The rising reliance on testing in American education and for licensure and certification has been accompanied by an escalation in cheating on tests at all levels. Edited by two of the foremost experts on the subject, the Handbook of Quantitative Methods for Detecting Cheating on Tests offers a comprehensive compendium of increasingly sophisticated data forensics used to investigate whether or not cheating has occurred. Written for practitioners, testing professionals, and scholars in testing, measurement, and assessment, this volume builds on the claim that statistical evidence often requires less of an inferential leap to conclude that cheating has taken place than do other, more common sources of evidence. This handbook is organized into sections that roughly correspond to the kinds of threats to fair testing represented by different forms of cheating. In Section I, the editors outline the fundamentals and significance of cheating, and they introduce the common datasets to which chapter authors' cheating detection methods were applied. Contributors describe, in Section II, methods for identifying cheating in terms of improbable similarity in test responses, preknowledge and compromised test content, and test tampering. Chapters in Section III concentrate on policy and practical implications of using quantitative detection methods. Synthesis across methodological chapters as well as an overall summary, conclusions, and next steps for the field are the key aspects of the final section.

First published in 1994. Routledge is an imprint of Taylor & Francis, an informa company.

In this volume, academics and researchers across disciplines including education, psychology and health studies come together to discuss personal, political and professional narratives of struggle, resilience and hope. Contributors draw from a rich body of auto/biographical research to examine the role of narrative and how it can be constructed to compose a life story, considering the roles of significant others, inspirational, educational and fictional characters, and those in myth and legend. The book discusses how personal narrative, often neglected in social and psychological enquiry, can be a valuable resource across a range of settings. Reference is made to the evolving role of narrative in education and health care, medicine and psychotherapy. This includes how particular narratives are hardwired into culture in ways that stifle personal and social understanding. Rather than providing a 'how to' guide, the book illustrates the range and power of narrative, including poetry, to re-awaken senses of self and agency in extremis. Each chapter draws on specific research, describing the context, explaining the methodology, and illuminating important findings. Discussing implications for research and practice, this book will be key reading for postgraduate and doctoral students in auto/biographical and narrative studies, and across a range of disciplines, including education, health and social care, politics, counselling and psychotherapy. It will be of interest to academics teaching research methods, and those developing biographical and auto/biographical narrative research.

Why should researchers be interested in their feelings and emotions as they carry out research? Emotion is what it is to exist, to be human, and is present in every sphere of our lives. All activities are infused with emotion, even those that are constructed as 'rational', because rationality and emotionality are interpenetrated and entwined because all thinking is tinged with feeling, and all feeling is tinged with thinking. This book illuminates the emotional processes of doing social and organizational research, and the implications of this for the outcomes of research. With contributions from leading academics and research practitioners, it addresses the significant issue of the sometimes intense emotional experiences involved in doing research and the implications it has for the theory and practice of social research. By examining the nature of feelings and emotions, it explores how we might understand researchers' emotions and experiences, and considers the often powerful feelings encountered in a variety of research contexts. Topics discussed include: power relations; psycho-social explanations of researcher emotions; paradoxical relations with research participants and the sometimes disturbing data that is gained; research supervision; the politics of research; gender; publishing, undergoing vivas and presenting at conferences. This book will therefore be a valuable companion to researchers and research students from the start of their career onwards.

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB (R) routines used for the examples are available on the authors' website. The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference. As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters.

See How Graphics Reveal Information Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA. Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.

An utterly absorbing collection of ten classic tales from the therapist’s chair by renowned psychiatrist and best-selling author Irvin D. Yalom.

Why was Saul tormented by three unopened letters from Stockholm? What made Thelma spend her whole life raking over a long-past love affair? How did Carlos's macho fantasies help him deal with terminal cancer?

In this engrossing book, Irvin Yalom gives detailed and deeply affecting accounts of his work with these and seven other patients. Deep down, all of them were suffering from the basic human anxieties—isolation, fear of death or freedom, a sense of the meaninglessness of life—that none of us can escape completely. And yet, as the case histories make touchingly clear, it is only by facing such anxieties head on that we can hope to come to terms with them and develop. Throughout, Dr. Yalom remains refreshingly frank about his own errors and prejudices; his book provides a rare glimpse into the consulting room of a master therapist.

