Originally published in 1987, this book presents papers from the First Conference of European Clinical Psychologists, held at the University of Kent Canterbury in July of that year. It shows some of the most exciting and recent developments in research and innovations in professional practice from many European countries with an overall theme of the WHO strategy of Health for all by the year 2000. The whole range of clinical psychology is covered, including: cognitive therapy, clinical psychology and WHO strategy, the mental health of ethnic minority groups, health psychology, care in the community, and many other topics. The book is likely to be of interest for anyone concerned with the recent history and policies in clinical psychology."

Volume I is the first of two volumes that document the three components of the CHILDES Project. It is divided into two parts which provide an introduction to the use of computational tools for studying language learning. The first part is the CHAT manual, which describes the conventions and principles of CHAT transcription and recommends specific methods for data collection and digitization. The second part is the CLAN manual, which describes the uses of the editor, sonic CHAT, and the various analytic commands. The book will be useful for both novice and experienced users of the CHILDES tools, as well as instructors and students working with transcripts of child language. Volume II describes in detail all of the corpora included in the CHILDES database. The conversational interactions in the corpora come from monolingual children and their caregivers and siblings, as well as bilingual children, older school-aged children, adult second-language learners, children with various types of language disabilities, and aphasic recovering from language loss. The database includes transcripts in 26 different languages.The CD-ROM that accompanies these volumes includes the transcript files described in Volume II. It runs on both Windows and Macintosh platforms. For more information or updates to the files, visit the CHILDES website at http: //childes.psy.cmu.ed

Referral and Termination Issues for Counselors guides trainee and practicing counselors through the practical issues surrounding the referral of clients, a procedure that may be necessary at any time during the counseling process. Stressing ethical issues and the need to be aware of competency limits, Anne Leigh provides clear and straightforward guidelines. The sensitive, ethical handling of termination is also an important part of this book, backed up by clear examples and the recognition of the emotional consequences of referral or termination for both counselor and client. She examines the situations most frequently calling for referral and the ways in which referral may take place responsibly and satisfactorily. This volume covers whether, how, to whom, and when to refer on, as well as how best to receive referrals from outside agencies.

This is the first comprehensive survey of Descriptive Psychology. It provides a systematic account of the basic formulations and characteristic methodologies of this discipline which was developed by Peter G. Ossorio of the University of Colorado at Boulder. Dr. Ossorio defines Descriptive Psychology as "a set of systematically related concepts which is designed to provide formal access to all the facts and possible facts concerning persons and behavioroand therefore everything else as well."

First published in 1985, Ethical Issues in Psychosurgery examines the continuing debate surrounding the treatment of psychiatric disorder by psychosurgery and its ethical implications. Psychosurgery represents a radical treatment and it therefore raises, in a particularly acute and challenging fashion, questions which are implicit In most therapy. The book offers a focussed study in bioethics, a model for bioethical inquiry, as well as introduction to some of the major problems in bioethics. These range from detailed discussions of informed consent, the sanctity of the brain, and the use of experimental therapies, to wider questions of social contract and professionalization. John Kleinig's balanced and informed treatment of the questions will make this book invaluable not only to those concerned with the philosophy of legal and medical ethics, but also to those in the fields of psychiatric practice and research.

Learn How to Use Growth Curve Analysis with Your Time Course Data An increasingly prominent statistical tool in the behavioral sciences, multilevel regression offers a statistical framework for analyzing longitudinal or time course data. It also provides a way to quantify and analyze individual differences, such as developmental and neuropsychological, in the context of a model of the overall group effects. To harness the practical aspects of this useful tool, behavioral science researchers need a concise, accessible resource that explains how to implement these analysis methods. Growth Curve Analysis and Visualization Using R provides a practical, easy-to-understand guide to carrying out multilevel regression/growth curve analysis (GCA) of time course or longitudinal data in the behavioral sciences, particularly cognitive science, cognitive neuroscience, and psychology. With a minimum of statistical theory and technical jargon, the author focuses on the concrete issue of applying GCA to behavioral science data and individual differences. The book begins with discussing problems encountered when analyzing time course data, how to visualize time course data using the ggplot2 package, and how to format data for GCA and plotting. It then presents a conceptual overview of GCA and the core analysis syntax using the lme4 package and demonstrates how to plot model fits. The book describes how to deal with change over time that is not linear, how to structure random effects, how GCA and regression use categorical predictors, and how to conduct multiple simultaneous comparisons among different levels of a factor. It also compares the advantages and disadvantages of approaches to implementing logistic and quasi-logistic GCA and discusses how to use GCA to analyze individual differences as both fixed and random effects. The final chapter presents the code for all of the key examples along with samples demonstrating how to report GCA results. Throughout the book, R code illustrates how to implement the analyses and generate the graphs. Each chapter ends with exercises to test your understanding. The example datasets, code for solutions to the exercises, and supplemental code and examples are available on the author's website.

