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Introduction to Nonparametric Item Response Theory de Klaas Sijtsma

Introduction to Nonparametric Item Response Theory

Autor Klaas Sijtsma, Ivo W. Molenaar
en Limba Engleză Paperback – 12 mar 2002
Introduction to Nonparametric Item Response Theory addresses an important and complex topic in test development in a manner that is precise and accurate, yet accessible to students and practitioners with a modest background in classical test theory. It also provides an excellent introduction to nonparametric IRT models for the more mathematically sophisticated student or researcher who will welcome the extensive additonal reading lists that are found at the conclusion of each chapter.
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Specificații

ISBN-13: 9780761908135
ISBN-10: 0761908137
Pagini: 182
Ilustrații: Illustrations
Dimensiuni: 140 x 216 x 10 mm
Greutate: 0.24 kg
Ediția:New.
Editura: Sage Publications, Inc
Locul publicării:Thousand Oaks, United States

Recenzii

“Introduction to Nonparametric Item Response Theory is an accessible introduction to constructing tests/scales using nonparametric IRT. It should be of great use to social scientists who construct their own measurement instruments, as well as those who provide them statistical support. For those who focus on larger scale standardized tests, the volume provides a refreshing step back to look at some important issues in the field and some alternative methods of analysis.”

Notă biografică

Vice dean of research Tilburg School of Social and Behavioral Sciences, program director research master, and professor of methodology and statistics

Descriere

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.