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors.

Compliance with federal equal employment opportunity regulations, including civil rights laws and affirmative action requirements, requires collection and analysis of data on disparities in employment outcomes, often referred to as adverse impact. While most human resources (HR) practitioners are familiar with basic adverse impact analysis, the courts and regulatory agencies are increasingly relying on more sophisticated methods to assess disparities. Employment data are often complicated, and can include a broad array of employment actions (e.g., selection, pay, promotion, termination), as well as data that span multiple protected groups, settings, and points in time. In the era of "big data," the HR analyst often has access to larger and more complex data sets relevant to employment disparities. Consequently, an informed HR practitioner needs a richer understanding of the issues and methods for conducting disparity analyses. This book brings together the diverse literature on disparity analysis, spanning work from statistics, industrial/organizational psychology, human resource management, labor economics, and law, to provide a comprehensive and integrated summary of current best practices in the field. Throughout, the description of methods is grounded in the legal context and current trends in employment litigation and the practices of federal regulatory agencies. The book provides guidance on all phases of disparity analysis, including: How to structure diverse and complex employment data for disparity analysis How to conduct both basic and advanced statistical analyses on employment outcomes related to employee selection, promotion, compensation, termination, and other employment outcomes How to interpret results in terms of both practical and statistical significance Common practical challenges and pitfalls in disparity analysis and strategies to deal with these issues

First published in 1990. Routledge is an imprint of Taylor & Francis, an informa company.

Narratives of Recovery from Mental Illness presents research that challenges the prevailing view that recovery from 'mental illness' must take place within the boundaries of traditional mental health services. While Watts and Higgins accept that medical treatment may be a vital start to some people's recovery, they argue that mental health problems can also be resolved through everyday social interactions, and through peer and community support. Using a narrative approach, this book presents detailed recovery stories of 26 people who received various diagnoses of 'mental illness' and were involved in a mutual help group known as 'GROW'. Drawing on an in-depth analysis of each story, chapters offer new understandings of the journey into mental distress and a progressive entrapment through a combination of events, feelings, thoughts and relationships. The book also discusses the process of ongoing personal liberation and healing which assists recovery, and suggests that friendship, social involvement, compassion, and nurturing processes of change all play key factors in improved mental well-being. This book provides an alternative way of looking at 'mental illness' and demonstrates many unexplored avenues and paths to recovery that need to be considered. As such, it will be of interest to researchers, academics and postgraduate students in the fields of psychiatry, psychology, nursing, social work and occupational therapy, as well as to service providers, policymakers and peer support organisations. The narratives of recovery within the book should also be a source of hope to people struggling with 'mental illness' and emotional distress

Practical Research with Children is designed to help the reader understand techniques for research with children, based on real world experience. The book describes a wide range of research methods, focusing equally on quantitative and qualitative approaches, and considers how different methods can be integrated. It highlights the benefits and challenges of each method and gives emphasis to best practice, with expert guidance on how to avoid potential pitfalls in order to obtain valuable insights into how children develop. The volume includes fifteen chapters arranged over three sections. Each chapter explores a particular method, or combination of methods, and discusses both theoretical and practical issues, using a diversity of domains, including different ages, cultures, populations and settings. Uniquely, the book includes newer methods (such as eye tracking and digital technologies) alongside well-established behavioural methods which are used for research with children. With contributions from internationally renowned researchers and practitioners from a range of disciplines, the book will be indispensable reading for a wide audience, including for students in psychology, education and nursing undertaking research projects with children, and also for anyone looking to understand the research behind current theories in child development.

