Gathering scholars from different disciplines, this book is the first on how to study emotions using sociological, historical, linguistic, anthropological, psychological, cultural, and mixed approaches. Bringing together the emerging lines of inquiry, it lays foundations for an overdue methodological debate. The volume offers entrancing short essays, richly illustrated with examples and anecdotes, that provide basic knowledge about how to pursue emotions in texts, interviews, observations, spoken language, visuals, historical documents, and surveys. The contributors are respectful of those being researched and are mindful of the effects of their own feelings on the conclusions. The book thus touches upon the ethics of research in vivid first person accounts. Methods are notoriously difficult to teach-this collection fills the gap between dry methods books and students' need to know more about the actual research practice.

The political and legislative changes which took place in South Africa during the 1990s, with the dissolution of apartheid, created a unique set of social conditions. As official policies of segregation were abolished, people of both black and white racial groups began to experience new forms of social contact and intimacy. By examining these emerging processes of intergroup contact in South Africa, and evaluating related evidence from the US, Racial Encounter offers a social psychological account of desegregation. It begins with a critical analysis of the traditional theories and research models used to understand desegregation: the contact hypothesis and race attitude theory. It then analyzes every day discourse about desegregation in South Africa, showing how discourse shapes individuals' conception and management of their changing relationships and acts as a site of ideological resistance to social change. The connection between place, identity and re-creation of racial boundaries emerge as a central theme of this analysis. This book will be of interest to social psychologists, students of intergroup relations and all those interested in post-apartheid South Africa.

Comprehensive and comprehensible, this classic text covers the basic and advanced topics essential for using factor analysis as a scientific tool in psychology, education, sociology, and related areas. Emphasizing the usefulness of the techniques, it presents sufficient mathematical background for understanding and applying its use. This includes the theory as well as the empirical evaluations. The overall goal is to show readers how to use factor analysis in their substantive research by highlighting when the differences in mathematical procedures have a major impact on the substantive conclusions, when the differences are not relevant, and when factor analysis might not be the best procedure to use. Although the original version was written years ago, the book maintains its relevance today by providing readers with a thorough understanding of the basic mathematical models so they can easily apply these models to their own research. Readers are presented with a very complete picture of the "inner workings" of these methods. The new Introduction highlights the remarkably few changes that the author would make if he were writing the book today. An ideal text for courses on factor analysis or as a supplement for multivariate analysis, structural equation modeling, or advanced quantitative techniques taught in psychology, education, and other social and behavioral sciences, researchers who use these techniques also appreciate this book's thorough review of the basic models. Prerequisites include a graduate level course on statistics and a basic understanding of algebra. Sections with an asterisk can be skipped entirely if preferred.

CODING MANUAL INFORMATION IS AVAILABLE FROM THE CHAPTER AUTHORS, AND THEIR E-MAIL ADDRESSES CAN BE FOUND ON PAGE XV OF THE BOOK. Family studies is an area that has enjoyed the benefits of conceptual and methodological advances in recent years including the widespread adoption of observational research techniques. The selection of an appropriate coding system is critical to achieving a better understanding of the complex family processes related to normative and pathological development. This book presents 14 examples of family observational coding systems, chosen for the wide range of constructs and phenomena they capture. Each system is described in detail, and excerpts from the coding manual are presented (links to the full coding manuals are available to purchasers of the book at LEA's Web site, www.erlbaum.com). Each chapter follows a consistent outline, so that the different coding systems can be more easily compared to one another. They include the theoretical underpinnings of the measure, its reliability and validity, the coding process, strategies for coder training, and examples of studies in which it has been used. This volume will prove invaluable to students and researchers in family studies, clinicians, and other practitioners who need to interpret data from family observations.

This book focuses on the practical issues and approaches to handling longitudinal and multilevel data. All data sets and the corresponding command files are available via the Web. The working examples are available in the four major SEM packages--LISREL, EQS, MX, and AMOS--and two Multi-level packages--HLM and MLn. All equations and figural conventions are standardized across each contribution. The material is accessible to practicing researchers and students. Users can compare and contrast various analytic approaches to longitudinal and multiple-group data including SEM, Multi-level, LTA, and standard GLM techniques. Ideal for graduate students and practicing researchers in social and behavioral sciences.