Incorporating a hands-on pedagogical approach, Nonparametric Statistics for Social and Behavioral Sciences presents the concepts, principles, and methods used in performing many nonparametric procedures. It also demonstrates practical applications of the most common nonparametric procedures using IBM's SPSS software. This text is the only current nonparametric book written specifically for students in the behavioral and social sciences. Emphasizing sound research designs, appropriate statistical analyses, and accurate interpretations of results, the text: Explains a conceptual framework for each statistical procedure Presents examples of relevant research problems, associated research questions, and hypotheses that precede each procedure Details SPSS paths for conducting various analyses Discusses the interpretations of statistical results and conclusions of the research With minimal coverage of formulas, the book takes a nonmathematical approach to nonparametric data analysis procedures and shows students how they are used in research contexts. Each chapter includes examples, exercises, and SPSS screen shots illustrating steps of the statistical procedures and resulting output.

Advanced and Multivariate Statistical Methods, Seventh Edition provides conceptual and practical information regarding multivariate statistical techniques to students who do not necessarily need technical and/or mathematical expertise in these methods. This text has three main purposes. The first purpose is to facilitate conceptual understanding of multivariate statistical methods by limiting the technical nature of the discussion of those concepts and focusing on their practical applications. The second purpose is to provide students with the skills necessary to interpret research articles that have employed multivariate statistical techniques. Finally, the third purpose of AMSM is to prepare graduate students to apply multivariate statistical methods to the analysis of their own quantitative data or that of their institutions. New to the Seventh Edition All references to SPSS have been updated to Version 27.0 of the software. A brief discussion of practical significance has been added to Chapter 1. New data sets have now been incorporated into the book and are used extensively in the SPSS examples. All the SPSS data sets utilized in this edition are available for download via the companion website. Additional resources on this site include several video tutorials/walk-throughs of the SPSS procedures. These "how-to" videos run approximately 5-10 minutes in length. Advanced and Multivariate Statistical Methods was written for use by students taking a multivariate statistics course as part of a graduate degree program, for example in psychology, education, sociology, criminal justice, social work, mass communication, and nursing.

Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.

This is the first book to demonstrate the application of power analysis to the newer more advanced statistical techniques that are increasingly used in the social and behavioral sciences. Both basic and advanced designs are covered. Readers are shown how to apply power analysis to techniques such as hierarchical linear modeling, meta-analysis, and structural equation modeling. Each chapter opens with a review of the statistical procedure and then proceeds to derive the power functions. This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, SAS, and SPSS. Readers can verify the power computation using the computer programs on the book's website. There is a growing requirement to include power analysis to justify sample sizes in grant proposals. Most chapters are self-standing and can be read in any order without much disruption.This book will help readers do just that. Sample computer code in R, SPSS, and SAS at www.routledge.com/9781848729810 are written to tabulate power values and produce power curves that can be included in a grant proposal. Organized according to various techniques, chapters 1 - 3 introduce the basics of statistical power and sample size issues including the historical origin, hypothesis testing, and the use of statistical power in t tests and confidence intervals. Chapters 4 - 6 cover common statistical procedures -- analysis of variance, linear regression (both simple regression and multiple regression), correlation, analysis of covariance, and multivariate analysis. Chapters 7 - 11 review the new statistical procedures -- multi-level models, meta-analysis, structural equation models, and longitudinal studies. The appendixes contain a tutorial about R and show the statistical theory of power analysis. Intended as a supplement for graduate courses on quantitative methods, multivariate statistics, hierarchical linear modeling (HLM) and/or multilevel modeling and SEM taught in psychology, education, human development, nursing, and social and life sciences, this is the first text on statistical power for advanced procedures. Researchers and practitioners in these fields also appreciate the book's unique coverage of the use of statistical power analysis to determine sample size in planning a study. A prerequisite of basic through multivariate statistics is assumed.

In this collection, international contributors come together to discuss how qualitative and quantitative methods can be used in psychotherapy research. The book considers the advantages and disadvantages of each approach, and recognises how each method can enhance our understanding of psychotherapy. Divided into two parts, the book begins with an examination of quantitative research and discusses how we can transfer observations into numbers and statistical findings. Chapters on quantitative methods cover the development of new findings and the improvement of existing findings, identifying and analysing change, and using meta-analysis. The second half of the book comprises chapters considering how qualitative and mixed methods can be used in psychotherapy research. Chapters on qualitative and mixed methods identify various ways to strengthen the trustworthiness of qualitative findings via rigorous data collection and analysis techniques. Adapted from a special issue of Psychotherapy Research, this volume will be key reading for researchers, academics, and professionals who want a greater understanding of how a particular area of research methods can be used in psychotherapy.

