Synthesizing many years' investigation into sexual identity and orientation, this book presents Dr Money's formulation of how sexual preference is determined. It includes a review of long-term follow-up studies on pre-natal influences on sexual identity, and discusses gender differentiation in childhood. The book concludes with an examination of the conflict between gender and sexual identities, and a description of the paraphilias.

Researchers, clinicians, and graduate students in psychology, biology, endocrinology, psychiatry, and family studies will find this volume of interest, as will anyone interested in gay and lesbian issues.

Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates.

Maximum Likelihood Estimation for Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data that account for the selection methods used, and includes all necessary background material on likelihood inference. It covers a range of data types, including multilevel data, and is illustrated by many worked examples using tractable and widely used models. It also discusses more advanced topics, such as combining data, non-response, and informative sampling.

The book presents and develops a likelihood approach for fitting models to sample survey data. It explores and explains how the approach works in tractable though widely used models for which we can make considerable analytic progress. For less tractable models numerical methods are ultimately needed to compute the score and information functions and to compute the maximum likelihood estimates of the model parameters. For these models, the book shows what has to be done conceptually to develop analyses to the point that numerical methods can be applied.

Designed for statisticians who are interested in the general theory of statistics, Maximum Likelihood Estimation for Sample Surveys is also aimed at statisticians focused on fitting models to sample survey data, as well as researchers who study relationships among variables and whose sources of data include surveys.

This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568.

The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced.

Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors' highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.

Psychoanalytic infant observation is frequently used in training psychoanalytic psychotherapists and allied professionals, but increasingly its value as a research method is being recognised, particularly in understanding developmental processes in vulnerable individuals and groups. This book explores the scope of this approach and discusses its strengths and limitations from a methodological and philosophical point of view.

Infant Observation and Research uses detailed case studies to demonstrate the research potential of the infant observation method. Divided into three sections this book covers

• infant observation as part of the learning process
• how infant observation can inform understanding and influence practice
• psychoanalytic infant observation and other methodologies.

Throughout the book, Cathy Urwin, Janine Sternberg and their contributors introduce the reader to the nature and value of psychoanalytic infant observation and its range of application. This book will therefore interest a range of mental health practitioners concerned with early development and infants' emotional relationships, as well as academics and researchers in the social sciences and humanities.

Description of time series, measurement, model building, and network methods for person-specific analysis Discussion of the statistical methods in the context of human research Empirical and simulated data examples used throughout the book R code for analyses provided as an online supplement Recorded lectures accompany each chapter

The Collective Unconscious in the Age of Neuroscience brings the connection between C. G. Jung's theory of a collective unconscious, neuroscience, and personal experiences of severe mental illness to life. Hallie B. Durchslag uses narrative analysis to examine four autobiographical accounts of mental illness, including her own, and illuminate the interplay between psychic material and human physiology that Jung intuited to exist. Durchslag's unique study considers the links between expressions of the collective unconscious, such as myth, fairy tales, folk tales, and 'big dreams', and the experiences of those diagnosed with severe mental illnesses, such as schizophrenia and bipolar I disorder. The author's personal narrative account of a psychotic episode is at its heart, bringing both an intimate foundation and exceptional insight to the book. With reference to neuroscientific and genetic research throughout, The Collective Unconscious in the Age of Neuroscience highlights gaps in depth psychological notions of etiology and treatment, highlights patterns of collective material in the qualitative experience of these genetic and biological disorders, and explores how the efficacy of pharmacological treatment sheds light on Jung's theoretical model. The Collective Unconscious in the Age of Neuroscience will be essential reading for academics and students of Jungian and post-Jungian studies, consciousness, neuroscience and mental health. It will also provide unique insight for analytical psychologists interested in severe mental illness and the collective unconscious.

Without question, statistics is one of the most challenging courses for students in the social and behavioral sciences. Enrolling in their first statistics course, students are often apprehensive or extremely anxious toward the subject matter. And while IBM SPSS is one of the more easy-to-use statistical software programs available, for anxious students who realize they not only have to learn statistics but also new software, the task can seem insurmountable. Keenly aware of students' anxiety with statistics (and the fact that this anxiety can affect performance), Ronald D. Yockey has written SPSS Demystified: A Simple Guide and Reference, now in its fourth edition. Through a comprehensive, step-by-step approach, this text is consistently and specifically designed to both alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS. Topics covered in the text are appropriate for most introductory and intermediate statistics and research methods courses. Key features of the text: Step-by-step instruction and screenshots Designed to be hands-on with the user performing the analyses alongside on their computer as they read through each chapter Call-out boxes provided, highlighting important information as appropriate SPSS output explained, with written results provided using the popular, widely recognized APA format End-of-chapter exercises included, allowing for additional practice SPSS datasets available on the publisher's website New to the Fourth Edition: Fully updated to SPSS 28 Updated screenshots in full color to reflect changes in SPSS software system (version 28) Exercises updated with up-to-date examples Exact p-values provided (consist with APA recommendations)