Comprehensive and comprehensible, this classic text covers the basic and advanced topics essential for using factor analysis as a scientific tool in psychology, education, sociology, and related areas. Emphasizing the usefulness of the techniques, it presents sufficient mathematical background for understanding and applying its use. This includes the theory as well as the empirical evaluations. The overall goal is to show readers how to use factor analysis in their substantive research by highlighting when the differences in mathematical procedures have a major impact on the substantive conclusions, when the differences are not relevant, and when factor analysis might not be the best procedure to use. Although the original version was written years ago, the book maintains its relevance today by providing readers with a thorough understanding of the basic mathematical models so they can easily apply these models to their own research. Readers are presented with a very complete picture of the "inner workings" of these methods. The new Introduction highlights the remarkably few changes that the author would make if he were writing the book today. An ideal text for courses on factor analysis or as a supplement for multivariate analysis, structural equation modeling, or advanced quantitative techniques taught in psychology, education, and other social and behavioral sciences, researchers who use these techniques also appreciate this book's thorough review of the basic models. Prerequisites include a graduate level course on statistics and a basic understanding of algebra. Sections with an asterisk can be skipped entirely if preferred.

Interest in the field of neurodevelopmental disorders has grown exponentially in recent years across a range of disciplines, including psychology, psychiatry, education and neuroscience. The research itself has become more sophisticated, using multidisciplinary methods to probe interdisciplinary questions. Neurodevelopmental Disorders: Research Challenges and Solutions provides a thorough overview of the key issues involved in researching neurodevelopmental disorders. The volume includes 14 chapters, arranged over three sections. Chapters in the first section address general research challenges for the study of neurodevelopmental disorders. The second section draws upon specific disorders (such as Williams syndrome, Autism Spectrum Disorders, Down Syndrome, Fragile X Syndrome, ADHD, and Language Disorders) to consider the syndrome-specific issues or challenges that may be crucial to advancing our understanding of aspects of cognition and behavior associated with them. The final section considers how research evidence may be translated into practice to begin making an impact upon the lives of individuals who have neurodevelopmental disorders and their families. Each chapter in the book also includes 'practical tips' for either conducting research with individuals who have neurodevelopmental disorders or considering wider practical issues. The book will be indispensable reading for advanced students, researchers, and practitioners in the fields of developmental psychology, developmental psychopathology, special needs education, neuropsychology, and neurodevelopmental disorders.

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.

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.

Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter. "

Written for non-mathematically-oriented readers, this text features:

• "
• A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data
• An emphasis on how the terminology, interpretation, and estimation" "of familiar general linear models relates to those of more complex models for longitudinal data
• Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers understanding of the overall model-building process
• Sample results sections for each example to provide useful templates for published reports
• Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and M"plus" at www.PilesOfVariance.com to help readers apply the models to their own data

The book opens with the building blocks of longitudinal analysis general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces "unconditional "longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. "Conditional "longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data.

Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book s accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text."

Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR). The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The next chapter discusses factor analysis for mixed data (FAMD), a little-known method for simultaneously analyzing quantitative and qualitative variables without group distinction. Focusing on MFA, subsequent chapters examine the key points of MFA in the context of quantitative variables as well as qualitative and mixed data. The author also compares MFA and Procrustes analysis and presents a natural extension of MFA: hierarchical MFA (HMFA). The final chapter explores several elements of matrix calculation and metric spaces used in the book.

Crafting Scholarship helps readers improve their writing and publishing success in academia. Framed within the context of the editorial and peer review process, the book explores writing, editing, and reviewing in academic publishing. As such it provides unique coverage of how successful writers work, how they manage criticism, and more. Examples from successful scholars provide helpful tips in writing articles, grants, books, book chapters, and reviews. Each chapter features tools that facilitate learning including "Best Practices" and "Writer s Resource" boxes to help maximize success, discussion questions and case studies to stimulate critical thinking, and recommended readings to encourage self exploration. A Facebook page provides an opportunity for readers to post writing updates and for instructors to share materials.

