In the decade of the 1970s, item response theory became the dominant topic for study by measurement specialists. But, the genesis of item response theory (IRT) can be traced back to the mid-thirties and early forties. In fact, the term "Item Characteristic Curve," which is one of the main IRT concepts, can be attributed to Ledyard Tucker in 1946. Despite these early research efforts, interest in item response theory lay dormant until the late 1960s and took a backseat to the emerging development of strong true score theory. While true score theory developed rapidly and drew the attention of leading psychometricians, the problems and weaknesses inherent in its formulation began to raise concerns. Such problems as the lack of invariance of item parameters across examinee groups, and the inadequacy of classical test procedures to detect item bias or to provide a sound basis for measurement in "tailored testing," gave rise to a resurgence of interest in item response theory. Impetus for the development of item response theory as we now know it was provided by Frederic M. Lord through his pioneering works (Lord, 1952; 1953a, 1953b). The progress in the fifties was painstakingly slow due to the mathematical complexity of the topic and the nonexistence of computer programs.

Generalizability theory offers an extensive conceptual framework and a powerful set of statistical procedures for characterizing and quantifying the fallibility of measurements. Robert Brennan, the author, has written the most comprehensive and up-to-date treatment of generalizability theory. The book provides a synthesis of those parts of the statistical literature that are directly applicable to generalizability theory. The principal intended audience is measurement practitioners and graduate students in the behavioral and social sciences, although a few examples and references are provided from other fields. Readers will benefit from some familiarity with classical test theory and analysis of variance, but the treatment of most topics does not presume specific background.

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.

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)

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.

Item response theory has become an essential component in the toolkit of every researcher in the behavioral sciences. It provides a powerful means to study individual responses to a variety of stimuli, and the methodology has been extended and developed to cover many different models of interaction. This volume presents a wide-ranging handbook to item response theory - and its applications to educational and psychological testing. It will serve as both an introduction to the subject and also as a comprehensive reference volume for practitioners and researchers. It is organized into six major sections: the nominal categories model, models for response time or multiple attempts on items, models for multiple abilities or cognitive components, nonparametric models, models for nonmonotone items, and models with special assumptions. Each chapter in the book has been written by an expert of that particular topic, and the chapters have been carefully edited to ensure that a uniform style of notation and presentation is used throughout. As a result, all researchers whose work uses item response theory will find this an indispensable companion to their work and it will be the subject's reference volume for many years to come.

PSYCHOLOGICAL TESTING: PRINCIPLES, APPLICATIONS, AND ISSUES, Ninth Edition explains the fundamentals of psychological testing, their important applications, and the controversies that emerge from those applications in clinical, education, industrial, and legal settings. Kaplan and Saccuzzo's engaging and thorough text demonstrates how psychological tests are constructed and used, both in a professional setting and in everyday lives. Part I focuses on the core concepts that affect the evaluation of all tests. Part II discusses the major types of psychological tests, while Part III looks at present-day issues affecting testing such as stereotype threat, bias, laws, and ethics. A multitude of test profiles and sample items illustrate how psychological testing is used and reported. Real-life case studies demonstrate the uses and misuses of psychological testing, while "Technical Example" boxes help you understand complex statistical concepts.

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

WINNER OF THE 2007 DEGROOT PRIZE

The prominence of finite mixture modelling is greater than ever. Many important statistical topics like clustering data, outlier treatment, or dealing with unobserved heterogeneity involve finite mixture models in some way or other. The area of potential applications goes beyond simple data analysis and extends to regression analysis and to non-linear time series analysis using Markov switching models.

For more than the hundred years since Karl Pearson showed in 1894 how to estimate the five parameters of a mixture of two normal distributions using the method of moments, statistical inference for finite mixture models has been a challenge to everybody who deals with them. In the past ten years, very powerful computational tools emerged for dealing with these models which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book reviews these techniques and covers the most recent advances in the field, among them bridge sampling techniques and reversible jump Markov chain Monte Carlo methods.

It is the first time that the Bayesian perspective of finite mixture modelling is systematically presented in book form. It is argued that the Bayesian approach provides much insight in this context and is easily implemented in practice. Although the main focus is on Bayesian inference, the author reviews several frequentist techniques, especially selecting the number of components of a finite mixture model, and discusses some of their shortcomings compared to the Bayesian approach.

The aim of this book is to impart the finite mixture and Markov switching approach to statistical modelling to a wide-ranging community. This includes not only statisticians, but also biologists, economists, engineers, financial agents, market researcher, medical researchers or any other frequent user of statistical models. This book should help newcomers to the field to understand how finite mixture and Markov switching models are formulated, what structures they imply on the data, what they could be used for, and how they are estimated. Researchers familiar with the subject also will profit from reading this book. The presentation is rather informal without abandoning mathematical correctness. Previous notions of Bayesian inference and Monte Carlo simulation are useful but not needed.

In this book, experts in statistics and psychometrics describe classes of linkages, the history of score linkings, data collection designs, and methods used to achieve sound score linkages. They describe and critically discuss applications to a variety of domains. They define what linking is, to distinguish among the varieties of linking and to describe different procedure for linking. Furthermore, they convey the complexity and diversity of linking by covering different areas of linking and providing diverse perspectives.

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.

