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"[A] fascinating read... Contrary to what the title might suggest, this is an upbeat exploration of suicide with a positive message." --Jeanine Connor, Therapy Today, December, 2018 This thought-provoking volume offers a distinctly human evolutionary analysis of a distinctly human phenomenon: suicide. Its 'pain and brain' model posits animal adaptations as the motivator for suicidal escape, and specific human cognitive adaptations as supplying the means , while also providing a plausible explanation for why only a relatively small number of humans actually take their own lives. The author hypothesizes two types of anti-suicide responses, active and reactive mechanisms prompted by the brain as suicide deterrents. Proposed as well is the intriguing prospect that mental disorders such as depression and addiction, long associated with suicidality, may serve as survival measures. Among the topics covered: * Suicide as an evolutionary puzzle. * The protection against suicide afforded to animals and young children. * Suicide as a by-product of pain and human cognition. * Why psychodynamic defenses regulate the experiencing of painful events. * Links between suicidality and positive psychology. * The anti-suicide role of spiritual and religious belief. In raising and considering key questions regarding this most controversial act, The Evolution of Suicide will appeal to researchers across a range of behavioral science disciplines. At the same time, the book's implications for clinical intervention and prevention will make it useful among mental health professionals and those involved with mental health policy.
This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets.
Unexpected events during an evaluation all too often send evaluators into crisis mode. This insightful book provides a systematic framework for diagnosing, anticipating, accommodating, and reining in costs of evaluation surprises. The result is evaluation that is better from a methodological point of view, and more responsive to stakeholders. Jonathan A. Morell identifies the types of surprises that arise at different stages of a program's life cycle and that may affect different aspects of the evaluation, from stakeholder relationships to data quality, methodology, funding, deadlines, information use, and program outcomes. His analysis draws on 18 concise cases from well-known researchers in a variety of evaluation settings. Morell offers guidelines for responding effectively to surprises and for determining the risks and benefits of potential solutions.
Using real-world data examples, this authoritative book shows how to use the latest configural frequency analysis (CFA) techniques to analyze categorical data. Some of the techniques are presented here for the first time. In contrast to such methods as log-linear modeling, which focus on relationships among variables, CFA allows researchers to evaluate differences and change at the level of individual cells in a table. Illustrated are ways to identify and test for cell configurations that are either consistent with or contrary to hypothesized patterns (the types and antitypes of CFA); control for potential covariates that might influence observed results; develop innovative prediction models; address questions of moderation and mediation; and analyze intensive longitudinal data. The book also describes free software applications for executing CFA.
This book will be invaluable to researchers and graduate students in psychology, education, management, public health, sociology, and other social, behavioral, and health science disciplines. It will also serve as a supplemental text in graduate-level courses on categorical data analysis, longitudinal analysis, and person-oriented research.
This edited volume presents examples of social science research projects that employ new methods of quantitative analysis and mathematical modeling of social processes. This book presents the fascinating areas of empirical and theoretical investigations that use formal mathematics in a way that is accessible for individuals lacking extensive expertise but still desiring to expand their scope of research methodology and add to their data analysis toolbox. Mathematical Modeling of Social Relationships professes how mathematical modeling can help us understand the fundamental, compelling, and yet sometimes complicated concepts that arise in the social sciences. This volume will appeal to upper-level students and researchers in a broad area of fields within the social sciences, as well as the disciplines of social psychology, complex systems, and applied mathematics.
Item response theory (IRT) is a latent variable modeling approach used to minimize bias and optimize the measurement power of educational and psychological tests and other psychometric applications.Designed for researchers, psychometric professionals, and advanced students, this book clearly presents both the "how-to" and the "why" of IRT. It describes simple and more complex IRT models and shows how they are applied with the help of widely available software packages. Chapters follow a consistent format and build sequentially, taking the reader from model development through the fit analysis and interpretation phases that one would perform in practice. The use of common empirical data sets across the chapters facilitates understanding of the various models and how they relate to one another.
Students and beginning researchers often discover that their introductory statistics and methods courses have not fully equipped them to plan and execute their own behavioral research studies. This indispensable book bridges the gap between coursework and conducting independent research. With clarity and wit, the author helps the reader build needed skills to formulate a precise, meaningful research question; understand the pros and cons of widely used research designs and analysis options; correctly interpret the outcomes of statistical tests; make informed measurement choices for a particular study; manage the practical aspects of data screening and preparation; and craft effective journal articles, oral presentations, and posters. Including annotated examples and recommended readings, most chapters feature theoretical and computer-based exercises; an answer appendix at the back of the book allows readers to check their work.