Without question, statistics is one of the most challenging courses for students in the social and behavioral sciences. Enrolling in their first statistics course, students are often apprehensive or extremely anxious toward the subject matter. And while IBM SPSS is one of the more easy-to-use statistical software programs available, for anxious students who realize they not only have to learn statistics but also new software, the task can seem insurmountable. Keenly aware of students' anxiety with statistics (and the fact that this anxiety can affect performance), Ronald D. Yockey has written SPSS Demystified: A Simple Guide and Reference, now in its fourth edition. Through a comprehensive, step-by-step approach, this text is consistently and specifically designed to both alleviate anxiety toward the subject matter and build a successful experience analyzing data in SPSS. Topics covered in the text are appropriate for most introductory and intermediate statistics and research methods courses. Key features of the text: Step-by-step instruction and screenshots Designed to be hands-on with the user performing the analyses alongside on their computer as they read through each chapter Call-out boxes provided, highlighting important information as appropriate SPSS output explained, with written results provided using the popular, widely recognized APA format End-of-chapter exercises included, allowing for additional practice SPSS datasets available on the publisher's website New to the Fourth Edition: Fully updated to SPSS 28 Updated screenshots in full color to reflect changes in SPSS software system (version 28) Exercises updated with up-to-date examples Exact p-values provided (consist with APA recommendations)

This text focuses on the experimental methods and the associated terminology encountered in the research literature of psychology. Initially, the content is kept simple, so as not to distract from the information on research technique and philosophy. Interesting psychological questions from well researched areas are then examined in detail, permitting a fuller discussion of the problems encountered in specific paradigms. It is in this fashion that the book offers both methods and content. Unique features of this text include: * a detailed discussion of the process of theorizing, coupled with a close examination of psychological constructs, offers the reader an opportunity to see how psychologists think about, develop, and modify their theories, and the part played by research in changing explanations of behavior. * Although it is common for psychologists to be self-conscious in their reasoning, it is uncommon to see an analysis of the logic that they use to draw conclusions. Presenting material that is rarely verbalized but readily acknowledged by experienced researchers, the text contains an overt analysis of the logic of drawing conclusions from research. * Instructors are given a choice among 15 chapters to focus on or combine to suit the course's concentration. For example, instructors have the option of focusing on experimental psychology or a broad-based course including material on research methods in experimental, social, clinical, and applied psychology. * Courses in experimental psychology or research methods are required for every psychology major. Statistical understanding is vital for this curriculum, and this text contains a comprehensive chapter on statistics making it ideal for courses that combine statistics and experimental methods. Other important coverage includes: * an all-inclusive summary of the material found in an introductory statistics class. Although courses in research methods and experimental psychology usually have a statistics prerequisite, the students rarely remember the material when entering the research course. This text provides the instructor with the option of simply assigning the statistics information as a review, rather than repeating the lectures. If the course requirements are such as to necessitate a joint statistics and research methods course -- with the instructor lecturing on both topics -- this text could serve as the single text for the course. A helpful discussion -- accompanied by a valuable table -- demonstrates how to choose an appropriate statistic. All necessary formulas and other familiar statistical procedures -- illustrating computational steps -- are also featured. * a detailed discussion of how to develop tests for use in research. Aside from the value of this information for any researcher, it can be particularly helpful to students who are required to develop original experiments. * an elaborate discussion of methodological issues in outcome research, using smoking cessation and weight reduction programs as examples. Test bank disks for Experimental Methods in Psychology, -- free to adopters -- consist of an average of six short-answer, 11 fill-in-the-blank, and 11 multiple-choice questions for each chapter. The files are in both ASCII and Word-for-Windows formats.

