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Optimal Linking Design for Response Model Parameters
Author(s) -
Barrett Michelle D.,
Linden Wim J.
Publication year - 2017
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/jedm.12145
Subject(s) - identifiability , computer science , selection (genetic algorithm) , item response theory , model selection , estimation , integer programming , estimation theory , mathematical optimization , algorithm , machine learning , statistics , mathematics , psychometrics , engineering , systems engineering
Linking functions adjust for differences between identifiability restrictions used in different instances of the estimation of item response model parameters. These adjustments are necessary when results from those instances are to be compared. As linking functions are derived from estimated item response model parameters, parameter estimation error automatically propagates into linking error. This article explores an optimal linking design approach in which mixed‐integer programming is used to select linking items to minimize linking error. Results indicate that the method holds promise for selection of linking items.