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Assessing Fit of Unidimensional Item Response Theory Models Using a Bayesian Approach
Author(s) -
Sinharay Sandip
Publication year - 2005
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2005.00021.x
Subject(s) - item response theory , guttman scale , bayesian probability , computer science , flexibility (engineering) , model checking , perspective (graphical) , bayesian statistics , machine learning , bayesian inference , artificial intelligence , econometrics , statistics , psychometrics , mathematics , algorithm
Even though Bayesian estimation has recently become quite popular in item response theory (IRT), there is a lack of works on model checking from a Bayesian perspective. This paper applies the posterior predictive model checking (PPMC) method ( Guttman, 1967 ; Rubin, 1984 ), a popular Bayesian model checking tool, to a number of real applications of unidimensional IRT models. The applications demonstrate how to exploit the flexibility of the posterior predictive checks to meet the need of the researcher. This paper also examines practical consequences of misfit, an area often ignored in educational measurement literature while assessing model fit.