advent of "Big Data" has brought with it a rapid diversification of data sources, requiring analysis that accounts for the fact that these data have often been generated and recorded for different reasons. Data integration involves combining data residing in different sources to enable statistical inference, or to generate new statistical data for purposes that cannot be served by each source on its own. This can yield significant gains for scientific as well as commercial investigations. However, valid analysis of such data should allow for the additional uncertainty due to entity ambiguity, whenever it is not possible to state with certainty that the integrated source is the target population of interest. Analysis of Integrated Data aims to provide a solid theoretical basis for this statistical analysis in three generic settings of entity ambiguity: statistical analysis of linked datasets that may contain linkage errors; datasets created by a data fusion process, where joint statistical information is simulated using the information in marginal data from non-overlapping sources; and estimation of target population size when target units are either partially or erroneously covered in each source. Covers a range of topics under an overarching perspective of data integration. Focuses on statistical uncertainty and inference issues arising from entity ambiguity. Features state of the art methods for analysis of integrated data. Identifies the important themes that will define future research and teaching in the statistical analysis of integrated data. Analysis of Integrated Data is aimed primarily at researchers and methodologists interested in statistical methods for data from multiple sources, with a focus on data analysts in the social sciences, and in the public and private sectors.

Textual Statistics with R comprehensively covers the main multidimensional methods in textual statistics supported by a specially-written package in R. Methods discussed include correspondence analysis, clustering, and multiple factor analysis for contigency tables. Each method is illuminated by applications. The book is aimed at researchers and students in statistics, social sciences, hiistory, literature and linguistics. The book will be of interest to anyone from practitioners needing to extract information from texts to students in the field of massive data, where the ability to process textual data is becoming essential.

Capture-recapture methods have been used in biology and ecology for more than 100 years. However, it is only recently that these methods have become popular in the social and medical sciences to estimate the size of elusive populations such as illegal immigrants, illicit drug users, or people with a drinking problem. Capture-Recapture Methods for the Social and Medical Sciences brings together important developments which allow the application of these methods. It has contributions from more than 40 researchers, and is divided into eight parts, including topics such as ratio regression models, capture-recapture meta-analysis, extensions of single and multiple source models, latent variable models and Bayesian approaches. The book is suitable for everyone who is interested in applying capture-recapture methods in the social and medical sciences. Furthermore, it is also of interest to those working with capture-recapture methods in biology and ecology, as there are some important developments covered in the book that also apply to these classical application areas.

Learn How to Infuse Leadership into Your Passion for Scientific Research Leadership and Women in Statistics explores the role of statisticians as leaders, with particular attention to women statisticians as leaders. By paying special attention to women's issues, this book provides a clear vision for the future of women as leaders in scientific and technical fields. It also shows how emerging and current leaders of both genders in many disciplines can expand their leadership potentials. Featuring contributions from leadership experts and statisticians at various career stages, this unique and insightful text: Examines leadership within the roles of statistician and data scientist from international and diverse perspectives Supplies a greater understanding of leadership within teams, research consulting, and project management Encourages reflection on leadership behaviors, promoting both natural and organizational leadership Identifies existing opportunities to foster creative outputs and develop strong leadership voices Includes real-life stories about overcoming barriers to leadership Leadership and Women in Statistics explains how to convert a passion for statistical science into visionary, ethical, and transformational leadership. Although the context focuses on statistics, the material applies to almost all fields of endeavor. This book is a valuable resource for those ready to consider leadership as an important element of their careers, and for those who are already leaders but want to deepen their perspectives on leadership. It makes an ideal text for group leadership training as well as for individual professional development.

1. This book is applicable to courses across the social and behavioral science on a wide range of quantitative methods courses. 2. The book is based on SPSS for EFA - one of the most popular statistics software packages used in behavioral sciences. 3. Clear step-by-step guidance combined with screen shots to show how to apply EFA to real data.

