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oai:www.ideals.illinois.edu:2142/20739 2023-07-10 col_2142_5131 col_2142_17362 com_2142_5130 com_2142_17361 com_2142_8903

Stout, William F. Chang, Hua-Hua 2011-05-07T12:47:52Z 2011-05-07T12:47:52Z 10000-01-01 1992 It has long been part of the Item Response Theory (IRT) folklore that under the usual empirical Bayes unidimensional IRT modeling approach, the posterior distribution of examinee ability given test response is approximately normal for a long test. Under very general non-parametric assumptions, we make this claim rigorous for a broad class of latent models. An asymptotic theory is developed by William Stout for an ordinal-scale-based ability estimation based procedure. Based on this theory a new joint MLE algorithm--Percentile Joint Maximum Likelihood Estimation (PJMLE) is developed for the estimation of examinee ability and item structure. An extensive simulation study has been conducted to examine the statistical properties of PJMLE and to compare the statistical performance of PJMLE with BILOG and LOGIST, the two most widely used procedures for joint estimation of ability and item structure. Made available in DSpace on 2011-05-07T12:47:52Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 9236416.pdf: 3575966 bytes, checksum: 8d400e58ae685b15ce18c64502e1fca6 (MD5) Previous issue date: 1992 Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:45:56Z Item is restricted indefinitely. Restriction data tranferred 2014-07-01T11:20:26-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission ETDs are only available to UIUC Users without author permission U of I Only AAI9236416 (UMI)AAI9236416 http://hdl.handle.net/2142/20739 eng Copyright 1992 Chang, Hua-Hua Statistics Psychology, Psychometrics Some theoretical and applied results concerning item response theory model estimation text Statistics Statistics University of Illinois at Urbana-Champaign Dissertation Ph.D.