Modelling partially cross-classified multilevel data

The British Journal of Mathematical and Statistical Psychology
Wen LuoLing Ning

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.

References

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Citations

Dec 22, 2015·Psychotherapy Research : Journal of the Society for Psychotherapy Research·Sonya K Sterba
Mar 18, 2016·Behavior Research Methods·Wen Luo
Jan 16, 2019·Multivariate Behavioral Research·Shuqiong Lin, Wen Luo
Dec 24, 2019·Annual Review of Public Health·David M MurrayStephanie M George
Oct 18, 2019·Educational and Psychological Measurement·Yaacov Petscher, Christopher Schatschneider
Jan 24, 2020·Social Science & Medicine·Jennifer PrattleyJames Nazroo
Dec 12, 2020·International Journal of Environmental Research and Public Health·Dawei BaoShuqing N Teng

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