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.

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.

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.

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

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.

Offering a deep look into the moral uncertainty in the contemporary social sciences and American society, this book explores an in-depth solution. This solution, as articulated by Pitirim A. Sorokin in the 20th century, is the theory of Integralism; a perspective dating back to Plato, Aristotle and Aquinas. Sorokin initially applied Integralism by locating and analyzing three dominant super socio-cultural systems over 2500 years of Graeco-Roman and Western history. Each super system was unified by a central philosophical principle based either on materialism (the senses), or the rational, or the supersensory/super-rational. A super system declines when it reaches the limits of its potential to achieve its true reality and value, to be replaced by another super system. Sorokin described a trendless rotation of the three super socio-cultural systems. The current dominant super socio-cultural system in the West is a materialist version emphasizing science and technology with little thought given to supersensory/super-rational reality. Sorokin asserted contemporary materialist culture was in a state of collapse due to the breakup of its moral values. As a consequence he saw a struggle for power occurring between egoistic individuals and groups often resulting in revolutions, wars and inter-human strife. In response to the one sided materialist view of reality the goal of Integralism is to unify all three forms of reality into an integral culture that harmoniously balances materialist and supersensory/super-rational orientations. A solution to the contemporary moral confusion, Sorokin argued, can be found in the application of supra-conscious intuition that would enable a human to know what is eternal in the ordinary and reach the transcendent; an experience not accessible to the senses or the rational intellect alone. The supra-conscious is the source for reaching the supreme moral value; creative unselfish altruism which can be shared by all cultures to produce peace and harmony in the world.

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.

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 EBBS Publications Series is designed to provide researchers and students with authoritative, topical reviews of major areas in the brain and behaviour sciences. Each volume includes specially commissioned and edited chapters by leading researchers, presented in a lively and accessible style ideal for the non-specialist. The study of appetite is of major interest to psychologists and neuroscientists, and is understood to involve components relating to both disciplines. Psychological research looks at the cues which guide appetitive behaviour, and the cognitive mechanisms used to interpret cues and influence choice of action. Neuroscience research looks at the neural substrates for these behavioural processes. This is the first volume to bring the two perspectives together covering the areas of eating, drinking, sexual behaviour, drug addiction, and gambling. It will be of interest to behavioural researchers in general, and to clinicians interested in abnormal forms of appetite.

How can we account for the sudden appearance of such dazzling artists and scientists as Mozart, Shakespeare, Darwin, or Einstein? How can we define such genius? What conditions or personality traits seem to produce exceptionally creative people? Is the association between genius and madness really just a myth? These and many other questions are brilliantly illuminated in The Origins of Genius. Dean Simonton convincingly argues that creativity can best be understood as a Darwinian process of variation and selection. The artist or scientist generates a wealth of ideas, and then subjects these ideas to aesthetic or scientific judgment, selecting only those that have the best chance to survive and reproduce. Indeed, the true test of genius is the ability to bequeath an impressive and influential body of work to future generations. Simonton draws on the latest research into creativity and explores such topics as the personality type of the genius, whether genius is genetic or produced by environment and education, the links between genius and mental illness (Darwin himself was emotionally and mentally unwell), the high incidence of childhood trauma, especially loss of a parent, amongst Nobel Prize winners, the importance of unconscious incubation in creative problem-solving, and much more. Simonton substantiates his theory by examining and quoting from the work of such eminent figures as Henri Poincare, W. H. Auden, Albert Einstein, Marie Curie, Charles Darwin, Niels Bohr, and many others. For anyone intrigued by the spectacular feats of the human mind, The Origins of Genius offers a revolutionary new way of understanding the very nature of creativity.

The Trouble with Twin Studies questions popular genetic explanations of human behavioral differences based upon the existing body of twin research. Psychologist Jay Joseph outlines the fallacies of twin studies in the context of the ongoing decades-long failure to discover genes for human behavioral differences, including IQ, personality, and the major psychiatric disorders. This volume critically examines twin research, with a special emphasis on reared-apart twin studies, and incorporates new and updated perspectives, analyses, arguments, and evidence.

