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Modeling Within-Item Dependencies in Parallel Data on Test Responses and Brain Activation

Published online by Cambridge University Press:  01 January 2025

Minjeong JeonOpen the ORCID record for Minjeong Jeon[Opens in a new window],
Paul De Boeck,
Jevan Luo,
Xiangrui Li and
Zhong-Lin Lu
Minjeong Jeon*
Affiliation:
University of California, Los Angeles
Paul De Boeck
Affiliation:
Ohio State University
Jevan Luo
Affiliation:
University of California, Los Angeles
Xiangrui Li
Affiliation:
Ohio State University
Zhong-Lin Lu
Affiliation:
New York University
*
Correspondence should be made to Minjeong Jeon, Department of Education, University of California, Los Angeles, 3141 Moore Hall, 457 Portola Avenue, Los Angeles, CA 90024, USA. Email: mjjeon@ucla.edu
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Abstract

In this paper, we propose a joint modeling approach to analyze dependency in parallel response data. We define two types of dependency: higher-level dependency and within-item conditional dependency. While higher-level dependency can be estimated with common latent variable modeling approaches, within-item conditional dependency is a unique kind of information that is often not captured with extant methods, despite its potential to shed new insights into the relationship between the two types of response data. We differentiate three ways of modeling within-item conditional dependency by conditioning on raw values, expected values, or residual values of the response data, which have different implications in terms of response processes. The proposed approach is illustrated with the example of analyzing parallel data on response accuracy and brain activations from a Theory of Mind assessment. The consequence of ignoring within-item conditional dependency is investigated with empirical and simulation studies in comparison to conventional dependency analysis that focuses exclusively on relationships between latent variables.

Keywords

joint analysisparallel dataconditional dependencyresponse accuracyfMRI activationstheory of mind

Information

Type
Original Paper
Information
Psychometrika , Volume 86 , Issue 1 , March 2021, pp. 239 - 271
Copyright
Copyright © 2021 The Psychometric Society

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Footnotes

Electronic Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11336-020-09741-2.

The authors are grateful to the Editor, the Associate Editor, and the anonymous reviewers for their constructive comments on our manuscript.

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