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.

Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and longitudinal studies, randomized clinical trials, complex sample surveys are used to illustrate both simple, and complex analyses. Version 0.3 of IVEware, the software developed by the University of Michigan, is used to illustrate analyses. IVEware can multiply impute missing values, analyze multiply imputed data sets, incorporate complex sample design features, and be used for other statistical analyses framed as missing data problems. IVEware can be used under Windows, Linux, and Mac, and with software packages like SAS, SPSS, Stata, and R, or as a stand-alone tool. This book will be helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems, along with examples of correct analysis techniques.

CHOSEN AS A BOOK OF THE YEAR BY THE TIMES AND DAILY TELEGRAPH 'A riveting chronicle of faulty science, false promises, arrogance, greed, and shocking disregard for the wellbeing of patients suffering from mental disorders. An eloquent, meticulously documented, clear-eyed call for change' Dirk Wittenborn In this masterful work, Andrew Scull, one of the most provocative thinkers writing about psychiatry, sheds light on its troubled history For more than two hundred years, disturbances of reason, cognition and emotion - the sort of things that were once called 'madness' - have been described and treated by the medical profession. Mental illness, it is said, is an illness like any other - a disorder that can treated by doctors, whose suffering can be eased, and from which patients can return. And yet serious mental illness remains a profound mystery that is in some ways no closer to being solved than it was at the start of the twentieth century. In this clear-sighted and provocative exploration of psychiatry, acclaimed sociologist Andrew Scull traces the history of its attempts to understand and mitigate mental illness: from the age of the asylum and surgical and chemical interventions, through the rise and fall of Freud and the talking cure, and on to our own time of drug companies and antidepressants. Through it all, Scull argues, the often vain and rash attempts to come to terms with the enigma of mental disorder have frequently resulted in dire consequences for the patient. Deeply researched and lucidly conveyed, Desperate Remedies masterfully illustrates the assumptions and theory behind the therapy, providing a definitive new account of psychiatry's and society's battle with mental illness.

Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine. It also provides flexible tools to describe the temporal trends, covariate effects and correlation structures of repeated measurements in longitudinal data. This book is intended for graduate students in statistics, data scientists and statisticians in biomedical sciences and public health. As experts in this area, the authors present extensive materials that are balanced between theoretical and practical topics. The statistical applications in real-life examples lead into meaningful interpretations and inferences. Features: * Provides an overview of parametric and semiparametric methods * Shows smoothing methods for unstructured nonparametric models * Covers structured nonparametric models with time-varying coefficients * Discusses nonparametric shared-parameter and mixed-effects models * Presents nonparametric models for conditional distributions and functionals * Illustrates implementations using R software packages * Includes datasets and code in the authors' website * Contains asymptotic results and theoretical derivations

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.

Unexpected events during an evaluation all too often send evaluators into crisis mode. This insightful book provides a systematic framework for diagnosing, anticipating, accommodating, and reining in costs of evaluation surprises. The result is evaluation that is better from a methodological point of view, and more responsive to stakeholders. Jonathan A. Morell identifies the types of surprises that arise at different stages of a program's life cycle and that may affect different aspects of the evaluation, from stakeholder relationships to data quality, methodology, funding, deadlines, information use, and program outcomes. His analysis draws on 18 concise cases from well-known researchers in a variety of evaluation settings. Morell offers guidelines for responding effectively to surprises and for determining the risks and benefits of potential solutions.

The new edition of An Introduction to Statistical Concepts is designed to help students really understand statistical concepts, the situations in which they can be used, and how to apply them to data. Hahs-Vaughn and Lomax discuss the most popular, along with many of the lesser-known, procedures and models, whilst also exploring nonparametric procedures used when standard assumptions are violated. They provide in-depth coverage of testing assumptions and highlight several online tools for computing statistics (e.g., effect sizes and their confidence intervals and power). This comprehensive, flexible, and accessible text includes a new chapter on mediation and moderation; expanded coverage of effect sizes; and discussions of sensitivity, specificity, false positive, and false negative, along with using the receiver operator characteristic (ROC) curve. In addition to instructions and screen shots for using SPSS, new to this edition is annotated script for using R. This book, noted for its crystal-clear explanations, and its inclusion of only the most crucial equations, is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral disciplines-from education, business, communication, exercise science, psychology, sociology and more.

