Modelling partially cross-classified multilevel data

作者：Luo Wen^*; Cappaert Kevin J; Ning Ling

来源：British Journal of Mathematical and Statistical Psychology, 2015, 68(2): 342-362.

DOI：10.1111/bmsp.12050

• cross-classified random effects models
• partially cross-classified data
• multilevel data

This article proposes an approach to modelling partially cross-classified multilevel data where some of the level-1 observations are nested in one random factor and some are cross-classified by two random factors. Comparisons between a proposed approach to two other commonly used approaches which treat the partially cross-classified data as either fully nested or fully cross-classified are completed with a simulation study. Results show that the proposed approach demonstrates desirable performance in terms of parameter estimates and statistical inferences. Both the fully nested model and the fully cross-classified model suffer from biased estimates of some variance components and statistical inferences of some fixed effects. Results also indicate that the proposed model is robust against cluster size imbalance.

• 出版日期2015-5