A Sound Basis for the Theory of Statistical Inference Measuring Statistical Evidence Using Relative Belief provides an overview of recent work on developing a theory of statistical inference based on measuring statistical evidence. It shows that being explicit about how to measure statistical evidence allows you to answer the basic question of when a statistical analysis is correct. The book attempts to establish a gold standard for how a statistical analysis should proceed. It first introduces basic features of the overall approach, such as the roles of subjectivity, objectivity, infinity, and utility in statistical analyses. It next discusses the meaning of probability and the various positions taken on probability. The author then focuses on the definition of statistical evidence and how it should be measured. He presents a method for measuring statistical evidence and develops a theory of inference based on this method. He also discusses how statisticians should choose the ingredients for a statistical problem and how these choices are to be checked for their relevance in an application.

This is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach, with excellent examples and good guidance on computing, the book is particularly popular for teaching outside statistics, i.e. in epidemiology, social science, business, etc. The sixth edition has been updated with a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses and a new section on generalized estimating equations and many new updates throughout. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians. Key Features: Provides a comprehensive, practical and accessible introduction to multivariate analysis. Keeps mathematical details to a minimum, so particularly geared toward a non-statistical audience. Includes lots of detailed worked examples, guidance on computing, and exercises. Updated with a new chapter on data visualization.

This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work. Garcia offers advanced coverage of Bayesian analysis, simulated data, exercises, implementable script code, and practical guidance on the latest R software packages. The book, also demonstrating the benefits to the L2 field of this type of statistical work, is a resource for graduate students and researchers in second language acquisition, applied linguistics, and corpus linguistics who are interested in quantitative data analysis.

In Systemic Constellations: Theory, Practice, and Applications, Damian Janus examines systemic constellations, a breakthrough method of psychotherapy, coaching, and consulting developed by Bert Hellinger. Janus examines numerous case studies and addresses the potential of Hellinger's approach for improving clients' mental health.

This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work. Garcia offers advanced coverage of Bayesian analysis, simulated data, exercises, implementable script code, and practical guidance on the latest R software packages. The book, also demonstrating the benefits to the L2 field of this type of statistical work, is a resource for graduate students and researchers in second language acquisition, applied linguistics, and corpus linguistics who are interested in quantitative data analysis.

1. This book is applicable to courses across the social and behavioral science on a wide range of quantitative methods courses. 2. The book is based on SPSS for EFA - one of the most popular statistics software packages used in behavioral sciences. 3. Clear step-by-step guidance combined with screen shots to show how to apply EFA to real data.

This edited volume recognizes that resilience, and the most effective means of harnessing it, differ across individuals, contexts and time. Presenting chapters written by a range of scholars and clinicians, the book highlights effective evidence-based approaches to nurturing resilience, before, during and after a traumatic experience or event. By identifying distinct therapeutic tools which can be used effectively to meet the particular needs and limitations associated with different age groups, clients and types of experience, the volume addresses specific challenges and benefits of nurturing resilience and informs best practice as well as self-care. Approaches explored in the volume include the use of group activities to teach resilience to children, the role of sense-making for victims of sex trafficking, and the ways in which identity and spirituality can be used to help young and older adults in the face of pain and bereavement. Chapters also draw on the lived experiences of those who have engaged in a personal or guided journey towards finding new meaning and achieving posttraumatic growth following experiences of trauma. The rich variety of approaches offered here will be of interest to clinicians, counsellors, scholars and researchers involved in the practice and study of building resilience, as well as trauma studies, psychology and mental health more broadly. The personal and practice-based real-life stories in this volume will also resonate with individuals, family and community members facing adversity.

Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market. The authors cover common categorical analysis methods and emphasize specific research questions that can be addressed by each analytic procedure, including how to obtain results using SPSS, SAS, and R, so that readers are able to address the research questions they wish to answer. Each chapter begins with a "Look Ahead" section to highlight key content. This is followed by an in-depth focus and explanation of the relationship between the initial research question, the use of software to perform the analyses, and how to interpret the output substantively. Included at the end of each chapter are a range of software examples and questions to test knowledge. New to the second edition: The addition of R syntax for all analyses and an update of SPSS and SAS syntax. The addition of a new chapter on GLMMs. Clarification of concepts and ideas that graduate students found confusing, including revised problems at the end of the chapters. Written for those without an extensive mathematical background, this book is ideal for a graduate course in categorical data analysis taught in departments of psychology, educational psychology, human development and family studies, sociology, public health, and business. Researchers in these disciplines interested in applying these procedures will also appreciate this book's accessible approach.

