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BUGSCode for Item Response Theory
Author(s) -
S. McKay Curtis
Publication year - 2010
Publication title -
journal of statistical software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.636
H-Index - 145
ISSN - 1548-7660
DOI - 10.18637/jss.v036.c01
Subject(s) - item response theory , computer science , code (set theory) , logistic regression , programming language , econometrics , statistics , machine learning , mathematics , psychometrics , set (abstract data type)
I present BUGS code to fit common models from item response theory (IRT), such as the two parameter logistic model, three parameter logisitic model, graded response model, generalized partial credit model, testlet model, and generalized testlet models. I demonstrate how the code in this article can easily be extended to fit more complicated IRT models, when the data at hand require a more sophisticated approach. Specifically, I describe modifications to the BUGS code that accommodate longitudinal item response data.

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