Statistical Methods for Multinomial Data with Multilevel Structures

Not Available "The ultimate objective of our research is the development of methodology and software for the analysis of multi-level binomial and multinomial data in longitudinal studies. Multi-level data is very common in psychological and biomedical studies. Ourresearch will make fundamental contributions to these studies by evaluating, recommending, and developing robust and efficient algorithms for handling such data. We will focus on parametric approach: generalized linear mixed models, and semiparametricapproaches: generalized estimating equation models and generalized additive mixed models. The algorithms will be implemented as an object-oriented software library in the S-Plus language and the approaches will be published in peer reviewed journals.Currently, there is no commercial software for handling multinomial data with multiple hierarchical structures. In the proposed work we will evaluate various algorithms and provide recommendations on when each algorithm should be used. A unified library ofwell-tested algorithms for performing these analyses will be available in Splus and ported to R. In addition, we will incorporate diagnostic techniques and graphical methods into the software, and we will develop a comprehensive case study guidebook usingreal problems. Generalized linear mixed effects models and generalized estimating equations models are widely used in psychology, social sciences, and other areas of scientific research. The computational algorithms we propose to investigate are aiming atcategorical data, particularly, binomial and multinomial responses. The product will be implemented as an S-Plus library and ported to R package. The software will be available to the public and will attract consulting projects and short courses forlongitudinal data analysis with S-Plus. The inclusion of a Java graphical user interface and guidebook will make these methods accessible to a wider audience of researchers."