Polls are conducted every day all around the world for almost everything (especially during elections). But not every poll is a good one. A lot depends on the type of questions asked, how they are asked and whether the sample used is truly representative. And these are not the only aspects of a poll that should be checked. So how does one separate the chaff from the wheat? That's where Understanding Public Opinion Polls comes in. Written by a well-known author with over thirty years of experience, the book is built around a checklist for polls that describes the various aspects of polls to pay attention to if one intends to use its results. By comprehensively answering the questions in the checklist, a good idea of the quality of the poll is obtained. Features: Provides readers with a deeper understanding of practical and theoretical aspects of opinion polls while assuming no background in mathematics or statistics Shows how to determine if a poll is good or bad Provides a historical perspective and includes examples from real polls Gives special attention to online and election polls The book gives an overview of many aspects of polls - questionnaire design, sample selection, estimation, margins of error, nonresponse and weighting. It is essential reading for those who want to gain a better understanding of the ins and outs of polling including those who are confronted with polls in their daily life or work or those who need to learn how to conduct their own polls.

It is quite common to reflect on what startles you. In the most diverse social contexts and cultures, the inescapable physiology of the reflex both shapes the experience of startle and biases the social usage to which the reflex is put. This book describes the ways in which the reflex is experienced, culturally elaborated, and socially used, and offers explanations for both patterned commonalities found across cultures, and for the culture-typical differences which differing cultural systems engender.

In 2010, the International Cyberbullying Think Tank was held in order to discuss questions of definition, measurement, and methodologies related to cyberbullying research. The attendees goal was to develop a set of guidelines that current and future researchers could use to improve the quality of their research and advance our understanding of cyberbullying and related issues. This book is the product of their meetings, and is the first volume to provide researchers with a clear set of principles to inform their work on cyberbullying. The contributing authors, all participants in the Think Tank, review the existing research and theoretical frameworks of cyberbullying before exploring topics such as questions of methodology, sampling issues, methods employed so far, psychometric issues that must be considered, ethical considerations, and implications for prevention and intervention efforts. Researchers as well as practitioners seeking information to inform their prevention and intervention programs will find this to be a timely and essential resource.

This important new collection provides not only a comprehensive overview of how organizational interventions can improve health and well-being in the workplace - addressing its causes rather than the symptoms - but also the practical issues faced in their design, implementation and evaluation. Drawing on a range of case studies and empirical investigations, it is the first book to seriously examine each element of the intervention process, and to recognize the individual, group, leader and organizational factors that researchers should consider. The authors describe the various challenges to such collaborative processes, as well as the specific methods and tools that can be used in response. Each chapter offers practical, evidence-based guidance. Featuring a final section examining new directions and approaches in organizational intervention research, the book features contributions from some of the leading international researchers in the field. It will be essential reading for any researcher or practitioner interested in the practical issues involved in improving the organization, design and management of the contemporary workplace.

Statistical Analysis of Contingency Tables is an invaluable tool for statistical inference in contingency tables. It covers effect size estimation, confidence intervals, and hypothesis tests for the binomial and the multinomial distributions, unpaired and paired 2x2 tables, rxc tables, ordered rx2 and 2xc tables, paired cxc tables, and stratified tables. For each type of table, key concepts are introduced, and a wide range of intervals and tests, including recent and unpublished methods and developments, are presented and evaluated. Topics such as diagnostic accuracy, inter-rater reliability, and missing data are also covered. The presentation is concise and easily accessible for readers with diverse professional backgrounds, with the mathematical details kept to a minimum. For more information, including a sample chapter and software, please visit the authors' website.

Research Methodology is meant to provide a broad guideline to facilitate and steer the whole of a research activity in any discipline. With the ambit and amount of research increasing by the day, the need for Research Methodology is being widely appreciated. Against this backdrop, we notice the dearth of well-written books on the subject. A Guide to Research Methodology attempts a balance between the generic approach to research in any domain and the wide array of research methods which are to be used in carrying out different tasks in any research. Discussions on these research methods appropriate in various disciplines have focused on the research tasks, keeping in mind the fact that a single such task like a comparison among alternatives may involve several methods from seemingly distinct areas. Unique features of this volume, as will be evident to a discerning reader, include: A detailed discussion on problem areas for research in several domains An illustrative and ampliated list of research problems drawn from different disciplines which can be pursued by interested research workers A comprehensive delineation of Research Design supported by illustrations An elaborate engagement with models with a note on model uncertainty Focus on recent and emerging models, methods and techniques A novel treatment of data analysis where the nature of data and the objective(s) of analysis justify drawing upon a variety of techniques for analysis This book will serve the purpose of a pre-PhD or a Master-level course-work for students of any discipline with a basic knowledge of quantitative analysis. In fact, anyone aspiring to take up meaningful research work will find the content useful and interesting.