"

Highlights include: "

-Insights on working with journal boards, reviewers, and contributors drawn from the author s 30 years of experience in editing journal articles and writing books.

-Describes writing quantitative and qualitative reports, theory and literature reviews, books and chapters, grants, and book reviews.

-Identifies common problems academics face in writing and publishing along with practical solutions.

-Explores best practices in writing peer reviews, responding to reviewers and editors, and how to calculate and interpret acceptance rates and impact factors.

-Addresses how to write each section of a journal article and select keywords that facilitate digital search engines to help potential readers find an article.

-Includes examples of published work and tips on writing research syntheses using meta-analytic techniques or narrative analyses.

-Examines the practices of successful writers, the pros and cons of collaborations, what publishers look for, and managing criticism.

-Reviews pertinent empirical literature on the core topics of writing, reviewing, and editing.

Intended for graduate or advanced undergraduate courses in professional development, writing in an academic field, or research methods taught in psychology, education, human development and family studies, sociology, communication, and other social sciences, this practical guide also appeals to those interested in pursuing an academic career and new and seasoned researchers. "

Crafting Scholarship helps readers improve their writing and publishing success in academia. Framed within the context of the editorial and peer review process, the book explores writing, editing, and reviewing in academic publishing. As such it provides unique coverage of how successful writers work, how they manage criticism, and more. Examples from successful scholars provide helpful tips in writing articles, grants, books, book chapters, and reviews. Each chapter features tools that facilitate learning including "Best Practices" and "Writer s Resource" boxes to help maximize success, discussion questions and case studies to stimulate critical thinking, and recommended readings to encourage self exploration. A Facebook page provides an opportunity for readers to post writing updates and for instructors to share materials.

"

Highlights include: "

-Insights on working with journal boards, reviewers, and contributors drawn from the author s 30 years of experience in editing journal articles and writing books.

-Describes writing quantitative and qualitative reports, theory and literature reviews, books and chapters, grants, and book reviews.

-Identifies common problems academics face in writing and publishing along with practical solutions.

-Explores best practices in writing peer reviews, responding to reviewers and editors, and how to calculate and interpret acceptance rates and impact factors.

-Addresses how to write each section of a journal article and select keywords that facilitate digital search engines to help potential readers find an article.

-Includes examples of published work and tips on writing research syntheses using meta-analytic techniques or narrative analyses.

-Examines the practices of successful writers, the pros and cons of collaborations, what publishers look for, and managing criticism.

-Reviews pertinent empirical literature on the core topics of writing, reviewing, and editing.

Intended for graduate or advanced undergraduate courses in professional development, writing in an academic field, or research methods taught in psychology, education, human development and family studies, sociology, communication, and other social sciences, this practical guide also appeals to those interested in pursuing an academic career and new and seasoned researchers. "

