title: Modelling partially cross-classified multilevel data.
abstract: 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.
authors.affiliation: Texas A&M University, College Station, Texas, USA.
authors.forename: Wen
authors.initials: W
authors.lastname: Luo
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Cappaert
authors.forename: Ling
authors.initials: L
authors.lastname: Ning
mesh_terms: D000124:Achievement; D001525:Behavioral Sciences / Q000706:statistics & numerical data*; D002648:Child; D003198:Computer Simulation; D003479:Curriculum / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D004521:Educational Measurement / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D055361:Multilevel Analysis; D011998:Recreation; D012942:Social Sciences / Q000706:statistics & numerical data*; D013334:Students / Q000706:statistics & numerical data
keywords: cross-classified random effects models; multilevel data; partially cross-classified data