Item Response Theory: Parameter Estimation Techniques, Second Edition

Series:
Statistics: A Series of Textbooks and Monographs
Published:
July 20, 2004 by CRC Press - 528 Pages
Editor(s):
Frank B. Baker, University of Wisconsin, Madison, USA; Seock-Ho Kim, University of Georgia, Athens, USA

Purchasing Options

Hardback
$107.95
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ISBN 9780824758257
Cat# DK2939
 

Features

  • Offers three new chapters discussing parameter estimation with multiple groups, parameter estimation for a test with mixed item types, and Markov chain Monte Carlo methods
  • Includes a CD-ROM containing updated BASIC and Java codes for estimation algorithms discussed in the book
  • Provides appendices illustrating the implementation of each estimation procedure presented in the text
  • Contains numerous examples drawn from various research studies that demonstrate the different concepts or relationships discussed in the text

    • Summary

    Item Response Theory clearly describes the most recently developed IRT models and furnishes detailed explanations of algorithms that can be used to estimate the item or ability parameters under various IRT models. Extensively revised and expanded, this edition offers three new chapters discussing parameter estimation with multiple groups, parameter estimation for a test with mixed item types, and Markov chain Monte Carlo methods. It includes discussions on issues related to statistical theory, numerical methods, and the mechanics of computer programs for parameter estimation, which help to build a clear understanding of the computational demands and challenges of IRT estimation procedures.

    Table of Contents

    The Item Characteristic Curve: Dichotomous Response
    Estimating the Parameters of an Item Characteristic Curve
    Maximum Likelihood Estimation of Examinee Ability
    Maximum Likelihood Procedures for Estimating Both Ability and Item Parameters
    The Rasch Model
    Marginal Maximum Likelihood Estimation and an EM Algorithm
    Bayesian Parameter Estimation Procedures
    The Graded Item Response
    Nominally Scored Items
    Markov Chain Monte Carlo Methods
    Parameter Estimation with Multiple Groups
    Parameter Estimation for a Test with Mixed Item Types

    Editorial Reviews

    "…an excellent resource for the serious investigator doing research involving estimation of IRT model parameters."
    -Journal of the American Statistical Association

    "…Baker has the unique ability to present complex material in a form that is easily understood….This book belongs on the bookshelf of every advanced student in psychometrics. It should also prove invaluable to students in statistics."
    -Journal of Educational Measurement

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    Resource OS Platform Updated Description Instructions
    IRTPET.zip Platform type March 27, 2008

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