首页    期刊浏览 2024年12月05日 星期四
登录注册

文章基本信息

  • 标题:The Impact of Ignoring a Crossed Factor in Cross-Classified Multilevel Modeling
  • 本地全文:下载
  • 作者:Kim, Soyoung ; Jeong, Yoonhwa ; Hong, Sehee
  • 期刊名称:Frontiers in Psychology
  • 电子版ISSN:1664-1078
  • 出版年度:2021
  • 卷号:12
  • 页码:567
  • DOI:10.3389/fpsyg.2021.637645
  • 出版社:Frontiers Media
  • 摘要: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.
  • 关键词:Cross-classified random effect modeling; multilevel data; Feeder; magnitute of coefficient; crossed factor; Monte-Carlo simulation study
国家哲学社会科学文献中心版权所有