Multilevel modeling is a data analysis technique used to analyze nested data. Nested data refers to data wherein units of analysis at one level are nested within units of analysis at higher levels. Multilevel data are observed in cross-sectional designs which sample individuals nested within groups. An example of this type of multilevel data is patients (level 1) nested within hospitals (level 2). Multilevel data are also found in repeated measures designs (e.g., multiwave longitudinal or experience sampling designs) which sample repeated reports nested within individuals. An example of this type of multilevel data is an experience sampling study where repeated reports of pain (level 1) are nested within individuals (level 2). In multilevel modeling, level thus refers to the structure of the data. The lower level (level 1) represents the most detailed unit of analysis and has the greatest number of data points. Level 2 represents the higher level within which level 1 observations are nested.