Item response theory : parameter estimation techniques /

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Bibliographic Details
Author / Creator:Baker, Frank B.
Edition:2. ed., rev. and expanded
Imprint:New York : M. Dekker, c2004.
Description:xx, 495 p. : ill. ; 24 cm. + 1 computer optical disc (4 3/4 in.)
Language:English
Series:Statistics : textbooks and monographs ; 176
Statistics, textbooks and monographs ; v. 176.
Subject:
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/5340333
Hidden Bibliographic Details
Other authors / contributors:Kim, Seock-Ho.
ISBN:0824758250
Notes:Accompanying CD-ROM contains "Basic and Java code."
Includes bibliographical references (p. [465]-481) and index.

MARC

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504 |a Includes bibliographical references (p. [465]-481) and index. 
505 0 0 |g 1.  |t The item characteristic curve : dichotomous response --  |g 2.  |t Estimating the parameters of an item characteristic curve --  |g 3.  |t Maximum likelihood estimation of examinee ability --  |g 4.  |t Procedures for estimating both ability and item parameters --  |g 5.  |t The Rasch model --  |g 6.  |t Parameter estimation via MMLE and an EM algorithm --  |g 7.  |t Bayesian parameter estimation procedures --  |g 8.  |t The graded item response --  |g 9.  |t Nominally scored items --  |g 10.  |t Parameter estimation for multiple group data --  |g 11.  |t Estimation of item parameters of mixed models --  |g 12.  |t Parameter estimation via Gibbs sampler --  |g A.  |t Implementation of maximum likelihood estimation of item parameters --  |g B.  |t Implementation of maximum likelihood estimation of examinee's ability --  |g C.  |t Implementation of JMLE procedure for the Rasch model --  |g D.  |t Implementation of item parameter estimation via MMLE/EM --  |g E.  |t Implementing the Bayesian approach --  |g F.  |t Implementation of parameter estimation under the graded response model --  |g G.  |t Implementation of MLE under nominal response scoring --  |g H.  |t Implementation of MMLE/EM for the Rasch model --  |g I.  |t Implementation of multiple groups estimation --  |g J.  |t Implementation of estimation for mixed models --  |g K.  |t Implementation of Gibbs sampler. 
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