*Unique and authoritative: the first-ever volume on the use of computational language analysis in psychological research. *Needed guide: new ways to use language analysis to explore foundational psychological questions are growing exponentially. *Interdisciplinary: these methods are also relevant to linguistics, communication, information sciences, and business. *From leading editors and contributors.

This comprehensive Handbook is the first to provide a practical, interdisciplinary review of ethical issues as they relate to quantitative methodology including how to present evidence for reliability and validity, what comprises an adequate tested population, and what constitutes scientific knowledge for eliminating biases. The book uses an ethical framework that emphasizes the human cost of quantitative decision making to help researchers understand the specific implications of their choices. The order of the Handbook chapters parallels the chronology of the research process: determining the research design and data collection; data analysis; and communicating findings. Each chapter: Explores the ethics of a particular topic Identifies prevailing methodological issues Reviews strategies and approaches for handling such issues and their ethical implications Provides one or more case examples Outlines plausible approaches to the issue including best-practice solutions. Part 1 presents ethical frameworks that cross-cut design, analysis, and modeling in the behavioral sciences. Part 2 focuses on ideas for disseminating ethical training in statistics courses. Part 3 considers the ethical aspects of selecting measurement instruments and sample size planning and explores issues related to high stakes testing, the defensibility of experimental vs. quasi-experimental research designs, and ethics in program evaluation. Decision points that shape a researchers' approach to data analysis are examined in Part 4 - when and why analysts need to account for how the sample was selected, how to evaluate tradeoffs of hypothesis-testing vs. estimation, and how to handle missing data. Ethical issues that arise when using techniques such as factor analysis or multilevel modeling and when making causal inferences are also explored. The book concludes with ethical aspects of reporting meta-analyses, of cross-disciplinary statistical reform, and of the publication process. This Handbook appeals to researchers and practitioners in psychology, human development, family studies, health, education, sociology, social work, political science, and business/marketing. This book is also a valuable supplement for quantitative methods courses required of all graduate students in these fields.

Provides researchers with a reproducible research workflow for using R/RStudio to make the entire researchprocess reproducible; from data gathering, to analysis, to presentation Includes instructions not only for creating reproducible research in R, but also extensively discusses how to take advantage of recent developments in RStudio. Emphasizes the presentation of reproducible research with non-print formats such as HTML5 slideshows, blogs, and other web-based content. Covers a range of techniques to organize and remotely store files at all stages of the research process. These techniques both streamline the research process, especially by making revisions easier, and enhance The book itself will be reproducible, as all of the data, analysis, and markup files will be made available online.

*First Bayesian SEM book specifically for social science researchers, not advanced statisticians; a strong background in calculus is not needed. *Engaging, worked-through examples help highlight problems that can arise during estimation, explore solutions, and provide guidance for writing up findings. *User-friendly features include take-home points, tips, warnings, sample data analysis plans, and more. *Each chapter contains excerpts of annotated code in both Mplus and R; the companion website supplies datasets, code, and output for all of the book's examples.