The book will be designed primarily for graduate students (or advanced undergraduates) who are learning psychometrics, as well as professionals in the field who need a reference for use in their practice. We would assume that users have some basic knowledge of using SPSS to read data and conduct basic analyses (e.g., descriptive statistics, frequency distributions). In addition, the reader should be familiar with basic statistical concepts such as descriptive statistics (e.g., mean, median, variance, standard deviation), percentiles and the rudiments of hypothesis testing. They should also have a passing familiarity with issues in psychometrics such as reliability, validity and test/survey scoring. We will not assume any more than basic familiarity with these issues, and will devote a portion of each chapter (as well as the entire first chapter) to reviewing many of these basic ideas for those not familiar with them. We envision the book as being useful either as a primary text for a course on applied measurement where SPSS is the main platform for instruction, or as a supplement to a more theoretical text. We also anticipate that readers working in government agencies responsible for testing and measurement issues at the local, state and national levels, and private testing, survey and market research companies, as well as faculty members needing a practical resource for psychometric practice will serve as a market for the book. In short, the readership would include graduate students, faculty members, data analysts and psychometricians responsible for analysis of survey response data, as well as educational and psychological assessments. The goal of the book is to provide readers with the tools necessary for assessing the psychometric qualities of educational and psychological measures as well as surveys and questionnaires. Each chapter will cover an issue pertinent to psychometric and measurement practice, with an emphasis on application. Topics will be briefly discussed from a theoretical/technical perspective in order to provide the reader with the background necessary to correctly use and interpret the statistical analyses that will be presented subsequently. Readers will then be presented with examples illustrating a particular concept (e.g., reliability). These examples will include a discussion of the particular analysis, along with the SPSS code necessary to conduct them. The resulting output will then be discussed in detail, focusing on the interpretation of the results. Finally, examples of how these results might be written up will also be included in the text. It is hoped that this mixture of theory with examples of actual practice will serve the reader both as a pedagogical tool and as a reference work. To our knowledge, no book outlining psychometric practice using commonly available software such as SPSS currently exists. Given that many practitioners in academia, government and private industry use SPSS for statistical analyses of testing data, we believe that our book will fill an important niche in the market. It will contain very practical information regarding how to conduct a wide variety of psychometric analyses, along with tips on interpretation of results and the appropriate format for reporting these results. We believe that it will prove useful to individuals in educational measurement, psychometrics, and survey and market research. Our text will add to the literature by providing users with a single reference containing the major ideas in applied psychometrics with instructions and examples for conducting the analyses in SPSS. In addition, we will provide original macros for estimating a variety of statistics and conducting analyses common in educational and psychological measurement.

What drives people toward their goals? Does motivation come from outside the individual or from within? This is a concise, engaging overview of leading theories and the wide body of research about this core concept in psychology. It draws from a broad spectrum of psychological models and disciplines, and focuses on how various theories of motivation define and examine different motivational attributes, such as rewards and goals. Real life case examples illuminate how various models explain behavior and connect the study of motivation to our daily lives.

An entertaining, affordable alternative to lengthy and expensive texts on the subject, Motivation 101 is unique in helping readers understand how each theory of motivation-behavioral, neurobiological, attribution, and other paradigms--views and defines a particular concept within the model. For example, each modality views the concept of "reward" from a different perspective. Tables within each chapter compare and contrast the thinking of different schools in regard to each construct. The book is also unique in its multidisciplinary focus, whereby research is drawn not only from different domains of psychology but also from such disciplines as education and business. The book considers cultural differences in the study of motivation and collaborative environments, and addresses changing research methodologies. The book concludes with a discussion of how motivation theory is applied in practical contexts to create change in the world. It will be an asset to undergraduate and graduate courses in motivation in psychology, education, and business.

Key Features: Provides a concise, engaging overview of motivation that encompasses leading theories and a broad body of research Compares and contrasts different schools of motivation theories and models including needs-based and cognitively based paradigms Draws from research across a wide range of domains within psychology, education, and business Connects the study of motivation to our daily lives through case examples and metaphors Demonstrates how applying theories of motivation can have tremendous impact on behavior change

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.

This is an easy-to-use reference to the most effective play therapy techniques and their substantiated results. Play therapy is not an approach based on guess, trial and error, or whims of the therapist at the moment. It is a well-thought-out, philosophically conceived, developmentally based, and research-supported method of helping children cope with and overcome the problems they experience in the process of living their lives. Concise digests of play therapy procedures explore the most difficult, as well as the most common problems encountered by play therapists. These digests cover play therapy approaches based on a variesty of theoretical positions for dealing with a broad range of specific problems.

