Journal
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume 91, Issue 9, Pages 1846-1866Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00949655.2021.1872078
Keywords
Estimation techniques; goodness-of-fit; marginal quasi likelihood (MQL); multilevel modelling; penalized quasi likelihood (PQL)
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Multilevel modelling is a novel approach for analyzing data with hierarchical structures. This study compares estimation methods for a goodness-of-fit test developed for binary response multilevel models, based on mathematical background, extensive simulations, and application to real-life data.
Multilevel modelling is a novel approach to analyse data which consist of a hierarchical or a nested structure. With advancements in multilevel modelling, there has been an advancement in the estimation techniques and also in goodness-of-fit tests which are vital to assess the fit of a model. However, these goodness-of-fit tests are not as yet tested to be suitable for models estimated using different estimation techniques. This study aims to conduct a comparison of methods of estimations for use in a goodness-of-fit test which is developed for binary response multilevel models. The comparison is based upon the mathematical background, extensive simulations and an application to a real-life dataset.
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