Handbook of item response theory / edited by Wim J. van der Linden, Pacific Metrics, Monterey, California.
Material type: TextSeries: Publisher: Boca Raton : CRC Press, Taylor & Francis Group, 2015-Description: xxv, 427 pages ; 26 cmContent type:- text
- unmediated
- volume
- 9781466514324 (alk. paper : vol. 2)
- BF 39.2.I84 .H191 2016 v.2
Item type | Current library | Collection | Call number | Status | Date due | Barcode | |
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Graduate Studies | DLSU-D GRADUATE STUDIES Graduate Studies | Graduate Studies | BF 39.2.I84 .H191 2016 v.2 (Browse shelf(Opens below)) | Available | 3AEA2015006155 |
Browsing DLSU-D GRADUATE STUDIES shelves, Shelving location: Graduate Studies, Collection: Graduate Studies Close shelf browser (Hides shelf browser)
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BD 450 .H288 2012 The Ascent of man : | BF 39 .F381s 1989 Statistical analysis in psychology in education / | BF 39 .M725 2016 Statistics for behavioural and social sciences / | BF 39.2.I84 .H191 2016 v.2 Handbook of item response theory / | BF 39.9 .P934 2002 Doing psychology critically : | BF 76 .C180 2007 Career paths in psychology : | BF 76.4 .H191 2005 Handbook of professional and ethical practice for psychologists, counsellors, and psychotherapist / |
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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