How Can You Improve Your Learning Capabilites? How Can You Enhance Your Potential for Change and Personal Growth? Most of us accept that education does not meet the needs of learners today, or their employers. This mismatch is a key reason why a high level of demotivated youth, as well as workers and managers remain unable to develop themselves. They have been other-organised and are unprepared for the world of work and the challenges of life. First published in 1991, this title offers a radical approach to human learning and personal change. Based on the reflective procedures of Learning Conversations, it enables a deep exploration of the learning process and allows individuals, teams and even whole organisations to create dynamic learning cultures capable of adaptive, constructive and continuing growth. Available again after some years this book is as relevant, if not of greater value, in our ever-changing society than when originally published.

This is the first book of its kind to include the personal accounts of people who have survived injury to the brain, along with professional therapists' reports of their progress through rehabilitation. The paintings and stories of survivors combine with experts' discussions of the theory and practice of brain injury rehabilitation to illustrate the ups and downs that survivors encounter in their journey from pre-injury status to insult and post-injury rehabilitation. Wilson, Winegardner and Ashworth's focus on the survivors' perspective shows how rehabilitation is an interactive process between people with brain injury, health care staff, and others, and gives the survivors the chance to tell their own stories of life before their injury, the nature of the insult, their early treatment, and subsequent rehabilitation. Presenting practical approaches to help survivors of brain injury achieve functionally relevant and meaningful goals, Life After Brain Injury: Survivors' Stories will help all those working in rehabilitation understand the principles involved in holistic brain injury rehabilitation and how these principles, combined with theory and models, translate into clinical practice. This book will be of great interest to anyone who wishes to extend their knowledge of the latest theories and practices involved in making life more manageable for people who have suffered damage to the brain. Life After Brain Injury: Survivors' Stories will also be essential for clinical psychologists, neuropsychologists, and anybody dealing with acquired brain injury whether they be a survivor of a brain injury themselves, a relative, a friend or a carer.

This book reviews the latest techniques in exploratory data mining (EDM) for the analysis of data in the social and behavioral sciences to help researchers assess the predictive value of different combinations of variables in large data sets. Methodological findings and conceptual models that explain reliable EDM techniques for predicting and understanding various risk mechanisms are integrated throughout. Numerous examples illustrate the use of these techniques in practice. Contributors provide insight through hands-on experiences with their own use of EDM techniques in various settings. Readers are also introduced to the most popular EDM software programs. A related website at http://mephisto.unige.ch/pub/edm-book-supplement/offers color versions of the book's figures, a supplemental paper to chapter 3, and R commands for some chapters. The results of EDM analyses can be perilous - they are often taken as predictions with little regard for cross-validating the results. This carelessness can be catastrophic in terms of money lost or patients misdiagnosed. This book addresses these concerns and advocates for the development of checks and balances for EDM analyses. Both the promises and the perils of EDM are addressed. Editors McArdle and Ritschard taught the "Exploratory Data Mining" Advanced Training Institute of the American Psychological Association (APA). All contributors are top researchers from the US and Europe. Organized into two parts--methodology and applications, the techniques covered include decision, regression, and SEM tree models, growth mixture modeling, and time based categorical sequential analysis. Some of the applications of EDM (and the corresponding data) explored include: selection to college based on risky prior academic profiles the decline of cognitive abilities in older persons global perceptions of stress in adulthood predicting mortality from demographics and cognitive abilities risk factors during pregnancy and the impact on neonatal development Intended as a reference for researchers, methodologists, and advanced students in the social and behavioral sciences including psychology, sociology, business, econometrics, and medicine, interested in learning to apply the latest exploratory data mining techniques. Prerequisites include a basic class in statistics.

Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation. Drawing on data sets from the author's many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author's website.

* Starts from the basics, focusing less on proofs and the high-level math underlying regressions, and adopts an engaging tone to provide a text which is entirely accessible to students who don't have a stats background * New chapter on integrity and ethics in regression analysis * Each chapter offers boxed examples, stories, exercises and clear summaries, all of which are designed to support student learning * Optional appendix of statistical tools, providing a primer to readers who need it * Code in R and Stata, and data sets and exercises in Stata and CSV, to allow students to practice running their own regressions * Author-created videos on YouTube * PPT lecture slides and test bank for instructors

This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group analysis, and model testing. New methodological topics are illustrated with real applications. The material presented brings together two traditions: psychometrics and structural equation modeling. Latent Variable and Latent Structure Models' thought-provoking chapters from the leading researchers in the area will help to stimulate ideas for further research for many years to come. This volume will be of interest to researchers and practitioners from a wide variety of disciplines, including biology, business, economics, education, medicine, psychology, sociology, and other social and behavioral sciences. A working knowledge of basic multivariate statistics and measurement theory is assumed.

