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

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

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

This book addresses issues related with researching sensitive topics in social work, focusing on marginalized, vulnerable and hard to reach people. It covers the definition, characteristics, challenges and opportunities of sensitive research, its philosophical roots and methodological debates, and the skills and values that are required along with the ethical, political and legal issues involved in conducting social work research. This book will cover innovative research methods appropriate for research on sensitive topics involving vulnerable people. It shines light on how to use traditional research methods sensitively, and how to generate data while minimizing the harm that can potentially be caused to research participants and researchers.

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.

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

YIS has been thought as an annual series of volumes collecting contributes aimed at developing the integration of idiographic and nomothetic approaches in psychological and more in general social science. At the beginning, 3 years ago, we got an agreement with an Italian publisher (FGP - Firera Publishing Group) interested in the scientific project and therefore willing to help the start up of this scientific enterprise. After publishing the first volume (YIS 2008- yet published in 2009 - the Volume is freely available on the FPG's website) we have had many positive feedbacks and signals of interests, as well as several submissions, from many parts of the world . This has provided an acceleration of the following issues - Above all, this led us to realize that it was time to give an editorial collocation to YIS that can be more consistent with the interest it has raised and that can ulteriorly raise. FPG does not put constraint on this perspective, being aware and agreed of the necessity of a worldwide context for the YIS's development. Moreover, there are no constraints in the possibility of going on in using the label "YIS," starting from Volume 4 The Series addresses a quite large potential public - students and researchers interested to theoretical and methodological development of psychology and, more in general, social science. Persons engaged with qualitative, dynamic informed models of analysis will find YIS a precious tool as well as a context enabling to develop a worlwide network of practices and cultures of research. The first three volumes' TOC witness how large and constantly increasing is the interest around the scientific project.

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

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

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

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

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

Horrified by the Holocaust, social psychologist Stanley Milgram wondered if he could recreate the Holocaust in the laboratory setting. Unabated for more than half a century, his (in)famous results have continued to intrigue scholars. Based on unpublished archival data from Milgram's personal collection, volume one of this two-volume set introduces readers to a behind the scenes account showing how during Milgram's unpublished pilot studies he step-by-step invented his official experimental procedure-how he gradually learnt to transform most ordinary people into willing inflictors of harm. Volume two then illustrates how certain innovators within the Nazi regime used the very same Milgram-like learning techniques that with increasing effectiveness gradually enabled them to also transform most ordinary people into increasingly capable executioners of other men, women, and children. Volume two effectively attempts to capture how step-by-step these Nazi innovators attempted to transform the Fuhrer's wish of a Jewish-free Europe into a frightening reality. By the books' end the reader will gain an insight into how the seemingly undoable can become increasingly doable.

* It is a straightforward, conversational introduction to statistics that delivers exactly what its title promises. * Each chapter begins with a brief overview of a statistic that describes what the statistic does and when to use it, followed by a detailed step-by-step explanation of how the statistic works and exactly what information it provides. * Chapters also include an example of the statistic (or statistics) in use in real-world research, "Worked Examples," "Writing It Up" sections that demonstrate how to write about each statistic, "Wrapping Up and Looking Forward" sections, and practice work problems. * A new chapter on person-centered analyses, including cluster analysis and latent class analysis (LCA) has been added (Chapter 16). * Person-centered analysis is an important alternative to the more commonly used variable-centered analyses (e.g., t tests, ANOVA, regression) and is gaining popularity in social-science research. * The chapter on non-parametric statistics (Chapter 14) was enhanced significantly with in-depth descriptions of Mann-Whitney U, Kruskall-Wallace, and Wilcoxon Signed-Rank analyses. * These non-parametric statistics are important alternatives to statistics that rely on normally distributed data. * This new edition also includes more information about the assumptions of various statistics, including a detailed explanation of the assumptions and consequences of violating the assumptions of regression (Chapter 13). * There is more information provided about the importance of the normal distribution in statistics (Chapters 4 and 7). * Each of the last nine chapters includes an example from the real world of research that employs the statistic, or statistics, covered in the chapter. * Altogether, these improvements provide important foundational information about how inferential statistics work and additional statistical tools that are commonly used by researchers in the social sciences. * The text works as a standalone or as a supplement and covers a range of statistical concepts from descriptive statistics to factor analysis and person-centered analyses.

