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

Frontiers in Psychology

Sehee Hong¹  Soyoung Kim²  Yoonhwa Jeong³ 

[1] Department of Education, Korea University, Seoul, South Korea;Institute of Educational Research, Korea University, Seoul, South Korea;Talent Development Group, Samsung Electronics Leadership Center, Yong-in, South Korea;

关键词: cross-classified random effect modeling;    multilevel data;    feeder;    magnitude of coefficients;    crossed factor;    Monte-Carlo simulation study;   

DOI  :  10.3389/fpsyg.2021.637645

来源: Frontiers

【 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.

【 授权许可】

CC BY