Human behavior is fascinating so it's no surprise that psychologists and neuroscientists spend their lives designing rigorous experiments to understand it. MATLAB is one of the most widely used pieces of software for designing and running behavioral experiments, and it opens up a world of quick and flexible experiment programming. This book offers a step-by-step guide to using MATLAB with Psychtoolbox to create customisable experiments. Its pocket size and simple language allow you to get straight to the point and help you to learn fast in order to complete your work in great time. In nine simple steps, it guides you all the way from setting parameters for your experiment to analysing the output. Gone are the daunting days of working through hundreds of irrelevant and complicated documents, as in this handy book, Erman Misirlisoy coaxes you in the right direction with his friendly and encouraging tricks and tips. If you want to learn how to develop your own experiments to collect and analyse behavioral data, then this book is a must-read. Whether you are a student in experimental psychology, a researcher in cognitive neuroscience, or simply someone who wants to run behavioral tasks on your friends for fun, this book will offer you the skills to succeed.

Dieser Buchtitel ist Teil des Digitalisierungsprojekts Springer Book Archives mit Publikationen, die seit den Anfangen des Verlags von 1842 erschienen sind. Der Verlag stellt mit diesem Archiv Quellen fur die historische wie auch die disziplingeschichtliche Forschung zur Verfugung, die jeweils im historischen Kontext betrachtet werden mussen. Dieser Titel erschien in der Zeit vor 1945 und wird daher in seiner zeittypischen politisch-ideologischen Ausrichtung vom Verlag nicht beworben.

A Step-by-Step Guide to Qualitative Data Coding is a comprehensive qualitative data analysis guide. It is designed to help readers to systematically analyze qualitative data in a transparent and consistent manner, thus promoting the credibility of their findings. The book examines the art of coding data, categorizing codes, and synthesizing categories and themes. Using real data for demonstrations, it provides step-by-step instructions and illustrations for analyzing qualitative data. Some of the demonstrations include conducting manual coding using Microsoft Word and how to use qualitative data analysis software such as Dedoose, NVivo and QDA Miner Lite to analyze data. It also contains creative ways of presenting qualitative findings and provides practical examples. After reading this book, readers will be able to: Analyze qualitative data and present their findings Select an appropriate qualitative analysis tool Decide on the right qualitative coding and categorization strategies for their analysis Develop relationships among categories/themes Choose a suitable format for the presentation of the findings It is a great resource for qualitative research instructors and undergraduate and graduate students who want to gain skills in analyzing qualitative data or who plan to conduct a qualitative study. It is also useful for researchers and practitioners in the social and health sciences fields.

Applied Power Analysis for the Behavioral Sciences is a practical "how-to" guide to conducting statistical power analyses for psychology and related fields. The book provides a guide to conducting analyses that is appropriate for researchers and students, including those with limited quantitative backgrounds. With practical use in mind, the text provides detailed coverage of topics such as how to estimate expected effect sizes and power analyses for complex designs. The topical coverage of the text, an applied approach, in-depth coverage of popular statistical procedures, and a focus on conducting analyses using R make the text a unique contribution to the power literature. To facilitate application and usability, the text includes ready-to-use R code developed for the text. An accompanying R package called pwr2ppl (available at https://github.com/chrisaberson/pwr2ppl) provides tools for conducting power analyses across each topic covered in the text.

This book is designed to introduce professors and administrators in higher education to the philosophical, theoretical, and research support for using a constructivist perspective on learning to guide the reconstruction of undergraduate education. It presents an original framework for systematically linking educational philosophy and learning theories to their implications for teaching practice. In this volume, Innes summarizes the sources he found most useful in developing his own set of teaching principles and course development process, and makes an argument for a particular perspective on learning--transactional constructivism--which is consistent with the philosophy of John Dewey and supported by current theory and research in learning science. Transactional constructivism, a combined approach, builds on the strengths of two competing views: psychological constructivism and the sociocultural perspective. Reconstructing Undergraduate Education: Using Learning Science to Design Effective Courses: *overviews the philosophical and theoretical underpinnings of the teaching model that is the focus of the volume; *presents a summary of Dewey's educational philosophy and connects his work to current theory and research in learning science; *examines psychological constructivism, one of the basic positions within the range of learning theories that takes a constructivist perspective; *offers a case study example of a course designed and taught from this perspective; *reviews the sociocultural and the transactional constructivist perspectives; *explores the quality of dialogue and disciplinary discourse in the classroom--an issue that is critical to the success of models derived from a transactional constructivist perspective on learning; and *explores broader issues related to reform in higher education. This volume is a vital resource for all professionals involved in undergraduate education.