The study of intuition and its relation to thoughtful reasoning is a burgeoning research topic in psychology and beyond. While the area has the potential to radically transform our conception of the mind and decision making, the procedures used for establishing empirical conclusions have often been vaguely formulated and obscure. This book fills a gap in the field by providing a range of methods for exploring intuition experimentally and thereby enhancing the collection of new data. The book begins by summarizing current challenges in the study of intuition and gives a new foundation for intuition research. Going beyond classical dual-process models, a new scheme is introduced to classify the different types of processes usually collected under the label of intuition. These new classifications range from learning approaches to complex cue integration models. The book then goes on to describe the wide variety of behavioural methods available to investigate these processes, including information search tracing, think aloud protocols, maximum likelihood methods, eye-tracking, and physiological and non-physiological measures of affective responses. It also discusses paradigms to investigate implicit associations and causal intuitions, video-based approaches to expert research, methods to induce specific decision modes as well as questionnaires to assess individual preferences for intuition or deliberation. By uniquely providing the basis for exploring intuition by introducing the different methods and their applications in a step-by-step manner, this text is an invaluable reference for individual research projects. It is also very useful as a course book for advanced decision making courses, and could inspire experimental explorations of intuition in psychology, behavioural economics, empirical legal studies and clinical decision making.

This book describes a series of ground-breaking residential workshops in therapeutic counselling in the 1960s, for people working in mental health and social care disciplines seeking to expand and deepen their reach. The work is unique in the scope of its research into the process and outcomes of such active immersive enquiry in this area. Besides a wealth of more systematic features, the author invites us into the initial conversations in the meeting room, and then follows the group members back into their lives, allowing us to see both early outcomes and the impact of participation up to ten years later. Finally, Barrett-Lennard reflects on the extended history of the intensive workshops and the related group work in other contexts they led into. He makes a compelling argument that such an intensive participatory process is as powerful today as it was in the 1960s. The blend of rich qualitative and empirical data and theory is a unique strength. It will be a great resource for students and scholars in applied psychology and psychotherapy, as well as for practicing therapists and trainees committed to meaningful work with their client groups.

Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors' collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. The authors show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions. The book makes two essential contributions to quantitative studies of time-related change. Through the introduction of the GLMM framework, it shows how innovative estimation methods and new model specifications can be used to tackle the "model identification problem" that has hampered the development and empirical application of APC analysis. The book also addresses the major criticism against APC analysis by explaining the use of new models within the GLMM framework to uncover mechanisms underlying age patterns and temporal trends. Encompassing both methodological expositions and empirical studies, this book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. It compares new and existing models and methods and provides useful guidelines on how to conduct APC analysis. For empirical illustrations, the text incorporates examples from a variety of disciplines, such as sociology, demography, and epidemiology. Along with details on empirical analyses, software and programs to estimate the models are available on the book's web page.

- The application of situational crime prevention theory to homicide by people with a serious mental disorder make this book a novel resource. - Research is reviewed with a focus on implications for prevention.

Psychologists are under increasing pressure to demonstrate the ecological validity of their assessment procedures--to show that the recommendations concluding their evaluations are relevant to urgent concerns in the legal and social policy arenas, such as predicting dangerousness, awarding compensation, and choosing a custodial parent. How much damage does a referred patient have? Who or what "caused" the damage? What impact will it have on his or her future life, work, and family? And what can be done to remediate the damage? The purpose of this book is to provide sound objective methods for answering these questions. It integrates the knowledge of experienced practitioners who offer state-of-the-art summaries of the best current approaches to evaluating difficult cases with that of basic theorists who describe emerging methods in both predictive and inferential statistics, such as Bayesian networks, that have proven their value in other scientific fields. Arguably, the enterprise of psychological assessment is so interdependent with that of data analysis that attempts to make inferences without consideration of statistical implications is malpractice. Prediction in Forensic and Neuropsychology: Sound Statistical Practices clarifies the process of hypothesis testing and helps to push the clinical interpretation of psychological data into the 21st century. It constitutes a vital resource for all the stakeholders in the assessment process--practitioners, researchers, attorneys, and policymakers.

Notwithstanding the mythical demise of "introspection," self-observation has always been an integral aspect of the social sciences. In the century following the "behavioral revolution," psychology has seen a reduction not so much in the frequency as in the rigor with which self-observation is practiced. A great deal of self-observation has been renamed or obscured (as, for example, "self-report"), but this has served only to defer and impoverish important theoretical and technical work.

This volume, which contributes to the development of a rigorous theory of self-observation, is organized around three general objectives: to re-animate a discourse on self-observation through a historical analysis of various self-observation traditions; to outline and begin to address some of the unique theoretical challenges of self-observation; and to elaborate some of the technical and practical details necessary for realizing a program of research dedicated to self-observation.