In this book the author's theoretical framework builds on linguistic and psychological research, arguing that similar image-schematic notions should be grouped together into interconnected family hierarchies, with complexity increasing with regard to the addition of spatial and conceptual primitives. She introduces an image schema logic as a language to model image schemas, and she shows how the semantic content of image schemas can be used to improve computational concept invention. The book will be of value to researchers in artificial intelligence, cognitive science, psychology, and creativity.

Cases and Stories of Transformative Action Research builds on its companion book, Principles and Methods of Transformative Action Research, by describing and analyzing dozens of examples of successful action research efforts pursued in the past five decades by students and faculty of the Western Institute for Social Research. Some projects are large-scale, and some are modest interventions in the everyday lives of those participating. Some are formal organizational efforts; others are the results of individual or small group initiatives. Included are chapters on community needs assessments and innovative grassroots approaches to program evaluation; the challenges of improving our decision-making during the crisis of the COVID-19 pandemic; strategies of intellectual activism in addressing the growing problem of workplace bullying; action research to preserve and share the history of the Omaha tribe; and plans for an innovative school-based project based on collaborative action-and-inquiry between students and Artificial Intelligence. In addition, there are a number of detailed stories about the use of transformative action research in such areas as somatic and trauma counseling, ethnic studies, health disparities, gender differences, grassroots popular education, and the improvement of statewide steps for preventing child abuse, among many others. This book can serve as an undergraduate or graduate social sciences text on research methods. It is also a guidebook for action-oriented research by academics, professionals, and lay people alike.

This book explores the ways in which the spatio-temporal contingency of human life is being conceived in different fields of research. Specifically, it looks at the relationship between the situatedness of human life, the situation or place in which human life is supposed to be situated, and the dimensions of space and time in which both situation and place are usually themselves supposed to be situated. Over the last two or three decades, the spatio-temporal contingency of human life has become an important topic of research in a broad range of different disciplines including the social sciences, the cultural sciences, the cognitive sciences, and philosophy. However, this research topic is referred to in quite different ways: while some researchers refer to it in terms of "situation", emphasizing the "situatedness" of human experience and action, others refer to it in terms of "place", emphasizing the "power of place" and advocating a "topological" or "topographical turn" in the context of a larger "spatial turn". Interdisciplinary exchange is so far hampered by the fact that the notions referred to and the relationships between them are usually not sufficiently questioned. This book addresses these issues by bringing together contributions on the spatio-temporal contingency of human life from different fields of research.

Research Design for the Behavioral Sciences fills an important gap for the helping professions by offering a blueprint for advanced concepts and an applied approach to understanding quantitative, qualitative, and mixed methods research design. This graduate-level text seamlessly weaves together the philosophy, science, and practical application of the most common methodological frameworks in practice. Advanced research design concepts are presented through clear and in-depth blueprints, applied case studies, myriad examples, and helpful learning activities.Written in detailed yet accessible language, this text describes the foundations of behavioral science research. The authors explore research-based philosophical integration, along with the technical application of every tradition. Through this philosophical and pragmatic approach, students will be able to attain a well-rounded and comprehensive understanding of behavioral science research. This text provides students with the opportunity to reach a greater level of research efficacy though the inclusion of methodological procedures, data analysis methods, reliability/validity standards, ethics, and directions on how to increase the rigor of each approach to research. Instructor resources include an instructor's manual, learning activities, test bank, and PowerPoints. Purchase includes digital access for use on most mobile devices and computers. Key Features: Provides clear, detailed, and contextually accurate examples of writing, quantitative, qualitative, and mixed methods procedures Reviews the paradigmatic hierarchy of each research tradition along with key analytic features in detail Delivers instructions for enhancing the methodological rigor of each approach Analyzes methodology-specific multicultural issues Demonstrates the application of a wide range of research methodologies with case studies Reviews the trends and history in research for counseling, psychology, social work, and marriage and family therapy Offers comprehensive instructor resources including manual, learning activities, test bank, and PowerPoint slides

