CORRECTION article
Front. Psychol.
Sec. Quantitative Psychology and Measurement
Volume 16 - 2025 | doi: 10.3389/fpsyg.2025.1686437
Correction: A random forest dynamic threshold imputation method for handling missing data in cognitive diagnosis assessments
Provisionally accepted
- Nanchang Normal University, Nanchang, China
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Correction: A random forest dynamic threshold imputation method for handling missing data in cognitive diagnosis assessments Correction on: A random forest dynamic threshold imputation method for handling missing data in cognitive diagnosis assessments. Author affiliation Adding: Affiliation [School of Mathematics and Information Science, Nanchang Normal University, Nanchang, China] was omitted for author [Xinai Xu]. This affiliation has now been added for author [Xinai Xu]. Removing: Author [Xinai Xu] was erroneously assigned to affiliation [Department of Educational Psychology, Faculty of Education, East China Normal University, Shanghai, China]. This affiliation has now been removed for author [Xinai Xu]. Incorrect affiliation In the published article, there was an error in affiliation(s) [2,3]. Instead of "[Department of Educational Psychology, Faculty of Education, East China Normal University, Shanghai, China]" and "[Faculty of Psychology, Beijing Normal University, Beijing, China]", it should be "[ ]". The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Keywords: missing data, cognitive diagnosis assessment, random forest threshold imputation, machine learning, Dynamic thresholds
Received: 15 Aug 2025; Accepted: 28 Aug 2025.
Copyright: © 2025 Xiaofeng, Jianqin and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xinai Xu, Nanchang Normal University, Nanchang, China
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