There have been many important changes in the participation of women and men in American society over the past quarter-century. Tests play a role in those changes by providing evidence of the diverse achievement and proficiency of women and men. They aid the learning process and reflect inequalities in opportunity to learn and participate. In addition, they provide useful information in considering what alternatives in education and work make most sense for individuals and influence views about groups of students, educational programs, and a wide range of issues. For all of these reasons, it is important that tests assess fairly and reflect accurately the ways young people are and are not achieving as well as desired. The test performance of women and men is a research topic of historical interest and has received much attention in recent years. Because of this increased interest, there is a great deal of new research and data available. The purpose of the study presented in this volume was to review this new information with two objectives in mind: *to clarify patterns of gender difference and similarity in test performance and related achievements, and *to see what implications those findings might have for fair assessment and, as a corollary, examine the assessment process as a possible source of gender differences. This study is interested in tests used in education to assess developed knowledge and skill. In order to gain a broader view of gender similarity and difference, the contributors looked at other types of measures and other characteristics of young women and men. Their hope is to contribute to a firmer basis for insuring fairness in tests--an objective which is particularly important as the field moves increasingly to new forms of assessment in which there is less experience.

Transforming the Measurement of Learning and Teaching in Higher Education proposes a dynamic new model for educational measurement by reconceptualizing the field of learning analytics. Revolving around the agency and daily work of those in the field, this book describes how colleges and universities can be better structured for quality learning, showcases new tools for gathering emergent feedback, and demonstrates how that feedback can be used effectively across higher education organizations. Leaders and practitioners at all levels are offered new approaches for organizational and technological design that ensure the type of data and the way it is gathered serve the ultimate goal of high quality learning and teaching.

Brue s Essentials Intellectual Disability is a concise, up-to-date overview of intellectual disability evaluation and assessment. This text offers a practical, concise overview of the nature of intellectual disability and adaptive skills functioning in children, adolescents, and adults. Coverage includes the latest information on prevalence, causes, differential diagnoses, behavioral and social concerns, test instruments, and the new DSM-5 diagnostic criteria. The discussion promotes a deeper understanding of the use of assessment data to inform interventions in clinical practice. Designed for easy navigation, each chapter highlights important points and key cautions to allow quick reference without sacrificing depth. A sample assessment report illustrates how findings should be communicated to better inform treatment, giving you a practical reference to ensure comprehensive reporting. In 2013, the DSM-5 conceptualization of intellectual disabilities was significantly changed. It's important for professionals to have access to the most current guidelines from a variety of sources, and this book compiles them all into a single reference.

This volume contains a collection of essays and discussions which serve as an introduction and guide to current research in the area of drawing inferences from self-selected samples. This topic is of direct interest to a professional audience of survey researchers, pollsters, market researchers, policymakers, statisticians, demographers, economists, and sociologists. The essays themselves and their associated critical discussions are clear and careful; the contributors are among the foremost experts in the field.

Designing and Implementing Effective Evaluations provides extensive real-life examples of program evaluations that illustrate the various elements and steps in conducting a successful evaluation. The detailed and diverse range of case studies show the common elements, methods, approaches, and processes of program evaluations, while also demonstrating the way that good evaluators adapt and tailor those methods to the specific characteristics and needs of a given program. The chapters explore the process of problem solving while navigating multiple stakeholders, competing agendas, and varying environments. The book introduces conversations concerning how to adapt evaluation processes and concepts with culturally different individuals and communities. It discusses the role of culture in navigating a meaningful evaluation process when significant cultural differences exist between the evaluator and individuals that make up the organization. The text is a vital resource for postgraduate students in program evaluation courses in Psychology, Education, Public Health, Social Work and related fields.

Presenting the proceedings of a conference held at Syracuse University in honor of S.S. Stevens, a pioneer in the scaling of sensory magnitudes and the originator of the method of magnitude estimation, this volume brings together the work of 20 authorities on the procedures of ratio scaling. These experts--psychophysicists, physiologists, and theoreticians--offer their views on whether or not psychological magnitudes can be measured and whether the judgments of psychological magnitudes constitute the basis for the construction of a ratio scale. Also discussed is the question of whether any single method could stand out as a potential standard technique for measuring psychological magnitudes.