Computerized Multistage Testing: Theory and Applications Contributor(s): Yan, Duanli (Editor), Von Davier, Alina A. (Editor), Lewis, Charles (Editor) |
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ISBN: 146650577X ISBN-13: 9781466505773 Publisher: CRC Press OUR PRICE: $133.00 Product Type: Hardcover - Other Formats Published: May 2014 |
Additional Information |
BISAC Categories: - Mathematics | Probability & Statistics - General |
Dewey: 150.287 |
LCCN: 2012276608 |
Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences |
Physical Information: 1.3" H x 6" W x 9.3" (2.00 lbs) 546 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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 |