Parameter estimation in Rasch Models for examinee-selected items

Chen Wei LIU, Wen Chung WANG

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2 Citations (Scopus)

Abstract

The examinee-selected-item (ESI) design, in which examinees are required to respond to a fixed number of items in a given set of items (e.g., choose one item to respond from a pair of items), always yields incomplete data (i.e., only the selected items are answered and the others have missing data) that are likely nonignorable. Therefore, using standard item response theory models, which assume ignorable missing data, can yield biased parameter estimates so that examinees taking different sets of items to answer cannot be compared. To solve this fundamental problem, in this study the researchers utilized the specific objectivity of Rasch models by adopting the conditional maximum likelihood estimation (CMLE) and pairwise estimation (PE) methods to analyze ESI data, and conducted a series of simulations to demonstrate the advantages of the CMLE and PE methods over traditional estimation methods in recovering item parameters in ESI data. An empirical data set obtained from an experiment on the ESI design was analyzed to illustrate the implications and applications of the proposed approach to ESI data. Copyright © 2017 by the National Council on Measurement in Education.

Original languageEnglish
Pages (from-to)518-549
JournalJournal of Educational Measurement
Volume54
Issue number4
Early online dateDec 2017
DOIs
Publication statusPublished - 2017

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Education
model theory
National Council
objectivity
simulation
experiment
education
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Citation

Liu, C.-W., & Wang, W.-C. (2017). Parameter estimation in Rasch Models for examinee-selected items. Journal of Educational Measurement, 54(4), 518-549. doi: 10.1111/jedm.12159