However, the commonly used hierarchical linear models HLMs are appropriate only when the outcome of interest is normally distributed. When you are dealing with outcomes that are not normally distributed binary, categorical, ordinal , a transformation and an appropriate error distribution for the response variable needs to be incorporated into the model. Save to Library.
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In your search for publications, if you work in a university you may be able to access Web of Knowledge subscribable service or, use Google Scholar. In recent years, there have been a growing number of books explaining how to undertake multilevel modelling. Here we have grouped them into these broad categories. If there are any important ones we have missed please email us - info-cmm bristol. Multilevel modelling books In your search for publications, if you work in a university you may be able to access Web of Knowledge subscribable service or, use Google Scholar.
General books on multilevel modelling aimed at a social science audience Books on longitudinal data analysis that emphasize multilevel random-coefficient models More specialised books that do spatial models, or are more technical accounts of mixed models, etc. They cover the following books: Multilevel Analysis: Multilevel Analysis for Applied Research: It's Just Regression. Guilford Press. There is an associated web site where you can download data at: De Boeck. Statistical modelling applied to social sciences. Cambridge University Press.
Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models
Multilevel Statistical Models. Some of the contents can be downloaded for from the following link, including updates and corrections: Techniques and Applications.
Lawrence Erlbaum Associates. Multilevel Modelling of Health Statistics. Longford, N. Random Coefficient Models. Oxford University Press.
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Luke, D. Multilevel Modeling. Raudenbush, S. Hierarchical Linear Models: Applications and Data Analysis Methods. Steve Raudenbush's personal web site has preprints and software for statistical power for multilevel models at http: Datasets for some of the examples in this book, with MLwiN macros, corrections and updates, can be found at: Bollen and Patrick J. Curran Applied Longitudinal Data Analysis: Singer and John B. Fitzmaurice, Nan M. Laird and James H. Laird, and J.
Applied Longitudinal Analysis. Longitudinal Data Analysis. There are a lot of useful downloads at tigger. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Topics in Modelling of Clustered Data. CRC Press. This book 'focuses on providing a comprehensive treatment of marginal, conditional, and random effects models using, among others, likelihood, pseudo-likelihood, and generalized estimating equations methods' Brown, H. Applied Mixed Models in Medicine.
This new edition presents an overview of the theory of mixed models applied to problems in medical research.
There is a web site for the book at www. Hierarchical Modelling for the Environmental Sciences: Statistical Methods. This edited collection deals with 'hierarchical Bayes and Markov Chain Monte Carlo methods for analysis …where information is heterogeneous and uncertain, processes are complex, and responses depend on scale'. Contains a number of chapters on spatial and spatial-temporal models Demidenko, E.
Mixed Models: Theory and Applications. This book aims to provide 'in-depth mathematical coverage of …linear, generalized linear, and nonlinear mixed models, along with diagnostics at both a graduate-level text and a reference' Heck, R. An Introduction to Multilevel Modeling Techniques. Lawrence ErlbaumAssociates. This book deals with 'multilevel regression models and multilevel models for covariance structures using hierarchical linear modelling and structural equation modelling' Lawson, A.
Browne, and C. John Wiley and Sons. This covers spatial models and how to fit the models in the named software. MLwiN Worksheets and macros at seis. Schnabel, and J.
Modeling Longitudinal and Multilevel Data: Practical Issues, Applied. Multilevel Modeling: Methodological Advances, Issues, and Applications. This edited collection aims to 'critically examine the real problems that occur when trying to use MLMs in applied research, such as power, experimental design, and model violations … includes topics such as growth modeling, repeated measures analysis, nonlinear modeling, outlier detection, and meta analysis.
Generalized Latent Variable Modeling: Web site: Steve Raudenbush's personal website has preprints and software for statistical power for multilevel models at http: Designed to be viewed electronically either in your browser or after saving to your computer and has coloured graphs and links for easy navigation round the document.