Item response theory modeling for examinee-selected items with rater effect

Journal article


Liu, Chen-Wei, Qiu, Xue-Lan and Wang, Wen-Chung. (2019). Item response theory modeling for examinee-selected items with rater effect. Applied Psychological Measurement. 43(6), pp. 435-448. https://doi.org/10.1177/0146621618798667
AuthorsLiu, Chen-Wei, Qiu, Xue-Lan and Wang, Wen-Chung
Abstract

Some large-scale testing requires examinees to select and answer a fixed number of items from given items (e.g., select one out of the three items). Usually, they are constructed-response items that are marked by human raters. In this examinee-selected item (ESI) design, some examinees may benefit more than others from choosing easier items to answer, and so the missing data induced by the design become missing not at random (MNAR). Although item response theory (IRT) models have recently been developed to account for MNAR data in the ESI design, they do not consider the rater effect; thus, their utility is seriously restricted. In this study, two methods are developed: the first one is a new IRT model to account for both MNAR data and rater severity simultaneously, and the second one adapts conditional maximum likelihood estimation and pairwise estimation methods to the ESI design with the rater effect. A series of simulations was then conducted to compare their performance with those of conventional IRT models that ignored MNAR data or rater severity. The results indicated a good parameter recovery for the new model. The conditional maximum likelihood estimation and pairwise estimation methods were applicable when the Rasch models fit the data, but the conventional IRT models yielded biased parameter estimates. An empirical example was given to illustrate these new initiatives.

Keywordsrater severity; examinee-selected items; missing not at random
Year2019
JournalApplied Psychological Measurement
Journal citation43 (6), pp. 435-448
PublisherSAGE Publications
ISSN0146-6216
Digital Object Identifier (DOI)https://doi.org/10.1177/0146621618798667
PubMed ID31452553
Scopus EID2-s2.0-85059681803
PubMed Central IDPMC6696873
Page range435-448
FunderResearch Grants Council, Hong Kong SAR (Special Administrative Region)
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online08 Oct 2018
Publication process dates
Deposited15 Nov 2023
Grant ID18613716
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