This workshop is concerned with contemporary methods for analysis and modeling of multilevel data that are increasingly more frequently collected in the behavioral, social, organizational, clinical, marketing and economics disciplines. The course commences with a discussion of clustering (nesting) effects, the key characteristic of multilevel modeling, and highlights the limitations of earlier popular approaches, such as aggregation and disaggregation. Two-level and three-level settings are then considered, where unconditional as well as conditional models are discussed and illustrated on data. Models for normal as well as for non-normal multilevel data are then focused on, including robust modeling of lower-level relationships. Multilevel models for categorical response variables are then dealt with and exemplified on data. Throughout the workshop, the increasingly popular package Stata is utilized, as are numerous examples employing empirical data. The course is geared toward graduate students and faculty members concerned with analysis and modeling of data from multilevel settings of growing relevance in the social, behavioral, clinical, marketing, and organizational disciplines.