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Joint Testlet Cognitive Diagnosis Modeling for Paired Local Item Dependence in Response Times and Response Accuracy

Overview of attention for article published in Frontiers in Psychology, April 2018
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Title
Joint Testlet Cognitive Diagnosis Modeling for Paired Local Item Dependence in Response Times and Response Accuracy
Published in
Frontiers in Psychology, April 2018
DOI 10.3389/fpsyg.2018.00607
Pubmed ID
Authors

Peida Zhan, Manqian Liao, Yufang Bian

Abstract

In joint models for item response times (RTs) and response accuracy (RA), local item dependence is composed of local RA dependence and local RT dependence. The two components are usually caused by the same common stimulus and emerge as pairs. Thus, the violation of local item independence in the joint models is called paired local item dependence. To address the issue of paired local item dependence while applying the joint cognitive diagnosis models (CDMs), this study proposed a joint testlet cognitive diagnosis modeling approach. The proposed approach is an extension of Zhan et al. (2017) and it incorporates two types of random testlet effect parameters (one for RA and the other for RTs) to account for paired local item dependence. The model parameters were estimated using the full Bayesian Markov chain Monte Carlo (MCMC) method. The 2015 PISA computer-based mathematics data were analyzed to demonstrate the application of the proposed model. Further, a brief simulation study was conducted to demonstrate the acceptable parameter recovery and the consequence of ignoring paired local item dependence.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 14%
Student > Master 5 14%
Student > Doctoral Student 3 9%
Student > Ph. D. Student 3 9%
Professor > Associate Professor 3 9%
Other 7 20%
Unknown 9 26%
Readers by discipline Count As %
Psychology 9 26%
Social Sciences 6 17%
Mathematics 2 6%
Linguistics 2 6%
Computer Science 2 6%
Other 2 6%
Unknown 12 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 April 2018.
All research outputs
#17,944,820
of 23,041,514 outputs
Outputs from Frontiers in Psychology
#20,802
of 30,339 outputs
Outputs of similar age
#236,950
of 326,529 outputs
Outputs of similar age from Frontiers in Psychology
#493
of 612 outputs
Altmetric has tracked 23,041,514 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
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