The third edition of Handbook of Marriage and the Family describes, analyzes, synthesizes, and critiques the current research and theory about family relationships, family structural variations, and the role of families in society. This updated Handbook provides the most comprehensive state-of-the art assessment of the existing knowledge of family life, with particular attention to variations due to gender, socioeconomic, race, ethnic, cultural, and life-style diversity. The Handbook also aims to provide the best synthesis of our existing scholarship on families that will be a primary source for scholars and professionals but also serve as the primary graduate text for graduate courses on family relationships and the roles of families in society. In addition, the involvement of chapter authors from a variety of fields including family psychology, family sociology, child development, family studies, public health, and family therapy, gives the Handbook a multidisciplinary and interdisciplinary framework.

This book summarizes a range of new analytic tools for multitrait-multimethod (MTMM) data. Providing an expository yet accessible approach to cutting-edge developments for MTMM analysis, a selection of quantitative researchers reveal their recent contributions to the field including non-technical summaries and empirical examples. The contributions inform quantitative social scientists of some of the most cutting-edge developments for MTMM analysis. A range of developments have emerged over the past decade for MTMM analyses, and this book presents these novel additions to the quantitative community as a cohesive narrative. This book makes these recent MTMM contributions accessible to applied researchers (most MTMM innovations are presented in less approachable journals for applied researchers) by providing non-technical summaries and empirical examples. This book will serve as a stepping stone for applied researchers seeking to adopt MTMM analysis into their program of research, and will be relevant to researchers, both within a professional and academic context, across the social and behavioral sciences.

- The application of situational crime prevention theory to homicide by people with a serious mental disorder make this book a novel resource. - Research is reviewed with a focus on implications for prevention.

This book offers different theoretical approaches about what clinical research is. Clinical Research in Psychoanalysis is a unique contribution to the attempts to bridge the gap between clinicians and researchers and to create a culture of a more rigorous and systematic inquiry. It provides an innovative experience because for the first time different methods and perspectives were used to analyse one same clinical material. This was done by analysts from different working parties of the International Psychoanalytical Association (IPA), from a range of different schools of psychoanalytic thought. This allows the reader to have a vision of the different methods that are currently being used by some working parties of the IPA and to learn about the strengths of each one for certain situations and types of research. This book revaluates clinical research, intending to make links between the analysts working through the working parties and the different ways of thinking in clinical research. By covering key topics, such as how working parties can facilitate different types of research; the place of metaphor in psychoanalytic research and practice; and the future for psychoanalytic research, this text is a fruitful dialogue between different theoretical conceptions and between clinicians and researchers, that will expand our perspectives on the evidence we find in clinical material and will broaden our views on the patient. This book offers a unique and invaluable experience to psychologists and psychoanalysts who are trying to improve their clinical practice and bring research evidence into their psychoanalytic practice. It is an invaluable contribution to psychoanalytic training of candidates, teachers, and students.

This book covers statistical consequences of breaches of research integrity such as fabrication and falsification of data, and researcher glitches summarized as questionable research practices. It is unique in that it discusses how unwarranted data manipulation harms research results and that questionable research practices are often caused by researchers' inadequate mastery of the statistical methods and procedures they use for their data analysis. The author's solution to prevent problems concerning the trustworthiness of research results, no matter how they originated, is to publish data in publicly available repositories and encourage researchers not trained as statisticians not to overestimate their statistical skills and resort to professional support from statisticians or methodologists. The author discusses some of his experiences concerning mutual trust, fear of repercussions, and the bystander effect as conditions limiting revelation of colleagues' possible integrity breaches. He explains why people are unable to mimic real data and why data fabrication using statistical models stills falls short of credibility. Confirmatory and exploratory research and the usefulness of preregistration, and the counter-intuitive nature of statistics are discussed. The author questions the usefulness of statistical advice concerning frequentist hypothesis testing, Bayes-factor use, alternative statistics education, and reduction of situational disturbances like performance pressure, as stand-alone means to reduce questionable research practices when researchers lack experience with statistics.

The Theoretical Framework in Phenomenological Research: Development and Application is an introduction to phenomenology in which the authors overview its origin, main ideas and core concepts. They show the application and relevancy of phenomenological tenets in practical qualitative research, as well as demonstrate how aligning theory and method enhances research credibility. In this detailed but digestible explanation of phenomenological theories, the authors explore the ideas of the main founders pertaining to the meaning of perceived reality and the meaning of being, and how these founders articulated their methodologies. In doing so, The Theoretical Framework in Phenomenological Research fills the well-documented gap between theory and practice within phenomenology by providing a much-needed bridge between the foundational literature and applied research on the subject, focusing equally on theory and practice. The book includes practical demonstrations on how to create theoretical/conceptual frameworks in applied phenomenological research. It also features detailed, step-by-step illustrations and examples regarding how researchers can develop frameworks and use their concepts to inform the development of themes at the data analysis stage. A reliable guide underpinned by foundational phenomenology literature, The Theoretical Framework in Phenomenological Research is an essential text for researchers, instructors, practitioners and students looking to design and conduct phenomenological studies in a manner that ensures credible outcomes.

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.

Quantitative Data Analysis for Language Assessment Volume II: Advanced Methods demonstrates advanced quantitative techniques for language assessment. The volume takes an interdisciplinary approach and taps into expertise from language assessment, data mining, and psychometrics. The techniques covered include Structural Equation Modeling, Data Mining, Multidimensional Psychometrics and Multilevel Data Analysis.Volume II is distinct among available books in language assessment, as it engages the readers in both theory and application of the methods and introduces relevant techniques for theory construction and validation. This book is highly recommended to graduate students and researchers who are searching for innovative and rigorous approaches and methods to achieve excellence in their dissertations and research. It is also a valuable source for academics who teach quantitative approaches in language assessment and data analysis courses.

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.

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.

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.

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 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.

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.

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.

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.

Advanced Methods in Automatic Item Generation is an up-to-date survey of the growing research on automatic item generation (AIG) in today's technology-enhanced educational measurement sector. As test administration procedures increasingly integrate digital media and Internet use, assessment stakeholders-from graduate students to scholars to industry professionals-have numerous opportunities to study and create different types of tests and test items. This comprehensive analysis offers thorough coverage of the theoretical foundations and concepts that define AIG, as well as the practical considerations required to produce and apply large numbers of useful test items.

Advanced Methods in Automatic Item Generation is an up-to-date survey of the growing research on automatic item generation (AIG) in today's technology-enhanced educational measurement sector. As test administration procedures increasingly integrate digital media and Internet use, assessment stakeholders-from graduate students to scholars to industry professionals-have numerous opportunities to study and create different types of tests and test items. This comprehensive analysis offers thorough coverage of the theoretical foundations and concepts that define AIG, as well as the practical considerations required to produce and apply large numbers of useful test items.

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.

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.

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.