This volume collects recent studies conducted within the area of medical education that investigate two of the critical components of problem-based curricula--the group meeting and self-directed learning--and demonstrates that understanding these complex phenomena is critical to the operation of this innovative curriculum. It is the editors' contention that it is these components of problem-based learning that connect the initiating "problem" with the process of effective "learning." Revealing how this occurs is the task taken on by researchers contributing to this volume. The studies include use of self-reports, interviews, observations, verbal protocols, and micro-analysis to find ways into the psychological processes and sociological contexts that constitute the world of problem-based learning.

Introducing Research and Data in Psychology shows how research design and data analysis are attainable and useful skills. It introduces both experimental and non-experimental methods of research and the analysis of data using both descriptive and inferential statistics. The uses, interpretation and calculation of common two sample statistical tests are explained. This comprehensive textbook includes the following designed features to help with technique:

• Practice exam answers to show how to achieve a higher grade
• Chapter summaries
• Glossary
• Case studies and examples
• Exercises and a full bibliography.

Written specifically for those with no prior programming experience and minimal quantitative training, this accessible text walks behavioral science students and researchers through the process of programming using MATLAB. The book explores examples, terms, and programming needs relevant to those in the behavioral sciences and helps readers perform virtually any computational function in solving their research problems. Principles are illustrated with usable code. Each chapter opens with a list of objectives followed by new commands required to accomplish those goals. These objectives also serve as a reference to help readers easily relocate a section of interest. Sample code and output and chapter problems demonstrate how to write a program and explore a model so readers can see the results obtained using different equations and values. A web site provides solutions to selected problems and the book's program code output and examples so readers can manipulate them as needed. The outputs on the website have color, motion, and sound. Highlights of the new edition include: *Updated to reflect changes in the most recent version of MATLAB, including special tricks and new functions. *More information on debugging and common errors and more basic problems in the rudiments of MATLAB to help novice users get up and running more quickly. *A new chapter on Psychtoolbox, a suite of programs specifically geared to behavioral science research. *A new chapter on Graphical User Interfaces (GUIs) for user-friendly communication. *Increased emphasis on pre-allocation of memory, recursion, handles, and matrix algebra operators. The book opens with an overview of what is to come and tips on how to write clear programs followed by pointers for interacting with MATLAB, including its commands and how to read error messages. The matrices chapter reviews how to store and access data. Chapter 4 examines how to carry out calculations followed by a review of how to perform various actions depending on the conditions. The chapter on input and output demonstrates how to design programs to create dialogs with users (e.g., participants in studies) and read and write data to and from external files. Chapter 7 reviews the data types available in MATLAB. Readers learn how to write a program as a stand-alone module in Chapter 8. In Chapters 9 and 10 readers learn how to create line and bar graphs or reshape images. Readers learn how to create animations and sounds in Chapter 11. The book concludes with tips on how to use MATLAB with applications such as GUIs and Psychtoolbox. Intended as a primary text for Matlab courses for advanced undergraduate and/or graduate students in experimental and cognitive psychology and/or neuroscience as well as a supplementary text for labs in data (statistical) analysis, research methods, and computational modeling (programming), the book also appeals to individual researchers in these disciplines who wish to get up and running in MATLAB.

This is the first practitioner-oriented guide to basic psychometrics--the science behind test construction and validation. It is an essential course text and clinical reference for anyone who needs to select and administer diagnostic tests or review test score data obtained by others. Without assuming any mathematical background, the author describes the fundamentals of diagnostic testing, how to interpret score data, and how specific tests are used in school psychology, special education, counseling, and other contexts. Concepts such as reliability, validity, and fairness and test bias are explained in an accessible, conversational way. Detailed case examples illustrate the dos and don'ts of presenting test score data to clients, parents, and teachers in oral feedback and written reports. Pedagogical Features: *End-of-chapter exercises that emphasize application and understanding, with answers at the end of the book. *Key terms are bolded throughout and accessibly explained in the end-of-book glossary. *Resource appendix--annotated guide to articles and books on more advanced topics in psychometrics, assessment, and testing.