Teaches the principles of sampling with examples from social sciences, public opinion research, public health, business, agriculture, and ecology. Has been thoroughly revised to incorporate recent research and applications. Includes a new chapter on nonprobability samples, and more than 200 new examples and exercises have been added.

N.N. Ladygina-Kohts earned her degree in comparative psychology at Moscow University in 1917, then became the first curator of the Darwin Museum in Moscow. Her pioneering work with the chimpanzee, Joni, was reported throughout the continent during her lifetime, earning her a series of honors in the Soviet Union. Unfortunately, Infant Chimpanzee and Human Child, her diary comparing Joni's development with that of her son, Rudy, had never been translated completely. This volume presents the first, complete English translation with 120 photographs, an introduction by Allen and Beatrix Gardner of the Center for Advanced Study at the University of Nevada and an Afterword by Lisa A. Parr, Signe Preuschoft, and Frans B. M. de Waal of the Living Links Center.

This book presents the conceptual and mathematical basis and the implementation of both electroencephalogram (EEG) and EEG signal processing in a comprehensive, simple, and easy-to-understand manner. EEG records the electrical activity generated by the firing of neurons within human brain at the scalp. They are widely used in clinical neuroscience, psychology, and neural engineering, and a series of EEG signal-processing techniques have been developed. Intended for cognitive neuroscientists, psychologists and other interested readers, the book discusses a range of current mainstream EEG signal-processing and feature-extraction techniques in depth, and includes chapters on the principles and implementation strategies.

Single Case Research in Schools addresses and examines the variety of cutting-edge issues in single case research (SCR) in educational settings. Featuring simple and practical techniques for aggregating data for evidence-based practices, the book delves into methods of selecting behaviors of interest and measuring them reliably. The latter part of Single Case Research in Schools is devoted to a step-by-step model of using SCR to evaluate practices in schools. This includes considerations such as measurement, date collection, length of phases, design consideratoins, calculating effect size and reliability of measures.

This volume presents the first wide-ranging critical review of validity generalization (VG)--a method that has dominated the field since the publication of Schmidt and Hunter's (1977) paper "Development of a General Solution to the Problem of Validity Generalization." This paper and the work that followed had a profound impact on the science and practice of applied psychology. The research suggests that fundamental relationships among tests and criteria, and the constructs they represent are simpler and more regular than they appear. Looking at the history of the VG model and its impact on personnel psychology, top scholars and leading researchers of the field review the accomplishments of the model, as well as the continuing controversies. Several chapters significantly extend the maximum likelihood estimation with existing models for meta analysis and VG. Reviewing 25 years of progress in the field, this volume shows how the model can be extended and applied to new problems and domains. This book will be important to researchers and graduate students in the areas of industrial organizational psychology and statistics.

Built around a problem solving theme, this book extends the intermediate and advanced student's expertise to more challenging situations that involve applying statistical methods to real-world problems. Data relevant to these problems are collected and analyzed to provide useful answers. Building on its central problem-solving theme, a large number of data sets arising from real problems are contained in the text and in the exercises provided at the end of each chapter. Answers, or hints to providing answers, are provided in an appendix. Concentrating largely on the established SPSS and the newer S-Plus statistical packages, the author provides a short, end-of-chapter section entitled Computer Hints that helps the student undertake the analyses reported in the chapter using these statistical packages.

The Generic Qualitative Approach to a Dissertation in the Social Sciences: A Step by Step Guide is a practical guide for the graduate students and faculty planning and executing a generic qualitative dissertation in the social sciences. Generic qualitative research is a methodology that seeks to understand human experience by taking a qualitative stance and using qualitative procedures. Based on Sandra Kostere and Kim Kostere's experiences of serving on dissertation committees, this book aims to demystify both the nuances and the procedures of qualitative research, with the aim of empowering students to conduct meaningful dissertation research and present findings that are rigorous, credible, and trustworthy. It examines the fundamental principles and assumptions underlying the generic qualitative method, then covers each stage of the research process including creation of research questions, interviews, and then offers three ways of analyzing the data gathered and presenting the results. With examples of the generic qualitative method in practice to show students how to conduct their research confidently, and chapters designed to walk the researcher through each step of the dissertation process, this book is specifically tailored for the accessible generic method, and will be useful for graduate students and faculty developing dissertations in Psychology, Education, Nursing and the social sciences.