Multilevel data are very common in many fields. Because of its hierarchical data structure, multilevel data are often analyzed using Linear Mixed-Effects (LME) models. The exploratory analysis, statistical modeling, and the examination of model-fit of LME models are more complicated than those of standard multiple regressions. A systematic modeling approach using visual-graphical techniques and LME models was proposed and demonstrated using the original AASHO road test flexible pavement data. The proposed approach including exploring the growth patterns at both group and individual levels, identifying the important predictors and unusual subjects, choosing suitable statistical models, selecting a preliminary mean structure, selecting a random structure, selecting a residual covariance structure, model reduction, and the examination of the model fit was further discussed.
Electrical Engineering and Applied Computing, Lecture Notes in Electrical Engineering (LNEE) Series 90, pp.351-363