Experimental design is important enough to merit a book on its own, without statistics, that instead links methodology to a discussion of how psychologists can advance and reject theories about human behaviour. The objective of this book is to fulfil this role.; The first four chapters lay the foundations of design in experimental psychology. The first chapter justifies the prominent role given to methodology within the discipline, whilst chapters two and three describe between-subject and within-subject designs. Chapter four compares and contrasts the traditional experimental approach with that of the quasi-experimental, or correlational approach, concluding that the consequences of not recognizing the value of the latter approach can be far-reaching.; The following three chapters discuss practical issues involved in running experiments. The first of these offers a comprehensive guide to the student researcher who wants to construct a good questionnaire, including a discussion of reliability and validity issues. The next chapter considers the basic tools of psychological research, whilst both discussing the theoretical problem of how a sample from a population is chosen and offering useful hints on the practical issue of finding adequate populations from which to select participants. The next chapter considers ethical practice within psychological research, written in large part so that psychology students will be better able to anticipate ethical problems in their studies before they occur.; The final two chapters consider reporting and reading psychological papers. Chapter eight details what should and should not be included in a laboratory report. The contributors use their collective experience of marking numerous lab reports to highlight common errors and provide solutions. Finally, chapter nine describes the various elements of a journal article, including tips on how to get the best out of your journal reading.

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Experimental design is important enough to merit a book on its own, without statistics, that instead links methodology to a discussion of how psychologists can advance and reject theories about human behaviour. The objective of this book is to fulfil this role. The first four chapters lay the foundations of design in experimental psychology. The first chapter justifies the prominent role given to methodology within the discipline, whilst chapters two and three describe between-subject and within-subject designs. Chapter four compares and contrasts the traditional experimental approach with that of the quasi-experimental, or correlational approach, concluding that the consequences of not recognizing the value of the latter approach can be far-reaching. The following three chapters discuss practical issues involved in running experiments. The first of these offers a comprehensive guide to the student researcher who wants to construct a good questionnaire, including a discussion of reliability and validity issues. The next chapter considers the basic tools of psychological research, whilst both discussing the theoretical problem of how a sample from a population is chosen and offering useful hints on the practical issue of finding adequate populations from which to select participants. The next chapter considers ethical practice within psychological research, written in large part so that psychology students will be better able to anticipate ethical problems in their studies before they occur. The final two chapters consider reporting and reading psychological papers. Chapter eight details what should and should not be included in a laboratory report. The contributors use their collective experience of marking numerous lab reports to highlight common errors and provide solutions. Finally, chapter nine describes the various elements of a journal article, including tips on how to get the best out of your journal reading.

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This comprehensive volume is widely regarded as the definitive practitioner resource and text in the field of biofeedback and applied psychophysiology. Leading experts cover basic concepts, assessment, instrumentation, clinical procedures, and professional issues. Chapters describe how traditional and cutting-edge methods are applied in treatment of a wide range of disorders, including headaches, temporomandibular disorders, essential hypertension, pelvic floor disorders, attention-deficit/hyperactivity disorder, tinnitus, and others. Applications for optimizing physical performance among artists and athletes are also reviewed. A wealth of information and empirical research is presented in an accessible style, including helpful glossaries. New to This Edition *Incorporates significant technological developments and new research areas. *Expanded focus on specialized applications, such as electroencephalographic (EEG) biofeedback/neurofeedback and heart rate variability biofeedback. *Chapters on surface electromyography, quantitative EEG, and consumer products. *Chapters on cognitive-behavioral therapy and relaxation training. *Chapters on additional clinical problems: anxiety disorders, asthma, work-related pain, traumatic brain injury, autism spectrum disorders, and substance use disorders.

