Human ratings are subject to various forms of error and bias. Since the early days of performance assessment, this problem has been sizeable and persistent. For example, expert raters evaluating the quality of an essay, an oral communication, or a work sample, often come up with different ratings for the very same performance. In cases like this, assessment outcomes largely depend upon which raters happen to provide the rating, posing a threat to the validity and fairness of the assessment. This book provides an introduction to a psychometric approach that establishes a coherent framework for drawing reliable, valid, and fair inferences from rater-mediated assessments, thus answering the problem of inevitably fallible human ratings: many-facet Rasch measurement (MFRM). Throughout the book, sample data taken from a writing performance assessment are used to illustrate key concepts, theoretical foundations, and analytic procedures, stimulating the readers to adopt the MFRM approach in their current or future professional context.

This book has two goals, each related to the validity of language assessment. The first goal is to explore the difference between fairness and justice in language assessment. The authors distinguish internal and external dimensions of the equitable and just treatment of individuals taking language tests which are used as gatekeeping devices to determine access to education and employment, immigrant status, citizenship, and other rights. The second goal is to show how the extent of test fairness can be demonstrated and improved using the tools of psychometrics, in particular the models collectively known as Rasch measurement. “This book will have an enormous impact on the field of language assessment. Using Rasch analysis models to explore and identify sources of unfairness, the authors make a compelling case for fairness in the design and implementation of language assessment instruments and for justice in the interpretation and use of test results. A real strength of the book is that it guides readers through analytical techniques in an accessible way.” Dan Douglas, Professor Emeritus, Applied Linguistics Program, Iowa State University.

Rasch Measurement Theory Analysis in R provides researchers and practitioners with a step-by-step guide for conducting Rasch measurement theory analyses using R. It includes theoretical introductions to major Rasch measurement principles and techniques, demonstrations of analyses using several R packages that contain Rasch measurement functions, and sample interpretations of results. Features: Accessible to users with relatively little experience with R programming Reproducible data analysis examples that can be modified to accommodate users’ own data Accompanying e-book website with links to additional resources and R code updates as needed Features dichotomous and polytomous (rating scale) Rasch models that can be applied to data from a wide range of disciplines This book is designed for graduate students, researchers, and practitioners across the social, health, and behavioral sciences who have a basic familiarity with Rasch measurement theory and with R. Readers will learn how to use existing R packages to conduct a variety of analyses related to Rasch measurement theory, including evaluating data for adherence to measurement requirements, applying the dichotomous, Rating Scale, Partial Credit, and Many-Facet Rasch models, examining data for evidence of differential item functioning, and considering potential interpretations of results from such analyses.

Written in an accessible style, this book facilitates a deep understanding of the Rasch model. Authors Bond and Fox review the crucial properties of the Rasch model and demonstrate its use with a wide range of examples including the measurement of educational achievement, human development, attitudes, and medical rehabilitation. A glossary and numerous illustrations further aid the reader's understanding. The authors demonstrate how to apply Rasch analysis and prepare readers to perform their own analyses and interpret the results. Updated throughout, highlights of the Second Edition include: a new CD that features an introductory version of the latest Winsteps program and the data files for the book’s examples, preprogrammed to run using Winsteps; a new chapter on invariance that highlights the parallels between physical and human science measurement; a new appendix on analyzing data to help those new to Rasch analysis; more explanation of the key concepts and item characteristic curves; a new empirical example with data sets demonstrates the many facets of the Rasch model and other new examples; and an increased focus on issues related to unidimensionality, multidimensionality, and the Rasch factor analysis of residuals. Applying the Rasch Model is intended for researchers and practitioners in psychology, especially developmental psychologists, education, health care, medical rehabilitation, business, government, and those interested in measuring attitude, ability, and/or performance. The book is an excellent text for use in courses on advanced research methods, measurement, or quantitative analysis. Significant knowledge of statistics is not required.

Using a meaning-based approach that emphasizes the "why" over the "how to," Psychometrics: An Introduction provides thorough coverage of fundamental issues in psychological measurement. Author R. Michael Furr discusses traditional psychometric perspectives and issues including reliability, validity, dimensionality, test bias, and response bias as well as advanced procedures and perspectives including item response theory and generalizability theory. The substantially updated Third Edition includes broader and more in-depth coverage with new references, a glossary summarizing over 200 key terms, and expanded suggested readings consisting of highly relevant papers to enhance the book’s overall accessibility, scope, and usability for both instructors and students. Online Resources Free PowerPoint® slides for instructors are available with this text. Contact your rep to learn more.