Written by one of the masters of the foundation of measurement, Louis Narens' new book thoroughly examines the basis for the measurement-theoretic concept of meaningfulness and presents a new theory about the role of numbers and invariance in science. The book associates with each portion of mathematical science a subject matter that the portion of science is intended to investigate or describe. It considers those quantitative or empirical assertions and relationships that belong to the subject matter to be meaningful (for that portion of science) and those that do not belong to be meaningless. The first two chapters of the Theories of Meaningfulness introduce meaningfulness concepts, their place in the history of science, and some of their traditional applications. The idea that meaningfulness will have different, but interrelated uses is then introduced. To provide formal descriptions of these, the author employs a powerful framework that incorporates pure mathematics, provides for qualitative objects and relations, and addresses the relationships between qualitative objects and pure mathematics. The framework is then applied to produce axiomatic theories of meaningfulness, including generalizations and a new foundation for the famous Erlanger Program of mathematics. The meaningfulness concept is further specialized with the introduction of intrinsicness, which deals with meaningful concepts and relations that are lawful and qualitativeness, which is concerned with qualitative concepts. The concept of empiricalness is then introduced to distinguish it from meaningfulness and qualitativeness. The failure to distinguish empiricalness from meaningfulness and qualitativeness has produced much confusion in the foundations of science literature and has generated many pseudo-controversies. This book suggests that many of these disappear when empiricalness is intersected with the other concepts to produce "meaningful and empirical relations," "empirical laws," and "qualitative and empirical concepts." A primary goal of this book is to show that the new theories of meaningfulness and intrinsicness developed in this book are not only descriptive but are also potent. Asserting that they do more than codify already existing concepts the book: *works out logical relationships between meaningfulness concepts that were previously unrecognized; *clarifies certain well-known and important debates by providing rich languages with new concepts and technical results (theorems) that yield insights into the debated issues and positions taken on them; and *provides new techniques and results in substantive scientific areas of inquiry. This book is about the role of mathematics in science. It will be useful to those concerned with the foundations of science in their respective fields. Various substantive examples from the behavioral sciences are presented.

Originally published in 1986, the impetus for this volume developed from a conference organized by Barbara Snell Dohrenwend and the editors on behalf of the Society for Life History Research in Psychopathology, the Society of the Study of Social Biology, and the Center for Studies of Mental Health of Aging at the National Institute of Mental Health. The theme of the conference was life span research on the prediction of psychopathology, and the goal was to bring together outstanding researchers who were engaged in longitudinal investigations at the time and whose work, collectively, covered the entire life-span, from infancy to old age. The papers that were presented at the conference were updated, so that the chapters that follow represented current, state-of-the-art considerations in some of the best ongoing studies concerned with the prediction of psychopathology at that time.

First Published in 1985. Routledge is an imprint of Taylor & Francis, an informa company.

In this volume academic and researchers from across disciplines including education, psychology and health studies come together to discuss personal, political and professional narratives of struggle, resilience and hope. Contributors draw from a rich body of auto-biographical research examining the role of narrative and how it can be constructed, considering the roles of significant others, inspirational, educational and fiction characters, as well as myth and legend, to compose a life story. The book discusses how personal narrative, often neglected in social and psychological enquiry, can be a valuable resource across a range of settings. Reference is made to the evolving role of narrative in studies of educational process, in health and other forms of professional practice, and in psychotherapy. The book also includes examples that go beyond words, demonstrating other ways of representing lives through research, using art, fiction and poetry. Rather than providing a 'how to' guide, this book illustrates the range of work possible using narrative.Each chapter outlines a specific study, describes the context, explains the methodology and illuminates important findings with examples from the research. Further implication for research and practice are also discussed making this book important reading for postgraduate and doctoral students, those teaching research methods, as well as academics and researchers developing biographical and auto/biographical narrative research. The text will appeal to those from a range of disciplines including education, health and social care, counselling and psychotherapy.

Stated Preference Methods Using R explains how to use stated preference (SP) methods, which are a family of survey methods, to measure people's preferences based on decision making in hypothetical choice situations. Along with giving introductory explanations of the methods, the book collates information on existing R functions and packages as well as those prepared by the authors. It focuses on core SP methods, including contingent valuation (CV), discrete choice experiments (DCEs), and best-worst scaling (BWS). Several example data sets illustrate empirical applications of each method with R. Examples of CV draw on data from well-known environmental valuation studies, such as the Exxon Valdez oil spill in Alaska. To explain DCEs, the authors use synthetic data sets related to food marketing and environmental valuation. The examples illustrating BWS address valuing agro-environmental and food issues. All the example data sets and code are available on the authors' website, CRAN, and R-Forge, allowing readers to easily reproduce working examples. Although the examples focus on agricultural and environmental economics, they provide beginners with a good foundation to apply SP methods in other fields. Statisticians, empirical researchers, and advanced students can use the book to conduct applied research of SP methods in economics and market research. The book is also suitable as a primary text or supplemental reading in an introductory-level, hands-on course.