Taking philosophical principles as a point of departure, this book provides essential distinctions for thinking through the history and systems of Western psychology. The book is concisely designed to help readers navigate through the length and complexity found in history of psychology textbooks. From Plato to beyond Post-Modernism, the author examines the choices and commitments made by theorists and practitioners of psychology and discusses the philosophical thinking from which they stem. What kind of science is psychology? Is structure, function, or methodology foremost in determining psychology's subject matter? Psychology, as the behaviorist views it, is not the same as the psychoanalyst's view of it, or the existentialist's, so how may contemporary psychology philosophically-sustain both pluralism and incommensurability? This book will be of great value to students and scholars of the history of psychology.

Awarded the 2019 Most Promising New Textbook Award by the Textbook & Academic Authors Association. This accessible and entertaining new textbook provides students with the knowledge and skills they need to understand the barrage of numbers encountered in their everyday lives and studies. Almost all the statistics in the news, on social media or in scientific reports are based on just a few core concepts, including measurement (ensuring we count the right thing), causation (determining whether one thing causes another) and sampling (using just a few people to understand a whole population). By explaining these concepts in plain language, without complex mathematics, this book prepares students to meet the statistical world head on and to begin their own quantitative research projects. Ideal for students facing statistical research for the first time, or for anyone interested in understanding more about the numbers in the news, this textbook helps students to see beyond the headlines and behind the numbers.

To reflect the importance of supervision and to widen understanding of its many facets, "The Third Eye" brings together contributions from some of the most experienced practitioners of group analysis with the reactions of those they have supervised. The contributors look at questions such as dual supervision, evaluation, block training at home and abroad, training of supervisors and ethical issues. They also give practical advice about maintaining group data, trainee presentations and appraisal techniques. The book concludes with a full reference bibliography of the main articles and books on supervision of group psychotherapy.
This book provides a panoramic exposition by seasoned practitioners of one of the more important but least documented skills demanded in group analysis. However the material extends in relevance beyond its field of origin and will be of significant interest to a wide readership of those concerned with the training, assessment and development of others working with or having responsibility for groups.

This book offers a critical perspective of the dominant discourses within the field of psychological trauma. It provides a challenge to normative western constructs and unsettles assumptions about accepted notions of universality and the nature of trauma. Traditionally the concept of psychological trauma has been widely accepted within mental health professions. However, in a post-positivist era, the language of mental health is shifting and making room for alternative discourses that include wider contextual influences, such as the impact of sociological, cultural, and technological developments. These wider discourses are illuminated as the authors draw together some of these arguments into one accessible text. Rather than claim definitive answers to the issues raised, readers are invited to engage with the discussions presented in order to position themselves in relation to the range of trauma discourses available.

A useful handbook, this text presents guidelines frequently followed by writers of reports of empirical research designed for publication in scientific business journals. The guidelines describe the types of information that should be included, how this information should be expressed, and where various types of information should be placed within a report. Excerpts from journal articles are used to illustrate most of the guidelines. At the end of each chapter, there are questions for classroom discussion.

Good Science is an account of psychological research emphasizing the moral foundations of inquiry. This volume brings together existing disciplinary critiques of scientism, objectivism, and instrumentalism, and then discusses how these contribute to institutionalized privilege and to less morally responsive research practices. The author draws on historical, critical, feminist, and science studies traditions to provide an alternative account of psychological science and to highlight the irreducibly moral foundations of everyday scientific practice. This work outlines a theoretical framework for thinking about and practicing psychology in ways that center moral responsibility, collective commitment, and justice. The book then applies this framework, describing psychological research practices in terms of the their moral dilemmas. Also included are materials meant to aid in methods instruction and mentoring.

Good Science is an account of psychological research emphasizing the moral foundations of inquiry. This volume brings together existing disciplinary critiques of scientism, objectivism, and instrumentalism, and then discusses how these contribute to institutionalized privilege and to less morally responsive research practices. The author draws on historical, critical, feminist, and science studies traditions to provide an alternative account of psychological science and to highlight the irreducibly moral foundations of everyday scientific practice. This work outlines a theoretical framework for thinking about and practicing psychology in ways that center moral responsibility, collective commitment, and justice. The book then applies this framework, describing psychological research practices in terms of the their moral dilemmas. Also included are materials meant to aid in methods instruction and mentoring.

