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.
Highlights
Hierarchical linear modeling (HLM) can be used when the levels in a multilevel data structure are strictly nested
The purpose of the current study is to compare the statistical performance of cross-classified random effect modeling (CCREM) and HLM when the correct model is generated by CCREM considering the magnitude of the coefficients and the number of feeders
CCREM and HLM were analyzed for each condition, and the results were represented as follows
Summary
Hierarchical linear modeling (HLM) can be used when the levels in a multilevel data structure are strictly nested. The HLM technique is a fairly common analysis method in educational settings (e.g., students are nested within schools). In the case of purely hierarchical data structures, lower-level entities are nested into only one higher-level entity (Raudenbush and Bryk, 2002). Behavioral scientists frequently encounter cross-classified data structures, i.e., there are multiple sources of membership for lower-level entities (Meyers and Beretvas, 2006). In the field of clinical and medical treatment, patients could have multiple sources of membership, such as doctors or nurses, while in the field of education, students could have multiple sources of membership, such as high schools and hometowns. HLM, which is purely a nested multilevel model, requires extending to reflect cross-classified multilevel data structures
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
