The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, S. | - |
dc.contributor.author | Jeong, Y. | - |
dc.contributor.author | Hong, S. | - |
dc.date.accessioned | 2021-12-03T10:42:05Z | - |
dc.date.available | 2021-12-03T10:42:05Z | - |
dc.date.created | 2021-08-31 | - |
dc.date.issued | 2021-03-03 | - |
dc.identifier.issn | 1664-1078 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/129101 | - |
dc.description.abstract | The present study investigated estimate biases in cross-classified random effect modeling (CCREM) and hierarchical linear modeling (HLM) when ignoring a crossed factor in CCREM considering the impact of the feeder and the magnitude of coefficients. There were six simulation factors: the magnitude of coefficient, the correlation between the level 2 residuals, the number of groups, the average number of individuals sampled from each group, the intra-unit correlation coefficient, and the number of feeders. The targeted interests of the coefficients were four fixed effects and two random effects. The results showed that ignoring a crossed factor in cross-classified data causes a parameter bias for the random effects of level 2 predictors and a standard error bias for the fixed effects of intercepts, level 1 predictors, and level 2 predictors. Bayesian information criteria generally outperformed Akaike information criteria in detecting the correct model. © Copyright © 2021 Kim, Jeong and Hong. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | Frontiers Media S.A. | - |
dc.title | The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hong, S. | - |
dc.identifier.doi | 10.3389/fpsyg.2021.637645 | - |
dc.identifier.scopusid | 2-s2.0-85102897932 | - |
dc.identifier.wosid | 000629355700001 | - |
dc.identifier.bibliographicCitation | Frontiers in Psychology, v.12 | - |
dc.relation.isPartOf | Frontiers in Psychology | - |
dc.citation.title | Frontiers in Psychology | - |
dc.citation.volume | 12 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Psychology | - |
dc.relation.journalWebOfScienceCategory | Psychology, Multidisciplinary | - |
dc.subject.keywordAuthor | cross-classified random effect modeling | - |
dc.subject.keywordAuthor | crossed factor | - |
dc.subject.keywordAuthor | feeder | - |
dc.subject.keywordAuthor | magnitude of coefficients | - |
dc.subject.keywordAuthor | Monte-Carlo simulation study | - |
dc.subject.keywordAuthor | multilevel data | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.