Why psychology is in peril as a scientific discipline-and how to save it Psychological science has made extraordinary discoveries about the human mind, but can we trust everything its practitioners are telling us? In recent years, it has become increasingly apparent that a lot of research in psychology is based on weak evidence, questionable practices, and sometimes even fraud. The Seven Deadly Sins of Psychology diagnoses the ills besetting the discipline today and proposes sensible, practical solutions to ensure that it remains a legitimate and reliable science in the years ahead. In this unflinchingly candid manifesto, Chris Chambers shows how practitioners are vulnerable to powerful biases that undercut the scientific method, how they routinely torture data until it produces outcomes that can be published in prestigious journals, and how studies are much less reliable than advertised. Left unchecked, these and other problems threaten the very future of psychology as a science-but help is here.

This text teaches readers how to plan, conduct, and write a research project and select and interpret data through its integrated approach to quantitative research methods. Although not a statistics book, students learn to master which technique to use when and how to analyze and interpret results, making them better consumers of research. Organized around the steps of conducting a research project, this book is ideal for those who need to analyze journal articles. With teaching experience in various departments, the authors know how to address the research problems faced by behavioral and social sciences students. Independent sections and chapters can be read in any order allowing for flexibility in assigning topics. Adopters applaud the book's clarity and applied interdependent approach to research. The book emphasizes five research approaches: randomized experimental, quasi-experimental, comparative, associational, and descriptive. These five approaches lead to three kinds of research designs which lead to three groups of statistics with the same names. This consistent framework increases comprehension while avoiding confusion caused by inconsistent terminology. Numerous examples, diagrams, tables, key terms, key distinctions, summaries, applied problems, interpretation questions, and suggested readings further promote understanding. This extensively revised edition features: More examples from published research articles to help readers better understand the research process. New Research in the Real World boxes that highlight actual research projects from various disciplines. Defined key terms in the margins and interpretation questions that help readers review the material. More detailed explanations of key concepts including reliability, validity, estimation, ethical and bias concerns, data security and assumptions, power analysis , and multiple and logistic regression. New sections on mediation and moderation analysis to address the latest techniques. More coverage of quasi-experimental design and qualitative research to reflect changing practices. A new appendix on how to write about results using APA guidelines to help new researchers. Online resources available at www.routledge.com/9781138852976 that provide instructors with PowerPoints, test questions, critical thinking exercises, a conversion guide, and answers to all of the book's problems and questions. Students will find learning objectives, annotated links to further readings and key concepts, and key terms with links to definitions. Intended for graduate research methods or design or quantitative/experimental research methods courses in psychology, education, human development, family studies, and other behavioral, social, and health sciences, some exposure to statistics and research methods is recommended.

This practical, activity-based textbook from well-known psychology author Cara Flanagan covers everything you need to know for the research methods part of your A Level psychology course.The skills, knowledge and understanding needed to get to grips with this challenging part of the course are delived in an appealing magazine-style layout. Appropriate for all AS and A Level Psychology specifications (AQA, OCR, WJEC/Eduqas and Edexcel), this book is perfect for learning about research methods either as a stand-alone or integrated part of the course.

Statistical Concepts-A Second Course presents the last 10 chapters from An Introduction to Statistical Concepts, Fourth Edition. Designed for second and upper-level statistics courses, this book highlights how statistics work and how best to utilize them to aid students in the analysis of their own data and the interpretation of research results. In this new edition, Hahs-Vaughn and Lomax discuss sensitivity, specificity, false positive and false negative errors. Coverage of effect sizes has been expanded upon and more organizational features (to summarize key concepts) have been included. A final chapter on mediation and moderation has been added for a more complete presentation of regression models. In addition to instructions and screen shots for using SPSS, new to this edition is annotated script for using R. This book acts as a clear and accessible instructional tool to help readers fully understand statistical concepts and how to apply them to data. It is an invaluable resource for students undertaking a course in statistics in any number of social science and behavioral science disciplines.

This book provides an overview of cutting-edge methods currently being used in cognitive psychology, which are likely to appear with increasing frequency in coming years. Once built around univariate parametric statistics, cognitive psychology courses now seem deficient without some contact with methods for signal processing, spatial statistics, and machine learning. There are also important changes in analyses of behavioral data (e.g., hierarchical modeling and Bayesian inference) and there is the obvious change wrought by the advancement of functional imaging. This book begins by discussing the evidence of this rapid change, for example the movement between using traditional analyses of variance to multi-level mixed models, in psycholinguistics. It then goes on to discuss the methods for analyses of physiological measurements, and how these methods provide insights into cognitive processing. New Methods in Cognitive Psychology provides senior undergraduates, graduates and researchers with cutting-edge overviews of new and emerging topics, and the very latest in theory and research for the more established topics.