Introduction to Nonparametric Item Response Theory
Language: English
Published by SAGE PUBN, 2002
- Hardcover
- New
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Über den AutorVice dean of research Tilburg School of Social and Behavioral Sciences, program director research master, and professor of methodology and statisticsInhaltsverzeichnisModels for Mental Measu.
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- Title
- Introduction to Nonparametric Item Response Theory
- Author
- Sijtsma, Klaas|Molenaar, Ivo W.
- Publisher
- SAGE PUBN
- Publication year
- 2002
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0761908129
- ISBN 13
- 9780761908128
"This manuscript addresses an important and complex topic in test development in a manner that is precise and accurate, yet very accessible to students and practitioners with a modest background in classical test theory and item response theory. It also provides an excellent introduction to nonparametric IRT models for the more mathematically sophisticated student or faculty member who will welcome the extensive additional reading lists that are found at the conclusion of each chapter."
―LINDA F. WIGHTMAN, School of Education, University of N. Carolina, Greensboro
"I thoroughly enjoyed this book, and liked the clear way the authors have worked through the chapters and examples. There are rich examples with plenty of exercises that encouraged me to try these methods with my own data. The quality of the interpretation is rich, particularly in the polytomous item domain. It is well worth having on the shelf as a reference tool and as available for graduate students who wish to know more."
―JOHN HATTIE, Head of the School of Education, University of Auckland, NZ
This book introduces social and behavioral science students and researchers to the theory and practice of the highly powerful methods of nonparametric item response theory (IRT). Anyone who uses or constructs tests or questionnaires for measuring abilities, achievements, personality traits, attitudes, or opinions will find nonparametric IRT useful for designing and improving such measurements. The authors show how the broadness of the nonparametric item response models allows them to fit many data sets and remain powerful enough for implying useful measurement properties, such as the ordering of persons using the simple total score (number-correct for dichotomous item tests and sum of rating scale score for polytomous item tests) and the ordering of the items using the item means. Many data analysis examples are given in the book, and a user-friendly computer program used throughout the book supports data analysis using nonparametric IRT. Given the importance of school admissions, certification, personnel selection, marketing, social policy evaluation, quality-of-life measurements, and assessments of deviant behavior, this book is a must read for students or researchers engaged in this work.
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