Psychology Research Methods: A Writing Intensive Approach provides instruction in critical concepts and processes in behavioral science research methods and skills in formulating and writing research papers. The book creates an experiential approach to learning, with chapters organized around the task of writing a complete APA-style research paper. The chapters consist of instructional text, excerpts from published research articles, and learning activities. The reading activities help students develop skills in reading scientific research, evaluating and analyzing scientific information, and assembling evidence to make a scientific argument. The writing activities help students to break down the process of writing a research paper into manageable and meaningful components. As students complete the chapter activities, they assemble their research paper. The book teaches research methods in a clinical context, inspired by the National Institute of Health's Science of Behavior Change Program. Students acquire knowledge about research methods as they read research articles about behavioral health disorders, including studies about their prevalence, causes, and treatment. Teaching research methods with a clinical focus helps students appreciate the value of psychological research. Psychology Research Methods: A Writing Intensive Approach provides instruction in critical concepts and processes in behavioral science research methods and skills in formulating and writing research papers. The book creates an experiential approach to learning, with chapters organized around the task of writing a complete APA-style research paper. The chapters consist of instructional text, excerpts from published research articles, and learning activities. The reading activities help students develop skills in reading scientific research, evaluating and analyzing scientific information, and assembling evidence to make a scientific argument. The writing activities help students to break down the process of writing a research paper into manageable and meaningful components. As students complete the chapter activities, they assemble their research paper. The book teaches research methods in a clinical context, inspired by the National Institute of Health's Science of Behavior Change Program. Students acquire knowledge about research methods as they read research articles about behavioral health disorders, including studies about their prevalence, causes, and treatment. Teaching research methods with a clinical focus helps students appreciate the value of psychological research. Psychology Research Methods: A Writing Intensive Approach provides instruction in critical concepts and processes in behavioral science research methods and skills in formulating and writing research papers. The book creates an experiential approach to learning, with chapters organized around the task of writing a complete APA-style research paper. The chapters consist of instructional text, excerpts from published research articles, and learning activities. The reading activities help students develop skills in reading scientific research, evaluating and analyzing scientific information, and assembling evidence to make a scientific argument. The writing activities help students to break down the process of writing a research paper into manageable and meaningful components. As students complete the chapter activities, they assemble their research paper. The book teaches research methods in a clinical context, inspired by the National Institute of Health's Science of Behavior Change Program. Students acquire knowledge about research methods as they read research articles about behavioral health disorders, including studies about their prevalence, causes, and treatment. Teaching research methods with a clinical focus helps students appreciate the value of psychological research.

Exploring the breadth of contemporary feminist research practices, this engaging text immerses the reader in cutting-edge theories, methods, and practical strategies. Chapters review theoretical work and describe approaches to conducting quantitative, qualitative, and community-based research with participants; doing content or media analysis; and evaluating programs or interventions. Ethical issues are addressed and innovative uses of digital media highlighted. The focus is studying gender inequities as they are experienced by individuals and groups from diverse cultural, racial, and socioeconomic backgrounds, and with diverse gender identities. Delving into the process of writing and publishing feminist research, the text covers timely topics such as public scholarship, activism, and arts-based practices. The companion website features interviews with prominent feminist researchers. Pedagogical Features *Case examples of feminist research. *Running glossary of key terms. *Boxes highlighting hot topics and key points for practice. *End-of-chapter discussion questions and activities. *End-of-chapter annotated suggested reading (books, articles, and online resources). *Sample letters to research participants. *Appendix of feminist scholars organized by discipline.

This volume provides an integrative review of the emerging and increasing use of network science techniques in cognitive psychology, first developed in mathematics, computer science, sociology, and physics. The first resource on network science for cognitive psychologists in a growing international market, Vitevitch and a team of expert contributors provide a comprehensive and accessible overview of this cutting-edge topic. This innovative guide draws on the three traditional pillars of cognitive psychological research-experimental, computational, and neuroscientific-and incorporates the latest findings from neuroimaging. The network perspective is applied to the fundamental domains of cognitive psychology including memory, language, problem-solving, and learning, as well as creativity and human intelligence, highlighting the insights to be gained through applying network science to a wide range of approaches and topics in cognitive psychology Network Science in Cognitive Psychology will be essential reading for all upper-level cognitive psychology students, psychological researchers interested in using network science in their work, and network scientists interested in investigating questions related to cognition. It will also be useful for early career researchers and students in methodology and related courses.

Shame remains at the core of much psychological distress and can eventuate as physical symptoms, yet experiential approaches to healing shame are sparse. Links between shame and art making have been felt, intuited, and examined, but have not been sufficiently documented by depth psychologists. Shame and the Making of Art addresses this lacuna by surveying depth psychological conceptions of shame, art, and the role of creativity in healing, contemporary and historical shame ideologies, as well as recent psychobiological studies on shame. Drawing on research conducted with participants in three different countries, the book includes candid discussions of shame experiences. These experiences are accompanied by Cluff's heuristic inquiry into shame with an interpretative phenomenological analysis that focuses on how participants negotiate the relationship between shame and the making of art. Cluff's movement through archetypal dimensions, especially Dionysian, is developed and discussed throughout the book. The results of the research are further explicated in terms of comparative studies, wherein the psychological processes and impacts observed by other researchers and effects on self-conscious maladaptive emotions are described. Shame and the Making of Art should be essential reading for academics, researchers, and postgraduate students engaged in the study of psychology and the arts. It will be of particular interest to psychologists, Jungian psychotherapists, psychiatrists, social workers, creativity researchers, and anyone interested in understanding the dynamics of this shame and self-expression.

