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.

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. 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.

This book offers a comprehensive introduction to the latest developments in the theory and practice of CAT. It can be used both as a basic reference and a valuable resource on test theory. It covers such topics as item selection and ability estimation, item pool development and maintenance, item calibration and model fit, and testlet-based adaptive testing, as well as the operational aspects of existing large-scale CAT programs.

The arrival of the computer in educational and psychological testing has led to the current popularity of adaptive testing---a testing format in which the computer uses statistical information about the test items to automatically adapt their selection to a real-time update of the test taker’s ability estimate. This book covers such key features of adaptive testing as item selection and ability estimation, adaptive testing with multidimensional abilities, sequencing adaptive test batteries, multistage adaptive testing, item-pool design and maintenance, estimation of item and item-family parameters, item and person fit, as well as adaptive mastery and classification testing. It also shows how these features are used in the daily operations of several large-scale adaptive testing programs.

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.

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.

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.