First Published in 2000. Routledge is an imprint of Taylor & Francis, an informa company.

Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain their assumptions and how tests work in future publications. Numerous examples from psychology, education, and other social sciences demonstrate varied applications of the material. Basic statistics and probability are reviewed for those who need a refresher.Mathematical derivations are placed in optional appendices for those interested in this detailed coverage. Highlights include: Unique coverage of categorical and nonparametric statistics better prepares readers to select the best technique for their particular research project but some chapters can be omitted entirely if preferred.Step by step examples of each test help readers see how the material is applied in a variety of disciplines. Although the book can be used with any program, examples of how to use the tests in SPSS & EXCEL foster conceptual understanding. Exploring the concept boxes integrated throughout prompt students to review key material and draw links between the concepts to deepen understanding. Problems in each chapter help readers test their understanding of the material. Emphasizes selecting tests that maximize power to help readers avoid marginally significant results. Website featuring datasets for the book's examples and problems, and for the instructor Power Points, author's course syllabus, and answers to the even numbered problems. Chapters 1-3 cover basic concepts in probability, especially the binomial formula followed by two chapters that address the analysis of contingency tables. Chapters 6-8 address nonparametric tests involving at least one ordinal variable, including testing for nonparametric interaction effects, a topic omitted from other texts. The book then turns to situations that involve one metric variable.Chapter 9 reviews concepts that are foundational to CDA, including linear regression and generalized linear models. Chapters 10-11 cover logistic, ordinal, and Poisson regression. Chapters 12 and 13 review loglinear models and the General Estimating Equations (GEE) methodology for measuring outcomes from multiple time points. For a deeper understanding of how various CDA techniques work, chapter 14 covers estimation methods, such as Newton-Raphson and Fisher scoring. The book concludes with a summary of factors that need to be considered when choosing the best statistical technique. Intended for individual or combined graduate or advanced undergraduate courses in categorical and nonparametric data analysis, cross-classified data analysis, advanced statistics and/or quantitative techniques taught in psychology, education, human development, sociology, political science, and other social and life sciences, the book also appeals to researchers in these disciplines. The nonparametric chapters can be deleted if preferred. Prerequisites include knowledge of t-tests and ANOVA.

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."

This controversial book argues that 20th century psychotherapy has been fundamentally characterized by serious disagreement on views of human nature, treatment rationales and goals. Focusing on the differences rather than the commonalities in therapy, eight eminent practitioners demonstrate the diversities in therapies and why, for the most part, it is not possible to tolerate or integrate with other approaches. `The book awakened me to understanding more about how a core belief or orientation can result in polarised attitudes towards the person. At the same time, in some cases, there is fundamental common ground which could potentially lead to genuine integration' - [ac]Eisteach, The Journal of the Irish Association for Counselling and Therapy `Eight distinguished practitioners address twelve different questions, aimed at identifying the distinctive qualities of their own approach and demonstrating how it has been arrived at. The result is a book that will allow both experienced practitioners and trainees to become familiar with and compare the current thinking of these well-known people... the very passion of these opposing and sometimes exclusive convictions may be the well-spring for the efficacy and achievements of these eminent practitioners and trainers' - Self & Society Each therapist highlights the distinctive properties of his or her orientation, and discusses questions such as: why and how they came to found, adapt or choose the approach they currently practise; what criticisms of the approach they consider to be valid; which approaches they consider to be ineffective, misleading or dangerous, and, conversely, more promising or effective; why their approach is more effective or comprehensive, and why it may be more suited to certain clients or client problems; and how they account for research which suggests that no one approach seems more effective than any other.

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

Capstone projects have long been used in other disciplines but are now being seen within psychology courses. This textbook is a one-stop-shop for anyone looking to undertake a capstone project or other final project. In a friendly and collaborative style, this book guides the student through everything they need to know to ensure a successful capstone or other final project.

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.