Factor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods. The social scientist's toolkit for factor analysis problems can be expanded to include the range of solutions this book presents. In addition to covering FA and PCA with orthogonal and oblique rotation, this book's coverage includes higher-order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance. Features of this book include: Numerous worked examples with replicable R code Explicit comprehensive coverage of data assumptions Adaptation of factor methods to binary, ordinal, and categorical data Residual and outlier analysis Visualization of factor results Final chapters that treat integration of factor analysis with neural network and time series methods Presented in color with R code and introduction to R and RStudio, this book will be suitable for graduate-level and optional module courses for social scientists, and on quantitative methods and multivariate statistics courses.

Statistical Concepts-A First Course presents the first 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for first and lower-level statistics courses, this book communicates a conceptual, intuitive understanding of statistics that does not assume extensive or recent training in mathematics and only requires a rudimentary knowledge of algebra. Covering the most basic statistical concepts, this book is designed to help readers really understand statistical concepts, in what situations they can be applied, and how to apply them to data. Specifically, the text covers basic descriptive statistics, including ways of representing data graphically, statistical measures that describe a set of data, the normal distribution and other types of standard scores, and an introduction to probability and sampling. The remainder of the text covers various inferential tests, including those involving tests of means (e.g., t tests), proportions, variances, and correlations. Providing accessible and comprehensive coverage of topics suitable for an undergraduate or graduate course in statistics, this book is an invaluable resource for students undertaking an introductory course in statistics in any number of social science and behavioral science disciplines.

In this fully revised Fourth Edition of Psychometrics: An Introduction, author R. Michael Furr centers his presentation around a conceptual understanding of psychometric core issues, such as scales, reliability, and validity. Focusing on purpose rather than procedure and the "why" rather than the "how to," this accessible book uses a wide variety of examples from behavioral science research so readers can see the importance of psychometric fundamentals in research. By emphasizing concepts, logic, and practical applications over mathematical proofs, this book gives students an appreciation of how measurement problems can be addressed and why it is important to address them. The book offers readers the most contemporary views of topics in psychometrics available in the nontechnical psychometric literature.

How to Structure a Thesis, Report or Paper provides concise practical guidance for students to help make their writing more structured at any level. It assists students in demonstrating what they have learned in the relevant course or degree programme in a way that is accessible to the supervisor and the examiner. Drawing on almost 20 years of supervision experience, the author presents the eight sections of a well-structured thesis, report or paper, together with discussing other relevant issues. Each chapter provides a detailed description of why each section of a thesis, report or paper is structured in the way it is, and its relationship to the whole piece of work. Good and bad examples are provided throughout the book, and there is a focus on key areas such as the six parts of an Introduction and its relationship to the Conclusion, how to phrase clear research questions and hypotheses to the use of references and how to make the thesis, report or paper easier to read. The structure presented in this book can be used to support many courses on the student's entire degree programme, as the structure can be adapted by re-arranging or deleting sections. This book is an invaluable aid to students at all stages in higher education, from their first report or paper until they write their final thesis. It provides clear guidelines for when students should ask their supervisors for advice, and when students can use their own initiative to learn the most. It makes writing a thesis, report or papers more straightforward!

The Process of Wellbeing develops an anthropological perspective on wellbeing as an intersubjective process that can be approached through the prism of three complementary conceptual framings: conviviality; care; and creativity. Drawing on ethnographic discussions of these themes in a range of cultural contexts around the world, it shows how anthropological research can help to enlarge and refine understandings of wellbeing, through dialogue with different perspectives and understandings of what it means to live well with others and the skills required to do so. Rather than a state or achievement, wellbeing comes into view here as an ongoing process that involves human and nonhuman others. It does not pertain to the individual alone, but plays out within the relations of care that constitute people, moving and thriving in circulation through affective environments.