This textbook is for graduate students and research workers in social statistics and related subject areas. It follows a novel curriculum developed around the basic statistical activities: sampling, measurement and inference. The monograph aims to prepare the reader for the career of an independent social statistician and to serve as a reference for methods, ideas for and ways of studying of human populations. Elementary linear algebra and calculus are prerequisites, although the exposition is quite forgiving. Familiarity with statistical software at the outset is an advantage, but it can be developed while reading the first few chapters.

Needs Assessment for Learning and Performance offers comprehensive coverage of the knowledge and skills needed to develop and conduct needs assessments and to analyze, interpret, and communicate results to clients and organizations. Though critical to planning any performance improvement system, needs assessments can feel abstract and vague to students who have not yet managed the process in a professional setting. This first-of-its-kind textbook uses a variety of real-world examples to connect major theories and models to effective principles for practice. Each chapter offers guiding questions, key terms and concepts, recommended readings, and case studies illustrating how needs assessment training can be applied. Graduate students and researchers of instructional design, human resources, performance improvement, program evaluation, and other programs will find this volume relevant to a range of academic and organizational contexts.

*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.

Understanding Statistics in Psychology with SPSS, eighth edition, offers students a trusted, straightforward, and engaging way of learning to do statistical analyses confidently using SPSS. Comprehensive and practical, the text is organised into short accessible chapters, making it the ideal text for undergraduate psychology students needing to get to grips with statistics in class or independently. Clear diagrams and full colour screenshots from SPSS make the text suitable for beginners while the broad coverage of topics ensures that students can continue to use it as they progress to more advanced techniques. Key features * Combines coverage of statistics with full guidance on how to use SPSS to analyse data. * Suitable for use with all versions of SPSS. * Examples from a wide range of real psychological studies illustrate how statistical techniques are used in practice. * Includes clear and detailed guidance on choosing tests, interpreting findings and reporting and writing up research. * Student-focused pedagogical approach including: o Key concept boxes detailing important terms. o Focus on sections exploring complex topics in greater depth. o Explaining statistics sections clarify important statistical concepts. . Dennis Howitt and Duncan Cramer are with Loughborough University.

*Accessible reference and text on a critical topic--testing pervades society, yet students find its core concepts challenging. *Class tested at two universities; the emphasis on conceptual understanding and application promotes critical thinking. *Flexible organization enables researchers and students to review the basics or delve into advanced concepts as needed. *Competing titles are pricey, dated, and not as reader friendly. *Examples and discussions walk readers through critical decisions in the selection, administration, scoring, interpretation, and development of measurement instruments.

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.

Validity is a clear, substantive introduction to the two most fundamental aspects of defensible testing practice: understanding test score meaning and justifying test score use. Driven by evidence-based and consensus-grounded measurement theory, principles, and terminology, this book addresses the most common questions of applied validation, the quality of test information, and the usefulness of test results. Concise yet comprehensive, this volume's integrated framework is ideal for graduate courses on assessment, testing, psychometrics, and research methods as well as for credentialing organizations, licensure and certification entities, education agencies, and test publishers.

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.

What should be done about airplane safety and terrorism, global warming, polluted water, nuclear power, and genetically engineered food? Decision-makers often respond to temporary fears, and the result is a situation of hysteria and neglect--and unnecessary illness and death. Risk and Reason explains the sources of these problems and explores what can be done about them. It shows how individual thinking and social interactions lead us in foolish directions. Offering sound proposals for social reform, it explains how a more sensible system of risk regulation, embodied in the idea of a "cost-benefit state," could save many thousands of lives and many billions of dollars too--and protect the environment in the process. Cass R. Sunstein is the Karl N. Llewellyn Distinguished Service Professor at the University of Chicago. Appointed by President Clinton to serve on the Advisory Committee on the Public Interest Obligations of Television Broadcasters. His many books include Republic.com (Princeton, 2001) and Designing Democracy (Oxford, 2001). He has worked in the United States Department of Justice and advised on law reform and constitution-making in many nations.

*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.

"This excellent book contains many different scripts, applicable to a number of special populations. It takes a practical approach and walks therapists step-by-step through the EMDR therapeutic process. Readers] will not be disappointed." Score:93, 4 stars --"Doody's"

Praise from a practicing EMDR therapist and user of Eye Movement Desensitization and Reprocessing (EMDR) Scripted Protocols:

"Kudos to...everyone who contributed to this important volume.... It] is an indispensable resource. Thank you, thank you, thank you " --Andrea B. Goldberg, LCSW
EMDRIA Certified EMDR Therapist
EMDRIA Consultant-in-training
Bloomfield and Newark, NJ

This book outlines some of the basic elements of the 11-Step Standard Procedure of EMDR and the Standard Three-Pronged EMDR Protocol. Unlike other EMDR books, however, this book focuses on applying EMDR scripted protocols to special populations.

Special populations discussed include children, adolescents, couples, and clients suffering from complex posttraumatic stress disorder, dissociative disorders, anxiety, addictive behaviors, and severe pain. This is a volume rich in wisdom and insight that every EMDR clinician working with special populations will need in his or her collection.

Key topics include:

• EMDR preparation, assessment, and desensitization phases for children
• Integrating EMDR into couples therapy
• EMDR protocol for treating sexual dysfunction
• EMDR-informed treatment approaches for dissociative disorders
• Clearing the pain of unrequited love with EMDR
• An EMDR approach to treating substance abuse and addiction
• EMDR for pain patients
• Self-care for EMDR practitioners