"Prospects for Immortality: A Sensible Search for Life After Death" theorizes how matters concerning the birth of the universe, its ultimate fate, and the creation, evolution, and final destiny of life on earth co-exist with regenerated consciousness. Readers of this volume will be prompted to think about the basic concepts of life, death, and consciousness in a unique way. Written in a style that entertains and informs, author J. Robert Adams speculates on how the transfer of memory and consciousness into the hereafter occurs in conjunction with the physical, chemical, and historical facts already established by science.

Praise for the first edition: "One of my biggest complaints when I teach introductory statistics classes is that it takes me most of the semester to get to the good stuff-inferential statistics. The author manages to do this very quickly....if one were looking for a book that efficiently covers basic statistical methodology and also introduces statistical software [this text] fits the bill." -The American Statistician Applied Statistical Inference with MINITAB, Second Edition distinguishes itself from other introductory statistics textbooks by focusing on the applications of statistics without compromising mathematical rigor. It presents the material in a seamless step-by-step approach so that readers are first introduced to a topic, given the details of the underlying mathematical foundations along with a detailed description of how to interpret the findings, and are shown how to use the statistical software program Minitab to perform the same analysis. Gives readers a solid foundation in how to apply many different statistical methods. MINITAB is fully integrated throughout the text. Includes fully worked out examples so students can easily follow the calculations. Presents many new topics such as one- and two-sample variances, one- and two-sample Poisson rates, and more nonparametric statistics. Features mostly new exercises as well as the addition of Best Practices sections that describe some common pitfalls and provide some practical advice on statistical inference. This book is written to be user-friendly for students and practitioners who are not experts in statistics, but who want to gain a solid understanding of basic statistical inference. This book is oriented towards the practical use of statistics. The examples, discussions, and exercises are based on data and scenarios that are common to students in their everyday lives.

Power Analysis of Trials with Multilevel Data covers using power and sample size calculations to design trials that involve nested data structures. The book gives a thorough overview of power analysis that details terminology and notation, outlines key concepts of statistical power and power analysis, and explains why they are necessary in trial design. It guides you in performing power calculations with hierarchical data, which enables more effective trial design. The authors are leading experts in the field who recognize that power analysis has attracted attention from applied statisticians in social, behavioral, medical, and health science. Their book supplies formulae that allow statisticians and researchers in these fields to perform calculations that enable them to plan cost-efficient trials. The formulae can also be applied to other sciences. Using power analysis in trial design is increasingly important in a scientific community where experimentation is often expensive, competition for funding among researchers is intense, and agencies that finance research require proposals to give thorough justification for funding. This handbook shows how power analysis shapes trial designs that have high statistical power and low cost, using real-life examples. The book covers multiple types of trials, including cluster randomized trials, multisite trials, individually randomized group treatment trials, and longitudinal intervention studies. It also offers insight on choosing which trial is best suited to a given project. Power Analysis of Trials with Multilevel Data helps you craft an optimal research design and anticipate the necessary sample size of data to collect to give your research maximum effectiveness and efficiency.

Structural equation modeling (SEM) is a very general and flexible multivariate technique that allows relationships among variables to be examined. The roots of SEM are in the social sciences. In writing this textbook, the authors look to make SEM accessible to a wider audience of researchers across many disciplines, addressing issues unique to health and medicine. SEM is often used in practice to model and test hypothesized causal relationships among observed and latent (unobserved) variables, including in analysis across time and groups. It can be viewed as the merging of a conceptual model, path diagram, confirmatory factor analysis, and path analysis. In this textbook the authors also discuss techniques, such as mixture modeling, that expand the capacity of SEM using a combination of both continuous and categorical latent variables. Features: Basic, intermediate, and advanced SEM topics Detailed applications, particularly relevant for health and medical scientists Topics and examples that are pertinent to both new and experienced SEM researchers Substantive issues in health and medicine in the context of SEM Both methodological and applied examples Numerous figures and diagrams to illustrate the examples As SEM experts situated among clinicians and multidisciplinary researchers in medical settings, the authors provide a broad, current, on the ground understanding of the issues faced by clinical and health services researchers and decision scientists. This book gives health and medical researchers the tools to apply SEM approaches to study complex relationships between clinical measurements, individual and community-level characteristics, and patient-reported scales.