In the first section of the book, three historians of psychology trace the evolution of self-observation. In the second, three scholars who are currently working in contemporary traditions of self-observation discuss the basic theoretical and practical challenges involved in conducting self-observation research. In the final two sections of the book, scholars from the phenomenological and narrative traditions trace the history, theory, and practice of self-observation in their respective traditions. Self-Observation in the Social Sciences continues the fine tradition set by Transaction's History and Theory of Psychology series edited by Jaan Valsiner. It is of interest to psychologists and to those who study methodology within the social sciences.

Design and Analysis in Educational Research Using jamovi is an integrated approach to learning about research design alongside statistical analysis concepts. Strunk and Mwavita maintain a focus on applied educational research throughout the text, with practical tips and advice on how to do high-quality quantitative research. Based on their successful SPSS version of the book, the authors focus on using jamovi in this version due to its accessibility as open source software, and ease of use. The book teaches research design (including epistemology, research ethics, forming research questions, quantitative design, sampling methodologies, and design assumptions) and introductory statistical concepts (including descriptive statistics, probability theory, sampling distributions), basic statistical tests (like z and t), and ANOVA designs, including more advanced designs like the factorial ANOVA and mixed ANOVA. This textbook is tailor-made for first-level doctoral courses in research design and analysis. It will also be of interest to graduate students in education and educational research. The book includes Support Material with downloadable data sets, and new case study material from the authors for teaching on race, racism, and Black Lives Matter, available at www.routledge.com/9780367723088.

This new edited volume features contributions from many of the leading scientists in probability and statistics from the latter part of the 20th century. It is the only book to assemble the views of these leading scientists--the pioneers in their respective fields. Stochastic Musings features contributions by: *Sir David Cox on statistics and econometrics; *C.R. Rao, M.B. Rao, and D.N. Shanbhag on convex sets of multivariate distributions and their extreme points; *Bradley Efron on the future of statistics; *David Freedman on regression association and causation; *Vic Barnett on sample ordering for effective statistical inference with particular reference to environmental issues; *David Bartholomew on a unified statistical approach to some measurement problems in the social sciences; *Joe Gani on scanning a lattice for a particular pattern; *Leslie Kish on new paradigms for probability sampling (his last paper); *Samuel Kotz and Norman L. Johnson on limit distributions of uncorrelated but dependent distributions on the unit square; *Samuel Kotz and Saralees Nadarajah on some new elliptical distributions; *Jef Teugels on the life span of a renewal; *Wolfgang Urfer and Katharina Emrich on maximum likelihood estimates of genetic effects; and **Vladimir M. Zolotarev on convergence rate estimates in functional limit theorems. The volume also includes the following contributions by faculty members of the Department of Statistics, Athens University of Economics and Business: *J. Panaretos, E. Xekalaki, and S. Psarakis on a predictive model evaluation and selection approach--the correlated gamma ratio distribution; *J. Panaretos and Z. Tsourti on extreme value index estimators and smoothing alternatives; *E. Xekalaki and D. Karlis on mixtures everywhere; and * Ir. Moustaki on latent variable models with covariates. Stochastic Musings will appeal to researchers, professionals, and students interested in the history and development of statistics and probability as well as in related areas, such as physics, biometry, economics, and mathematics. Academic and professional statisticians will benefit from the book's coverage of the latest developments in the field, as well as reflections on the future directions of the discipline.

This book covers statistical consequences of breaches of research integrity such as fabrication and falsification of data, and researcher glitches summarized as questionable research practices. It is unique in that it discusses how unwarranted data manipulation harms research results and that questionable research practices are often caused by researchers' inadequate mastery of the statistical methods and procedures they use for their data analysis. The author's solution to prevent problems concerning the trustworthiness of research results, no matter how they originated, is to publish data in publicly available repositories and encourage researchers not trained as statisticians not to overestimate their statistical skills and resort to professional support from statisticians or methodologists. The author discusses some of his experiences concerning mutual trust, fear of repercussions, and the bystander effect as conditions limiting revelation of colleagues' possible integrity breaches. He explains why people are unable to mimic real data and why data fabrication using statistical models stills falls short of credibility. Confirmatory and exploratory research and the usefulness of preregistration, and the counter-intuitive nature of statistics are discussed. The author questions the usefulness of statistical advice concerning frequentist hypothesis testing, Bayes-factor use, alternative statistics education, and reduction of situational disturbances like performance pressure, as stand-alone means to reduce questionable research practices when researchers lack experience with statistics.

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.

Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods in Psychology is the complete tool box to deliver the most valid and generalizable answers to today's complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences. Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences.