The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling

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...

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Bibliographic Details
Published in:Frontiers in psychology 2021-03, Vol.12, p.637645-637645
Main Authors: Kim, Soyoung, Jeong, Yoonhwa, Hong, Sehee
Format: Article
Language:English
Subjects:
cross-classified random effect modeling
crossed factor
feeder
magnitude of coefficients
Monte-Carlo simulation study
multilevel data
Psychology
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Summary: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.
ISSN:1664-1078
1664-1078
DOI:10.3389/fpsyg.2021.637645