Partial least squares structural equation modelling (PLS-SEM) is becoming a popular statistical framework in many fields and disciplines of the social sciences. The main reason for this popularity is that PLS-SEM can be used to estimate models including latent variables, observed variables, or a combination of these. The popularity of PLS-SEM is predicted to increase even more as a result of the development of new and more robust estimation approaches, such as consistent PLS-SEM. The traditional and modern estimation methods for PLS-SEM are now readily facilitated by both open-source and commercial software packages. This book presents PLS-SEM as a useful practical statistical toolbox that can be used for estimating many different types of research models. In so doing, the authors provide the necessary technical prerequisites and theoretical treatment of various aspects of PLS-SEM prior to practical applications. What makes the book unique is the fact that it thoroughly explains and extensively uses comprehensive Stata (plssem) and R (cSEM and plspm) packages for carrying out PLS-SEM analysis. The book aims to help the reader understand the mechanics behind PLS-SEM as well as performing it for publication purposes. Features: Intuitive and technical explanations of PLS-SEM methods Complete explanations of Stata and R packages Lots of example applications of the methodology Detailed interpretation of software output Reporting of a PLS-SEM study Github repository for supplementary book material The book is primarily aimed at researchers and graduate students from statistics, social science, psychology, and other disciplines. Technical details have been moved from the main body of the text into appendices, but it would be useful if the reader has a solid background in linear regression analysis.

With over two decades of classroom experience, Michael Passer knows how to guide students through the ins and outs of research methods. In this remarkable text, Passer's experience leads to chapters filled with clear explanations, resonant examples, and contemporary research from across the breadth of modern psychology, all while anticipating common questions and misunderstandings. The new edition has been fully updated to reflect the latest APA style guidelines, as well as the updated APA Code of Conduct and ethical principles. It features full-page infographics summarizing key concepts and fully updated research. It can be packaged FREE with Worth Publishers' LaunchPad Solo for Research Methods-the ideal online component for the text, featuring videos and activities that put students in the role of either experimenter or research subject.

Prevention and developmental sciences have many complementary goals and much to gain by collaboration. With random assignment to conditions and long-term multivariate follow-up of individuals across significant years in the life span, fundamental basic and applied research questions can now be addressed using new statistical methods. This special issue includes four empirical papers that used growth modeling techniques (hierarchical linear modeling, latent growth curve analyses) to examine direct and indirect effects of theory-based, longitudinal prevention experiments on developmental trajectories of children's and adolescents' substance use, delinquency, and school bonding.

Concept analysis is an established genre of inquiry in nursing, introduced in the 1970s. Currently, over 100 concept studies are published annually, yet the methods used within this field have rarely been questioned. In Concept Analysis in Nursing: A New Approach, Paley provides a critical analysis of the philosophical assumptions that underpin nursing's concept analysis methods. He argues, provocatively, that there are no such things as concepts, as traditionally conceived. Drawing on Wittgenstein and Construction Grammar, the book first makes a case for dispensing with the traditional concept of a 'concept', and then provides two examples of a new approach, examining the use of 'hope' and 'moral distress'. Casting doubt on the assumption that 'hope' always stands for an 'inner' state of the person, the book shows that the word's function varies with the grammatical construction it appears in. Similarly, it argues that 'moral distress' is not the name of a mental state, but a normative classification used to bolster a narrative concerning nursing's identity. Concept Analysis in Nursing is a fresh and challenging book written by a philosopher interested in nursing. It will appeal to researchers and postgraduate students in the areas of nursing, health, philosophy and linguistics. It will also interest those familiar with the author's previous book, Phenomenology as Qualitative Research.