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Used worldwide in assessment and professional certification contexts, computerized adaptive testing (CAT) offers a powerful means to measure individual differences or make classifications. This authoritative work from CAT pioneer David J. Weiss and Alper Şahin provides a complete how-to guide for planning and implementing an effective CAT to create a test unique to each person in real time. The book reviews the history of CAT and the basics of item response theory used in CAT. It walks the reader through developing an item bank, pretesting and linking items, selecting required CAT options, and using simulations to design a CAT. Available software for CAT delivery is described, including links to free and commercial options. Engaging multidisciplinary examples illustrate applications of CAT for measuring ability, achievement, proficiency, personality, attitudes, perceptions, patients'reports of their symptoms, and academic or clinical progress.

The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased. Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers'engagement in testing. In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices.

This book aims to introduce the use of computerized adaptive testing (CAT) – a novel and ever increasingly used method of a test administration – applied to the field of Kinanthropology. By adapting a test to an individual respondent's latent trait level, computerized adaptive testing offers numerous theoretical and methodological improvements that can significantly advance testing procedures.In the first part of the monograph, the theoretical and conceptual basis of CAT, as well as a brief overview of its historical origins and basic general principles are presented. The discussion necessarily includes the description of Item Response Theory (IRT) to some extent, since IRT is almost exclusively used as the mathematical model in today's CAT applications. Practical application of CAT is then evaluated using Monte-Carlo simulations involving adaptive administration of the Physical Self-Description Questionnaire – an instrument widely used to assess physical self-concept in the field of sport and exercise psychology.

The goal of this guide and manual is to provide a practical and brief overview of the theory on computerized adaptive testing (CAT) and multistage testing (MST) and to illustrate the methodologies and applications using R open source language and several data examples. Implementation relies on the R packages catR and mstR that have been already or are being developed by the first author (with the team) and that include some of the newest research algorithms on the topic. The book covers many topics along with the R-code: the basics of R, theoretical overview of CAT and MST, CAT designs, CAT assembly methodologies, CAT simulations, catR package, CAT applications, MST designs, IRT-based MST methodologies, tree-based MST methodologies, mstR package, and MST applications. CAT has been used in many large-scale assessments over recent decades, and MST has become very popular in recent years. R open source language also has become one of the most useful tools for applications in almost all fields, including business and education. Though very useful and popular, R is a difficult language to learn, with a steep learning curve. Given the obvious need for but with the complex implementation of CAT and MST, it is very difficult for users to simulate or implement CAT and MST. Until this manual, there has been no book for users to design and use CAT and MST easily and without expense; i.e., by using the free R software. All examples and illustrations are generated using predefined scripts in R language, available for free download from the book's website.

Peterson's Master the GRE® 2015 is your one-stop guidebook for preparing for the GRE test. This comprehensive eBook is organized into six parts that include basic information about the test, a diagnostic and three practice tests, and detailed test prep for the Analytical Writing, Verbal Reasoning, and Quantitative Reasoning sections of the GRE. Inside you'll find 7 full-length practice tests, including access to 3 online, with detailed answer explanations and a thorough review of all GRE® General Test subject areas: Analytical Writing, Verbal Reasoning, and Quantitative Reasoning. Also included is expert advice on GRE® question formats, scoring, and what to expect on test day. Additional resources include valuable guidance to help you score high on the Argument and Issue Task essays, helpful tips for using the on-screen calculator and answering numeric entry questions strategies—everything you need to boost your GRE score.

Testing of second language pragmatics has grown as a research area but still suffers from a tension between construct coverage and practicality. In this book, the authors describe the development and validation of a web-based test of second language pragmatics for learners of English. The test has a sociopragmatic orientation and strives for a broad coverage of the construct by assessing learners'metapragmatic judgments as well as their ability to co-construct discourse. To ensure practicality, the test is delivered online and is scored partially automatically and partially by human raters. We used the argument-based approach to validation, which showed that the test can support low-stakes decisions about learners'knowledge of sociopragmatics in English.

Provides the tools needed to successfully perform adaptive tests across a broad range of datasets Adaptive Tests of Significance Using Permutations of Residuals with R and SAS illustrates the power of adaptive tests and showcases their ability to adjust the testing method to suit a particular set of data. The book utilizes state-of-the-art software to demonstrate the practicality and benefits for data analysis in various fields of study. Beginning with an introduction, the book moves on to explore the underlying concepts of adaptive tests, including: Smoothing methods and normalizing transformations Permutation tests with linear methods Applications of adaptive tests Multicenter and cross-over trials Analysis of repeated measures data Adaptive confidence intervals and estimates Throughout the book, numerous figures illustrate the key differences among traditional tests, nonparametric tests, and adaptive tests. R and SAS software packages are used to perform the discussed techniques, and the accompanying datasets are available on the book's related website. In addition, exercises at the end of most chapters enable readers to analyze the presented datasets by putting new concepts into practice. Adaptive Tests of Significance Using Permutations of Residuals with R and SAS is an insightful reference for professionals and researchers working with statistical methods across a variety of fields including the biosciences, pharmacology, and business. The book also serves as a valuable supplement for courses on regression analysis and adaptive analysis at the upper-undergraduate and graduate levels.

This celebrated primer presents an introduction to all of the key ingredients in understanding computerized adaptive testing technology, test development, statistics, and mental test theory. Based on years of research, this accessible book educates the novice and serves as a compendium of state-of-the-art information for professionals interested in computerized testing in the areas of education, psychology, and other related social sciences. A hypothetical test taken as a prelude to employment is used as a common example throughout to highlight this book's most important features and problems.Changes in the new edition include: •a completely rewritten chapter 2 on the system considerations needed for modern computerized adaptive testing; •a revised chapter 4 to include the latest in methodology surrounding online calibration and in the modeling of testlets; and •a new chapter 10 with helpful information on how test items are really selected, usage patterns, how usage patterns influence the number of new items required, and tools for managing item pools.

Modern computer technology has opened up several new possibilities for optimizing the administration of educational and psychological tests. In computer adaptive testing (CAT), tests are automatically tailored to the proficiency level of the individual examinees. Currently, nearly all large-scale testing programs in the western world are already adaptive or in the process of becoming so. Written by active CAT researchers from Europe and North America, the chapters offer a comprehensive introduction to the latest developments in the theory and practice of CAT. The book can be used both as a basic reference on the state of the art in CAT and a valuable resource in graduate courses on test theory. The theoretical chapters in this book cover such topics as item selection and ability estimation, item pool development and maintenance, item calibration and model fit, and testlet-based adaptive testing. The practical chapters describe the operational aspects of existing large-scale CAT programs.