Introduction to Mixed Modelling:
Beyond Regression and Analysis of Variance
September 2006
NEW! The Download Material now includes a list of corrections to the book and
solutions to exercises using the statistical software SAS, GenStat and R.
Mixed modelling is one of the most promising and exciting areas of statistical analysis, enabling more powerful interpretation of data through the recognition of random effects. However, many perceive mixed modelling as an intimidating and specialized technique. This book introduces mixed modelling analysis in a simple and straightforward way, allowing the reader to apply the technique confidently in a wide range of situations.
The book first introduces the criterion of REstricted Maximum Likelihood (REML) for the fitting of a mixed model to data before illustrating how to apply mixed model analysis to a wide range of situations, how to estimate the variance due to each random-effect term in the model, and how to obtain and interpret Best Linear Unbiased Predictors (BLUPs) – estimates of individual effects that take account of their random nature.
It is intended to be an introductory guide to a relatively advanced, specialised topic, and to convince the reader that mixed modelling is neither so specialised nor so difficult as it may at first appear.