Handbook of item response theory / edited by Wim J. van der Linden, Pacific Metrics, Monterey, California.

Contributor(s): Material type: TextTextSeries: Publisher: Boca Raton : CRC Press, Taylor & Francis Group, 2015-Description: xxv, 427 pages ; 26 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781466514324 (alk. paper : vol. 2)
Subject(s): LOC classification:
  • BF 39.2.I84 .H191 2016 v.2
Incomplete contents:
-- Volume 2. Statistical tools --
Summary: Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT). While (IRT) heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void. Summary: Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods.
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Holdings
Item type Current library Collection Call number Status Date due Barcode
Graduate Studies Graduate Studies DLSU-D GRADUATE STUDIES Graduate Studies Graduate Studies BF 39.2.I84 .H191 2016 v.2 (Browse shelf(Opens below)) Available 3AEA2015006155

Includes bibliographical references and index.

-- Volume 2. Statistical tools --

Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT). While (IRT) heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void.

Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods.

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