The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling
- Authors
- Kim, S.; Jeong, Y.; Hong, S.
- Issue Date
- 3-3월-2021
- Publisher
- Frontiers Media S.A.
- Keywords
- cross-classified random effect modeling; crossed factor; feeder; magnitude of coefficients; Monte-Carlo simulation study; multilevel data
- Citation
- Frontiers in Psychology, v.12
- Indexed
- SSCI
SCOPUS
- Journal Title
- Frontiers in Psychology
- Volume
- 12
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/129101
- DOI
- 10.3389/fpsyg.2021.637645
- ISSN
- 1664-1078
- 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.
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