Students across the behavioural and social sciences
This fresh, new textbook provides a thorough and student-friendly guide to the different techniques used in cognitive neuroscience. Given the breadth of neuroimaging techniques available today, this text is invaluable, serving as an approachable text for students, researchers, and writers. This text provides the right level of detail for those who wish to understand the basics of neuroimaging and also provides more advanced material in order to learn further about particular techniques. With a conversational, student-friendly writing style, Aaron Newman introduces the key principles of neuroimaging techniques, the relevant theory and the recent changes in the field.
As a transdisciplinary profession, evaluation has much to offer to global change interventions that work toward a sustainable future across national boundaries, sectors, and issues. This book introduces Blue Marble evaluation, which provides a framework for developing, adapting, and evaluating major systems change initiatives involving complex networks of stakeholders. Michael Quinn Patton demonstrates how the four overarching principles and 12 operating principles of this innovative approach allow evaluators, planners, and implementers to home in on sustainability and equity issues in an intervention. Compelling case examples, bulleted review lists, charts, and 80 original exhibits and graphics connect the global and local, the human and ecological. Rooted in utilization-focused, developmental, and principles-focused evaluation, Blue Marble evaluation is designed to tackle problems outside the reach of traditional evaluation practice.
Statistical Concepts-A Second Course presents the last 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for second and upper-level statistics courses, this book highlights how statistics work and how best to utilize them to aid students in the analysis of their own data and the interpretation of research results. In this new edition, Hahs-Vaughn and Lomax discuss sensitivity, specificity, false positive and false negative errors. Coverage of effect sizes has been expanded upon and more organizational features (to summarize key concepts) have been included. A final chapter on mediation and moderation has been added for a more complete presentation of regression models. In addition to instructions and screen shots for using SPSS, new to this edition is annotated script for using R. This book acts as a clear and accessible instructional tool to help readers fully understand statistical concepts and how to apply them to data. It is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral science disciplines.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work. The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding. The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses. Features Integrates working code into the main text Illustrates concepts through worked data analysis examples Emphasizes understanding assumptions and how assumptions are reflected in code Offers more detailed explanations of the mathematics in optional sections Presents examples of using the dagitty R package to analyze causal graphs Provides the rethinking R package on the author's website and on GitHub
Thoroughly revised and updated, this engaging text has given thousands of students and new evaluators the practical information and expert advice needed to conduct or use evaluations. In 26 concise sections, the book describes how to articulate answerable evaluation questions, collect and analyze data using both quantitative and qualitative methods, and deal with contingencies that might alter the traditional sequence of an evaluation. Special strengths of the text are its attention to individual, organizational, and community culture and emphasis on building collaborative relationships with stakeholders. An in-depth case study and related end-of-section exercises (including group activities) help students put themselves in the evaluator role. Other pedagogical features include section titles written as questions, bulleted recaps of each section, "Thinking Ahead" and "Next Steps" pointers, cautionary notes, and suggestions for further reading. New to This Edition *New and expanded topics: evaluation contracts, budgeting, surveys, data visualization, qualitative coding and memoing, factors affecting evaluation use, and context-sensitive evaluation. *Revised case study with extended exercises that guide the reader to complete a simulated evaluation. *End-of-section "Quick Read" links to recommended American Evaluation Association blog posts. *Four entirely new sections (such as "How Do You Strengthen Relationships with Stakeholders?" and "How Do We Plan a Process-Focused Evaluation Design?"), plus other changes and additions throughout.
Daten sind Gold. Dieses Gold liegt auf der Strasse allerdings meistens in unansehnlichen Erzklumpen. Man findet es beispielsweise in Unternehmensdatenbanken oder bei speziellen Erhebungen. Wie man das Goldgewinnt- alsoDaten richtig auswertet und die Auswertung interpretiert dasfindet sich in"Daten und Statistik" furSozialwissenschaftler und Psychologen. Dieses leicht verstandlich geschriebeneKompaktbuch zum systematischen und anwendungsnahen Einstieg richtet sich an Studierende, die in wissenschaftlichen Arbeiten mit diversen Daten konfrontiert sind, und an in statistischen Methoden unerfahrene Praktiker. Mit den anschaulichen undalltagsnahen Beispielen wird es fur jeden Leser eine wertvolle Hilfe sein."
This book explores test adaptation, a scientific and professional activity now spanning all of the social and behavioural sciences. Adapting tests to various linguistic and cultural contexts is a critical process in today's globalized world, and requires a combination of knowledge and skills from psychometrics, cross-cultural psychology and others. This volume provides a step-by-step approach to cross-cultural test adaptation, emphatically presented as a melange between science and practice. The volume is driven by the first-hand practical experience of the author in a large number of test adaptation projects in various cultures, and is supported by the consistent scientific body of knowledge accumulated over the last several decades on the topic. It is the first of its kind: an in-depth treatise and guide on why and how to adapt a test to a new culture in such a way as to preserve its psychometric value.
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