Pavement performance data is a very common example of multilevel data. While analyzing this type of data using conventional regression techniques, the normality assumptions with random errors and constant variance were often violated. 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 rigid pavement data. The proposed LME modeling process including selecting a preliminary mean structure, selecting a random structure, selecting a residual covariance structure, model reduction, and examining the model fit will be further discussed in this paper.