The goal of this book is to present and evaluate the concept of dynamic testing. Unlike "static" tests such as the SAT or IQ tests, it emphasizes learning potential rather than past learning accomplishments. This book is unique in its wide-ranging review of virtually all approaches to dynamic testing. It also suggests alternatives to the typical kinds of tests given in the United States--tests that often seem to stifle rather than encourage the development of human potential.

This book provides an up-to-date review of commonly undertaken methodological and statistical practices that are sustained, in part, upon sound rationale and justification and, in part, upon unfounded lore. Some examples of these "methodological urban legends," as we refer to them in this book, are characterized by manuscript critiques such as: (a) "your self-report measures suffer from common method bias"; (b) "your item-to-subject ratios are too low"; (c) "you can t generalize these findings to the real world"; or (d) "your effect sizes are too low."

Historically, there is a kernel of truth to most of these legends, but in many cases that truth has been long forgotten, ignored or embellished beyond recognition. This book examines several such legends. Each chapter is organized to address: (a) what the legend is that "we (almost) all know to be true"; (b) what the "kernel of truth" is to each legend; (c) what the myths are that have developed around this kernel of truth; and (d) what the state of the practice should be. This book meets an important need for the accumulation and integration of these methodological and statistical practices.

This book explains how to develop more effective risk communications using the Carnegie Mellon mental-model approach. Such communications are designed to contain, in readily usable form, the information that people need to make informed decisions about risks to health, safety, and the environment. The approach draws together methods from the natural and social sciences, providing a framework for interdisciplinary collaboration. It is demostrated with varied examples including electromagnetic fields, climate change, radon, and sexually transmitted diseases.

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.

This volume, Statistical Methods in Psychiatry Research and SPSS, now going into its second edition, has been helping psychiatrists expand their knowledge of statistical methods and fills the gaps in their applications as well as introduces data analysis software. It addresses the statistical needs of physicians and presents a simplified approach. The book emphasizes the classification of fundamental statistical methods in psychiatry research that are precise and simple. Professionals in the field of mental health and allied subjects without any mathematical background will easily understand all the relevant statistical methods and carry out the analysis and interpret the results in their respective field without consulting any statistician. This new volume has over 100 pages of new material, including several new appendixes. The sequence of the chapters, the sections within the chapters, the subsections within the sections, and the points within the subsections have all been arranged to help professionals in classification refine their knowledge in statistical methods and fills the gaps.

A Handbook of Process Tracing Methods demonstrates how to better understand decision outcomes by studying decision processes, through the introduction of a number of exciting techniques. Decades of research have identified numerous idiosyncrasies in human decision behavior, but some of the most recent advances in the scientific study of decision making involve the development of sophisticated methods for understanding decision process-known as process tracing. In this volume, leading experts discuss the application of these methods and focus on the best practices for using some of the more popular techniques, discussing how to incorporate them into formal decision models. This edition has been expanded and thoroughly updated throughout, and now includes new chapters on mouse tracking, protocol analysis, neurocognitive methods, the measurement of valuation, as well as an overview of important software packages. The volume not only surveys cutting-edge research to illustrate the great variety in process tracing techniques, but also serves as a tutorial for how the novice researcher might implement these methods. A Handbook of Process Tracing Methods will be an essential read for all students and researchers of decision making.

The Ozone Layer is an accessible history of stratospheric ozone, from its discovery in the nineteenth century to current investigations of the Antarctic ozone hole. Drawing directly on the scientific literature, Christie uses the story of ozone as a case study for examining fundamental issues relating to the practice of modern science and the conduct of scientific debate. Linking key debates in the philosophy of science to an example of real-world science it is an excellent and thought-provoking introduction to the philosophy of science.

Unrealistic public policies have increasingly concerned clinicians who fear being held responsible for their decisions in a legal climate that expects them to accurately predict the future. Clinical Assessment of Dangerousness provides a forum in which a group of internationally recognized scholars present the major conceptual issues and themes in their areas of violence research. Each chapter includes an issue-based essay that makes the research findings clinically relevant for assessment and prediction of violence. This book provides a reference that outlines key information for conducting more effective risk assessments with different populations.

This path-breaking book reviews psychological research on practical intelligence and describes its importance in everyday life. The authors reveal the importance of tacit knowledge--what we have learned from our own experience, through action. Although it has been seen as an indispensable element of expertise, intelligence researchers have found it difficult to quantify. Based on years of research, Dr. Sternberg and his colleagues have found that tacit knowledge can be quantified and can be taught. This volume thoroughly examines studies of practical intelligence in the United States and in many other parts of the world as well, and for varied occupations, such as management, military leadership, teaching, research, and sales.