A number of disciplines in the social and behavioral sciences address data
that are quantitative and hierarchical. Examples include quantitative data
on students who attend various schools, psychiatric patients who are treate
d by different mental health specialists, and workers who are employed by d
ifferent types of firms. Longitudinal data are also hierarchical in the sen
se that data on individuals are collected at different time points. Similar
to students being nested in schools, observations may he nested within ind
ividuals. This paper discusses a set of very useful statistical tools, know
n as multilevel models, that may be used to examine hierarchical data. It b
egins with a general description of these models and then provides specific
examples that address common social science research issues. It also discu
sses software that makes these models available for even the novice social
statistician.