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Item Response Theory: Parameter Estimation Techniques (Second edition, REvised and Expanded)

Item Response Theory: Parameter Estimation Techniques (Second edition, REvised and Expanded)

Item Response Theory: Parameter Estimation Techniques (Second edition, REvised
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Item Response Theory: Parameter Estimation Techniques (Second edition, REvised and Expanded) Hardback - 2004 - 2nd Edition

by Baker, Frank B., and Seock-Ho Kim

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Offering more than 25 percent new material, this reference describes the mostrecently utilized IRT models and furnishes detailed explanation of algorithmsthat can be used to estimate the item or ability parameters under various IRTmodels.

Used - Good

Description

New York: Marcel Dekker, 2004. Second Edition, Revised and Enlarged . Hardcover. Good/No Dust Jacket. An ex-library copy in original laminated pictorial hard covers of the revised and expanded second edition. The usual ex-libris markings. The binding is sound, the text is clean/unmarked, and there is little cover wear. No dust jacket, apparently as issued.

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Details

  • Title Item Response Theory: Parameter Estimation Techniques (Second edition, REvised and Expanded)
  • Author Baker, Frank B., and Seock-Ho Kim
  • Binding Hardback
  • Edition number 2nd
  • Edition Second Edition, Revised and Enlarged
  • Condition Used - Good
  • Pages 524
  • Volumes 1
  • Language ENG
  • Publisher Marcel Dekker, New York
  • Publication date 2004
  • Features Bibliography, Index
  • Bookseller's Inventory # 062988
  • ISBN 9780824758257 / 0824758250
  • Weight 1.85 lbs (0.84 kg)
  • Dimensions 9.18 x 6.24 x 1.28 in (23.32 x 15.85 x 3.25 cm)
  • Category Psychology
  • Library of Congress subjects Parameter estimation
  • Library of Congress Catalogue Number 2004559891
  • Dewey Decimal Code 519.544
  • Quantity available 1

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Reader reviews for Item Response Theory: Parameter Estimation Techniques (Second edition, REvised and Expanded)

From the publisher

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.
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