This comprehensive guide offers a rich introduction to research methods, experimental design and data analysis techniques in developmental science, emphasizing the importance of an understanding of this area of psychology for any student or researcher interested in examining development across the lifespan. The expert contributors enhance the reader's knowledge base, understanding of methods, and critical thinking skills in their area of study. They cover development from the prenatal period to adolescence and old age, and explore key topics including the history of developmental research, ethics, animal models, physiological measures, eye-tracking, and computational and robotics models. They accessibly explore research measures and design in topics including gender identity development, the influence of neighborhoods, mother-infant attachment relationships, peer relationships in childhood, prosocial and moral development patterns, developmental psychopathology and social policy, and the examination of memory across the lifespan. Each chapter ends with a summary of innovations in the field over the last ten years, giving students and interested researchers a thorough overview of the field and an idea of what more is to come. Conducting Research in Developmental Psychology is essential reading for upper-level undergraduate or graduate students seeking to understand a new area of developmental science, developmental psychology, and human development. It will also be of interest to junior researchers who would like to enhance their knowledge base in a particular area of developmental science, human development, education, biomedical science, or nursing.

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.

This Fifth Edition of Neil J. Salkind's Statistics for People Who (Think They) Hate Statistics: Using Microsoft Excel, presents an often intimidating and difficult subject in a way that is clear, informative, and personable. Opening with an introduction to Excel, including coverage of how to use functions and formulas, this edition shows students how to install the Excel Data Analysis Tools option to access a host of useful analytical techniques. New to the Fifth Edition is new co-author Bruce Frey who has added a new feature on statisticians throughout history (with a focus on the contributions of women and people of color). He has updated the "Real-World Stats" feature, and added more on effect sizes, updated the discussions on hypotheses, measurement concepts like validity and reliability, and has more closely tied analytical choices to the level of measurement of variables.

*First comprehensive introduction and guide to social fiction, an arts-based research approach. *Part graduate text, part reference for students and researchers in education, sociology, psychology, communications, nursing, human services, and related fields. *Engaging, humorous writing interweaves how-tos with examples from the author's fiction, along with insights and tips. *Provides evaluation criteria for social fiction.

Item response theory (IRT) is widely used in education and psychology and is expanding its applications to other social science areas, medical research, and business as well. Using R for Item Response Theory Model Applications is a practical guide for students, instructors, practitioners, and applied researchers who want to learn how to properly use R IRT packages to perform IRT model calibrations with their own data. This book provides practical line-by-line descriptions of how to use R IRT packages for various IRT models. The scope and coverage of the modeling in the book covers almost all models used in practice and in popular research, including: dichotomous response modeling polytomous response modeling mixed format data modeling concurrent multiple group modeling fixed item parameter calibration modelling with latent regression to include person-level covariate(s) simple structure, or between-item, multidimensional modeling cross-loading, or within-item, multidimensional modeling high-dimensional modeling bifactor modeling testlet modeling two-tier modeling For beginners, this book provides a straightforward guide to learn how to use R for IRT applications. For more intermediate learners of IRT or users of R, this book will serve as a great time-saving tool for learning how to create the proper syntax, fit the various models, evaluate the models, and interpret the output using popular R IRT packages.

This new volume reviews longitudinal models and analysis procedures for use in the behavioral and social sciences. Written by distinguished experts in the field, the book presents the most current approaches and theories, and the technical problems that may be encountered along the way. Readers will find new ideas about the use of longitudinal analysis in solving problems that arise due to the specific nature of the research design and the data available. Divided into two parts, Longitudinal Models in the Behavioral and Related Sciences opens with the latest theoretical developments. In particular, the book addresses situations that arise due to the categorical nature of the data, issues related to state space modeling, and potential problems that may arise from network analysis and/or growth-curve data. The focus of part two is on the application of longitudinal modeling in a variety of disciplines. The book features applications such as heterogeneity on the patterns of a firm's profit, on house prices, and on delinquent behavior; non-linearity in growth in assessing cognitive aging; measurement error issues in longitudinal research; and distance association for the analysis of change. Part two clearly demonstrates the caution that should be taken when applying longitudinal modeling as well as in the interpretation of the results. Longitudinal Models in the Behavioral and Related Sciences is ideal for advanced students and researchers in psychology, sociology, education, economics, management, medicine, and neuroscience.