Signal detection theory--as developed in electrical engineering and based on statistical decision theory--was first applied to human sensory discrimination 40 years ago. The theoretical intent was to provide a valid model of the discrimination process; the methodological intent was to provide reliable measures of discrimination acuity in specific sensory tasks. An analytic method of detection theory, called the relative operating characteristic (ROC), can isolate the effect of the placement of the decision criterion, which may be variable and idiosyncratic, so that a pure measure of intrinsic discrimination acuity is obtained. For the past 20 years, ROC analysis has also been used to measure the discrimination acuity or inherent accuracy of a broad range of practical diagnostic systems. It was widely adopted by methodologists in the field of information retrieval, is increasingly used in weather forecasting, and is the generally preferred method in clinical medicine, primarily in radiology. This book attends to both themes, ROC analysis in the psychology laboratory and in practical diagnostic settings, and to their essential unity. The focus of this book is on detection and recognition as fundamental tasks that underlie most complex behaviors. As defined here, they serve to distinguish between two alternative, confusable stimulus categories, which may be perceptual or cognitive categories in the psychology laboratory, or different states of the world in practical diagnostic tasks. This book on signal detection theory in psychology was written by one of the developers of the theory, who co-authored with D.M. Green the classic work published in this area in 1966 (reprinted in 1974 and 1988). This volume reviews the history of the theory in engineering, statistics, and psychology, leading to the separate measurement of the two independent factors in all discrimination tasks, discrimination acuity and decision criterion. It extends the previous book to show how in several areas of psychology--in vigilance and memory--what had been thought to be discrimination effects were, in reality, effects of a changing criterion. The book shows that data plotted in terms of the relative operating characteristic have essentially the same form across the wide range of discrimination tasks in psychology. It develops the implications of this ROC form for measures of discrimination acuity, pointing up the valid ones and identifying several common, but invalid, ones. The area under the binormal ROC is seen to be supported by the data; the popular measures d' and percent correct are not. An appendix describes the best, current programs for fitting ROCs and estimating their parameters, indices, and standard errors. The application of ROC analysis to diagnostic tasks is also described. Diagnostic accuracy in a wide range of tasks can be expressed in terms of the ROC area index. Choosing the appropriate decision criterion for a given diagnostic setting--rather than considering some single criterion to be natural and fixed--has a major impact on the efficacy of a diagnostic process or system. Illustrated here by separate chapters are diagnostic systems in radiology, information retrieval, aptitude testing, survey research, and environments in which imminent dangerous conditions must be detected. Data from weather forecasting, blood testing, and polygraph lie detection are also reported. One of these chapters describes a general approach to enhancing the accuracy of diagnostic systems.

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.

Originally published in 1987, this book presents papers from the First Conference of European Clinical Psychologists, held at the University of Kent Canterbury in July of that year. It shows some of the most exciting and recent developments in research and innovations in professional practice from many European countries with an overall theme of the WHO strategy of 'Health for all by the year 2000.' The whole range of clinical psychology is covered, including: cognitive therapy, clinical psychology and WHO strategy, the mental health of ethnic minority groups, health psychology, care in the community, and many other topics. The book is likely to be of interest for anyone concerned with the recent history and policies in clinical psychology.

This edited volume provides both conceptual and practical information for conducting and evaluating evidence-based outcome studies. It encompasses psychotherapy research for traditional mental health disorders (eg. depression, anxiety), as well as psychosocial-based treatments provided to medical patient populations to have impact either on the disease process itself (pain, cardiovascular risk) or to improve the quality of life of such individuals. This is a hands-on book, whose major emphasis is on the practical nuts-and-bolts implementation of psychosocial-based RCTs from conception to completion.

* 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.

Prepares readers to analyze data and interpret statistical results using the increasingly popular R more quickly than other texts through LessR extensions which remove the need to program. By introducing R through less R, readers learn how to organize data for analysis, read the data into R, and produce output without performing numerous functions and programming first. Readers can select the necessary procedure and change the relevant variables without programming. Quick Starts introduce readers to the concepts and commands reviewed in the chapters. Margin notes define, illustrate, and cross-reference the key concepts. When readers encounter a term previously discussed, the margin notes identify the page number to the initial introduction. Scenarios highlight the use of a specific analysis followed by the corresponding R/lessR input and an interpretation of the resulting output. Numerous examples of output from psychology, business, education, and other social sciences demonstrate how to interpret results and worked problems help readers test their understanding. www.lessRstats.com website features the lessR program, the book's 2 data sets referenced in standard text and SPSS formats so readers can practice using R/lessR by working through the text examples and worked problems, PDF slides for each chapter, solutions to the book's worked problems, links to R/lessR videos to help readers better understand the program, and more. New to this edition: o upgraded functionality and data visualizations of the lessR package, which is now aesthetically equal to the ggplot 2 R standard o new features to replace and extend previous content, such as aggregating data with pivot tables with a simple lessR function call.

Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data. New to the Second Edition Reorganized to focus on unbalanced data Reworked balanced analyses using methods for unbalanced data Introductions to nonparametric and lasso regression Introductions to general additive and generalized additive models Examination of homologous factors Unbalanced split plot analyses Extensions to generalized linear models R, Minitab (R), and SAS code on the author's website The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.

This engaging book helps readers identify and then discard 52 misconceptions about data and statistical summaries. The focus is on major concepts contained in typical undergraduate and graduate courses in statistics, research methods, or quantitative analysis. Interactive Internet exercises that further promote undoing the misconceptions are found on the book's website. The author's accessible discussion of each misconception has five parts: The Misconception - a brief description of the misunderstanding Evidence that the Misconception Exists - examples and claimed prevalence Why the Misconception is Dangerous - consequence of having the misunderstanding Undoing the Misconception - how to think correctly about the concept Internet Assignment - an interactive activity to help readers gain a firm grasp of the statistical concept and overcome the misconception. The book's statistical misconceptions are grouped into 12 chapters that match the topics typically taught in introductory/intermediate courses. However, each of the 52 discussions is self-contained, thus allowing the misconceptions to be covered in any order without confusing the reader. Organized and presented in this manner, the book is an ideal supplement for any standard textbook. An ideal supplement for undergraduate and graduate courses in statistics, research methods, or quantitative analysis taught in psychology, education, business, nursing, medicine, and the social sciences. The book also appeals to independent researchers interested in undoing their statistical misconceptions.

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.

Devising tests that evaluate a nation's educational standing and implement efficacious educational reforms requires a careful balance among the contributions of technology, psychometrics, test design, and the learning sciences. Unlike other forms of adaptive testing, multistage testing (MST) is highly suitable for testing educational achievement because it can be adapted to educational surveys and student testing. Computerized Multistage Testing: Theory and Applications covers the methodologies, underlying technology, and implementation aspects of this type of test design. The book discusses current scientific perspectives and practical considerations for each step involved in setting up an MST program. It covers the history of MST, test design and implementation for various purposes, item pool development and maintenance, IRT-based and classical test theory-based methodologies for test assembly, routing and scoring, equating, test security, and existing software. It also explores current research, existing operational programs, and innovative future assessments using MST. Intended for psychologists, social scientists, and educational measurement scientists, this volume provides the first unified source of information on the design, psychometrics, implementation, and operational use of MST. It shows how to apply theoretical statistical tools to testing in novel and useful ways. It also explains how to explicitly tie the assumptions made by each model to observable (or at least inferable) data conditions. Winner of the 2016 AERA Award for Significant Contribution to Educational Measurement and Research Methodology The 2016 American Education Research Association (AERA) Div. D award committee for Significant Contributions to Educational Measurement and Research Methodology has recognized unanimously this collaborative work advancing the theory and applications of computerized MST. This annual award recognizes published research judged to represent a significant conceptual advancement in the theory and practice of educational measurement and/or educational research methodology. The 2016 award was made under the heading: Measurement, Psychometrics, and Assessment. This collective work, published in 2014 as an edited volume titled Computerized Multistage Testing: Theory and Applications, was cited by the committee both for the originality of the conceptual foundations presented in support of multistage testing and for arguing persuasively for its potential impact on the practice of educational measurement.

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.

Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages. The book provides broad, but unified, coverage, and the authors integrate the concepts and ideas shared across models and types of data, especially regarding conceptual links between discrete and continuous limited dependent variables. The authors argue that these dependent variables are, if anything, more common throughout the human sciences than the kind that suit linear regression. They cover special cases or extensions of models, estimation methods, model diagnostics, and, of course, software. They also discuss bounded continuous variables, boundary-inflated models, and methods for modeling heteroscedasticity. Wherever possible, the authors have illustrated concepts, models, and techniques with real or realistic datasets and demonstrations in R and Stata, and each chapter includes several exercises at the end. The illustrations and exercises help readers build conceptual understanding and fluency in using these techniques. At several points the authors bring together material that has been previously scattered across the literature in journal articles, software package documentation files, and blogs. These features help students learn to choose the appropriate models for their purpose.

Test fairness is a moral imperative for both the makers and the users of tests. This book focuses on methods for detecting test items that function differently for different groups of examinees and on using this information to improve tests. Of interest to all testing and measurement specialists, it examines modern techniques used routinely to insure test fairness. Three of these relevant to the book's contents are: * detailed reviews of test items by subject matter experts and members of the major subgroups in society (gender, ethnic, and linguistic) that will be represented in the examinee population * comparisons of the predictive validity of the test done separately for each one of the major subgroups of examinees * extensive statistical analyses of the relative performance of major subgroups of examinees on individual test items.